Applied Science, Faculty of
Mining Engineering, Keevil Institute of
DSpace
UBCV
Gillis, Andrew
Doctor of Philosophy - PhD
University of British Columbia
The mining industry has a long history of generating low shareholder returns. Over the past two decades, the average Canadian mining company has returned -8%, while the TSX composite index returned +7%. Prior studies have failed to reach generalizable conclusions about the factors that drive mining firm performance over the long run. This research project investigated over 100 Canadian mining companies' performance from 2003 to 2016 and identified the most significant long-run performance factors. The analysis of these Canadian mining firms found that commodity prices explained the majority of firm performance on an annual basis, but mineral asset impairments had a much more considerable influence on performance over the long run. Over the study period, 90% of Canadian mining companies declared a mineral asset impairment. On average, companies wrote off 40% of a mine’s value when they reported an impairment. After collecting data on almost 300 operating mines, this research project found that declines in metal prices, lower than expected ore grade, and poorer than expected ground conditions contributed significantly to mineral asset impairments, along with several other factors. This research project also found that mineral asset impairments were more likely to occur when mines were located in developing countries, when a mine was located in a geographic region unfamiliar to the operating company, and when a mine employed a mining method or a mineral recovery method that was unfamiliar to the operating company. Finally, this study discussed mineral asset impairments through the frame of project forecasting errors to identify possible root causes of impairments and suggest methods for reducing impairments. It identified technical errors, optimism bias, and principal-agent conflicts as potential root causes. The investigation of over 250 unique impairment events found that approximately 30% were caused by technical error and 70% by optimism bias or principal-agent conflict. The practice of using distributional forecasting error information from completed projects, known as reference class forecasting, was presented as a method to improve cost and benefit forecasts, reduce mineral asset impairments, and improve mining firm performance over the long run.
How to Avoid the Worst Financial Outcomes in the Mining Industry by ANDREW GILLIS BASc, The University of British Columbia, 2007 MBA, Simon Fraser University, 2015 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Mining Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2021 © Andrew Gillis, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: HOW TO AVOID THE WORST FINANCIAL OUTCOMES IN THE MINING INDUSTRY Submitted by Andrew Gillis in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mining Engineering Examining Committee: John Steen, Mining Engineering, UBC Supervisor W Scott Dunbar, Mining Engineering, UBC Supervisory Committee Member Andrew von Nordenflycht, Beedie School of Business, SFU Supervisory Committee Member Shaun Barker, Earth, Ocean and Atmospheric Sciences, UBC University Examiner Werner Antweiler, Sauder School of Business, UBC University Examiner iii Abstract The mining industry has a long history of generating low shareholder returns. Over the past two decades, the average Canadian mining company has returned -8%, while the TSX composite index returned +7%. Prior studies have failed to reach generalizable conclusions about the factors that drive mining firm performance over the long run. This research project investigated over 100 Canadian mining companies' performance from 2003 to 2016 and identified the most significant long-run performance factors. The analysis of these Canadian mining firms found that commodity prices explained the majority of firm performance on an annual basis, but mineral asset impairments had a much more considerable influence on performance over the long run. Over the study period, 90% of Canadian mining companies declared a mineral asset impairment. On average, companies wrote off 40% of a mine’s value when they reported an impairment. After collecting data on almost 300 operating mines, this research project found that declines in metal prices, lower than expected ore grade, and poorer than expected ground conditions contributed significantly to mineral asset impairments, along with several other factors. This research project also found that mineral asset impairments were more likely to occur when mines were located in developing countries, when a mine was located in a geographic region unfamiliar to the operating company, and when a mine employed a mining method or a mineral recovery method that was unfamiliar to the operating company. Finally, this study discussed mineral asset impairments through the frame of project forecasting errors to identify possible root causes of impairments and suggest methods for reducing impairments. It identified technical errors, optimism bias, and principal-agent conflicts as potential root causes. The investigation of over 250 unique impairment events found that iv approximately 30% were caused by technical error and 70% by optimism bias or principal-agent conflict. The practice of using distributional forecasting error information from completed projects, known as reference class forecasting, was presented as a method to improve cost and benefit forecasts, reduce mineral asset impairments, and improve mining firm performance over the long run. v Lay Summary The mining industry has a long history of generating low shareholder returns. This research project investigated the performance of over 100 Canadian mining companies from 2003 to 2016 and identified the most significant long-run performance factors. The analysis of these Canadian mining firms found that commodity prices explained the majority of firm performance on an annual basis, but mine write-downs had a much more significant influence on performance over the long run. Over the study period, 90% of Canadian mining companies wrote down a mining operation, and, on average, 40% of a mine’s value was written off when this occurred. This study identified that mine write-downs are likely caused by poor forecasting at the beginning of mining projects. It suggests mining companies learn from the forecasting mistakes of past projects to improve current project forecasts, reduce mine write-downs, and improve mining firm performance over the long run. vi Preface This dissertation is original, unpublished, independent work by the author, A. Gillis. vii Table of Contents Abstract ............................................................................................................................. iii Lay Summary .................................................................................................................... v Preface ............................................................................................................................... vi Table of Contents ............................................................................................................ vii List of Tables ................................................................................................................... xii List of Figures ................................................................................................................. xiv Acknowledgements ......................................................................................................... xv Dedication ....................................................................................................................... xvi Introduction ....................................................................................................................... 1 Literature Review: Mining Company Performance .............................................................. 2 Primary Research Questions .................................................................................................... 6 Research Outline ....................................................................................................................... 7 Significance of the Research ..................................................................................................... 8 Chapter 1: Replicating Prior Studies ............................................................................ 11 Introduction ............................................................................................................................. 11 Data Set..................................................................................................................................... 12 Shapiro et al. - Geographic Scope and Product Scope ......................................................... 15 Analysis ................................................................................................................................. 17 Results ................................................................................................................................... 19 Discussion ............................................................................................................................. 20 Camus et al. - Mineral Reserve Growth ................................................................................ 21 viii Data Set ................................................................................................................................. 24 Analysis ................................................................................................................................. 24 Results ................................................................................................................................... 26 Discussion ............................................................................................................................. 28 MacDiarmid et al. - Commodity Price and Revenue ............................................................ 29 Dataset ................................................................................................................................... 32 Analysis ................................................................................................................................. 33 Results ................................................................................................................................... 35 Discussion ............................................................................................................................. 36 Conclusion ................................................................................................................................ 38 Chapter 2: Mining Firm Performance .......................................................................... 40 Introduction ............................................................................................................................. 40 Defining Performance ............................................................................................................. 41 Accounting and Market Measures......................................................................................... 43 Specific Market Measures ..................................................................................................... 45 Investigating Firm Performance ............................................................................................ 50 Data Set..................................................................................................................................... 53 Analysis ..................................................................................................................................... 54 Method .................................................................................................................................. 56 Results ....................................................................................................................................... 56 Discussion ................................................................................................................................. 61 Conclusion ................................................................................................................................ 64 Chapter 3: Mining Firm Performance Factors ............................................................ 66 Introduction ............................................................................................................................. 66 Firm-Level Performance Factors ........................................................................................... 67 ix Dataset ...................................................................................................................................... 70 Analysis ..................................................................................................................................... 71 Independent Variable: Mineral Reserve Growth .................................................................. 71 Independent Variable: Impairment........................................................................................ 73 Independent Variable: Cash Costs ........................................................................................ 73 Dependent Variable ............................................................................................................... 75 Control Variables .................................................................................................................. 76 Method .................................................................................................................................. 76 Results ....................................................................................................................................... 77 Multivariable Model .............................................................................................................. 79 Discussion ................................................................................................................................. 82 Conclusions .............................................................................................................................. 85 Chapter 4: Mining Firm Impairments .......................................................................... 86 Introduction ............................................................................................................................. 86 Impairments ............................................................................................................................. 86 Definition .............................................................................................................................. 86 Impairment Literature Review .............................................................................................. 89 The Major Project Frame ....................................................................................................... 93 Data Set..................................................................................................................................... 97 Analysis ................................................................................................................................... 100 Comparison of Mining Project Financial Scale to Major Project Financial Scale .............. 100 Mining Operation Impairments in Developed versus Developing Countries ..................... 101 Routine vs Non-Routine Mining Operations ...................................................................... 103 Results ..................................................................................................................................... 105 Mining Project Financial Scale ........................................................................................... 105 x Mining Operation Impairments ........................................................................................... 105 Mines Located in Developed versus Developing Countries ............................................... 108 Routine vs Non-Routine Mining Operations ...................................................................... 109 Discussion ............................................................................................................................... 110 Conclusion .............................................................................................................................. 113 Chapter 5: Causes of Impairments ............................................................................. 116 Introduction ........................................................................................................................... 116 Project Forecasting Errors ................................................................................................... 117 Bad Luck or Technical Error ............................................................................................... 117 Optimism Bias ..................................................................................................................... 118 Principal-Agent Conflict ..................................................................................................... 120 Mining Project Impairments ................................................................................................ 121 Data Set................................................................................................................................... 128 Analysis ................................................................................................................................... 129 Results ..................................................................................................................................... 132 Discussion ............................................................................................................................... 141 Optimism Bias and Principal-Agent Conflict ..................................................................... 141 Bad luck or technical errors ................................................................................................ 145 Conclusions ............................................................................................................................ 145 Chapter 6: Reducing Mining Impairments ................................................................ 148 Introduction ........................................................................................................................... 148 Reference Class Forecasting ................................................................................................. 149 Mine Project Forecasting ...................................................................................................... 154 Information Availability ...................................................................................................... 156 Incentives ............................................................................................................................ 160 xi The Role of Corporate Governance ..................................................................................... 161 Conclusions ............................................................................................................................ 162 Conclusion ..................................................................................................................... 164 References ...................................................................................................................... 171 Appendix A .................................................................................................................... 183 Appendix B .................................................................................................................... 184 Appendix C .................................................................................................................... 188 xii List of Tables Table 1: Data set descriptive statistics .............................................................................. 15 Table 2: Return on assets by strategic group .................................................................... 19 Table 3: TRvI and TSR descriptive statistics .................................................................... 27 Table 4: Camus et al. replication linear regression model results .................................... 28 Table 5: MacDiarmid et al. analysis results ...................................................................... 32 Table 6: Descriptive statistics for replicated MacDiarmid et al. variables ....................... 35 Table 7: MacDiarmid et al. replication linear regression model results ........................... 36 Table 8: Annual average return vs. annualized first and last price ................................... 49 Table 9: Descriptive statistics for ΔCP and ΔSR .............................................................. 56 Table 10: Regression model results for ΔCP vs ΔSR ....................................................... 57 Table 11: Descriptive statistics for aCP, aTSR and CAaTSR .......................................... 57 Table 12: aCP vs aTSR linear regression model summary ............................................... 58 Table 13: Descriptive statistics for EV, Years, FR, FI, Fc, and aTSR .............................. 77 Table 14: Correlation table for the multivariable model factors ....................................... 80 Table 15: Variance inflation factors for the multivariable model factors ......................... 81 Table 16: FR, FI and, Fc, vs aTSR linear regression model summary ............................. 81 Table 17: Summary of mine and mine project identification ........................................... 99 Table 18: Descriptive statistics for mines that declared impairments ............................ 106 Table 19: Descriptive statistics for the impairment percentage ...................................... 108 Table 20: Regression model results for developing country vs impairment percentage 109 Table 21: Routine vs non-routine mining operation analysis results .............................. 109 Table 22: Mining operation benefits and costs ............................................................... 125 xiii Table 23: Mapping mining impairments to project forecasting error root causes .......... 127 Table 24: Reported reasons for mining impairments ...................................................... 130 Table 25: Impairment category summary statistics ........................................................ 134 Table 26: Mapping mining impairments to project forecasting error root causes .......... 139 Table 27: Simplified mining operation benefits and costs .............................................. 156 Table 28: Pinos Altos forecasted mine parameters ......................................................... 158 Table 29: Pinos Altos forecasted and actual mine parameters ....................................... 159 Table 30: Mining companies included in Shapiro et al. (2007) study ............................ 183 Table 31: Mining companies included in Camus et al. (2009) study ............................. 183 Table 32: Mining companies included in MacDiarmid et al. (2018) study .................... 183 Table 33: Market exchange listings of all data set companies ........................................ 184 Table 34: Status of all data set mine and mining project occurrences ............................ 188 xiv List of Figures Figure 1: MSCI World Metals and Mining Index, December 2000-2017 .......................... 2 Figure 2: MSCI Metals Price Index, July 1999 to July 2019............................................ 14 Figure 3: Box and whisker plot of return on assets for each strategic group.................... 19 Figure 4: Analysis of TRI vs. TSR by Camus et al. ......................................................... 23 Figure 5: TRvI vs TSR for my expanded data set ............................................................ 27 Figure 6: aTSR histogram ................................................................................................. 59 Figure 7: CAaTSR histogram ........................................................................................... 60 Figure 8: Book value of developed mining projects at first revenue, in 2015 dollars .... 105 Figure 9: Distribution of impairments per unique mine occurrence ............................... 106 Figure 10: Regional distribution of all mines in the data set .......................................... 107 Figure 11: Regional distribution of mines that declared impairments ............................ 107 Figure 12: Total number of impairment events recorded in each impairment category . 132 Figure 13: Total value of impairment events recorded in each impairment category .... 133 Figure 14: Mining impairments by type of forecasting error (count) ............................. 140 Figure 15: Mining impairments by type of forecasting error (value) ............................. 140 Figure 16: New mine development or acquisition and metal prices over time .............. 142 Figure 17: Impairment events and metal prices over time .............................................. 143 Figure 18: Hypothetical cost overrun data ...................................................................... 151 Figure 19: Hypothetical cost estimate escalation factors ................................................ 152 xv Acknowledgements Thanks to Prof. John Steen for your encouragement, support, and guidance. Thanks to Prof. Andrew von Nordenflycht for teaching me to ascribe the same level of skepticism to my own thoughts that I typically ascribe to the thoughts of others. Thanks to Prof. Scott Dunbar for convincing me to do this and for seeing it through to the end. Thanks to all three for having the faith that I would run out of arrogance before I ran out of motivation. I appreciate it. xvi Dedication For Sarah – your patience and support are boundless. 1 Introduction “The definition of a world class resource is one that survives five generations of incompetent management.” – South American GM1 In a 2001 publication, Phillip Crowson identified that the mining industry had generated low financial returns for investors since the 1970s. From 1977-1990 the mining industry underperformed 10-year US government bonds while exhibiting significantly more volatility; through the 1990’s the overall industry return was below its cost of capital. A recent report from Ernst & Young identified competitive shareholder returns as the second greatest risk facing mining and metals companies in 2018. It went on to state that “the sector has consistently underperformed in terms of returns to shareholders in recent years” (EYGM Limited 2018). Figure 1 shows the MSCI World Metals and Mining Index from 2000 to 2017. Taking the period of January 1st, 2003 to January 1st, 2016 as the most recent commodity super-cycle, the mining industry's returns have indeed been disappointing. The average annualized return for all operating mining companies listed on the Toronto Stock Exchange (TSX) was -8%. For comparison, the TSX Composite Index returned 7% annually while inflation averaged 1.7%. Canadian mining companies destroyed significant shareholder value over this period. 1 Steen et al. 2017 2 Figure 1: MSCI World Metals and Mining Index, December 2000-2017 (Source: Bloomberg, 2018) The purpose of any for-profit business is to earn a return for owners above the company’s cost of capital. Over the most recent commodity super-cycle, most Canadian mining firms did not achieve this - the average Canadian mining company destroyed significant shareholder value. And in the process, these mining companies spent billions of dollars, disturbed millions of hectares of land, and occupied thousands of intelligent, talented, and dedicated individuals for over a decade. The purpose of this research project is to determine what, if anything, can be done to improve mining industry returns. Literature Review: Mining Company Performance Several studies in peer-reviewed journals and academic theses have addressed mining industry and company performance from several angles. Some studies have looked at financial performance directly, while others have focused on the impacts of various levers of corporate strategy on performance (e.g., outsourcing specific business operations, a particular geographic or commodity focus, general operational efficiency). Further, performance has been defined in various ways, such as returns to investors, profitability, and returns on assets. 3 Crowson (2001) gives the most comprehensive assessment of mining company performance in the academic literature. He focuses on various periods from 1969 to 1999 and uses several different financial metrics to illustrate mining firms' poor aggregate performance. Crowson identifies that in over half the years from 1977 to 1990, the Australian mining industry did not recover its cost of capital. He also identifies that through the 1990s, global base metal producers generated lower market returns than US ten-year bonds while exhibiting greater price volatility. Crowson shows that this poor financial performance existed during rising metal demand and metal prices and suggests that this poor performance was due to the supply-side of the industry (mining operators). He theorizes that any value created by cost-cutting measures accrued to mining company customers rather than shareholders. O’Shannassy (2008) uses BHP as a minor case study in his academic discussion of the link between competitive advantage and firm performance by tracing BHP's rise, fall, and recovery through the 1990s. However, the treatment is brief. The author identifies periods of high and low performance for BHP without defining any specific performance dimensions and then deduces competitive advantage from the periods of high performance. Using Porter’s (2004) framework for competitive advantage (being driven by strategic positions of either cost leadership, differentiation, or focus), Heili (2005) states that mining companies must adopt a cost leadership position to achieve high performance. He identifies that the mining industry is commodity-based, and firms have no choice but to take global prices for the products they sell. Companies do not have the option to pursue differentiation or focus strategies when faced with this industry dynamic. He points out several mining companies in the Zambian copper belt refer to cost leadership in their Mission and Objective statements. 4 Camus et al. (2009), Garcia et al. (2010), and Garcia & Camus (2011) take the opposite position and argue that mining companies should focus much less on cost reduction. They demonstrate a relationship between mineral reserve growth and total shareholder return for 14 large mining companies from 2000-2008 and conclude that mining companies could improve returns by putting more organizational focus on mineral exploration and resource evaluation to drive reserve growth. They compare the mining industry to the oil and gas industry, specifically focusing on the concept of “upstream” oil reserve management and “downstream” refining and marketing activities. They advocate for creating an executive position in resource management and separating financial reporting within mining companies to identify value creation in the “upstream” and “downstream” activities. While Camus et al., Garcia et al., and Garcia & Camus show a general trend in the data which suggests increasing reserves correlate with increasing shareholder returns, the top performers in this data set are primarily gold producers, and there is a significant amount of dispersion among firms with average reserve growth. The authors acknowledge a few limitations of the research. First, they identify that the analysis could be improved by including more companies and looking at different periods. In hindsight, we can see that the period from 2000-2008 represents a significant increase in metal prices. A substantial and protracted period of declining metal prices was yet to come. Second, they identify that the markets punished the poor-performing outliers in the data set for taking on heavy debt loads, suggesting that disciplined financial management may be another core competency not investigated in this research. Third, they acknowledge that this research does not explore why some companies are better than others at increasing mineral reserves. 5 On the topic of mining company strategy, Shapiro et al. (2007) studied 26 of the world's largest hard rock miners in 2005 to determine whether companies appeared to pursue similar or differing strategies to one another. They also evaluated the aggregate financial performance of any strategic clusters (companies pursuing similar strategies) they could identify. To create strategic clusters, they compared companies along the dimensions of geographic scope, product scope, exploration and R&D spend relative to sales, and capital investment relative to the company’s number of employees. They defined financial performance as the average return on assets (ROA) from 2002 through 2004. The researchers identified one large strategic cluster and two smaller strategic clusters. Companies with high geographic and product scope (diversified multinational miners) made up the large cluster. Companies with low geographic and product scope (national single-commodity miners) and companies with a high geographic scope and low product scope (multinational gold miners) made up the two smaller clusters. They found the average ROA among all companies to be 10% during the study period. The diversified multinationals earned an ROA of 8%, the national single-commodity producers earned 20%, and the multinational gold miners earned 4%. The researchers concluded that an outlying strategic position, meaning a strategic position different from most firms in the analysis, correlates with higher economic returns, provided the company chooses the correct outlying strategy. MacDiarmid et al. (2018) directly study mining company performance factors with a data set of four diversified, multinational companies. Using enterprise value from 2006-2015 as the measure of performance, they find that revenue, commodity prices, and EBITDA multiple are the primary drivers of company performance. While the authors comment on a relationship between revenue and commodity price, there is no quantitative analysis of the potential 6 collinearity between those two variables. The authors find no significant and consistent relationship between production output, EBITDA margin, leverage ratio, net debt to EBITDA ratio, or return on capital employed and their performance measure of enterprise value. Primary Research Questions The existing research fails to draw any rigorous, generalizable conclusions about factors that affect mining firm performance. Earlier studies have either taken a small number of firms for analysis, studied short periods during rising commodity prices, or both. Further, there has been no investigation to parse factors under management control (like mineral reserve growth or the type of commodity produced) and those that are not (like commodity prices or market valuations). Crowson (2001) looks at a large population of mining companies over several decades but aggregates company performance for his analysis. In a study aimed at investigating industry versus firm performance, Hawawini et al. (2003) found that most of the value captured and destroyed within industries results from just a few firms. The authors hypothesize that “superior (or poor) management leads to superior (or poor) firm performance irrespective of industry structure." Following this research, the question remains of whether some mining companies exhibited market performance substantially above or below the average. Regarding factors that drive mining firm performance, Crowson (2001) asserted that poor industry performance is due to supply expansion (new mine development) without any attention to future demand. Still, there is no supporting analysis to test this theory. More recent studies have attempted to identify factors related to mining firm performance using quantitative analyses. However, these studies have used limited sample sizes over much shorter periods. Camus et al. (2009), Garcia et al. (2010), and Garcia & Camus (2011) studied 14 companies over 7 eight years, Shapiro et al. (2007) studied 26 companies over three years, and MacDiarmid et al. (2018) studied only four companies over nine years. O’Shannassy (2008) and Heili (2005) both commented on mining company performance without conducting any qualitative analysis. These studies show little overlap among factors investigated and no agreement among the conclusions. This research project answers the following questions: 1. Do mining companies generate returns that are significantly different than the commodities they produce? 2. Are there factors within the control of mining company management that lead to significantly higher and lower firm performance? 3. What can mining companies do to improve firm performance? Research Outline This research project uses a data set of every operating metal mining company listed on the Toronto Stock Exchange (TSX) between January 1st, 2003 and January 1st, 2016 with a minimum of 3 continuous years of operation – a total of 105 companies and almost 900 firm-year observations. This data set also includes information on every mining project developed or acquired by these firms over the study period. In total, this includes almost 500 operating mines and pre-revenue development projects that I identified by manually reviewing mining company Annual Reports, Financial Statements, and Annual Information Forms. Although this research project used data from companies listed on the TSX, it includes companies with head offices and mining operations in many regions. The findings are likely valid for mining companies around the world. In the first chapter, I test the results from the existing literature against this more comprehensive data set and find that the only supported relationship is between commodity price 8 and shareholder returns. The results from studies using smaller company samples and shorter periods during rising commodity price environments are not generalizable to my more extensive data set. In the second chapter, I identify that commodity prices play a significant role in explaining shareholder returns on an annual basis; however, the explanatory power of commodity prices drops considerably over the long run. In the third chapter, I find that mining asset impairments have the most significant effect on shareholder returns over the long run. I develop a multivariable model that explains almost 60% of firm performance using firm impairments, production costs, and commodity prices. In the fourth chapter, I investigate mining asset impairments and compare them to major project forecasting errors. I develop a theory that links mining asset impairments to project forecasting errors. In the fifth chapter, I investigate the root causes of mining asset impairments through the lens of project forecasting errors. I find that roughly 30% of asset impairments are due to technical errors, and 70% are caused by optimism bias or principal-agent conflict in the project planning and development process. Finally, the sixth chapter describes reference class forecasting and suggests this as a promising method to reduce forecasting errors and asset impairments in the mining industry, ultimately leading to improved shareholder returns. Significance of the Research From the perspective of mining company leadership and mining industry investors, it is essential to know if low investor returns were caused by unavoidable industry effects (like commodity prices) or if some companies created significant value for investors despite low average industry returns. If some mining companies generated significantly above-average returns, were there unique factors contributing to these high returns? And conversely, were there unique factors that contributed to low returns? 9 This research contributes to the mining firm performance literature, the business performance literature, and the project forecasting literature. First, this research project identifies that previous studies investigating mining firm performance using shorter periods of analysis during rising commodity price environments have not produced results that are generalizable to longer periods spanning varying commodity price environments. This finding also has implications for business performance research in other cyclical industries. Second, this research project identifies annualized total shareholder return as the most appropriate measure of performance for a long-term investor in the mining industry. Further, it identifies that using average annual returns can hide the influence of factors or events with effects spanning more than one fiscal year and suggests using long-term, annualized share price data to uncover these factors. This finding also has implications for business performance research in general, where firm or industry-level events with effects spanning more than one year may significantly impact firm performance. The third contribution applies this finding to the mining industry. It shows that commodity prices significantly influence annual shareholder returns, but asset impairments have a more significant influence on shareholder returns over the long run. Fourth, this research project found similarities between mining asset impairments and major project forecasting errors and then used the project forecasting error literature to identify the root causes of mining impairments. This research supports the project forecasting error literature (which has primarily studied public infrastructure projects) and identifies the mining industry as a potential area of future forecasting error research. It also provides the first quantitative assessment of the relative frequency of forecasting error root causes. Finally, this research project identifies reference class forecasting as a method to reduce mining forecasting errors and impairments. This study 10 concludes by suggesting future research to develop reference class forecasting methods for the mining industry. From a practical point of view, I expect companies can use this information to make decisions that are more likely to create value for shareholders and reduce the risk of destroying shareholder value. I also expect the findings will enable lenders and investors to improve capital allocation among mining companies by identifying companies more likely to generate higher future returns. With the knowledge generated by this research, prudent and thoughtful investors should earn returns at the top of the mining sector rather than the bottom. Mining companies pursuing intelligent corporate strategies will be rewarded with a lower cost of capital and easier corporate funding. In contrast, mining companies pursuing value-destroying strategies will be unable to raise capital and subsequently destroy value. Mining companies and mining financiers will benefit from a better understanding of the decisions that mining companies ought to make to achieve higher investor returns. 11 Chapter 1: Replicating Prior Studies Introduction The purpose of this chapter is to test whether the findings from the existing literature on mining firm performance are valid when applied to a comprehensive data set of all Canadian mining companies over an entire commodity cycle, from 2003 to 2016. This chapter will test the studies by Shapiro et al. (2007), Camus et al. (2009), and MacDiarmid et al. (2018). The other literature reviewed in the Introduction did not have sufficient quantitative analysis to be replicated here. Shapiro et al. (2007) use the mining industry to test the impact of strategic decisions on return on assets (ROA). Using a 3-year period from 2002 through 2004, they selected 26 global mining companies and compared the scope of products sold and the scope of geographic operations to ROA. The authors found that companies operating in a single region and producing a single commodity generated above-average ROA while multinational gold producers generated below-average ROA. I test these findings with a data set of every Canadian-listed metal mining company from 2003 to 2016 and conclude that the results of Shapiro et al. are not valid for this more extensive data set. Camus et al. (2009) set out to prove that mineral reserve growth is the most important factor in generating high shareholder returns. Using a 9-year period from 2000 to 2008, they selected 14 global mining companies and compared a mineral reserve growth factor to total shareholder return (TSR). The authors conclude that mineral reserve growth is positively correlated to increases in TSR. I test these findings with my data set of Canadian-listed metal mining companies from 2003 to 2016 and conclude that the results of Camus et al. are not valid for this more extensive data set. 12 MacDiarmid et al. (2018) attempt to identify the performance factors that drive company performance over rising, volatile, and falling commodity price environments. Using a 10-year period from 2006 to 2015, they select four global mining companies and compare production output, commodity price, revenue, EBITDA multiple, EBITDA margin, leverage ratio, net debt to EBITDA, and return on capital employed to their performance measure of company enterprise value. The authors conclude that revenue and commodity price are correlated to enterprise value while identifying a spurious correlation to EBITDA multiple. I test revenue and commodity price against enterprise value using a firm-year analysis with over 700 observations and conclude that the findings of MacDiarmid et al. hold up when replicated with this more extensive data set. Data Set My data set comprises publicly listed metal mining companies listed on the Toronto Stock Exchange (TSX) from January 1st, 2003 to January 1st, 2016. I chose publicly listed mining companies for the availability of company financial and operational information. While many private mining companies exist, it would be impractical or impossible to collect enough data to compare their performance. I look only at mining companies on the TSX to collect consistent and comparable information through SEDAR, the TSX reporting database. Some of the largest mining companies in the world list on the London Stock Exchange (LSE), Australian Stock Exchange (ASX) and the New York Stock Exchange (NYSE). However, the TSX provides the largest population of publicly listed mining companies, with approximately 1,200 companies having an industry sector classification of “metals and mining” (TSX Group Inc 2019). For comparison, the ASX has around 700 (ASX Limited 2015), and the LSE has fewer than 200 (London Stock Exchange plc 2019). The TSX not only has the greatest number of operating mining companies, but many companies on the LSE, ASX and NYSE are cross-listed on the 13 TSX, and those companies are included in this study (see Appendix B for the exchange listings of each company). Studies of cross-listed stocks on exchanges in different counties have found that prices are cointegrated and mutually adjust (e.g., Eun and Sabherwal 2003; Lok and Kalev 2006). Werner and Kleidon (1996) found that cross-listed stocks' intraday trading patterns closely resemble similar non-cross-listed stocks. Combining these conclusions from prior studies, I believe the findings from this study are generalizable to mining companies listed on the other major exchanges. Operating mining companies generate revenue from the exploitation of minerals. In contrast, junior mining companies typically hold mineral properties but have no mining operations and no declared revenue from metal or mineral sales. This research assumes that materially different factors drive the performance of operating mining companies and junior mining companies. While there are interesting questions about the performance of junior mining companies and the transition from junior to operator, these questions are outside this research scope. I cleaned the data set of junior mining companies and left only operating mining companies. To be included in the dataset, I required a minimum of 3 annual data points of commercial operation during the sample period. Further, I removed companies involved only in coal, fertilizer, aggregate, and diamond production. This research project assumes that metal mining companies' market performance is materially different from those involved in other types of mined products. For example, many Canadian coal mines have long-term sale contracts with coal-fired power plants. From a financial perspective, many appear to have more in common with public utilities than traditional mining companies. The world diamond market is primarily controlled by De Beers, which results in an opaque pricing market that does not follow commodity price movements. 14 The study period from January 1st, 2003 to January 1st, 2016 was chosen to evaluate company performance across rising, volatile, and falling commodity price environments (Marañon and Kumral 2018). Figure 2 below shows the MSCI Commodity Metals Price Index with the study period identified in a shaded box. Figure 2: MSCI Metals Price Index, July 1999 to July 2019 (Source: MSCI, 2018) There is a rising period from Q1 2003 to Q1 2008, then a volatile period from Q1 2008 to Q1 2011, and finally a falling period from Q1 2011 to Q4 2015. Different strategic or operational factors may result in above and below-average performance depending on the commodity price environment. However, this research project intentionally captures different commodity price environments to identify generalizable performance factors across all commodity price environments. From the estimated 1,200 publicly listed companies categorized by the Bloomberg Industry Classification System and the North American Industry Classification System (NAICS) as being involved in the metals and mining sector, I identified 115 mining companies with a minimum of 3 continuous years of mineral or metal product sales over the study period. Of those 15 115 operating mining companies, ten were removed from the data set because they were not principally involved in metal mining. 105 TSX-listed operating metal mining companies remained for this research project. Throughout the period, companies came into and out of commercial operation. Thirty companies were operating at the start of the period. Seventy companies were operating by the end of the period. 2012 had the highest number of operating companies with 88. In total, the dataset includes 899 firm-year observations. I show the descriptive statistics for the data set in the table below. Table 1: Data set descriptive statistics Total number of firms 105 Total firm-year observations 899 Minimum number of years per firm 2 Maximum number of years per firm 13 Average years per firm in the data set 8 Number of firms in each year (at Jan 1) 2003 30 2010 77 2004 33 2011 87 2005 40 2012 88 2006 46 2013 87 2007 57 2014 78 2008 64 2015 74 2009 68 2016 70 Shapiro et al. - Geographic Scope and Product Scope Shapiro et al. (2007) used the mining industry to analyze the impact of strategic decisions on company performance. More specifically, they tested the relative performance of groups of companies that made similar strategic decisions regarding the geographic scope (the geographic regions where the companies operate); product scope (the commodities the companies produce); 16 exploration and R&D expenditure as a percentage of revenue; and capital intensity (calculated as the dollar value of plant, property, and equipment per employee). The authors referred to companies that are similar along some or all strategic dimensions as strategically homogeneous and companies that are dissimilar along some or all strategic dimensions as strategically heterogeneous. Return on assets (ROA), as defined below, was used as the measure of firm performance. 𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝐴𝑠𝑠𝑒𝑡𝑠 =𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝐵𝑒𝑓𝑜𝑟𝑒 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑎𝑛𝑑 𝑇𝑎𝑥𝑒𝑠𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 The data set for the study by Shapiro et al. includes 26 companies (listed in Appendix A), and the authors collected data from the 2002-2004 fiscal years. The authors selected the companies from a report produced by a 2005 PriceWaterhouseCoopers report listing the largest mining companies in the world and a list of attendees at BMO Nesbitt Burn’s 2005 Global Resources Conference. To be included in the data set, companies needed to be primarily engaged in hard rock metal/mineral mining and have revenues over $1 billion. Shapiro et al. used descriptive statistics, Pearson correlations, cluster analysis, and scatter plots to evaluate strategic measures and identify strategic clusters. The authors identified three strategic clusters: Cluster 1 had a variable geographic scope and moderate to broad product scope (national and multinational multi-commodity producers, 18 companies); Cluster 2 had narrow geographic and product scope (single region, single commodity producers, six companies); and Cluster 3 had a broad geographic scope and narrow product scope (all multinational gold producers in this sample, two companies). The authors used scatter plots to identify performance outliers and defined outlying performance as plus or minus one standard deviation from the average performance. For this sample of companies, the mean ROA was 10% and the standard deviation was 7%, with 17 performance ranging from 3% to 32%. Looking at the performance of different strategic clusters, Cluster 1 (the dominant cluster) had an average ROA of 8%, Cluster 2 had an average ROA of 20%, and Cluster 3 had an average ROA of 4%. Shapiro et al. found that single region, single commodity producers demonstrated performance higher than one standard deviation above the mean and multinational gold producers demonstrated performance almost one standard deviation below the mean. From these results, I derive the following hypotheses: H1: Operation in a single region with the production of a single commodity results in above-average returns on assets. H2: Multinational gold mining results in below-average returns on assets. Analysis For this specific analysis, I removed one company from the data set because it had insufficient assets to generate a meaningful return on assets calculation for several years in the analysis period, bringing the total number of companies in this analysis to 104. I categorized each of these 104 companies by product scope and geographic scope. I based both measures of categorization on simplified versions of the methods by Shapiro et al. I determined product scope by collecting data on company revenue by commodity type. I categorized companies as either having “narrow” or “broad” product scope. I coded companies with only a single commodity accounting for more than 10% of revenue into the narrow product scope category. I coded companies with more than one commodity accounting for at least 10% of revenue into the broad product scope category. I determined geographic scope by identifying the regions where companies had operating mines. I coded companies into either “narrow” or “broad” geographic scope categories. Like the 18 regional classification by Shapiro et al., the regions used were North America, South America, Eurasia, Africa, and Asia Pacific. I coded companies with operations in a single region into the narrow geographic scope category and companies with operations in two or more regions into the broad geographic scope category. I categorized companies into strategic groups mimicking those used by Shapiro et al., based on the identification of narrow or broad product scope along with a narrow or broad geographic scope. Group 1 consisted of companies with broad product scope and narrow or broad geographic scope. Group 2 consisted of companies with narrow geographic scope and narrow product scope, and Group 3 consisted of companies with narrow product scope and broad geographic scope. Like the findings of Shapiro et al., Group 3 consisted almost entirely of multinational gold producers except for two multinational copper producers. When a company changed strategy, I counted it in each strategic position it occupied during the study period. This movement among strategic positions generated 142 total firm-position observations. I grouped data based on the time a company occupied each strategic group. These strategic groups stand as the independent variable in my analysis. I calculated return on assets (ROA) to stand as the dependent variable. Shapiro et al. calculated average ROA from annual financial data for the years 2002-2004. For my analysis, I took the average ROA across the entire period that a company occupied a particular strategic group. Following Shapiro et al., I analyzed the influence of strategic grouping on ROA by calculating the mean and standard deviation for each group and comparing this to the mean and standard deviation for all companies in the data set. 19 Results I present the analysis results for ROA in the table below and the following box and whisker plot (which shows the quartiles). Table 2: Return on assets by strategic group All Firms Group 1: Broad Product Variable Geography Group 2: Narrow Product Narrow Geography Group 3: Narrow Product Broad Geography Observations 142 44 75 23 Min -101.9% -29.9% -101.9% -31.7% Max 41.5% 20.5% 41.5% 18.3% Mean -1.6% 1.2% -3.1% -1.8% Median -0.5% 1.8% -1.1% -0.6% Std Dev 16.0% 10.3% 19.6% 10.3% Figure 3: Box and whisker plot of return on assets for each strategic group Shapiro et al. suggest performance more than ±1 standard deviation from the mean represents a meaningful difference in performance. With a total sample mean of -1.6% and a 20 standard deviation of 16.0%, we see from the table above that no strategic group came close to generating an ROA 1 standard deviation above or below the mean. We see that Group 1, referred to by Shapiro et al. as the “dominant strategic cluster,” had the largest deviation from the total sample mean; however, it still falls only 0.18 standard deviations away from the mean. Shapiro et al. used a data set of the largest global mining companies. In contrast, my dataset includes companies with a wide range of revenue and market capitalization values. To test for the effects of company size, I also performed the analysis using various revenue cutoffs. The results are robust to company size. Discussion From the analysis above, I can draw conclusions for both proposed hypotheses. Group 2 consisted of companies producing a single commodity in a single region. This group did not demonstrate above-average performance relative to the entire sample of firms as measured by ROA, so hypothesis H1: Operation in a single country with production of a single commodity results in above-average returns on assets is not supported. In fact, the analysis indicated slightly below-average performance for this strategic group. Group 3 mainly consisted of multinational gold producers. This group did not demonstrate below-average performance relative to the entire sample of firms as measured by ROA, so hypothesis H2: Multinational gold mining results in below-average returns on assets is not supported. In fact, the analysis indicated slightly above-average performance for this strategic group. There are three significant differences between the analysis done by Shapiro et al. and the analysis I performed here. First, the size and composition of the data sets are significantly different. Shapiro et al. used 78 firm-year observations (26 very large mining companies over 21 three years), while I draw from almost 900 firm-year observations among a wide range of company sizes. My analysis is robust to company size, so I do not believe results differ due to company size effects. I infer that the study period is the primary reason for the difference in results, leading to the second significant difference. Shapiro et al. use a much shorter period of 3 years than my study period of 13 years. It is plausible that certain strategic positions result in different mining firm performance under different economic conditions or that the performance benefits of strategic positions are fluid and change as companies in the mining industry change strategic positions. An evaluation of long-term performance would not capture effects that may only be present over shorter periods. Finally, Shapiro et al. use a more complex method for determining strategic position along geographic and product scopes. While using the same calculation method may have categorized some companies differently, I do not believe that the difference would have been meaningful because of the considerable size of the standard deviation of ROA in the complete data set and the strategic groups. Looking at the overall picture of these results, I conclude that specific strategic decisions around product and geographic scope do not materially affect mining firm ROA over the long run. Camus et al. - Mineral Reserve Growth Camus et al. (2009) explicitly set out to prove that mineral reserve growth is the most important factor in generating shareholder value, stating that “This paper aims to prove that the ‘one big thing’ [companies must focus on] in the mining business is mineral reserves growth.” The authors analyzed the relationship between mineral reserve growth and shareholder return for 14 companies from 2000 to 2008. The authors chose the companies to “broadly represent the whole mining industry,” although they acknowledge that “a few important mining companies are 22 missing” due to a lack of available information. (See Appendix A for the list of companies used in the Camus et al. data set). The independent variable in the analysis is a combination of mineral reserve growth and production, defined as Total Reserve Increment (TRI): 𝑇𝑅𝐼 =𝑅𝑖 − 𝑅𝑖−1 + 𝛴𝑖𝑃𝑟𝑜𝑑𝑅𝑖−1 where: 𝑅𝑖 attributable, recoverable reserves at the end of period i 𝑅𝑖−1 attributable, recoverable reserves at the beginning of period i Prod attributable production during the period i Camus et al. recorded and compared recoverable reserves on an ‘equivalent commodity’ basis, meaning they expressed minor products in terms of the company's primary product. For example, if gold accounts for most of a company’s revenue but it also produces copper and silver, all reserves and production were expressed in equivalent gold ounces. The authors calculated equivalent commodities using average prices and metallurgical recoveries for each minor commodity. The authors explain the choice to include mineral production in the TRI variable by stating, “. . . the counterpart of the company share price is mineral reserves. For operating mining companies, dividends are frequently the matching part of production.” Camus et al. assume mineral reserves exploited in the past and mineral reserves available to the company in the future drive TSR. However, if investors’ expectations about the future drive share value, depleted reserves may harm the share price. It appears to me that Camus et al. are conflating a backward-looking metric (past production) and a forward-looking metric (mineral reserves), which may not be appropriate when attempting to explain stock price movements. Camus et al. use Total Shareholder Return (TSR) as the dependent variable in their analysis and calculate TSR as follows: 23 𝑇𝑆𝑅 =𝑃𝑖 − 𝑃𝑖−1 + 𝛴𝑖𝐷𝑖𝑣𝑃𝑖−1 where: 𝑃𝑖 share price at the end of period i 𝑃𝑖−1 share price at the beginning of period i Div dividends paid in the period i The authors analyzed the relationship between the dependent and independent variables by graphing and visual analysis without any statistical methods. The authors used the logarithm of TRI and TSR to construct the graph used for the analysis (shown in the figure below). Because the log of a negative number is undefined, a significant and critical limitation of this analysis method is the exclusion of any companies with negative TSR or TRI. Figure 4: Analysis of TRI vs. TSR by Camus et al. (Source: Camus et al., 2009) Camus et al. visually identify a correlation between increases in the logarithm of TRI and increases in the logarithm of TSR while acknowledging several outliers. They explain Inmet, 24 Antofagasta, and BHP generate relatively high TSR from large mineral resources that may be converted to future reserves. (Mineral resources are areas of a mineral deposit that have not yet demonstrated economic viability, whereas mineral reserves have demonstrated economic viability). However, the authors perform no analysis of the influence of mineral resources on TSR and give no information about the other companies' mineral resource holdings. On the other hand, they explain the relatively low TSR of Teck, Anglo American, and Barrick by citing high debt loads and production hedging activities. Again, the authors perform no analysis of the influence of debt or hedging on TSR and give no information about other companies' debt loads. Nevertheless, Camus et al. conclude that mineral reserve growth plus production is positively related to total shareholder return, and we have the following hypothesis: H3: Mineral reserve growth plus mineral production is positively correlated to shareholder return. Data Set Beginning with my complete dataset described earlier in this chapter, I removed eight companies because they had no declared mineral reserves during the study period, bringing the total number of companies used for analysis to 97. Analysis I used a total reserve growth factor as the independent variable; however, I used the dollar value of mineral reserves and production rather than metal units (ounces of gold, pounds of copper, tonnes of lead, etc.). I use the dollar value of mineral reserves and production rather than the production unit to account for changes in a company’s primary commodity and overall commodity mix over time. I controlled for the effect of commodity price changes by calculating all mineral reserve values in terms of 2016 commodity prices. I adjusted production revenue to 25 2016 commodity prices for each company. I call this the Total Reserve Value Increment (TRvI), and it is defined as follows: 𝑇𝑅𝑣𝐼 =𝑉𝑖 − 𝑉𝑠 + 𝛴𝑖𝑅𝑒𝑣𝑉𝑠 where: 𝑉𝑖 value of attributable, recoverable reserves in 2016 commodity prices at the end of period i 𝑉𝑠 value of attributable, recoverable reserves in 2016 commodity prices at the start of period i Rev value of production in 2016 commodity prices during the period i I follow Camus et al. by using the first and last available data points in the series to calculate TSR and TRvI. Camus et al. do not provide an explanation for the decision to use the first and last data points for each company to calculate TSR and TRI, rather than annual, semi-annual, or quarterly data as we will see in the analysis done by MacDiarmid et al. (2018) in the following section. However, I agree with this approach because it mitigates the impact of the timing of mineral reserve declarations on stock prices. Like mineral reserve declarations, impairments result from business conditions or decisions visible to investors before they are declared and persist afterward. Studies of asset write-offs and impairments in the business literature have found stock price impacts spanning multiple years before and after the impairment announcement (Elliott and Shaw 1988; Bartov, Lindahl, and Ricks 1998). I believe that mineral reserve declarations are similar because companies release indicative information (e.g., drill results and multiple stages of resource declaration) well before mineral reserves are officially declared. They will also release reserve and feasibility study updates after the declaration of mineral reserves. For this reason, I believe that taking as long a period as possible will stand the best chance of identifying any long-term relationship between mineral reserve growth and firm performance if one exists. 26 Like Camus et al., I calculate total shareholder return to stand as the dependent variable. Unlike the method used by Camus et al., I used the value of TSR and TRvI directly and did not convert them to logarithms. This method allowed me to include companies with negative TSR and TRvI in the analysis. Using only companies with positive TSR and TRvI would have eliminated 59 of the 97 companies from the analysis. With that said, I also perform the analysis using only companies with positive TSR and TRvI. I graphed the results and then conducted a statistical analysis to check for correlation. I chose regression analysis as a standard method of testing for a relationship between business performance and a performance factor (e.g., Rumelt 1991; R. Hoskisson et al. 1999; R. Johnson and Soenen 2003; Fama and French 2006; MacDiarmid, Tholana, and Musingwini 2018). I used the statistical software R Studio for the analysis. Results The figure below shows the plotting of TRvI vs TSR with 97 companies. The figure below shows a significant number of positive and negative TSR values, and there is no discernable correlation. There are several outlying data points for both TSR and TRvI. 27 Figure 5: TRvI vs TSR for my expanded data set I present the descriptive statistics for TRvI and TSR in the table below. Table 3: TRvI and TSR descriptive statistics TRvI TSR Count 97 97 Min -0.98 -1.00 Max 72.24 11.44 Mean 5.16 0.38 Median 1.37 -0.28 Standard Deviation 10.60 2.10 -11357911-5 15 35 55 75Total Shareholder ReturnTotal Reserve Increment28 The linear regression produced a coefficient of determination (R²) of 0.00 and a probability value (p-value) of 0.52. These results indicate that there is no statistically significant correlation between TSR and TRvI. To mimic the original study, I also performed an analysis using the logarithms of TSR and TRvI. Because the logarithm of a negative number is undefined, I could only include companies with positive values of each variable. This method resulted in a positively biased sample of 38 companies. I did not find any statistically significant relationship. I also analyzed the entire sample of 97 companies using enterprise value to control for company size and the number of operating years to control for differences in operating duration. In each case, the regression still failed to achieve statistical significance. The following table summarizes the results for each linear regression model. Table 4: Camus et al. replication linear regression model results Model Effect Size R2 Std. Error p-value Full sample, no control variables 0.01 0.00 0.02 0.52 Full sample, enterprise value control 0.01 -0.02 0.02 0.82 Full sample, operating years control 0.01 -0.02 0.02 0.86 Logarithm of TSR and TRvI -0.10 -0.02 0.21 0.60 Discussion The statistical analysis showed no significant relationship between total shareholder return and reserve growth plus production; therefore, hypothesis H3: Mineral reserve growth plus mineral production is positively correlated to shareholder return is not supported. This conclusion is valid for both the broader dataset of 97 companies using direct values and the smaller and positively biased sample of 38 companies using the logarithms of the values. Further, the results of this analysis are robust to company size. The conclusions drawn by Camus 29 et al. using eight large mining companies in a rising commodity price environment do not appear to be generalizable to a much larger sample of companies over a longer time horizon containing different commodity price environments. As discussed earlier in this section, it is likely a theoretical error to include mineral production in the reserve growth factor. Camus et al. relate mineral production in their TRI factor to dividends in the TSR factor; however, I believe the parallel they identify is incorrect. Outside of the mining industry, companies typically issue dividends from free cash flow generated from assets. The generation of this cash does not directly represent an erosion of the company’s asset base. An investor is likely to believe that a history of regular dividend payments indicates sustainable future dividend payments. In the mining industry, cash is generated precisely through a reduction in mineral assets. The faster a company produces and sells minerals, the less likely it is to produce in the future unless it is regularly replenishing the depleted mineral assets. And in this analysis, the inclusion of production was not trivial – on average, the production component of TRvI made up 45% of the numerator. Despite this theoretical issue, Camus et al. graphically identify a relationship between their reserve growth plus production data and shareholder return data. Perhaps this relationship is only valid during rising commodity prices or for the limited sample of companies in their data set. However, I do believe that the theory linking mineral reserve growth to stock prices is likely correct. The following chapter will outline this theory in more detail and investigate the impact of reserve growth on shareholder returns without a mineral production component. MacDiarmid et al. - Commodity Price and Revenue MacDiarmid et al. (2018) identify factors that drive company value over rising, volatile, and falling commodity price environments. They determine that “the period 2006–2008 was 30 characterized by a commodity price boom which saw mining companies making extraordinary profits as commodity prices were increasing” then “this was followed by a commodity price downturn with the onset of the [global financial crisis] in 2008 and subsequent recovery during the period 2010–2011. Then from 2011 to 2015, there was a steady downward trend in commodity prices that increased pressure on mining companies to reduce costs in order to adjust to the declining commodity prices.” This identification of increasing, volatile, and falling commodity price periods corroborates the periods I identified during my dataset's initial construction. The only difference is the starting year: MacDiarmid et al. begin in 2006, while the starting year for my dataset is 2003. Although they use a much longer sample period than the studies by Shapiro et al. (2007) and Camus et al. (2009), they use a limited sample size of only four companies. (I provide a list of these companies in Appendix A). The authors use the change in enterprise value as the dependent variable. They define enterprise value as equity value plus total debt, plus preferred stock, plus minority interests, minus cash and cash equivalents. They calculate the dependent variable as the ratio of ending enterprise value divided by starting enterprise value. 𝛥𝐸𝑉 =(𝑃𝑖 × 𝑆𝑖) + 𝐷𝑖 − 𝐶𝑖(𝑃𝑖−1 × 𝑆𝑖−1) + 𝐷𝑖−1 − 𝐶𝑖−1 where: Pi share price at the end of period i Pi-1 share price at the beginning of period i Si diluted number of shares outstanding at the end of period i Si-1 diluted number of shares outstanding at the beginning of period i Di total debt, preferred, stock and minority interests at the end of period i Di-1 total debt, preferred stock, and minority interests at the beginning of period i Ci cash and cash equivalents at the end of period i Ci-1 cash and cash equivalents at the beginning of period i 31 The authors select eight potential ‘value drivers’ as independent variables. They are production output; commodity price; revenue; EBITDA multiple (i.e., EBITDA divided by stock price); EBITDA margin (i.e., EBITDA divided by revenue); leverage ratio; net debt to EBITDA ratio; and return on capital employed (ROCE). The authors used either annual data (ROCE, net debt to EBITDA, EBITDA multiple), semi-annual data (leverage ratio, EBITDA margin, revenue, production output), or quarterly data (commodity price) over the 10-year period to create a longitudinal data set for each independent variable for each company. The authors do not explain the reason for the different frequencies of data collection within the period. The relationship between each independent variable and the dependent variable was then investigated for each company independently over the time series. The authors first use visual inspection of graphs and then use Microsoft Excel to calculate Pearson correlation coefficients (r) and p-values. The authors base their analysis, discussion, and conclusions on the Pearson correlation coefficient; however, they also report R-square and adjusted R-square values in their study. MacDiarmid et al. assert that “[it] is generally assumed that a correlation coefficient between 0.1 and 0.3 indicates a low correlation, while a correlation coefficient between 0.3 and 0.5 and that between 0.5 and 1.0 indicate medium and high correlations, respectively.” The authors combine this framework with significance testing at the 95% level (p-value <0.05) and conclude that “it was established that revenue, commodity price and EBITDA multiple are primary drivers of company value.” I summarize their findings in the table below. ‘High,’ ‘Medium,’ and ‘Low’ refer to the strength of the correlation as defined by the authors, and the results in bold indicate a p-value <0.05. 32 Table 5: MacDiarmid et al. analysis results Variable Anglo BHP Rio Tinto CVRD (Vale) Production Low. None Medium*** Low. Commodity Price Low** High*** High*** Low*** Revenue High*** High*** High*** Medium*** EBITDA multiple Medium* None Medium* Medium* EBITDA margin Medium** None None None Leverage ratio Low* None None None Net debt to EBITDA Medium* Low Low None ROCE Low* None Low Low Significance (p-value) codes: 0=‘***’ | 0.001=‘**’ | 0.01=‘*’ | 0.05=‘.’ | 0.1=‘ ’ Based on this analysis, the authors conclude that that revenue, commodity price, and EBITDA multiple “are primary drivers of company value.” However, the authors define the EBITDA multiple as EV/EBITDA, so EV is a confounding variable in this analysis –both variables in the analysis will increase as EV increases. Leaving EBITDA multiple aside, I will test the following hypotheses: H4: Revenue increases positively correlate with increases in enterprise value for mining firms. H5: Commodity price increases positively correlate with enterprise value for mining firms. Dataset MacDiarmid et al. collected 20 revenue data points and 40 commodity price data points for each of the four companies over a 10-year period. In total, they had 83 observations for the 33 analysis of revenue versus enterprise value and 162 observations for the analysis of commodity price versus enterprise value. However, since the authors analyzed each company individually, the maximum number of observations in each analysis was 21 and 41 for revenue and commodity price, respectively. The data set for this analysis uses annual information from the 105 study companies for a firm-year analysis. In total, I have 786 observations for both changes in revenue versus enterprise value and changes in commodity prices versus enterprise value. I took revenue information directly from company financial statements. Commodity prices were taken from several online sources and cross-checked for accuracy and reliability when possible. Some commodities have widely quoted and transparent prices (e.g., gold, silver, copper). I needed to source others from metal trading databases such as Johnson Matthey Base Prices (e.g., palladium, rhodium). Some needed to be taken directly from company financial statements (e.g., cobalt, antimony, molybdenum). In all cases, I took prices for each commodity in my data set from a single source to maintain consistency over the study period. Analysis Firm revenue and commodity price were the independent variables in the analysis. I represented both variables in the analysis as ratios of the year-over-year change. Like MacDiarmid et al., I based the change in commodity price on a weighted average of the commodities produced by each company at each observation. For example, if a company generated revenue through 70% gold sales and 30% copper sales, the corresponding commodity price for that firm-year observation was based 70% on the gold price change and 30% on the copper price change. I calculated the ratio for commodity price using the following equation: 34 𝛥𝐶𝑃 =∑ (𝐶𝑗𝑖 × 𝑃𝑗𝑖𝑛𝑗=1 )∑ (𝐶𝑗𝑖−1 × 𝑃𝑗𝑖−1)𝑛𝑗=1 where: 𝐶𝑗𝑖 percentage of revenue derived from commodity j at the end of period i 𝐶𝑗𝑖−1 percentage of revenue derived from commodity j at the beginning of period i 𝑃𝑗𝑖 price of commodity j at the end of period i 𝑃𝑗𝑖−1 price of commodity j at the beginning of period i I calculated the ratio for revenue changes using the following equation: 𝛥𝑅𝑒𝑣 =𝑅𝑒𝑣𝑖𝑅𝑒𝑣𝑖−1 where: 𝑅𝑒𝑣𝑖 company revenue at the end of period i 𝑅𝑒𝑣𝑖−1 company revenue at the beginning of period i I calculated enterprise value changes in the same way as in the MacDiarmid et al. study. I calculated the enterprise value variable as the ratio of ending enterprise value to starting enterprise value: 𝛥𝐸𝑉 =(𝑃𝑖 × 𝑆𝑖) + 𝐷𝑖 − 𝐶𝑖(𝑃𝑖−1 × 𝑆𝑖−1) + 𝐷𝑖−1 − 𝐶𝑖−1 where: Pi share price at the end of period i Pi-1 share price at the beginning of period i Si diluted number of shares outstanding at the end of period i Si-1 diluted number of shares outstanding at the beginning of period i Di total debt, preferred, stock and minority interests at the end of period i Di-1 total debt, preferred stock, and minority interests at the beginning of period i Ci cash and cash equivalents at the end of period i Ci-1 cash and cash equivalents at the beginning of period i MacDiarmid et al. analyze the change in revenue and the change in commodity price against enterprise value for each of the four companies in their study individually and then draw conclusions by comparing the company results. In a way, they are performing a manual fixed-35 effect analysis but do not mathematically combine the results. I first graphed the data for my analysis and then conducted a fixed-effect regression analysis with the statistical software R Studio. Results The table below shows the descriptive statistics for the analysis variables. Table 6: Descriptive statistics for replicated MacDiarmid et al. variables 𝜟𝑪𝑷 𝜟𝑹𝒆𝒗 𝜟𝑬𝑽 Count 786 786 786 Min 0.39 0 -11.78 Max 2.26 66.67 12.01 Mean 1.09 1.59 1.27 Median 1.06 1.15 1.07 Standard Deviation 0.28 3.12 1.20 The fixed-effect regression analysis of revenue versus enterprise value found a 1.00% change in revenue correlated with a 0.11% change in enterprise value. The coefficient of determination (adjusted R²) was 0.57, and the probability value (p-value) was <2.2e-16. The guidelines set out by MacDiarmid et al. classify the correlation as “high” while the very low p-value indicates high statistical significance. The fixed-effect regression analysis of commodity price versus enterprise value found that a 1.00% change in weighted average commodity price correlated with a 1.36% change in enterprise value. The coefficient of determination (adjusted R²) was 0.58, and the probability value (p-value) was <2.2e-16. The guidelines set out by MacDiarmid et al. classify the ROA correlation as “high” while the very low p-value indicates high statistical significance. I also ran all analyses using enterprise value as a control variable for company size with no material change in the effect size or statistical significance of the results, indicating that the 36 results are robust to company size. The table below summarizes the results of the linear regression models. Table 7: MacDiarmid et al. replication linear regression model results Model Effect Size Std. Error R2 P-Value 𝛥𝑅𝑒𝑣 vs 𝛥𝐸𝑉, no control variable 0.11 0.01 0.57 <2.2e-16 𝛥𝐶𝑃 vs 𝛥𝐸𝑉, no control variable 1.36 0.15 0.58 <2.2e-16 𝛥𝑅𝑒𝑣 vs 𝛥𝐸𝑉, firm size control variable 0.08 0.01 0.58 <2.2e-16 𝛥𝐶𝑃 vs 𝛥𝐸𝑉, firm size control variable 1.40 0.17 0.58 <2.2e-16 Discussion The results show a statistically significant relationship between increases in firm revenue and enterprise value. Therefore, hypothesis H4: Revenue increases positively correlate with increases in enterprise value is supported. The results also show a statistically significant relationship between commodity price increases and enterprise value. Therefore, hypothesis H5: Commodity price increases positively correlate with increases in enterprise value is supported. I found that both changes in revenue and changes in commodity price explained almost 60% of the variability in enterprise value of individual firms. However, the commodity price effect size is an order of magnitude stronger than revenue (1.36% vs 0.11%). An important point to note in this analysis (and the study by MacDiarmid et al.) is that commodity prices are present in the revenue variable - revenue for a mining firm can be calculated as commodity price multiplied by commodity production. MacDiarmid et al. found that changes in production are not correlated to changes in enterprise value. The results of my analysis indirectly support this additional result from the MacDiarmid et al. study. I infer from the relative influence of commodity price and revenue on enterprise value that the addition of production information in the revenue factor weakens its effect relative to commodity price 37 alone. This result may also support the conclusions from the prior section where mineral reserve growth plus production was uncorrelated to total shareholder return. In both that analysis and this analysis, it seems that higher production does not correlate with higher market valuations. If resource holdings are the primary measure of market value for mining companies, both production and revenue should work against mining companies' market returns. I will investigate the connection between mineral reserves (on their own) and company value in the following chapter. Unlike the studies by Shapiro et al. (2007) and Camus et al. (2009), MacDiarmid et al. used a more extended study period that included rising, volatile, and falling commodity price environments. Given that my analysis did not support the Shapiro et al. and Camus et al. findings, it may be the case that the commodity price environment has a significant influence on the factors affecting mining firm performance. What works in one commodity price environment may not work in others. A strategy that creates high performance in a rising commodity price environment may not be generalizable over the long run for a mining company. It may be of academic interest to study the impact of different strategic decisions for mining companies in different commodity price environments. However, from a practical point of view, applying these finding would require the successful prediction of commodity prices and if the management of a mining company could successfully predict commodity prices then I offer that they do not need to bother with mining at all and simply trade commodity futures to make a large fortune. The fifth chapter in this study will shed some light on the ability of mining company management to predict future metal prices. Another interpretation of this chapter's findings is that commodity prices largely determine mining firm performance, and the strategic decision-making of mining company 38 management plays a relatively minor role in firm performance. The commodity price versus enterprise value fixed-effect regression model showed that commodity prices alone explained 58% of the variance in firm returns. However, this does leave 42% of the variance in enterprise value unaccounted for. Further, it says nothing about how commodity price influences on individual firms may vary within the data set. Among the three quantitative studies of mining firm performance that I tested in this chapter, the MacDiarmid et al. study was the only one that looked at commodity prices as an independent variable and none of the studies controlled for commodity prices when investigating other factors. These three studies leave unanswered questions about the effect of commodity prices on individual firms within the data set and the influence of factors other than commodity price on firm performance. Conclusion The purpose of this chapter was to test whether the findings from previous studies of mining firm performance are valid for my more extensive data set of all Canadian mining firms from 2003 to 2016. I tested the conclusions from Shapiro et al. (2007), Camus et al. (2009), and MacDiarmid et al. (2018). Shapiro et al. studied 26 global mining companies over a 3-year period. They found that a company strategy of single commodity production in a single region generated above-average returns on assets (ROA) and a company strategy of gold production in multiple regions generated below average ROA. Using the same analysis method as Shapiro et al., I found no strategic groups generated ROA significantly above or below the average. Camus et al. studied the mineral reserve growth of 14 global mining companies over a 9-year period. They graphed the logarithm of reserve growth against the logarithm of total shareholder return and visually identified a correlation between the two variables. I performed an 39 analysis using linear regression on my more extensive data set and did not find any statistically significant relationship between reserve growth plus production and total shareholder return. MacDiarmid et al. used a data set of 4 companies over a 10-year period to investigate factors that may affect mining firms' enterprise value. They found that changes in revenue and commodity price changes positively correlate to enterprise value changes. Using my more extensive data set, I confirmed the findings of MacDiarmid et al. and concluded that both revenue and commodity prices are positively correlated to enterprise value. Further, I found a fixed-effect regression model using commodity prices as the independent variable had an effect size on enterprise value that was approximately ten times greater than one using revenue as the independent variable. It appears that taking a longer study period over different commodity price environments is more important than taking a larger sample of companies when trying to draw general conclusions about long-term performance factors in the mining industry. The studies by Shapiro et al. and Camus et al. both looked at periods of rising commodity prices. In contrast, MacDiarmid et al. took a longer period that included rising, volatile, and falling commodity price environments. The conclusion that changes in commodity prices have a large and significant impact on company value further supports this observation. Picking a specific point in time while ignoring the effects of commodity price can result in conclusions about mining firm performance that are not generalizable to other periods. Further, there are unanswered questions about the influence of factors other than commodity price on firm performance. The following two chapters will address these questions by identifying whether individual mining firm performance was significantly different from commodity price performance and by identifying factors within the control of mining company management that positively and negatively affect performance. 40 Chapter 2: Mining Firm Performance Introduction In the last chapter, I reviewed the current academic literature on mining firm performance and tested the conclusions from these studies against my more extensive data set of Canadian mining firms over a 13-year period that included rising, volatile, and falling commodity price environments. The only supported conclusion from the existing literature was a correlation between commodity prices and firm enterprise value. There are two primary purposes to this chapter. First, this chapter will identify an appropriate performance measure from a long-term mining company shareholder's perspective. The studies of mining firm performance analyzed in the last chapter had each used a different performance measure as the dependent variable, so I must select a performance measure that clearly aligns with my stated research frame. Second, this chapter will determine whether firm performance was significantly different than commodity price performance to identify whether there may be firm-level factors that can generate performance materially higher or lower than commodity price performance. This chapter starts by defining mining firm performance from a long-term mining company shareholder's perspective. I review the business performance literature and discuss the advantages and limitations of various accounting measures of performance and market measures of performance. Ultimately, I determine that annualized total shareholder return (aTSR) is the most appropriate performance measurement for long-term shareholders in the mining industry. I then test the effect of commodity prices on aTSR to understand the impact of commodity prices on firm performance from the point of view of a long-term mining company shareholder. Finally, I compare the aTSR of individual firms to the market return of a basket of commodities representing each firm's unique commodity production mix to calculate a commodity-adjusted 41 return. I then determine the difference between firm performance and commodity price performance for each mining company in the data set. The analysis of commodity price on aTSR and the investigation of commodity adjusted returns indicated that there are likely to be firm-level factors that significantly affect mining company returns over the long run. Defining Performance The previous chapter identified four prior studies of mining firm performance which used four different ways of measuring firm performance: return on invested capital (Crowson 2001); shareholder returns (Crowson 2001; Camus et al. 2009); return on assets (Shapiro et al. 2007); and enterprise value (MacDiarmid et al. 2018). The purpose of this section is to identify the performance measure that is most appropriate when taking the view of a long-term shareholder in the mining industry. I start with a review of the business literature to identify the range of potential performance metrics. Ford and Schellenberg (1982) define an organization as “an open system and as a coalition of diverse [stakeholders], each with a specifiable aspiration or expectation level as to what it expects in exchange for continued membership in the coalition.” They identify that an organization can be assessed as good on some dimensions and bad on others, or both good and bad on the same dimensions by different stakeholders. Gentry and Shen (2010) suggest that “researchers should consider clearly defining the construct or the specific aspect of firm financial performance they intend to study first, and then using it to guide theory and hypothesis development.” Venkatraman and Ramanujam (1986) support this point of view, identifying that different fields of study should use different performance measures based on their unique research questions. These studies illustrate that there is no single, correct performance measure 42 and that stakeholders with different interests will view firm performance differently. To properly guide the current research, I must choose a clear frame or perspective to determine an appropriate performance measure. Johnson et al. (1985) are forceful in their assertion that a corporation's purpose is to create shareholder wealth, and “corporate excellence” ought to be judged on this basis. However, mining operations have significant environmental, social, economic, and developmental purposes and impacts that reach much further than simple shareholder wealth creation (Richards 2009). Mining company leadership must consider many important stakeholders when making strategic decisions. However, I must choose one specific point of view so a precise research question can be posed and then clearly answered. In this research project, I am solely concerned with the interests of long-term company investors. Therefore, I will attempt to select the most appropriate measure of firm performance to represent long-term shareholders. Researchers studying firm performance have identified a wide range of dependent variables to measure firm performance. The existing business performance literature has broadly categorized performance measures into either accounting measures or market measures. Accounting measures use accounting or financial information typically available to the public through published reports and business databases. These measures are generally derived from earnings or profits, and examples include earnings divided by assets (Return on Assets or ROA), earnings divided by sales (Return on Sales or ROS), and earnings divided by paid-in capital and retained earnings (Return on Invested Capital or ROIC). Market measures are based on a company’s stock price performance or total market value, often combined with financial measures. Examples include share price appreciation plus dividends (Total Shareholder Return or TRS), market value plus debt and minus cash (Enterprise Value), market value divided by 43 total assets (Tobin’s q) and several measures that look to compare stock returns to risk-free return rates and volatility (e.g., Jensen’s Alpha, Sharpe Ratio, Treynor Ratio). In a paper about the persistence of competitive advantage over time, Wiggins and Ruefli (2005) reviewed several prior studies. They found that “most of the studies use ROA as their primary or only measure of performance.” Gentry and Shen (2010) found that market to book and stock market returns are “the most widely used measures of stock market performance in management research.” In a study of corporate divestitures, Hoskisson et al. (1994) use the accounting measures ROA, ROS and ROE along with the market-based Sharpe Ratio, Treynor Ratio and Jensen’s Alpha. Lubatkin and Rogers (1989) identify that previous studies have examined return without accounting for risk. To address this shortcoming in their research on firm diversification strategies, they create a modified version of Jensen’s Alpha to measure performance. Johnson et al. (1985) use the total rate of return to shareholders as “a simple, direct measure of economic value created” in their paper about the concept of corporate excellence. These past studies illustrate that there is no single, correct measure of firm performance. Therefore, I will need to identify which performance measure is most appropriate for long-term mining company shareholders. The first step is to determine whether an accounting measure or a market measure is most appropriate. Accounting and Market Measures Gentry and Shen (2010) assert that management researchers should accept both accounting and market measures as valid measures of firm financial performance. However, there have been conflicting studies on the equivalency of accounting and market measures. Looking at ten different accounting measures of performance, R. Johnson and Soenen (2003) found ROA to be significantly correlated to market measures. On the other hand, Gentry and 44 Shen found accounting profitability and market measures to have a covariance of less than 10% and stated that one or the other needs to be selected to evaluate performance. Although some studies have indicated that accounting measures correlate with market measures, there are some conceptual problems with accounting measures when taking a company shareholder's viewpoint. The principal issue with accounting measures is that they do not take risk into account when assessing performance (W. B. Johnson, Natarajan, and Rappaport 1985; Lubatkin and Rogers 1989; Hawawini, Subramanian, and Verdin 2003). A company could improve accounting measures by taking more financial risk while negatively affecting company value and market performance. Because market values are based on future expectations while accounting measures are based on past performance, it is understandable that investors would react negatively to the prospect of increased company risk in the future (R. E. Hoskisson, Johnson, and Douglas 1994; Gentry and Shen 2010). For example, a company could improve ROA by deferring the replacement of critical assets to keep asset value low. However, this deferral could create operational risks of unplanned production losses that put downward pressure on the stock price and market value. Further, there is a significant complication for mining company investors when relying on any measure that uses the book value of assets. Despite mineral reserves and resources generally being a mining company’s most significant asset, their value of reserves and resources is not accurately reflected on company financial statements. Mineral asset valuation is typically forward-looking since it represents the stock of raw materials that a mining company can potentially convert into revenue and profits; however, IFRS and US GAAP financial accounting standards are typically backward-looking (Njowa and Musingwini 2018). The book value of a company’s assets may be entirely out of line with its mineral asset value because of standard 45 accounting practices' backward-looking nature. For example, two companies with identical mineral reserves can have significantly different asset values because one company acquired an operating mine, and the other discovered the mineral asset through greenfield exploration. The acquiring company would report the cost of the acquisition on its balance sheet through assets and goodwill. In contrast, the discovering company would only have the capitalized exploration costs, which would likely be much lower. Comparisons of accounting ratios like ROA and asset-based market measures like Tobin’s q (market value divided by asset value) become inappropriate when applied to mining companies. In summary, accounting measures are typically backward-looking and do not take risk into account, while market measures are typically forward-looking and take risk into account. Further, any accounting measure that takes the book value of assets into account will likely not be appropriate for any study of mining firm performance because of the accounting practices used to report asset values for mining companies. Taking these deficiencies with accounting-based measures into account, I believe that a market-based measure is most appropriate for comparing performance among mining companies, given the viewpoint of a long-term company shareholder. Specific Market Measures Now that I have identified a market-based measure (rather than an accounting-based measure) as most appropriate for the chosen research frame, this section first reviews the business literature's standard market-based measures. It then identifies the most appropriate performance measure for long-term mining company investors. The business literature has commonly used Total Return to Shareholders (e.g., Johnson et al. 1985; Camus et al. 2009), Enterprise Value (e.g., MacDiarmid et al. 2018), the Sharpe ratio, 46 the Treynor ratio and Jensen's alpha (e.g., Hoskisson et al. 1994), and combinations thereof (e.g., Lubatkin and Rogers 1989) to evaluate or compare company performance. Total Return to Shareholders (TRS) is the change in stock price plus dividends. It can be presented as a total return over a given period, an average annual return, or an average annualized return. TRS represents the most straightforward measure of ultimate shareholder benefit. Enterprise value is the total equity of a company plus total debt minus cash. While enterprise value gives a theoretical “all-cash purchase price” for a company, it does not account for differences in its ability to secure debt financing. For example, a company with assets in a politically unstable country or region may be unable to secure debt financing, costing owners more to purchase or develop than assets in a politically stable country or region. The Sharpe Ratio (Sharpe 1994) has been widely used to compare investment strategies (Ledoit and Wolf 2008) and is defined as: 𝑆 =(𝑅𝑖 − 𝑅𝑏)𝜎 where: 𝑅𝑖 security or portfolio return 𝑅𝑏 benchmark portfolio return 𝜎 the standard deviation of 𝑅𝑖 − 𝑅𝑏 Some researchers have used a variation of the Sharpe Ratio to investigate risk-adjusted firm performance (rather than fund or portfolio performance) by using the difference between the firm market return and the risk-free rate as the numerator along with the total variance in firm returns as the denominator ( Hoskisson et al. 1994). 47 Jensen’s Alpha (α) is a risk-adjusted measure of the absolute market performance of a security or portfolio of securities (Hübner 2005). It is defined as: 𝑅𝑖 − 𝑅𝑓 = 𝛼 + 𝛽(𝑅𝑚 − 𝑅𝑓) where: 𝑅𝑖 security or portfolio return 𝑅𝑓 risk-free return 𝑅𝑚 market return 𝛽 security or portfolio volatility relative to the market volatility The Treynor Ratio has been variously defined as (Hoskisson et al. 1994): 𝑇 =(𝑅𝑖 − 𝑅𝑓)𝛽 where: 𝑅𝑖 security or portfolio return 𝑅𝑓 risk-free return 𝛽 security or portfolio volatility relative to the market volatility and as (Hübner 2005): 𝑇 =𝛼𝛽 where: 𝛼 Jensen’s Alpha 𝛽 security or portfolio volatility relative to the market volatility The Treynor ratio represents the excess or abnormal return of a security or portfolio of securities per unit of risk. The common thread among the Sharpe Ratio, Treynor Ratio, and Jensen’s Alpha is that they use firm or portfolio price volatility to measure relative risk. A Sharpe Ratio can be compared to the Sharpe ratio of other securities or portfolios. In contrast, the Treynor Ratio and Jensen’s Alpha compare security or portfolio volatility to the broader market, generally defined 48 as the exchange on which the security or portfolio of securities trade. They use a “risk-free rate,” typically defined as short-term US Treasury bills when compared to a security or portfolio of securities traded on a US stock exchange. To determine the most appropriate market measure for a long-term mining company shareholder, I first eliminate any market measures that include company assets in the calculation. (I described the discrepancy between reported asset values on financial statements and mineral asset values earlier in this chapter). Next, I eliminate any ratios or factors using a risk-free rate of return or adjusting for stock market volatility relative to the overall market, such as the Sharpe Ratio, Treynor Ratio and Jensen’s Alpha. Because I am taking a mining industry investor viewpoint (as opposed to an investor looking to make capital allocation decisions across asset classes or industries), I do not need to make comparisons to other industries, asset classes or a risk-free rate. I am simply interested in the relative performance among companies within the mining industry. Lastly, I eliminate enterprise value because there is a significant value for mining company owners in securing financing for large capital assets. Enterprise value does not take a company’s ability to finance assets into account. For these reasons, I take total shareholder return (TRS) as the most appropriate and straightforward measure of mining firm performance when taking the perspective of a long-term company investor. TRS directly reflects the value created (or destroyed) for shareholders, allows for a direct and transparent comparison of risk-adjusted value creation (and destruction) among mining companies, and is not subject to asset value reporting issues that affect other performance metrics. 49 I can calculate TRS as a total return over a period, an average annual value, or an annualized value. Because the mining companies in my data set were operating for different durations within the study period, each company’s total return is not comparable. The average annual value takes the average value of share price changes for each year: 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐴𝑛𝑛𝑢𝑎𝑙 𝑅𝑒𝑡𝑢𝑟𝑛 = ∑ (𝑎𝑛𝑛𝑢𝑎𝑙 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒 𝑐ℎ𝑎𝑛𝑔𝑒)𝑛𝑖=0(𝑦𝑒𝑎𝑟𝑠 𝑜𝑓 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 − 1) However, calculating and averaging percentages can hide the impact of underperforming years. The table below illustrates a case where two companies start and end a 3-year period with the same share prices but an inter-year price change causes a significantly different average annual return compared to the annualized value, which uses the first and last price for its calculation: Table 8: Annual average return vs. annualized first and last price Year 0 1 2 3 Company A share price $10 $5 $10 $10 Company A annual return - -50% 100% 0% Average of annual returns 17% First and last price, annualized 0% Company B share price $10 $10 $10 $10 Company B annual return - 0% 0% 0% Average of annual returns 0% First and last price, annualized 0% For this reason, I consider the annualized total shareholder return to be the most representative calculation of performance for a long-term shareholder. I calculate annualized total shareholder return (aTSR) in the following way: 50 𝑎𝑇𝑆𝑅 = (1 +(𝑃𝑖 + ∑ 𝐷𝑖𝑣𝑖 − 𝑃𝑠)𝑃𝑠)(1𝑡𝑖−𝑡𝑠)− 1 where: P𝑖 share price at the end of period i P𝑠 share price at the start of period i 𝐷𝑖𝑣 dividends paid in the period i ti ending year of the period i ts starting year of the period i With an appropriate performance metric now selected, I can now move on to the investigation of mining firm performance. Investigating Firm Performance The purpose of this section is to determine the influence of commodity prices on annualized shareholder return (aTSR) and to identify whether there is a significant component of performance that may be explained by firm-level factors within the control of mining company management. The previous chapter confirmed MacDiarmid et al.’s (2018) findings and identified a strong correlation between commodity price and enterprise value. Chapter 1 found that annual commodity price changes explained almost 60% of the variance in yearly enterprise value changes using a fixed-effect regression model. MacDiarmid et al. used enterprise value to measure mining firm performance, whereas I chose annualized total shareholder return as the most appropriate performance metric for this study. As a starting point in this investigation, I will repeat the analysis of annual commodity price changes on annual firm performance from the last chapter, but I will use aTSR to measure performance. Enterprise value is defined as equity value plus total debt, plus preferred stock, plus minority interests, minus cash and cash equivalents. Equity value is defined as the share price multiplied by the total number of shares outstanding, so the share price is included in the 51 enterprise value calculation. Of all the enterprise value calculation components, the share price is the most short-term and the most reactive because it fluctuates based on investor sentiment each trading day. The share count and the value of debt, preferred stock, minority interests and cash balances are within the company's control and ought to be more stable over time. For this reason, I believe that commodity prices should affect total shareholder return at least as much as enterprise value. I will test this relationship through the following hypothesis: H6: Annual increases in commodity prices positively correlate with annual shareholder returns for mining firms. From the viewpoint of a long-term investor, I will also try to determine whether this relationship between commodity prices and mining company returns persists over the long run. In a study of 264 companies over 5- and 15-year periods, Mauri and Michaels (1998) found that the variance explained by industry effects (rather than firm effects) decreased over time. They theorize that this may represent long periods of convergence punctuated by industry reorientations from a company strategy standpoint. Relating these findings to the results from the last chapter, it may be the case that commodity price effects (assumed here to be an industry effect for mining firms) are dominant on an annual basis, but firm-effects may become more important to performance over the long run. Previous studies of mining firm performance have differed in the time frame used to measure firm performance. Camus et al. (2009) used the first and last data points from their study period to analyze mining firm performance and mineral reserve growth. On the other hand, MacDiarmid et al. (2018) used annual, semi-annual, and quarterly panel data for each firm to test the effect of various factors on firm performance. Neither study explained the choice of data frequency or method of analysis. Suppose it is the case that industry effects have less explanatory power over 52 time. In that case, it may be that commodity prices have less influence over longer periods, and firm-level factors become more critical. To investigate this theory, I will test the following hypothesis: H7: The relationship between commodity prices and annualized total shareholder return weakens over the long run. Turning to individual firm performance, Hawawini et al. (2003) studied over 500 firms across 55 industries to determine whether firm performance was due to firm effects or industry effects. The authors found that most of the value created and destroyed within industries resulted from just a few firms. Industry effects drove the performance of average firms; however, firm effects drove the performance of the firms that captured and destroyed most of the value within industries. They identify that “firms in the bottom 20 percent report returns that amount to less than one-third of those gleaned by their average peers.” In this chapter, I will compare company returns to the returns generated by a basket of commodities representing the commodities produced by each company on a weighted average basis. I will then directly compare each company’s performance on a commodity-adjusted basis. Taking together the influence of commodity prices identified in the previous chapter and the findings of Hawawini et al. that just a few firms created and destroyed most value within industries, I will test the following hypothesis: H8: A relatively small number of firms in the Canadian mining industry generate annualized total shareholder returns significantly above and below the average on an absolute basis and commodity adjusted basis. 53 Data Set This analysis dataset contains 105 TSX-listed operating metal mining companies from January 1st, 2003 to January 1st, 2016, and is described in more detail in the previous chapter. I collected share price, dividend data, percentage of revenue per commodity and commodity price for each company over its operating period. Commodity prices were taken from several online sources and cross-checked for accuracy and reliability when possible. Some commodities have widely quoted and transparent prices (e.g., gold, silver, copper). In contrast, I needed to source others from metal trading databases such as Johnson Matthey Base Prices (e.g., palladium, rhodium). Some needed to be taken directly from company financial statements (e.g., cobalt, antimony, molybdenum). In all cases, I took prices for each commodity in my data set from a single source to maintain consistency over the study period. For companies that started operations after January 1st, 2003, I took the share price and commodity price on January 1st, following the first revenue generated from metal or mineral product sales as the opening share price and commodity price. For acquired companies, I used the acquiring share price for the closing price. I used the share price and commodity price on January 1st following cessation of operations as the closing share price and commodity price for companies that ceased operation. I use the share price and commodity price following cessation of operations to capture the effect of factors or events that completely stopped mining operations. I decided to use the share price and commodity price on January 1st of any given year to mitigate the effects of intra-year price changes among companies in the data set, especially due to the limited availability of price data for some commodities. 54 Analysis The first analysis tests the effect of annual commodity price changes (ΔCP) on annual shareholder return (ΔSR). It uses the same fixed-effect regression model used to analyze commodity prices on enterprise value. I based the change in commodity price on a weighted average of the commodities produced by each company at each annual observation. I calculated the annual change in commodity price using the following equation: 𝛥𝐶𝑃 =∑ (𝐶𝑗𝑖 × 𝑃𝑗𝑖)𝑛𝑗=1∑ (𝐶𝑗𝑠 × 𝑃𝑗𝑠)𝑛𝑗=1 where: 𝐶𝑗𝑖 percentage of revenue derived from commodity j at the end of period i 𝐶𝑗𝑠 percentage of revenue derived from commodity j at the start of period i 𝑃𝑗𝑖 price of commodity j at the end of period i 𝑃𝑗𝑠 price of commodity j at the start of period i I calculated the annual shareholder return by comparing the share price at the end of the year plus dividends declared to the share price at the start of the year. 𝛥𝑆𝑅 =𝑃𝑖 + 𝐷𝑖𝑣𝑃𝑠 where: Pi share price at the end of period i Ps share price at the start of period i Div dividends declared during the period i I tabulated, graphed, visually inspected, and cleaned the data. Then a fixed-effect regression analysis was performed using the statistical software R Studio. The second analysis tests the persistence of commodity price effects using a longer time horizon. For this analysis, I calculated the annualized commodity price return and annualized total shareholder return for each company using the first and last commodity price and share 55 price within the study period. The weighted average annualized commodity price performance for each company was calculated based on the formula below: 𝑎𝐶𝑃 = (1 +∑ (𝐶𝑗𝑖 × 𝑃𝑗𝑖)𝑛𝑗=1∑ (𝐶𝑗𝑠 × 𝑃𝑗𝑠)𝑛𝑗=1)(1𝑡𝑖−𝑡𝑠)− 1 where: 𝐶𝑗𝑖 percentage of revenue derived from commodity j at the end of period i 𝐶𝑗𝑠 percentage of revenue derived from commodity j at the start of period i 𝑃𝑗𝑖 price of commodity j at the end of period i 𝑃𝑗𝑠 price of commodity j at the start of period i ti ending year of the period i ts starting year of the period i I calculated the annualized total shareholder return (aTSR) for each company based on the following formula: 𝑎𝑇𝑆𝑅 = (1 +(𝑃𝑖 + ∑ 𝐷𝑖𝑣𝑖 − 𝑃𝑠)𝑃𝑠)(1𝑡𝑖−𝑡𝑠)− 1 where: P𝑖 share price at the end of period i P𝑠 share price at the start of period i 𝐷𝑖𝑣 dividends paid in the period i ti ending year of the period i ts starting year of the period i I tabulated, graphed, visually inspected, and cleaned the data. Then a fixed-effect regression analysis was performed using the statistical software R Studio. The third analysis compares aTSR to aCP to determine whether firms in the data set significantly outperformed and significantly underperformed based on aTSR and a commodity-adjusted basis. I took commodity adjusted annualized shareholder return (CAaTSR) as the difference between aTSR and aCP based on the following formula: 𝐶𝐴𝑎𝑇𝑆𝑅 = 𝑎𝑇𝑆𝑅 − 𝑎𝐶𝑃 56 Hawawini et al. (2003) offer that “exact definitions of a value leader or loser are debatable” and take the firms in the top and bottom 20 percent of cumulative value creation or destruction for each industry over their sample period as the leaders and losers, respectively. This analysis takes exceptionally good and bad performance to be aTSR and CAaTSR at least one standard deviation above and below the mean. Method I collected, tabulated, and graphed the data in Microsoft Excel. I performed a statistical analysis using linear regression in R Studio. The coefficient of determination (R²) measures the amount of variation in the dependent variable that is explained by the independent variable, and the probability value (p-value) measures the probability of generating the R² value without an actual statistical relationship (i.e., the null hypothesis). I report both measures and the effect size of each variable of interest. Results The table below shows the descriptive statistics for the annual change in commodity price and annual change in total shareholder return. Compared to the commodity price versus enterprise value analysis performed in the last chapter, I removed two observations from this data set due to outlying ΔSR values. Table 9: Descriptive statistics for ΔCP and ΔSR 𝜟𝑪𝑷 ΔSR Count 784 784 Min 0.39 0 Max 2.26 12 Mean 1.09 1.17 Median 1.06 0.95 Standard Deviation 0.28 1.08 57 The fixed-effect linear regression analysis of annual commodity price changes (ΔCP) versus annual shareholder return (ΔSR) found a 1.00% change in commodity price correlated with a 1.72% change in shareholder return on an annual basis. The coefficient of determination (adjusted R²) was 0.62, and the probability value (p-value) was <2.2e-16. I summarize the results of this model in the table below. Table 10: Regression model results for ΔCP vs ΔSR Model Effect Size Std. Error R2 p-value ΔCP vs ΔSR 1.72 0.13 0.62 <2.2e-16 The table below shows the descriptive statistics for the annualized commodity price performance, annualized total shareholder return, and commodity adjusted annualized total shareholder return based on each company's first and last share price and weighted average commodity price during the study period. Table 11: Descriptive statistics for aCP, aTSR and CAaTSR aCP aTSR CAaTSR Count 105 105 105 Min -0.16 -1.00 -1.19 Max 0.29 0.78 0.84 Mean 0.05 -0.08 -0.14 Median 0.07 -0.06 -0.11 Standard Deviation 0.10 0.30 0.27 The linear regression analysis of long-term annualized commodity price performance (aCP) versus long-term annualized total shareholder return (aTSR) found a 1.00% change in aCP correlated with a 1.48% change in aTSR. The coefficient of determination (adjusted R²) was 0.23, and the probability value (p-value) was 1.57e-07. The equation for this linear regression model is as follows: 58 aTSR = -0.16 + 1.48aCP I performed analyses that retarded each company's end date by one year and advanced each company's start date by one year to determine whether these results were unique to the specific start and end dates used for each company. In the former case, a 1.00% change in aCP correlated with a 1.56% change in aTSR with an adjusted R² of 0.36 and in the latter case, 1.00% change in aCP correlated with a 1.14% change in aTSR with an adjusted R² of 0.13. Both results were significant, with p-values <0.00. I summarize the results of these regression models in the table below. Table 12: aCP vs aTSR linear regression model summary Model Effect Size Std. Error R2 p-value aCP vs aTSR 1.48 0.26 0.23 <1.57e-07 aCP vs aTSR, end date -1 year 1.56 0.21 0.36 2.41e-11 aCP vs aTSR, start date +1 year 1.14 0.29 0.13 1.27e-04 59 Below is a histogram of the annualized shareholder return (aTSR) of the 105 data set mining companies, presented in percentages. One standard deviation from the mean is shown as solid red lines. Two standard deviations from the mean is shown as dotted red lines. Figure 6: aTSR histogram The data set contains 13 companies (12%) that generated an aTSR one standard deviation above the mean and four companies (4%) that generated an aTSR two standard deviations above the mean. On the other hand, there were 12 companies (11%) with an aTSR one standard deviation below the mean and four companies (4%) with an aTSR two standard deviations below the mean. Below is a histogram of the commodity-adjusted annualized shareholder return (CAaTSR) of the 105 data set mining companies, presented in percentages. One standard 1sd = 22% 2sd = 52% 1sd = -38% 2sd = -68% 60 deviation from the mean is shown as solid red lines. Two standard deviations from the mean is shown as dotted red lines. Figure 7: CAaTSR histogram The data set contains 11 companies (10%) that generated CAaTSR one standard deviation above the mean, and two companies (2%) generated CAaTSR two standard deviations above the mean. On the other hand, there were ten companies (10%) with CAaTSR one standard deviation below the mean and four companies (4%) with aTSR two standard deviations below the mean. 10 of the 11 top performers by CAaTSR were in the top-performing group by aTSR, and 9 of the ten bottom performers by CAaTSR were in the bottom performing group by aTSR. 1sd = 13% 2sd = 40% 1sd = -40% 2sd = -67% 61 Discussion The first analysis looked at the effect of annual commodity price changes on annual shareholder return to compare to the effect of commodity price on enterprise value from the previous chapter. When using annual panel data for each company in a fixed-effect linear regression model, the effect size of commodity price on annual returns is more significant than enterprise value (1.72% versus 1.36% for a 1.00% change in commodity price). The coefficient of determination (R2) is slightly higher as well (0.62 versus 0.58). The effect of annual commodity price changes on annual shareholder returns is higher than for annual enterprise value changes. The regression model also explains slightly more of the variance, so hypothesis H6: Annual increases in commodity prices positively correlate with annual shareholder returns for mining firms is supported. These results also confirm that, on an annual basis, the effect of commodity price explains most of the variance in shareholder returns (R2 > 0.5). The share price of mining companies can act as somewhat of a ‘lever’ on commodity prices; on average, a mining company's share price will move 1.72 times more than a basket of commodities produced by that company. For example, if the price of gold increases by 10% in a given year, we can expect that, on average, gold mining companies will return 17.2% over the same year. However, this analysis does not uncover any asymmetry in price movement that may exist. There may be a non-proportional relationship between increases and decreases in commodity prices and shareholder returns. For example, Kahneman and Tversky (1979) identified that people feel losses more acutely than gains, so it could be the case that mining company investors react much more negatively to commodity price decreases than increases. The investigation of this possible asymmetric reaction could be investigated by comparing the relationship between commodity price and shareholder returns in rising price environments and 62 falling price environments; however, this investigation is outside of the current study's scope and will be left to future research. The subsequent analysis looked at the relationship between commodity prices and shareholder returns over the long run by using the first and last available commodity price and share price over the entire study period. While commodity prices still had a strong effect on aTSR (1.14% to 1.56% from a 1.00% change in commodity price over a range of start and end dates), commodity price explained much less of the variation in shareholder return over the long run (R2 = 0.13 to 0.36 over a range of start and end dates). Therefore, hypothesis H7: The relationship between commodity prices and annualized total shareholder return weakens over the long run is supported. The weakening effect of commodity prices over longer periods indicates that some factors or events affect mining companies on a less-than-annual basis and have a material impact on shareholder returns. For example, mineral discoveries, significant acquisitions, and unexpected adverse events such as geotechnical failures and strong community opposition to mining projects will significantly impact mining company share prices but are all very unlikely to happen on an annual basis and may affect share prices across several calendar years. The impact will only show up when taking a long-term view of performance. This observation supports the choice of Camus et al. (2009) to use the first and last share price when they tested the effects of mineral reserve growth on shareholder returns - large changes in mineral reserves typically do not happen on an annual basis and likely do not have a share price impact at a single point in time. Given the frame of a long-term mining company investor, I have now identified that there are likely factors occurring on a less-than-annual basis with more-than-annual share price effects and materially affect mining company performance over the long term. I will investigate these factors in more detail in the following chapter. 63 The final analysis looked at the dispersion of mining company returns on an absolute and commodity-adjusted basis to determine whether some mining companies significantly outperformed and underperformed the average. These results showed a relatively small number of firms (approximately 10% in each case) that generated significantly above average and below-average shareholder returns on an absolute and commodity-adjusted basis. Further, it was almost exclusively the same companies outperforming and underperforming in both cases. Despite the strong influence that commodity prices may exert on mining companies, it was likely the presence of firm-level factors that resulted in significantly above and below-average performance. Therefore, hypothesis H8: A relatively small number of firms in the Canadian mining industry generate annualized total shareholder returns significantly above and below the average on an absolute basis and commodity adjusted basis is supported. It is worth noting that, in general, the returns generated by mining companies over the long run were meagre. Crowson (2001) identifies that the long-term cost of capital for mining companies is in the order of 8%. On that basis, less than 25% of companies in the data set recovered their cost of capital over the entire study period. Similarly, less than 25% of companies generated a positive return on a commodity-adjusted basis. Very plainly, this means that investors in more than 75% of companies over this study period would have been better off simply holding commodity investments directly rather than investing in mining companies. However, roughly 25% of companies were able to generate higher returns than their cost of capital and outperform (in some cases significantly outperform) the commodities they were producing. There may be lessons to learn from these companies (and the significant underperformers) that other companies can apply to improve overall industry performance. I will investigate these firm-level factors in the following chapter to determine if there are actions or 64 decisions that mining company management may take to increase the likelihood of generating high shareholder returns and decrease the likelihood of the worst outcomes for investors. Conclusion This chapter's first purpose was to identify the most appropriate measure of mining firm performance for a long-term shareholder. The second purpose was to determine whether the companies in this study demonstrated materially different performance than the commodities they produced. The chapter began with a review of the performance measures typically used in the business literature. I discussed the difference between accounting and market measures. I determined that a market measure would avoid many of the problems associated with asset accounting in the mining industry and best reflect a mining company shareholder's perspective. Ultimately, I chose annualized Total Shareholder Return (aTSR) as the most appropriate measure of performance for a long-term mining company shareholder. Next, a fixed-effect linear regression analysis of commodity prices versus shareholder return found that, on an annual basis, commodity prices explain over 60% of the variation in mining firm returns. In contrast, a linear regression analysis using long-term commodity price and share price data found that, over the long run, commodity prices explain less than 30% of the variation in mining firm returns. Based on these results, it seems likely that firm-level effects are occurring on a less-than-annual basis (e.g., mineral reserve declarations, asset impairments) and reducing the impact of commodity prices on firm performance over the long run. Finally, an analysis of the dispersion of absolute and commodity adjusted returns identified a relatively small number of companies that significantly outperformed and underperformed the average returns. While average returns were low (-8%), some companies 65 generated high market returns during the study period. And some companies managed to destroy most or all shareholder value. Further, companies in both groups demonstrated significantly different performance than the underlying commodities they were producing. This result also points to the likely existence of firm-level effects that strongly influence long-run shareholder returns in the mining industry. The following chapter will attempt to identify these less-than-annual, firm-level factors so that mining company management can try to emulate the best-performing firms' practices and avoid the mistakes of the worst-performing. 66 Chapter 3: Mining Firm Performance Factors Introduction The previous chapter identified the possibility there are long-term, firm-level factors that significantly impact the market returns generated by mining companies. The purpose of this chapter is to identify firm-level factors that affect the market returns of mining companies. With an understanding of the firm-level performance factors, mining company management may adopt the practices and strategic decisions that are more likely to result in higher shareholder returns while avoiding those that are more likely to destroy shareholder value. First, I generate hypotheses to identify firm-level factors that may have a significant effect on shareholder returns. Using a theoretical framework that describes mining company value creation as the profitable exploitation of mineral reserves, I identify three performance factors: mineral reserve growth, mineral asset impairments, and production costs. I test each of these factors against annualized total shareholder returns (aTSR) using linear regression models that control for years of operation and company size. These analyses determine that mineral asset impairments and production costs both have a statistically significant relationship to aTSR. Finally, I create a multivariable linear regression model using impairment, production cost, and commodity price factors that explains almost 60% of the long-term mining firm return variance. The impairment factor has an effect size that is an order of magnitude larger than the production cost and commodity price factors. This chapter concludes that mineral asset impairments are a firm-level effect that significantly impacts mining company returns over the long run. 67 Firm-Level Performance Factors As discussed in the previous chapter, accounting measures report on what has happened in the firm and are typically backward-looking, while stock prices reflect investor expectations about the firm's future (Gentry and Shen 2010). Since this analysis is principally concerned with stock price performance, I will attempt to identify factors that materially change investors’ perceptions of future company value. The concept of a material change in perception is essential to affect stock prices. For example, suppose investors have high expectations of future company value at the start of the period and equally high expectations at the end of the period. In that case, we should not expect to see a significant relative change in the stock price (Thaler 2005). Fundamentally, a mining company attempts to generate profits by taking raw materials, transforming them into a saleable product, and then selling these products for more than they cost to produce. The raw materials are mineral reserves. The transformation of mineral ore into metal concentrate sets the company’s capital and operating costs. Global commodity prices set the sale price. A fundamental equation to describe this process could be: expected profits = mineral reserves x (commodity price – production costs) The previous chapter identified that commodity prices impact annualized total shareholder return (aTSR) for mining companies. It also determined that there are likely factors beyond commodity prices that affect individual mining companies' market returns. This chapter focuses on investigating these firm-level factors, specifically factors that are likely to change an investor’s perception of future company value: mineral reserves and production costs. Camus et al. (2009) identified a correlation between mineral reserve growth plus production and shareholder return. However, I found the relationship between total mineral reserve growth plus mineral production and total shareholder return to be unsupported when 68 tested against my more extensive data set of Canadian mining companies. The production component of Camus et al.’s independent variable is likely detrimental to investor expectations because it represents a reduction in a company’s ability to create future value. For example, if a company has completely exhausted its mineral reserve base, then the Camus et al. reserve factor (ending reserves plus production divided by starting reserves) would equal 1.0; however, without any prospect of future value creation, its share price would likely go to zero. On average, over my data set, the production component made up 45% of the numerator in the reserve growth factor, so it was very significant in the analysis. This chapter will test the relationship between mineral reserve growth and shareholder return without including a mineral production component through the following hypothesis: H9: Mineral reserve growth positively correlates with annualized total shareholder return. On the other hand, mineral-related asset impairments represent lowered future expectations of value. In this study, mineral-related assets include fixed assets like processing plants, goodwill associated with acquisitions, unsold concentrate inventory, and capitalized exploration or mine development costs. When the asset's recorded value on a company’s balance sheet exceeds its market value or its value in use, an impairment must be declared (The International Financial Reporting Standards Foundation 2017). In contrast with depreciation of a fixed asset or amortization of an intangible asset that follows a planned and predictable reduction in value over the asset's life, asset impairments in the mining industry are unplanned and often significant. Indicators of impairment commonly listed in mining firm financial statements include, but are not limited to, significant declines in market capitalization or commodity prices; significant increases in production costs; significant revisions to mine plans; serious mine 69 accidents; and changes in government regulation, laws or tax rates. The declaration of a mineral asset impairment is a direct admission that the asset will not generate the value expected from that asset. I expect that impairments will subsequently lower investor expectations of future value creation and result in lower shareholder returns. I will test this theory through the following hypothesis: H10: Mineral asset impairments negatively correlate with annualized total shareholder returns. In a 2001 study of mining industry performance, Crowson describes several decades of poor industry performance and assigns blame for this low profitability to the mining companies themselves. He asserts that a surge in demand for metals drove investment into too many large mining projects, resulting in oversupply. The implication is that companies put marginal cost projects into production due to growing demand, which then became unprofitable when supply caught up to demand, and metal prices declined. In a study of the Zambian copper industry, Heili (2005) asserts that cost leadership was the only strategy available to mining companies in achieving an adequate return on investment to shareholders. While Crowson was commenting on profitability and Heili was discussing strategy in general, changes in production costs will likely affect an investor’s perception of a mining company’s ability to operate profitably in the future, and that ought to be reflected in the company’s share price performance. I will test this relationship through the following hypothesis: H11: Decreases in production costs positively correlate with annualized total shareholder returns. 70 Dataset This analysis's total dataset comprises the 105 TSX-listed operating metal mining companies described in the first chapter. I collected mineral reserve, impairment, and cost data directly from public disclosures by each mining company. I accessed these documents through company websites and SEDAR (the electronic reporting database for publicly listed companies in Canada). I collected mineral reserve data from annual information forms, annual reports, and management discussion & analysis documents. Throughout the sample period, companies listed on the TSX were required to conform to Canadian Institute of Mining (CIM) Definition Standards for Mineral Resources and Mineral Reserves (CIM Standing Committee on Reserve Definitions 2014) as mandated by National Instrument 43-101 – Standards of Disclosure for Mineral Projects (Canadian Securities Administrators 2016). Through these industry standards, mining companies must report mineral reserves in a consistent manner under the direction of a qualified technical person, and I can be confident that the amount and value of reserves reported by the different data set companies are reasonably comparable. I collected the impairment data from audited company financial statements that were accessed and downloaded through SEDAR. This data is for impairments specifically related to mineral-related assets in the form of mineral property assets, plant and equipment assets, and goodwill. I excluded impairments of financial assets (investments, loans, etc.) from the data set. I collected cost information from annual reports and management discussion & analysis documents. The World Gold Council introduced the concept of cash cost reporting in 1996. Cash costs typically include only mining and production costs, and companies report them on a commodity unit basis (e.g., dollars of cost per ounce of gold). To better capture the full lifecycle 71 cost of production, the World Gold Council introduced the concepts of All-In Sustaining Costs (AISC) and All-In Costs (AIC) in 2013 (Whelan 2013). These measures include costs related to exploration, closure, SG&A and other income or cash flow statement items. All these costs are non-GAAP measures, but gold miners and most other mining companies typically reported them. Since AISC and AIC were introduced in the middle of the study period, this analysis uses cash costs. Cash costs are the most consistently reported cost information over the study period. Share price and dividend data for each company were collected from Bloomberg data services and accessed through a Bloomberg terminal. Analysis Independent Variable: Mineral Reserve Growth I calculated a mineral reserve growth factor for each company by taking the reserves at the end of the period, subtracting the reserves at the start of the period, and dividing that difference by the reserves at the beginning of the period. Using data from the start and end of each company's operating period follows Camus et al. (2009). It aligns with the previous chapter’s results, which identified the influence of less-than-annual events on long-run mining company performance. Company disclosure practices are another reason to use first and last data points for mineral reserve declarations over the study period. Companies release information indicating future mineral reserve declarations (e.g., drill results and multiple stages of resource declaration) well before mineral reserves are officially declared. Companies will also release reserve and feasibility study updates after the declaration of mineral reserves. These public news releases can cause investor reactions and share price movements for years before and after the official declaration of mineral reserves. For this reason, I believe that taking as long a period as 72 possible stands the best chance of identifying any long-term relationship between mineral reserve growth and shareholder returns if one exists. Camus et al. (2009) use physical mineral reserves measures (weights and volumes) when calculating their mineral reserve growth factor to control for the effects of commodity price changes over their study period. They express all commodities produced by a particular company in terms of the “major commodity” produced by the company. However, several companies in my data set changed which commodity was produced in the highest quantity or generated the highest percentage of revenue during the study period. In some cases, this changed several times for a single company. To avoid selecting a single “major commodity” for each company, I convert all reserves to a dollar value using constant 2016 commodity prices. In this way, I can easily compare each company’s change in mineral reserves over the study period while controlling for the impact of commodity price changes throughout the period. The calculation for mineral reserve growth is as follows: 𝐹𝑅 =(𝑉𝑖 − 𝑉𝑖−1)𝑉𝑖−1 where: 𝑉𝑖 value of mineral reserves in 2016 commodity prices at the end of the operating period 𝑉𝑖−1 value of mineral reserves in 2016 commodity prices at the start of the operating period I removed eight companies from this analysis because they had no declared mineral reserves during the study period; I used the remaining 97 companies in the analysis. If companies declared bankruptcy or were liquidated, I recorded the residual value to shareholders (if any) as the ending value. 73 Independent Variable: Impairment The impairment independent variable represents the total percentage of a company’s mineral property assets impaired over the study period. I used the entire study period to mitigate the impacts of the timing of impairment declarations. Impairments result from business conditions or decisions visible to investors before the impairment is declared and persist afterward. Studies of asset write-offs/impairments in the business literature reported stock price impacts spanning multiple years before and after the impairment announcement (e.g., Elliott and Shaw 1988; Bartov et al. 1998). I calculated the impairment factor as follows: 𝐹𝐼 =𝛴𝑖𝐼(𝛴𝑖𝐼 + 𝐴𝑖) where: 𝐼 value of mineral asset impairments during the operating period 𝐴𝑖 mineral asset value at the end of the period I removed one company from the analysis for declaring no impairments and ending the period with no declared mineral assets. This left 104 companies in the dataset for this analysis. If companies declared bankruptcy or were liquidated, I assumed that the entire mineral property value was impaired less any residual value. Independent Variable: Cash Costs Since all hypotheses in this study revolve around changes in investor expectations, the cash cost factor will compare the cash costs to commodity prices for each company at the start and end of the company’s operating period. The comparison is made to commodity prices to reflect the potential for profit creation. If costs are reduced by 10%, but the commodity value falls by 20%, investors should expect less value creation. I used the first and last cash costs in the whole operating period (rather than annual values) to reflect the frame of a long-term investor 74 and align with the periods of analysis chosen for mineral reserves and mineral asset impairments. To account for companies changing their product mix over the study period, I compare a weighted average cash cost and commodity value at each data point. While the inclusion of commodity price in the calculation introduces a factor that correlates with shareholder returns, its inclusion is necessary to appropriately address investors’ expectations of company profitability and management’s ability to reduce production costs in the face of commodity price pressure. To identify a possible relationship between the cost factor and commodity prices, I check and report the correlation between these two variables in the results. Ninety-five companies reported cash cost data directly in Management Discussion & Analysis documents or Annual Reports. For these companies, I used the following formula to calculate the independent variable: 𝐹𝐶 =𝐶𝑖𝑃𝑖𝐶𝑖−1𝑃𝑖−1− 1 where: 𝐶𝑖 commodity weighted average cash cost of production at the end of the operating period 𝐶𝑖−1 commodity weighted average cash cost of production at the start of the operating period 𝑃𝑖 weighted average commodity price at the end of the operating period 𝑃𝑖−1 weighted average commodity price at the start of the operating period Ten companies did not directly report cash cost data. In these cases, I calculated cost changes based on the operational costs and revenue information from the income statement in company Financial Reports using the following formula: 75 𝐹𝐶 =𝑂𝑖𝑅𝑒𝑣𝑖𝑂𝑖−1𝑅𝑒𝑣𝑖−1− 1 where: 𝑂𝑖 operating costs at the end of the operating period 𝑂𝑖−1 operating costs at the start of the operating period 𝑅𝑒𝑣𝑖 revenue at the end of the operating period 𝑅𝑒𝑣𝑖−1 revenue at the start of the operating period I calculated the independent variable for each company in the same way at the start and end of the period for consistency. When companies reported cash cost data for more than one commodity, the change in the cost of each commodity produced was weighted based on the revenue generated from each commodity. Dependent Variable Following from the definition of mining firm performance that I selected in the previous chapter, mining firm performance is represented in this analysis by annualized total shareholder return (aTSR): 𝑎𝑇𝑆𝑅 = (1 +(𝑃𝑖 + ∑ 𝐷𝑖𝑣𝑖 − 𝑝𝑠)𝑝𝑠)(1𝑡𝑖−𝑡𝑖−1)− 1 where: P𝑖 share price at the end of period i P𝑖−1 share price at the beginning of period i 𝐷𝑖𝑣 dividends paid in the period i ti ending year of the period i ti−1 starting year of the period i Data set companies operated for different durations during the study period, from a minimum of 3 years for inclusion in the data set to the maximum of 13 years. I introduced 76 control variables in the analysis to account for the differences in operating duration and other factors known to affect company performance. Control Variables When investigating firm performance, business research typically controls for firm size and other unique factors related to the industry, period, and sample under study that the researchers have identified (e.g., Wiggins and Ruefli 2005; Fama and French 2006; Dorobantu and Odziemkowska 2017). Total asset value is a typical measure of firm size; however, in the first chapter, I identified the problems of comparing the reported book value of assets among mining firms. Instead, I use the enterprise value for each firm at the start of the period as a firm size measure. I also introduce years of operation in the sample period as a control variable to account for the difference in operating years among companies in the dataset. I do not control for operation during different commodity price environments (rising, volatile or falling commodity prices) to better identify factors that are generalizable across commodity price environments. Method I collected, tabulated, and graphed the data in Microsoft Excel. I performed a statistical analysis using multivariable linear regression in R Studio. The coefficient of determination (R²) measures the amount of variation in the dependent variable that is explained by the independent variable, and the probability value (p-value) measures the probability of generating the R² value without an actual statistical relationship (i.e., the null hypothesis). I report both measures and the effect size of each variable of interest in the following results section, and I use them to draw conclusions about the hypotheses. 77 Results The table below shows the descriptive statistics for the control variables (enterprise value at the start of the period in millions of dollars and years of operation), the independent variables (mineral reserve growth, mineral asset impairment, and cash cost), and the dependent variable (annualized total shareholder return). Table 13: Descriptive statistics for EV, Years, 𝐅𝐑, 𝐅𝐈, 𝐅𝐜, and aTSR EV Years 𝑭𝑹 𝑭𝑰 𝑭𝑪 𝒂𝑻𝑺𝑹 Count 105 105 97 104 105 105 Min 2.37 2 -1.00 0.00 -0.79 -1.00 Max 21,099 13 51.94 1.00 2.44 0.78 Mean 980 7.56 3.41 0.37 0.21 -0.08 Median 279 8 0.60 0.32 0.06 -0.06 Standard Deviation 2,610 3.39 8.53 0.30 0.64 0.30 The mineral reserve growth factor had a range from -1.00 (a complete reduction of reserves by the end of the period) to 52 (52 times more reserves at the end of the period than the start. The median of the data set was 0.60, and the mean was 3.41, reflecting some large positive values. Overall, 35 companies increased reserves, 61 decreased reserves, and one company had no change in declared reserves. The analysis of the effect of mineral reserve growth on mining firm performance produced a coefficient of determination (R²) of -0.04 and the probability value (p-value) of 0.96 after controlling for company size and years of operation. I do not consider this R² value to be meaningful nor the p-value to be statistically significant. I also performed the analysis using starting reserves as a control variable to check for reserve size effects, and there was no material change to the results. The mineral asset impairment factor had a range from a minimum of 0.00 (no asset impairments) to 1.00 (complete impairment of all mineral assets). Eleven companies declared no 78 impairments during the study period, and 93 companies declared some impairment related to mineral-related assets. Six companies impaired more than 90% of mineral-related assets. The median of the data set was 0.32, and the mean was 0.37; an average company impaired more than 30% of its mineral assets during the study period! The analysis of the effect of mineral property impairments on mining firm performance produced a coefficient of determination (R²) of 0.47 and the probability value (p-value) of 2.299e-13. I consider this R² value to be meaningful and the p-value to be statistically significant. In the corresponding regression model, a 1% increase in mineral asset impairment correlated to a 1.35% decrease in annualized total shareholder return after controlling for company size and years of operation during the study period. The equation for this regression line is as follows: aTSR = 0.348 – 1.35FI - 0.00si-1 – 0.02y + 0.00FI*s + 0.08FI*y where: 𝐹𝐼 mineral impairment factor (defined above) 𝑠𝑖−1 enterprise value at the start of the period 𝑦 years of operation during the study period From the model, we can see that the impairment factor is two orders of magnitude larger than all other factors, and the model is insensitive to company size. The cash cost factor ranged from -0.79 (79% reduction in cash costs) to 2.44 (244% increase in cash costs). The median was 0.06, and the mean was 0.21. There was a wide dispersion in cost changes, with 41 companies experiencing decreases in costs, 61 reporting increases in costs, and two reporting no change in cash costs relative to commodity prices. The analysis of the effect of cash costs on annualized total shareholder return produced a coefficient of determination (R²) of 0.19 and the probability value (p-value) of 1.09e-4 after controlling for 79 company size and years of operation. I consider this R² value to be meaningful and the p-value to be statistically significant. In the corresponding regression model, a 1% increase in the production cost factor correlated to a 0.45% decrease in annualized total shareholder return after controlling for company size and years of operation during the study period. The equation for this regression line is as follows: aTSR = 0.04 – 0.45FC - 0.00si-1 – 0.01y + 0.00FC*s + 0.03FC*y where: 𝐹𝐶 cash cost factor (defined above) 𝑠𝑖−1 enterprise value at the start of the period 𝑦 years of operation during the study period From the model, we can see that the impairment factor is one order of magnitude larger than all other factors, and the model is insensitive to company size. Also, I performed a linear regression analysis to check for correlation between the cost factor (which included commodity prices in the calculation) and commodity price changes. This analysis produced a coefficient of determination (R²) of 0.02 and a probability value (p-value) of 0.06. Every 1% change in commodity price resulted in a -0.11% change in the cost factor. I interpret these results to mean that commodity prices do not materially affect the cost factor in this analysis. Multivariable Model A multivariable model including the impairment factor (FI), cost factor (FC), and the annualized commodity price from the previous chapter (𝑎𝐶𝑃) was created and tested for correlation against aTSR. I used operating duration as a control variable for FI, FC, and aCP (I discarded company size as a control variable based on the results from the linear models described above). This analysis produced a coefficient of determination (R²) of 0.59 and the 80 probability value (p-value) of <2.2e-16. I consider this R² value to be meaningful and the p-value to be statistically significant. In the corresponding regression model, a 1% increase in FI correlates to a 0.96% decrease in aTSR, a 1% increase in FC correlates to a 0.21% decrease in aTSR, and a 1% increase in 𝑎𝐶𝑃 correlates to a 0.41% increase in aTSR after controlling for years of operation during the study period. The equation for this regression line is as follows: aTSR = 0.29 – 0.96FI – 0.21FC + 0.41𝑎𝐶𝑃 - 0.03y + 0.05FI*y + 0.02FC*y + 0.07𝑎𝐶𝑃 *y A check of collinearity among all factors in the multivariable model was performed in RStudio and the results are presented in the table below: Table 14: Correlation table for the multivariable model factors Impairment (FI) Cost (FC) Commodity Price (𝑎𝐶𝑃) Years (y) Impairment (FI) 1.00 Cost (FC) 0.25 1.00 Commodity Price (𝑎𝐶𝑃) -0.24 -0.28 1.00 Years (y) 0.09 -0.08 0.14 1.00 This analysis shows a low (>0.30) correlation between all independent variables. As a further check, I also calculated the variance inflation factor (VIF) in RStudio. The VIF measures the strength of the correlation between the predictor variables in a regression model. Values of 1 indicate no correlation between any predictor variables, and values above 5 indicate a potentially severe correlation. I present the results of the variance inflation factor analysis in the table below: 81 Table 15: Variance inflation factors for the multivariable model factors Impairment (FI) Cost (FC) Commodity Price (𝑎𝐶𝑃) Years (y) VIF 1.13 1.14 1.15 1.04 These results confirm that the multivariable model presented above is likely not affected by any collinearity between the model's predictor variables. Combining the range of values and model coefficient for each factor, I can calculate the relative impact of each factor on annualized total shareholder return. For example, the FI values range from 0.00 to 1.00 with an average of 0.37, and the multivariable model coefficient is 0.96. Therefore, FI has a potential effect on aTSR of 0.00 to 0.96 with an average value of 0.36. Making a similar calculation with the other two factors, FC has a potential effect on aTSR of -0.51 to 0.17 with an average value of -0.04, and aCP has a potential effect on aTSR of -0.07 to 0.12 with an average value of 0.02. Through this comparison, we see that the impairment factor has, on average, an order of magnitude larger effect on aTSR than the production cost factor and the commodity price factor. I present a summary of the results from all regression models in the table below. Table 16: 𝐅𝐑, 𝐅𝐈 and, 𝐅𝐜, vs aTSR linear regression model summary Model Effect Size Std. Error R2 p-value 𝐹𝑅 vs aTSR, EV and Years controls 0.01 0.02 -0.04 0.96 𝐹𝐼 vs aTSR, EV and Years controls -1.35 0.21 0.47 2.299e-13 𝐹𝐶 vs aTSR, EV and Years controls -0.45 0.11 0.19 1.09e-4 Multivariable Model 0.59 <2.2e-16 𝐹𝐼 -0.96 0.19 𝐹𝑐 -0.21 0.09 𝑎𝐶𝑃 0.41 0.59 82 Discussion Hypothesis H9: Mineral reserve growth positively correlates with annualized total shareholder return was not supported by this analysis; however, hypothesis H10: Mineral asset impairments negatively correlate with annualized total shareholder returns was supported. In isolation, I am shocked to find that mineral reserve growth does not correlate with annualized shareholder returns over the long run. However, considering that mineral asset impairments have a significant relationship with shareholder returns, I can offer two possible explanations. Studies have shown that people experience a loss approximately twice as intensely as an equal gain (Kahneman and Tversky 1979b). It may be that investors do react positively to increases in mineral reserves, but that positive reaction is outweighed by any subsequent impairment declaration (remembering that only 11 companies in the data set declared no impairments). The question of investor reactions to reserve declarations and impairments could be investigated directly through an event study of stock price movements around these announcements. Second, it may be that investors are skeptical of the economic viability of reserve declarations. Fundamentally, mining companies should have a strong incentive to increase their reserve base, especially if companies are regularly depleted their reserves through production. The mining companies themselves commission the technical reports required to declare mineral reserves (NI 43-101 reports). While they require vetting by industry professionals, they may be influenced by optimistic forecasts and misaligned interests between mining companies and investors. It could also be the case that skepticism reduces investor reactions to reserve declarations. In contrast, impairments provide certainty that companies have destroyed value. Investor reactions may reflect the relative certainty of these two types of 83 disclosures. I will investigate the question of over-optimistic forecasts and misaligned interests in the following chapters. It is worth noting that only balance sheet items can be impaired - a company that acquires a mineral property may report a significantly different asset value than a company that discovers a mineral property of similar reserve quantity and quality. The explanatory quality of impairment as a performance factor is limited by the consistency of asset valuation among operating mining companies; however, I believe it is conservative because companies are likely to report mineral asset impairments only when necessary. I also found cash cost changes to have a statistically significant effect on aTSR. Hypothesis H11: Decreases in cash costs per unit of production positively correlate with annualized total shareholder return is supported by this analysis. While the effect of cash cost changes on aTSR was an order of magnitude less than the effect of impairments when combined in a multivariable model, it is likely that the full extent of increases in cash costs was not wholly reflected in the cost factor. Extreme increases in costs relative to commodity prices likely drove companies to shut down and impair high-cost operations, thus removing them from operating cost calculations. I calculated the cost factor at the firm level, so the closure and impairment of some operations likely reduced the cost factor's impact. In contrast, the impairment factor probably better reflected the overall business impact. The multivariable model combining the effects of impairments, cash costs and commodity prices explained almost 60% of the variation mining company returns over the long run. Combining the range of variation in each independent variable with the model coefficient for each variable also found that the effect of impairments on aTSR is an order of magnitude larger than cash costs or commodity prices. This result supports the theory from the previous 84 chapter that there are effects that occur on a less-than-annual basis and have effects spanning more than one fiscal year, which significantly affect mining companies’ market returns. Further, the explanatory power of the commodity price variable dropped significantly when combined with the impairment variable in a multivariable model. This observation indicates that commodity prices may only matter to the extent that they cause asset impairments. Turning back to the topic of individual company performance, the previous chapter identified 11 companies that demonstrated significant outperformance and ten that demonstrated significant underperformance relative to the commodities they were producing. Except for one company, all outperforming companies impaired less than 10% of their mineral assets during the study period, and 5 of the 11 declared no impairments. On the other hand, half of the underperforming companies impaired more than 90% of their mineral assets and 8 of 10 impaired 60% or more of their mineral asset value. With this comparison between impairments and commodity-adjusted returns, I conclude that mineral asset impairments play a significant role in a mining company's ability to decouple market returns (both positively and negatively) from commodity price effects over the long run. It does appear that there are actions or decisions that mining company management can make at the firm-level to make impairments more or less likely and have a significant effect on shareholder returns. The following chapters will conduct a detailed investigation into the reasons and causes of the mining industry's impairments. I will ultimately make recommendations that can be used by mining company management to avoid impairments and by mining company investors to avoid the worst investment returns in the mining industry. 85 Conclusions This chapter's stated purpose was to identify the long-term, firm-level factors that significantly impact the long-run returns for mining company investors. I developed a theory that identified mineral reserve growth, mineral asset impairments, and production costs as possible drivers of annualized total shareholder return (aTSR) for mining companies. Each of these factors was tested against aTSR using linear regression analysis. The analysis found that both asset impairments and changes in production costs are significantly correlated to aTSR. A multivariable model using mineral asset impairment, production cost, and commodity price factors explained almost 60% of the long-term mining company return variation. The impairment factor had an effect size one order of magnitude larger than the production cost and commodity price factors. Overall, this chapter found that mineral asset impairments are a less-than-annual, firm-level factor that significantly affects mining company performance over the long run. The following chapters will investigate the reasons and causes of mineral asset impairments so that mining company management can make strategic and operational decisions to reduce the likelihood of declaring impairments and improve returns to investors. 86 Chapter 4: Mining Firm Impairments Introduction In the previous chapter, I identified asset impairments as the primary driver of annualized total shareholder return for mining companies. The overall purpose of this chapter is to find an existing body of knowledge in the academic literature that will help us better understand the causes of impairments in the mining industry. This chapter begins with a definition of impairments and reviews the business literature around asset impairments. It then discusses the business impairment literature in relation to mining firm asset impairments. After identifying several critical deficiencies in applying the business impairment literature to mining companies, I present a theory that draws parallels between mining impairments and major project forecasting errors. I then perform an analysis to test this theorized comparison. This chapter finds that, like major project forecasting errors, mining operations are more likely to experience impairments when constructed in developing countries and when the mine has a significantly novel attribute for the operating company. In this analysis, novelty meant the mining operation was built in a region unfamiliar to the operating company or used a mining or processing method unfamiliar to the operating company. Impairments Definition When the asset's recorded value on a company’s balance sheet exceeds its market value or its value in use, an impairment must be declared (The International Financial Reporting Standards Foundation 2017). When a mining company intends to sell a property, the company lists the property as ‘held for sale’ and records its market value on the company’s balance sheet. 87 The market value is typically determined by looking at recent sales of similar mining properties and converting these sales values to a price per unit of metal reserves. For example, past sales will be compared on a “dollars per ounce of gold reserves” basis. An average value based on past sales will be calculated, and then this average value will be multiplied by the number of ounces of gold reserves in the subject property. On the other hand, the ‘value in use’ is the sum of all cash flows that the company expects the asset to generate, discounted to the present value. The value in use for a mining operation is the sum of the cash flows that the company expects the operation to generate over its life, discounted to the present value. The company can increase this value when, for example, new mineral reserves are discovered, when metal prices rise, or when costs decrease. On the other hand, the company must reduce the value when, for example, declared mineral reserves are decreased, when metal prices decline, when costs increase, or when mines must be shut down prematurely. When one of these value-destroying events occurs, the company may need to declare an impairment. Impairments are typically due to unplanned or unexpected events. If the events, or their financial impacts, were predictable, they would be included in the value in use calculation. Impairments contrast with depreciation of a fixed asset or amortization of an intangible asset which follows a planned and predictable reduction in value over the asset's life. In this study, mineral-related assets include balance sheet items like processing plants, goodwill associated with acquisitions, unsold concentrate inventory, and capitalized exploration or mine development costs. There are specific accounting guidelines for mining firms that identify when assets need to be tested for impairment. Common indicators of impairment for mining firms include but are not limited to significant declines in market capitalization or commodity prices; significant increases in production costs; significant revisions to mine plans; serious mine accidents; and changes in 88 government regulation, laws or tax rates. Further, companies must test goodwill for impairment annually (PricewaterhouseCoopers 2012). Mineral reserves and resources may be the largest sources of market value for mining firms. Junior mining companies (with no revenue but holding a promising mineral property) can have market capitalizations well beyond many operating mining companies. It is also corroborated by the previous chapter's findings, which identified that unexpected reductions in mineral asset values have a significant effect on market returns for mining companies over the long run. However, accounting practices dictate that the value of a mineral property reported in the company's financial statements reflects the historical costs to either find and develop or acquire the property unless the value has been reduced due to an impairment (PricewaterhouseCoopers 2012). When the market value or value in use of these assets is lower than the reported value on the balance sheet, the company records an impairment charge to reduce the asset's declared value. This practice of recording historical costs rather than the market value of mineral reserves means that two companies with identical mineral reserves may record different impairment charges depending on the historical costs associated with the reserves. Suppose Company A spent $300MM exploring and developing its mineral property, and Company B acquired an identical mineral property for $500MM. Each would record those respective values as the historical cost of the mineral asset on their balance sheets. Now suppose that current commodity prices put the value in use of those mineral reserves at $400MM. Company B would need to record an impairment charge of $100MM ($500M - $400MM = $100MM). Company A would not need to record an impairment charge because the carrying value of $300MM is lower than the recoverable value of $400MM. This example illustrates that reported impairments should 89 represent a conservative estimate of the potential value lost by mining firms. There are likely firms with relatively low historical costs associated with their mineral projects that do not need to recognize impairments when there is a significant decline in expected future value. Despite the conservative nature of mining firm impairments, the previous chapter's analysis concluded that impairments were a significant cause of low shareholder returns for mining companies. Impairment Literature Review The academic literature uses the terms “write-off,” “write-down,” and “impairment” to refer to the reduction in the value of different types of assets, and in some cases, these terms are used interchangeably (e.g., Francis, Hanna, and Vincent 1996; Cotter, Stokes, and Wyatt 1998; Alciatore et al. 2000). For this research, I will refer to all unexpected reductions in asset value as impairments unless referring specifically to a study that uses different nomenclature. Much of the impairment literature has focused either on the relationship between the timing of impairment disclosures and changes in stock prices or on the idea of discretionary impairments, where identifiable corporate factors contribute to the timing and magnitude of impairment disclosures. Elliott and Shaw (1988) investigate the relationship between stock returns and impairments and identify that stock returns are significantly below industry medians in the three years preceding significant impairments. They explain this phenomenon by saying that “assets suffer an impairment of value, management realizes that impairment, and then an accounting entry is created to record the impairment.” While they also identify a small one- and two-day negative share return associated with the impairment disclosure, the overall conclusion is that investors have largely priced in the impairment ahead of the disclosure. Bartov et al. (1998) expand the study period around impairment announcements and find that company returns are significantly worse than average in both the preceding and subsequent 90 two years around impairment announcements. They attribute these long tails of poor performance to impairments being “infrequent and . . . often ambiguous in the information they convey,” leaving past and future earnings announcements to convey the full scope of the financial impact that the impairments may represent. (Bartov et al. 1998) Alciatore et al. (2000) study impairments in the oil and gas industry and conclude that contemporaneous returns and impairments are significantly correlated with one another. However, they find a stronger correlation when the return data is lagged by one quarter, suggesting that some of the market’s reaction to the asset impairment occurred in the preceding fiscal quarter. The authors collected data from a period of declining oil and gas prices, which could be a confounding variable in this analysis. Declining oil and gas prices could have impacted the announcement of impairments as low oil prices reduced resource values and reduced share prices as investors sell off oil and gas equities in anticipation of further commodity price declines. Investigating reasons for impairments, Strong and Meyer (1987) find that “the most important determinant of a write-down decision is apparently a change in senior management.” Francis et al. (1996) expand on this research by separating impairments into different types (inventory; goodwill; property, plant, and equipment (PPE); and restructuring charges) and looking at the impact of corporate factors on each type independently. Their analysis finds that management changes, years with unusually good earnings, and years with unusually bad earnings are significant in explaining impairments associated with goodwill and restructuring charges. On the other hand, they find no significant relationship between these factors and inventory or PPE impairments. From these results they conclude that management incentives (bonus contracts, debt covenants, etc.) play a significant role in the reporting of these “discretionary” impairments, but not in the reporting of inventory and PPE impairments. Further, 91 they find negative share price reactions to inventory impairments but positive reactions to restructuring charges. Cotter et al. (1998) also investigate factors that influence asset impairments; however, they identify an issue facing impairment research for mining firms: “As with prior studies of asset revaluations, financial institutions (banks, insurance companies, and credit unions) and mining companies are excluded from the initial sampling index because they are subject to particular debt structures and statutory reporting requirements.” While most published studies in the business literature may exclude mining companies from their datasets, Hilton and O’Brien (2009) provide a mining-focused example of impairment disclosure through a case study of Inco’s Voisey’s Bay project. Inco’s Voisey’s Bay project represents a unique case where a tracking stock existed for the project; its only asset was a 25% interest in the Voisey’s Bay mineral project. Due to declines in the price of nickel and challenges with the local government, the tracking stock declined by 75%, but Inco failed to record any impairment until four years later. Inco ultimately reported an impairment representing 44% of the project’s book value and 16% of Inco’s total asset value. This delay in disclosure supports the literature emphasizing management discretion in impairment reporting. The authors identify that “the undiscounted cash flow criterion [for impairment calculations], intended to prevent unnecessary write-downs, can be effective only in lower volatility settings.” The authors suggest that in high volatility settings (such as mining companies dealing with commodity price movements), the undiscounted cash flow criterion gives management too much discretion around the declaration of asset impairments. (The undiscounted cash flow criteria is a feature of US Generally Accepted Accounting Principles (GAAP). Current IFRS accounting standards, which were described earlier and used by most 92 companies in my data set, require discounting of cash flows for impairment testing and allow for impairment reversals). The authors also find no significant share price impact on the days around the impairment disclosure. This result supports the conclusion from the existing literature that some amount of the impairment is already reflected in the stock price by the time of the announcement and that it will take more time for investors to price in the full impact of the impairment. The existing business literature assesses stock price movements before, concurrent with, and after impairment announcements, supporting the impairment analysis's overall frame in this research study. Studies from the literature have generally found that stock returns are affected several years before and after significant impairment declarations. This research study takes a long-term view of shareholder returns and impairments by looking at shareholder returns and impairments for each company over the complete duration of company operation within the 13-year study period. An analysis done over only a few years or using annual impairment and return data would likely miss the longer-term share price effects of significant impairments. The existing business literature also explains how management may make discretionary impairment decisions for asset types like goodwill and restructuring charges. However, it says nothing about the causes for required impairments of PPE and inventory. This is highly relevant for mining firms as mineral reserves represent a massive raw material inventory to convert into saleable products. Company management has no incentive to impair its reserve base unless absolutely necessary. Unfortunately, the business literature cannot explain why non-discretionary impairments in the mining industry may occur, which is the primary question from the previous chapter. In the following section, I will identify a different reference frame for mining projects 93 that may allow me to draw from an existing body of research to uncover specific causes of mining impairments. The Major Project Frame A significant amount of academic research describes project forecasting errors, which lead to the financial benefits of projects not being realized. In a study of 258 transportation infrastructure projects, Flyvbjerg et al. (2002) found that project cost underestimation has been the rule rather than the exception for the past 70 years. Flyvbjerg et al. (2009) found that “cost overruns and benefit shortfalls of 50 percent are common [while] cost overruns above 100 percent are not uncommon.” These findings are supported by several other studies that have confirmed significant project cost underestimation and benefit shortfall (e.g., Flyvbjerg 2007; Flyvbjerg et al. 2005). While many of these studies are based on transportation infrastructure projects, comparative studies have shown that estimating and forecasting errors of this magnitude apply to a wide range of other industries, including mining (Flyvbjerg et al. 2002; Flyvbjerg 2009). In this chapter, I will investigate whether mining operations are similar to the types of projects that have been studied extensively in the project management literature and whether mining firm impairments are likely a result of forecasting errors. The Project Management Institute (2020) defines a project as “a temporary endeavour undertaken to create a unique product, service or result.” Further, Flyvbjerg (2011) identifies a “major project” as one costing a hundred million dollars or more. With these definitions in mind, I can first analyze whether it is appropriate to view a mining operation through a major project lens. Qualitatively, there are many similarities between mining projects and major construction or infrastructure projects. Both require significant upfront capital investment, take years of 94 planning, face many complex risks through project delivery, and require assumptions and forecasts many years into the future to quantify the expected benefits (Flyvbjerg 2011). The development of a mine can be considered a project on its own. It consists of an open pit or underground mine and the construction of all required infrastructure, including a mineral recovery process and plant, all of which are designed to suit the unique mineral resource's specific needs. Mining companies will mobilize significant but temporary engineering and construction resources for this development phase. Further, an individual mine can be viewed as a temporary endeavour because the purpose is to exploit and exhaust a specific mineral resource over a finite duration with a particular closure plan defined before the development of the mine begins. While some areas may host mining operations for decades (or centuries), all modern mines start with a defined mine life expected to come to a planned end. We can see that mining operations meet the PMI definition of a project by being temporary and creating unique designs, assets, and processes to exploit a specific mineral resource. From a quantitative perspective, I can compare the development cost of mines within my data set to Flyvbjerg’s classification of “major projects.” I will test the similarity in financial scale between mines and major projects through the following hypothesis: H12: Mining operations typically cost more than $100MM to develop. Given the frequency of mining project impairments, I can also identify another common feature with major projects from other industries: costs are often underestimated and forecasted benefits are often overestimated. Mining companies will develop a positive financial forecast for a mining project to receive the necessary funding, showing the discounted future cash flows covering the initial investment and providing a satisfactory return. These cash flows are determined based on 95 detailed mine and production plans, including many different assumptions and projections related to mineral reserves, mining rates, metal recovery, operating costs, and commodity prices. When a mining company needs to revise these assumptions significantly, the mining operation's assets must be tested for impairment. The company will declare an impairment when these discounted cash flows are less than the operation's book value, which is based on the cost to develop or acquire the mine (unless an impairment has already reduced the value). In summary, a mining company will declare an impairment when reality diverges in a significant and negative way from the original forecast of costs and benefits. From a theoretical perspective, this reasoning identifies a plausible connection between mining project impairments and forecasting errors. To corroborate the link between mining firm impairments and project forecasting errors, I can test my data set of mining firm impairments against factors from the project management literature that have been identified to increase the likelihood of forecasting errors. The existing literature around project forecasting errors has identified two broad factors contributing to increases in forecasting errors. First, Flyvbjerg et al. (2002) tested the effects of geographical location on cost underestimation. Using a sample of 258 transportation infrastructure projects with an average value of US$350 million, they categorized the geographic location of these projects into Europe, North America, and “other geographical areas,” which consisted of 10 developing countries plus Japan. While the authors did go into any detail about the reasons for including Japan in a category with ten developing countries, they did conclude that “cost underestimation appears to be more pronounced in developing nations than in North American and Europe.” They found that the difference in cost underestimation between North American projects and European projects was not statistically significant. My data set consists of 96 mining operations spread among many countries and regions around the world and across a broad spectrum of economic development so I can test whether mining project impairments were similarly higher in developing countries than developed countries, which leads to the following hypothesis: H13: Mining project impairments will be larger for projects in developing countries. Next, the literature identifies several psychological biases that contribute to forecasting errors. These include the planning fallacy (Kahneman and Tversky 1973; Tversky and Kahneman 1974; Kahneman and Tversky 1979b; Roger et al. 1994), prospect theory (Kahneman and Tversky 1979a) and anchoring (Tversky and Kahneman 1974; Kahneman and Lovallo 1993; Ascher 1993). Kahneman and Lovallo (1993) identify that these biases are likely to be greater for initiatives that a company has never attempted before, such as applying new manufacturing technologies or entering new markets. Flyvbjerg (2006) adds that these biases are likely to be most significant in the planning of “non-routine projects, understood as projects that managers and decision-makers in a certain locale or organization have never attempted before – like building new plants or infrastructure, or catering to new types of demand.” Due to the unique nature of mineral resources, several attributes may make a mining project non-routine for an operating company. First, the proposed mine's location may be in a geographic region unfamiliar to the company. Second, depending on the depth of the orebody below surface or the orebody's physical orientation, the mine design will dictate whether mining is conducted using an open pit or an underground mining method. The choice of one or the other requires equipment, technical expertise, and operational knowledge unique to that mine type. An open pit mine typically utilizes large haul trucks and shovels for mining and requires expert engineering knowledge in slope stability. An underground mine uses smaller loading and hauling equipment suited for 97 underground operation and requires expert engineering knowledge in rock mechanics, tunneling and ground support. Third, different minerals require different processing methods. Gold tank leaching, copper heap leaching, and polymetallic flotation (just to name a few different mineral extraction processes) all require specific process engineering and operational expertise. Connecting the literature around project forecasting errors with the opportunity for mining companies to embark on non-routine projects, I will test the following hypothesis: H14: Mining project impairments will be larger for mining projects that are not routine for operating companies. I collected data on every mining project developed by a mining company listed on the TSX from 2003 to 2016 to test these three hypotheses. Data Set To investigate mining project forecasting errors, I need to identify the gap between economic expectations and reality for individual mines and mining projects. Compared to the previous chapters' analysis, this chapter moves away from a frame of analysis based on individual mining companies to a frame of analysis focused on individual mining operations. For this analysis, I identified every unique occurrence of a mining operation or mining project over the study period of January 1st, 2003 to January 1st, 2016. I defined a mining operation as a mine that was generating revenue. I defined a mining project as a pre-revenue mineral property that a company identified as a “material property” based on GAAP or IFRS accounting standards or as a past-producing-but-currently-dormant mine that a company had acquired. I defined a unique occurrence of a mine or mining project as the combination of a unique mine or mining project and a unique operating company. For example, the El Chanate gold mine was developed by Capital Gold in 2007, acquired by Aurico Gold in 2011, then 98 merged into Alamos Gold in 2015. I considered these three unique occurrences. The mine development created an initial operational book value for the mining operation's assets, then the two subsequent acquisitions each reset the book value of those assets based on each acquiring or merging company's acquisition cost. In total, I identified 489 unique occurrences of operating mines and pre-revenue mining projects by manually reviewing company Annual Reports, Financial Statements and Annual Information Forms downloaded from SEDAR. Some mining companies would group several operations into one segment by country, geographic region or commodity type, so detailed information at the project/mine level was unavailable. 119 of these unique occurrences did not have adequate segment disclosure that allowed me to collect financial data on an individual mine or project basis. I was left with a data set of 370 unique operating mines and mining projects, and I collected detailed financial information for each one. These 370 unique occurrences included 72 occurrences of projects that had not started construction, development, or redevelopment (in the case of dormant mines) and 298 occurrences of operating mines that had either been developed or acquired. The 298 operating occurrences included 114 occurrences of development and 183 occurrences of acquisition. However, the total number of operating mines in the data set was 253. The discrepancy between the 114 occurrences of development and 253 unique operating mines is due to TSX-listed companies in the data set acquiring mines from companies that were not listed on the TSX and therefore not identified in the data set during development. I summarize this in the table below. I list all 370 unique occurrences of operating mines and pre-revenue mining projects in Appendix C, along with their coding as a pre-revenue project, developed mine, or acquired mine. 99 Table 17: Summary of mine and mine project identification Total mines and mining projects identified 489 Mines and projects with adequate segment disclosure 370 Operating mine occurrences 298 Developed mines 114 Acquired mines 183 Unique operating mines 253 I collected data on the development or acquisition cost and the impaired value for each mine to estimate forecasting errors for individual mining operations. I removed projects from this part of the analysis because companies had not made or acted on an investment decision to develop the mine; I assume that they had not forecasted a positive economic case for commercial mine development during the study period. On the other hand, I assume that companies developing or acquiring a mine have forecasted a positive economic case for investment. I defined cost in the following ways depending on the occurrence category: Developed mine: year-end book value of the related assets in the first year that revenue is generated from the mine, in 2015 dollars. Acquired mine: year-end book value of the related assets in the year of acquisition, in 2015 dollars. A limitation of this method of cost recording is that it does not capture capital additions that may occur (and in some cases may be significant) over the mine's life. On the other hand, it is not subject to reductions in asset values due to scheduled depreciation and amortization, which are based on accounting guidelines and may not reflect the actual market value or value in use of the assets. Collecting year-by-year capital addition information and estimating the “true” asset value through adjustments to depreciation and amortization for every mining operation would be prohibitively time-consuming for this research project's scope. The proposed analysis method 100 based on the value at the start of operations or the acquisition value in 2015 dollars provides an approximate but consistent way to compare mining operations' relative financial scale. I recorded all impairments over the study period for each unique occurrence of both operating mines and projects listed as “material properties.” Summing all impairments for each unique occurrence generated a picture of the total impairments incurred during the study period for each occurrence of a mine or mining project. The impairment data is limited by the fact that I only collected impairment information over the study period. Pre-revenue mining projects may have declared impairments outside of the study period, and developed or acquired mines may have declared impairments after the study period. I also collected data on the mine or mining project location, the existing countries and regions of operation for the operating firm, the mining method and processing method of each mine, and the existing mining and processing methods employed by the operating firm. Analysis Comparison of Mining Project Financial Scale to Major Project Financial Scale I compared mine development and acquisition costs from my data set to the major project benchmark of $100 million to test whether mining projects are similar in financial scale to major projects from the project management literature. It is not entirely straightforward to determine how much it costs to start a mine, mainly because there is no consensus around when mine development ends and commercial operations officially begin. It is common for mining companies to declare “commercial production” based on self-determined criteria that are not necessarily consistent among companies or projects (Lavigne and Tang 2011). One company may declare commercial production when the mine has been handed over to the owner’s team from external construction contractors; another may declare commercial production when 101 mineral recovery is within 10% of the design target. Other companies may not bother with a formal “commercial production” declaration at all. I took the mining project's approximate financial scale to be the book value of the assets associated with the mining project at the end of the first year that mineral sales occurred from the mine. The equation is 𝑀𝑖𝑛𝑒 𝑃𝑟𝑜𝑗𝑒𝑐𝑡 𝐶𝑜𝑠𝑡2015 = 𝑃𝑃𝐸𝑖 + 𝑀𝑖𝑛𝑖 where: 𝑃𝑃𝐸𝑖 = Book value of property, plant and equipment associated with the mine at the end of the first year that revenue is generated from the project, in 2015 dollars 𝑀𝑖𝑛𝑖 = Book value of mineral properties associated with the mine at the end of the first year that revenue is generated from the project, in 2015 dollars I calculated the average mine project cost, median mine project cost, and percentage of mining projects with a cost greater than $100 million for comparison to the major project financial benchmark of $100 million. Mining Operation Impairments in Developed versus Developing Countries I performed a linear regression analysis using project impairment as the dependent variable, a categorical variable based on project location as the independent variable, and project operating years as a control variable to test whether mining projects in developing countries experienced higher impairments than mining projects in developed countries. The major project literature investigated cost overruns and benefit shortfalls of completed infrastructure projects (specifically comparing rail projects between developing and developed countries). For this analysis, the data set consists of the 237 mining projects developed or acquired during the study period (January 1st, 2003 to January 1st, 2016). This removed mines that may have declared 102 impairments before the study period but is limited because impairments may have been declared after the end of the study period. In the analysis by Flyvbjerg et al. (2002), the authors calculated the cost overrun variable as the actual project costs minus forecast costs in the percentage of forecast costs. In this analysis, I am using mining project impairments as an indication of project forecasting errors. In a similar way to the calculation of firm impairments from the previous chapter, I calculate the project impairment variable in the following way: 𝑃𝐼 =𝛴𝑖𝐼𝑝(𝛴𝑖𝐼𝑝 + 𝐴𝑝𝑖) where: 𝐼𝑝 value of mining project impairments during the study period 𝐴𝑝𝑖 mineral project value at the end of the period While Flyvbjerg et al. could compare an actual value to a forecasted value directly, my calculation can only be considered a rough approximation of forecasting errors. Two considerations limit it. First, only using impairment data will not account for positive forecasting errors and may miss negative forecasting errors hidden by positive forecasting errors within the same project. For example, suppose a project has a capital cost overrun of 20% during a period where metal prices increase by a similar amount. In that case, the company may not need to declare any impairment. Second, the ending asset value for a mining project has been affected by accounting depreciation over the operating period. It may not represent the project's actual asset value unless an impairment has recently reduced the reported value to the mine’s value in use. For these reasons, the impairment variable used in this analysis can only be considered a rough approximation of forecasting errors. 103 Flyvbjerg et al. (2002) compared projects in North America (presumably defined as Canada and the United States), Europe, and “other geographical areas (a group of 10 developing nations plus Japan).” The authors ultimately conclude that forecasting errors are more pronounced in developing nations than in North America and Europe. I coded mining projects in my analysis by their location in “developed economies” or in “developing economies” as defined by the United Nations Conference on Trade and Development (UNCTAD 2020). The classification also defines “transition economies,” and I coded these as “developing economies” for this analysis. In this analysis, the “developed economies” were Australia, Canada, New Zealand, The United States, and European countries. A dummy variable (0 or 1) was assigned to each category to stand as the independent variable. Finally, to account for the different operating periods among the mining projects in the data set, years of operation was introduced as a control variable. The linear regression analysis was performed in R Studio. Routine vs Non-Routine Mining Operations I performed a linear regression analysis using project impairment as the dependent variable, a categorical variable that differentiated routine and non-routine projects as the independent variable, and project operating years as a control variable to test whether non-routine mining projects experienced higher impairments than routine mining projects. I calculated the impairment variable in the same way as the analysis for projects in developed versus developing countries: 𝑃𝐼 =𝛴𝑖𝐼𝑝(𝛴𝑖𝐼𝑝 + 𝐴𝑝𝑖) where: 𝐼𝑝 value of mining project impairments during the study period 𝐴𝑝𝑖 mineral project value at the end of the period 104 I created the routine versus non-routine independent variable by coding mining projects based on whether the project was in a region where the operating company had current or past mining operations or whether the project used a mining or mineral recovery method the company had used before. In addition to operational data collected during the study period, I reviewed historical company information like Annual Reports and Annual Information Forms to identify regions of operation, mining methods, and mineral recovery methods that each company had used before the study period. Only thirty firms were operating at the start of the study period, and only seven were in operation before 1990, so the historical investigation was not overly demanding. Following Shapiro et al. (2007), I defined geographic regions as North America, South America, Europe, Africa, and Asia/Pacific. I defined unique mining methods as open pit mining or underground mining, and I defined unique mineral recovery methods as tank leaching, heap leaching, flotation, or new commodity production. For example, suppose a company only had a gold mine in Brazil that used open pit mining and heap leaching. I would code the development of an open pit, heap leach gold mine in Peru as routine (same mining method, same mineral recovery method, and same region). Now suppose the same company developed an underground tin mine in Brazil (new mining method and new commodity, same region) or an open pit, heap leach gold mine in Zimbabwe (same mining method, same mineral recovery method, new region). I would code those operations as non-routine. The analysis tested the impact of entering a new region and using a new mining or mineral recovery method independently. It also tested the effect of any non-routine attribute on project impairments. A dummy variable (0 or 1) was assigned to stand as the independent variable in each case. 105 Again, to account for the different operating periods among the mining projects in the data set, I introduced years of operation as a control variable. I performed the linear regression analysis in R Studio. Results Mining Project Financial Scale The following plot shows the book value of mines when revenue is first reported, in 2015 dollars. The average value is $749 million, and the median value is $208 million. 84 of the 114 mines had values over $100 million when revenue was first recorded, representing 74% of the developed projects. Figure 8: Book value of developed mining projects at first revenue, in 2015 dollars Mining Operation Impairments Of the 297 unique occurrences of mining operations in the data set, 156 (53%) declared impairments during the study period. Some projects only declared one impairment during the 1 10 100 1,000 10,000 100,000Value ($MM, in 2015 dollars)Mining Projects at First Revenue106 study period, while many declared several impairments. The following chart shows the distribution of declared impairments per mine. Figure 9: Distribution of impairments per unique mine occurrence The following table presents the descriptive statistics for mining operations that declared impairments. Table 18: Descriptive statistics for mines that declared impairments Number of mines that declared impairments 156 Total value $63B Minimum value $1MM Maximum value $7.7B Average value $406MM Median value $106MM The following two charts show the regional distribution of all mines in the data set and the regional distribution of mines that declared impairments. 0 1 2 3 4 5 107 Figure 10: Regional distribution of all mines in the data set Figure 11: Regional distribution of mines that declared impairments South America37%Africa17%Asia/Pacific13%North America25%Europe8%South America42%Africa16%Asia/Pacific10%North America26%Europe6%108 Mines Located in Developed versus Developing Countries The table below summarizes the descriptive statistics for the dependent variable. Table 19: Descriptive statistics for the impairment percentage 𝑷𝑰 Count 237 Min 0.00 Max 1.00 Mean 0.31 Median 0.20 Standard Deviation 0.36 Of the 237 unique mine occurrences used in the analysis of impairments for mines in developed vs developing countries, 87 (37%) were in countries identified as “developed,” and 150 (63%) were in counties identified as “developing.” The linear regression model found that being in a developing country correlated with 17% higher impairments than mines in developed countries (p-value = 0.02). I express impairments as a percentage of a mine’s asset value. Hence, a 17% increase in impairments means that mines in developing countries, on average, impaired 17% more of their asset values than mines in developed countries. The equation for the regression line is as follows: 𝑃𝐼 = 25.62 – 17.08d + 1.00y + 2.36d*y where: 𝑃𝐼 impairments (%) 𝑑 developed country categorical variable (developed = 1, developing = 0) 𝑦 years of operation during the study period The table below summarizes the results of the logistic regression model. 109 Table 20: Regression model results for developing country vs impairment percentage Model Effect Size (%) Std. Error R2 P-Value 𝑑 vs 𝑃𝐼, Years control -17.08 10.01 0.03 0.02 Routine vs Non-Routine Mining Operations I used 237 unique mine occurrences to analyze impairments for routine versus non-routine mines. I identified 120 (51%) as being in a non-routine region, 97 (41%) as using a non-routine mining method, and 93 (39%) as producing a non-routine commodity or using a non-routine mineral recovery method. I identified 68 (29%) operations as being in a non-routine region, using a non-routine mining method, and using a non-routine mineral recovery method. In many cases, these were the first mining operations for a particular company. I present the results of the linear regression analysis in the table below. Table 21: Routine vs non-routine mining operation analysis results Non-Routine Attribute Effect Size (%) Std. Error R2 P-Value Region 3.23 9.75 0.03 0.01 Mining Method 10.43 10.81 0.02 0.04 Recovery Method 8.87 11.07 0.02 0.04 Any non-routine attribute 3.56 9.91 0.02 0.03 All non-routine attributes 20.30 13.40 0.03 0.02 Here is the equation for the non-routine region factor: 𝑃𝐼 = 17.56 + 3.23Rr + 2.65y – 1.53r*y where: 𝑃𝐼 impairments (%) 𝑟 non-routine region categorical variable (non-routine = 1, routine = 0) 𝑦 years of operation during the study period 110 Here is the equation for the non-routine mining method factor: 𝑃𝐼 = 16.07 + 10.42Rm + 2.37y – 1.50Mm*y where: 𝑃𝐼 impairments (%) Mm non-routine mining method categorical variable (non-routine = 1, routine = 0) 𝑦 years of operation during the study period Here is the equation for the non-routine mineral recovery factor: 𝑃𝐼 = 16.50 + 8.87Rp + 2.46y – 1.65Mp*y where: 𝑃𝐼 impairments (%) Mp non-routine mineral recovery method categorical variable (non-routine = 1, routine = 0) 𝑦 years of operation during the study period Here is the equation for any non-routine factor being present: 𝑃𝐼 = 17.23 + 3.56RAny + 2.61y – 1.26RAny*y where: 𝑃𝐼 impairments (%) RAny any non-routine factor categorical variable (non-routine = 1, routine = 0) 𝑦 years of operation during the study period Here is the equation for all non-routine factors being present: 𝑃𝐼 = 15.68 + 20.30RAll + 2.45y – 2.80RAll*y where: 𝑃𝐼 impairments (%) RAll all non-routine factors categorical variable (non-routine = 1, routine = 0) 𝑦 years of operation during the study period Discussion I found that 74% of developed mining projects had a book value above $100 million when the project first generated revenue. I consider this percentage conservative since many of these projects continued to spend money on subsequent project development to reach a commercial production declaration or expand operations. Based on this information, hypothesis 111 H12: Mining operations typically cost more than $100MM to develop is supported. Most mining projects can be considered similar in financial scale to major projects, as defined in the project management literature. The analysis of mines in developed versus developing countries found that being in a developing country correlated with impairment of 17% more mineral asset value, on average. The regression model was statistically significant. The independent variable had a very high effect size (17% difference on an average value of 31% asset impairment among all mines in the data set). Based on this result, the analysis found that hypothesis H13: Mining project impairments will be larger for projects in developing countries is supported. The UN classification of developing economies, economies in transition (grouped with developing for this analysis), and developed economies relies mainly on differences in per capita gross national income, and the classification reflects “basic economic country conditions” (UNCTAD 2020). Looking for reasons that may explain the impairment differences between mining operations in developing and developed countries, I am more likely to find explanations in forecasted costs than in revenue/benefits. In almost all cases, mining companies sell mineral products into global commodity markets, so there will be no difference in the price realized for mineral or metal concentrates between mines in developed or developing countries. On the cost side, it may be the case that capital costs to build projects are inaccurately forecast or that operating costs turn out to be higher than forecast. Engineering firms in developed countries design most mining projects (Canada and Australia are significant hubs for mining project engineering). It may be that engineers working in developed countries underestimate the logistical and infrastructure challenges and costs of executing projects in developing countries. Companies preparing project feasibility studies (also often in Canada and Australia) may 112 underestimate the availability of skilled labour and engineering/management staff in developing countries and underestimate the mine's operating costs, both through higher labour costs and lower operational performance. Finally, to the extent that there is a relationship between economic development and the rule of law or government corruption, mines in developing countries may be at more risk of arbitrary withdrawal of permits or outright expropriation. The analysis of routine versus non-routine factors for operating mines found that mines in non-routine regions, using non-routine mining methods, or using non-routine mineral recovery methods generated higher impairments. Mines in the routine regions, using routine mining methods, or using routine mineral recovery methods generated lower impairments. The effect size of non-routine mining methods and mineral processing methods were larger (10.43% and 8.87%, respectively) than the effect size of non-routine regions (3.32%). Further, operations that combined all non-routine attributes studied in this analysis impaired 20.30% more of their asset values than mines with no non-routine characteristics. Based on these results, the research found that hypothesis H14: Mining project impairments will be larger for mining projects that are not routine for operating companies is supported. One explanation for the smaller effect size of non-routine regions is that most mining operations are remote from corporate head offices, so a mine’s host country may have a smaller impact than the number of kilometres or time zones from the head office. Perhaps hiring local personnel from the operating region can mitigate many of the challenges of developing or acquiring a mine in a new region. It may be easier to hire a Peruvian general manager for a new Peruvian mining operation than to find a local general manager who is an expert in your new mineral recovery method or relocate an expert ex-pat to the new operation. However, the relative ease or difficulty of hiring expertise would not explain forecasting errors unless there is a gap in 113 the perception between the real and perceived difficulty in overcoming the challenges of entering a new region. Another possible explanation that speaks more directly to forecasting errors is that technical/engineering errors related to mining or mineral recovery methods are more difficult to predict and quantify than errors associated with entering new geographic regions. As a direct comparison to the major project literature, these analyses are limited because impairments are an imprecise measure of forecasting error since positive forecasting errors (e.g., metal prices above forecasted values) can hide negative forecasting errors. Further, impairments are limited by the book value of the mine’s assets, which are subject to accounting depreciation that may not have a meaningful effect on initial project forecasts. With that said, the regression models were all statistically significant at the 95% level, and all produced high effect sizes relative to the average impaired value in the data set. Taking all the results from these analyses together – along with the limitations stated above - I conclude that, in general, mining projects can be classified as major projects and that mining impairments have similar characteristics to project forecasting errors. Based on this conclusion, the following chapter will investigate mining firm impairments through the lens of the existing project forecasting error research. The next chapter will review the major project literature on the root causes of forecasting errors and investigate whether these root causes may explain the mining impairments this chapter identified. Conclusion This chapter's stated purpose was to find an existing body of knowledge in the academic literature to help understand the causes of impairments observed in the mining industry. I first defined mining firm impairments and discussed the significance of asset impairments to mining companies. I then reviewed the existing literature on asset impairments 114 and identified a significant gap regarding mining firm asset impairments. Most published literature has studied discretionary impairments to accounting entries like goodwill and explicitly removed the mining sector (among others) from the data sets because of the industry's unique statutory reporting requirements. I then identified major project forecasting errors as a possible analogue to mining firm impairments and reviewed the relevant literature on this topic. To determine whether the literature on project forecasting error is relevant to this study of mining firm impairments, I first compared the financial scale of mining operations to the financial scale of major projects. I then tested my data set of mining firm impairments against factors known to increase major project forecasting errors. I specifically investigated project location and the introduction of novel or non-routine project attributes. The analysis in this chapter found that mining operations were similar in financial scale to major projects. Like project forecasting errors, mining operation impairments were higher in developing countries. They were also higher when a key feature of the mining operation was novel or non-routine for the operating company. Specifically, impairments were higher when the mining operation was in a region unfamiliar to the operating company or used a mining method or mineral recovery method unfamiliar to the operating company. This chapter concluded that the literature around major projects and project forecasting errors could be applied to mining operations and mining impairments. Following the analysis in this chapter, I offered some possible reasons for mining impairments. Errors in forecasting the costs, challenges, and risks of operating in developing countries and accurately gauging the likelihood of technical failures were both put forward as possible explanations. The following chapter will create a framework to understand the reasons behind mining impairments and 115 compare these to the known and suspected reasons for project forecasting errors from the major project literature. 116 Chapter 5: Causes of Impairments Introduction The previous chapter compared mining impairments to forecasting errors from the project management literature. It found that mines are similar in financial scale to major projects. Like project forecasting errors, mine impairments are higher in developing countries and higher when mines have a significantly non-routine attribute for the operating company. The chapter concluded that mining firm impairments could be viewed through the lens of major project forecasting errors. This chapter investigates the root causes of impairments in the mining industry by leveraging the literature on the root causes of project forecasting errors. This chapter reviews the literature around the root causes of project forecasting errors. It identifies three leading causes of forecasting errors: technical error, optimism bias, and principal-agent conflict. Following the project forecasting error literature review, I develop a theoretical framework to identify the potential causes of mine forecasting errors and then relate these to the root causes identified in the project management literature. Using a data set of every impairment event declared for an operating mine from January 1st, 2003 to January 1st, 2016, I coded all impairment events into the theoretical framework for mine forecasting errors. The analysis found that impairments were declared for almost all theoretical categories and mapped well to the project forecasting root causes of technical error and a combined category of optimism bias and principal-agent conflict. The largest category of reported mining impairments was declining metal prices (mapped to optimism bias and principal-agent conflict), followed by issues with expected ore grade and access to ore (mapped 117 to technical error). Overall, roughly 30% of impairments resulted from technical error, and 70% resulted from optimism bias or principal-agent conflict. This chapter concludes with a discussion of the relationship between the disclosed reasons for mining impairments and the root causes from the project forecasting literature and then offers suggestions for future research. Project Forecasting Errors The previous chapter identified that mining operations are similar in financial scale to major projects and that mining impairments can reasonably be viewed through the lens of project forecasting errors. Major project forecasting errors have been studied extensively, and several theories have been put forward to explain these large and persistent errors. Wachs (1990) notes that the complexity of forecasts is often misleading because the most critical components are not the equations or databases but rather the assumptions that forecasters need to make about the future. In a 2002 article, Flyvbjerg et al. put forward a framework for categorizing estimating and forecasting errors. They identify technical, economic, psychological, and political errors. In a 2011 article, Flyvbjerg simplified these categories to bad luck or technical error, optimism bias, and economic-political causes. For improved clarity in this research study, I will refer to the bad luck or technical error category simply as ‘technical error,’ and the economic-political category as ‘principal-agent conflict.’ Technical Error This category refers to forecasting errors from sources like inadequate data, honest mistakes, unlucky probabilistic outcomes, and lack of experience (Flyvbjerg et al. 2002). Examples include geological or environmental problems, safety mishaps, and other events that may be out of project managers' direct control but could have been anticipated with more 118 information and better technical understanding. These errors could have been avoided because the necessary information for an accurate forecast existed at the project planning and forecasting stage. Flyvbjerg (2009) asserts that while it may be impossible to precisely predict what may go wrong in a single given project, the significant case history of technical forecasting errors means that it is still possible to predict the overall project risk (and associated cost implications) based on past outcomes. Optimism Bias Optimism bias is a term for several psychological biases such as the planning fallacy (Kahneman and Tversky 1973; Tversky and Kahneman 1974; Kahneman and Tversky 1979b; Roger et al. 1994), prospect theory (Kahneman and Tversky 1979a) and anchoring (Tversky and Kahneman 1974; Kahneman and Lovallo 1993; Ascher 1993). The planning fallacy refers to people’s tendency to neglect the reliability of representative information when making predictions and to discount or explain away past prediction errors. Prospect theory relates to people’s risk aversion in situations involving sure gain and risk seeking in situations involving certain loss. Anchoring refers to the tendency for a starting point or value to inappropriately influence people’s predictions. It also refers to people's tendency to underestimate the overall probability in the presence of many conjunctive, low probability events. Research has identified that both laypeople and experts alike are prone to these biases, even after being made aware of them (Tversky and Kahneman 1974). Kahneman and Lovallo (1993, 2003) characterize these biases as the “inside view.” The inside view is the overwhelmingly preferred, intuitive approach that involves focusing intensely on the case at hand. They state that “the natural way to think about a complex project is to focus on the project itself – to bring to bear all one knows about it, paying special attention to its 119 unique or unusual features.” Conversely, the “outside view” involves looking at the distributional data from past experiences to make predictions while intentionally ignoring the unique and unusual attributes from the case at hand. The outside view aims to counteract people’s natural psychological biases that cause significant forecasting errors. However, the authors note that the outside view can be “intolerably demoralizing” and can be rejected for seeming crude and superficial. Translating this work into the frame of project forecasting, Flyvbjerg et al. (2002) identified optimism among project promoters and forecasters at the project appraisal phase as a significant source of forecasting error. Flyvbjerg et al. (2009) state that adopting the inside view in project forecasting causes executives to focus on the case at hand and ignore past outcomes. The authors explicitly call out the planning fallacy as the reason for underestimating project completion times. They identify anchoring to an initial plan as one of the primary biases of judgement in project forecasting. Over time, there has been some evolution about whether these psychological biases are legitimate causes of project forecasting failure. In conflict with Roger et al. (1994), Flyvbjerg et al. (2002) reject optimism bias as a primary cause of project cost underestimation and assert that it should have been reduced or eliminated over time as professional project forecasters identify these biases and learn from past mistakes. However, Flybjerg takes a more balanced approach in a 2011 publication, stating that optimism bias may be part of the explanation but not the whole story. In a 2013 publication, Flyvbjerg acknowledges that most individuals and organizations are inclined to take the inside view as the conventional and intuitive approach. 120 Principal-Agent Conflict Researchers have euphemistically referred to this category of error as “strategic misrepresentation,” or put more plainly, the self-interested lying of various project participants (Flyvbjerg et al. 2002; Flyvbjerg 2004). Wachs (1989) identified that planners must walk a line between science and advocacy when developing forecasts. Interviews of project planners, engineers, and economists have reported material forecast revisions to increase the likelihood of project approval (Wachs 1990). Engineering or construction companies may underestimate capital costs in the estimating stage to secure follow-on work (Wachs 1990; Flyvbjerg et al. 2002; Flyvbjerg 2004). There is a multi-level principal-agent conflict inherent in most project forecasting exercises (Flyvbjerg 2004). A significant information asymmetry exists between project contractors (e.g., planners, engineering firms, construction firms) and project proponents and between project proponents and project funders. Contractors have an incentive to present aggressive estimates to win design and construction work orders of magnitude more valuable than forecasting or estimating work. Project proponents have an incentive to submit optimistic financial forecasts to secure funding in a competitive investment environment (Kahneman and Lovallo 2003; Flyvbjerg et al. 2009). A less overt but still pernicious cause of misrepresentation comes from the simple discomfort people feel delivering unpleasant or pessimistic information (Kahneman and Lovallo 1993). Many organizations have a culture where risk communication is seen as taboo and information is suppressed to avoid discomfort among stakeholders (Kutsch and Hall 2010; Stingl and Geraldi 2017). 121 Given the similarity between major projects and mining projects identified in the previous chapter, I will compare the literature on the project forecasting error root causes to the reported reasons for mining impairments. While there has been considerable discussion of forecasting error root causes in the existing major project literature, there has been no quantitative assessment of the relative size or frequency of the forecasting error root causes. This research study provides the first quantitative assessment of forecasting error root causes. Mining Project Impairments To create a theoretical framework for understanding mining impairments as forecasting errors, I will identify the components of a mining project forecast and then map these onto the root causes identified in the project management literature. Like major projects described in the project management literature, benefits and costs make up a mining project forecast's highest conceptual level. Mining companies will often list many of the qualitative benefits of a mining operation, such as regional economic development and meeting consumer demand for metals. However, I focus only on the direct economic benefit captured in company financial statements: revenue from mineral and metal sales. Revenue is derived from the amount of commodity produced and the market price for that commodity. A metal mine will generate a mineral concentrate or a metal product at some saleable level of purity. For example, a copper mine will often produce a copper-sulphide mineral concentrate of approximately 30% copper and sell it to a copper smelter, sometimes through an intermediary metal trading company. On the other hand, gold mines will often produce semi-pure bars of precious metal they will sell to refiners. In both cases, the value received is related to the product's metal content and either the commodity's spot (current) price or a negotiated contract price. 122 Since an orebody is a finite resource, there is an expected total benefit over a mine's life. When a mining company issues a feasibility study for a mine, it reports a specific quantity of economically minable minerals (the mineral reserves) based on several critical assumptions. First, a metal price forecast is created, determining the economic viability of mining different orebody areas. Increases in metal prices over the mine's life can increase reserves by making new areas of the orebody economically viable. A decrease in metal prices can render areas of the orebody sub-economic and reduce the overall amount of mineral reserves. Second, the company makes assumptions about subsurface conditions around the orebody. It estimates the orebody's mineral content by drilling information, geological interpretation, and computer modelling. There can often be hundreds of meters between drill holes, and it is up to accurate geological interpretation to determine the degree of ore continuity between holes. The ore body grade estimate may go up or down as the company mines the mineral deposit and obtains more accurate grade information. Further, the company uses drilling information to estimate the ground conditions that feed into the choice of mining method and overall mine design. Different mining methods and mine designs are used based on rock competency and ground stability. For example, more competent rock can safely accommodate larger underground openings, while a technique like block caving requires a fractured rock mass to generate predictable caving. Third, mineral recovery rates are estimated based on laboratory test work ahead of any commercial mining activity. These test work programs often consist of a few tonnes of samples that are expected to represent an orebody composed of millions of tonnes of material. These test work programs can overestimate and underestimate the mineral recovery rate, directly affecting the conversion of ore into saleable products. Sometimes, the samples tested will not represent a significant section of the orebody. A completely different process will be required for effective 123 mineral recovery, resulting in the construction of a new recovery plant or a complete loss for that section of the orebody. Finally, a mining company typically operates at the pleasure of the community, region, and country where the ore body is located (referred to here as a “social license to operate”). Labour unrest, community opposition, and mine nationalization are all events that have resulted in significant value destruction for numerous mining companies operating around the world. All these factors work together to affect the expected benefit of proposed and operating mines. From a cost perspective (again focusing only on direct costs captured in company financial statements), I distinguish between capital and operating costs. Capital costs are the costs spent developing or acquiring a mining project and typically show up on financial statements as property, plant, and equipment (PPE) or goodwill. These capital costs usually consist of project exploration costs, equipment purchases, and other pre-production activities for a developed project. Pre-production activities include constructing site access roads, installing power infrastructure, underground development (ramps, shafts, and tunnels), open pit pre-stripping (removing waste above an orebody), mineral recovery plant construction, and tailings dam construction. Any of these costs may be subject to over or underestimation at the forecasting stage. While some mining companies may use an internal project team to create a mine feasibility study, it is much more common for an arms-length engineering firm to produce the study. There may be a disconnect between the firm doing the forecasting at the feasibility stage and the engineering and construction management firm that the mining company contracts for development. The construction company may use different methods or approaches than the engineering company envisioned. These differences may generate additional costs compared to 124 the forecast. On the other hand, the engineering firm preparing the feasibility study may be overly aggressive in its forecast to secure the follow-on construction work. And many of these activities, specifically those dealing with earthworks, are subject to errors in understanding the site conditions, leading to significant cost overruns. For an acquired project, the capital cost includes the acquisition price plus any immediate refurbishment necessary to bring the operation into regular production. While an acquisition price may appear to be a fixed number, forecasting errors can be associated with the eventual purchase price and the acquired assets. A company may enter purchase discussions or negotiations for a mine, only to end up in an aggressive bidding war and eventually pay significantly more than it initially expected (see The Winner’s Curse by Richard Thaler (1988)). On the other hand, despite conducting detailed and deliberate due diligence, it may come to pass that the purchased assets do not measure up to expectations. Perhaps some items were overlooked during the due diligence stage, or perhaps there were adverse conditions known to the selling company that it did not proactively disclose. The other cost component is operating costs, which are the costs spent on an ongoing basis to generate a saleable product. These typically include fixed costs such as labour (site management and equipment or process operators), equipment leases, and variable expenses such as fuel, electricity, mechanical consumables, and chemicals. Mining operations are large consumers of energy through fuel and electricity. Sometimes, electricity is supplied by grid power under long-term contracts with a public utility. Other times, power is generated on-site using diesel or natural gas generators. Most open pit mines utilize diesel truck fleets for ore transport. Overall, most mines have high financial exposure to global oil prices as a significant operating cost. Further, as commodity cycles go up, the demand for skilled labour and mining-125 specific consumables can become substantial. These demand changes can dramatically impact operating costs and carry significant operating risks if the mine cannot quickly source critical materials. For example, in 2011, the spot price for haul truck tires was double the typical contract price due to limited supply (Matrix 2011). Like metal prices, operating costs rely on projections well into the future to build forecasts for mining operations. I summarize the benefits and costs described above in the table below. Table 22: Mining operation benefits and costs Benefits Costs Revenue Reserves Capital Costs Operating Costs - Metal prices - Product grade - Metal prices - Ore grade - Ore access - Process recovery - Social license - Mine development - Equipment - Plant construction - Acquisition cost - Labour - Fuel & Electricity - Materials With the components of a mining project forecast identified, I can now attempt to map these onto the root causes of forecasting errors identified in the project management literature. The root causes of forecasting errors identified by the project management literature are technical error, optimism bias, and principal-agent conflict. Technical error refers to inadvertent mistakes, a lack of adequate data, and inexperience. I map product grade, ore grade, ore access, and process recovery to this root cause category. These forecast elements are derived from data that can be collected and interpreted at the planning and forecasting stages of a mining project. With enough time, money, and expertise, these elements could be known with complete certainty. They correspond to physical rock and mineral properties that exist before the start of the project and are unchanging over the project planning period. Big, adverse surprises related to rock or mineral properties during project development 126 and mine operation result from insufficient information, inexperience, and poor judgement. In contrast, the other forecast elements correspond to future values that must be predicted during the planning and forecasting stages. They cannot be known with complete certainty, regardless of the amount of data collection and interpretation, Optimism bias refers to several types of psychological biases that compel people to overestimate future benefits and underestimate risk systematically. I map metal prices, social license, capital costs items, and operating cost items to this root cause category. Metal prices, construction costs and operating costs can be subject to anchoring where recent prices and costs may have an outsized impact on forecast values. Mine development and construction costs can be further subject to the planning fallacy by discounting past project cost overruns and anchoring bias by discounting a very high number of low probability project execution risks. The planning fallacy may also be present around the social license to operate when mining firms discount information about local project opposition or the threat of government intervention. Principal-agent conflicts stem from the principal-agent dynamic present in most major projects that creates an incentive for some project participants to misrepresent costs and benefits. I also map metal prices, social license, capital cost items, and operating cost items to this root cause category. These forecast elements can be misrepresented or manipulated by project agents depending on the principal’s level of knowledge and the forecasting control structures that are in place. For example, a project promoter can create complex and intentionally over-optimistic metal price forecasts to push a project forward. Engineering firms can deliberately underestimate capital construction costs to win a valuable construction contract. Local personnel can misrepresent the community or government support behind a proposed mine for personal gain. 127 The following table maps the components of a mining project forecast to the root causes identified in the project management literature. Table 23: Mapping mining impairments to project forecasting error root causes Benefits Costs Revenue Reserves Capital Costs Operating Costs Forecasting Error Causes Technical Error Product grade Ore grade Ore access Process recovery Optimism Bias or Principal-Agent Conflict Metal prices Metal prices Social license Mine development Mining equipment Plant construction Acquisition Labour Fuel & Electricity Materials I have combined the root cause categories of optimism bias and principal-agent conflict because I do not believe these causes can be parsed with publicly available information. This category contains the forecasting elements that cannot be known with perfect accuracy in the project planning phase and require some judgment when constructing the project forecast. Ultimately, the difference between a forecasting error caused by optimism bias or principal-agent conflict comes down to the forecaster's belief and intention. If the forecaster believes the forecast is correct and has no deceptive intent, it can be identified as optimism bias. If the forecaster believes the forecast is incorrect and intends to deceive other project participants, it can be identified as a principal-agent conflict. Forecasting errors caused by principal-agent conflicts have been discussed extensively in the project forecasting literature, and principal-agent conflicts are repeatedly cited as a root cause of forecasting errors (e.g., Wachs 1989; Flyvbjerg et al. 2005; Flyvbjerg 2009; Awojobi and 128 Jenkins 2016). To identify this root cause, the project forecasting literature relies on (typically anonymous) personal interviews rather than any quantitative data collection exercise. The anonymous nature of the interviews seems to indicate project participants are unwilling to publicly disclose or speculate on the lying and manipulation associated with the principal-agent conflict. The current research is completely limited on this topic because it only makes use of publicly available information. Data Set While the previous chapter looked at the sum of impairments for individual mining operations, this chapter looks at each impairment declaration independently. During the study period of January 1st, 2003 to January 1st, 2016, I treated each impairment declaration with a value of at least $1 million and adequate reporting to tie the impairment to a specific mining operation as a unique impairment event. I generated a total population of 320 unique impairment events. I removed impairment events associated with mining projects that had not started construction to align with the major project literature, which deals only with constructed projects. Removing projects left 266 impairment events associated with completed, operating mines. For each of these impairment events, I collected data on the impairment value, the asset's residual value at the time of the impairment, and the reported reason for the impairment. The data set is limited by the fact that I only collected impairment information over the study period. Some mining operations identified within the study period may have declared impairments outside of the study period. 129 Analysis The analysis of impairment events consisted of coding the reported reasons for mining impairments into the mining project forecast components summarized in Tables 10 and 11 above. I coded reported reasons for mining impairments as “other” if I could not map them to the theorized forecast components. On the other hand, some companies attributed impairments to several different factors. For example, poor ground conditions can cause underground mining dilution (when waste rock is inadvertently mixed with ore), which then causes lower mining production rates because more rock must be mined for the same amount of ore, and higher milling costs because the waste rock is sent to the mill along with ore. In this example, the company may list poor ground conditions, lower mining rates, and higher milling costs as causes of impairment. However, the root cause is a gap between the technical understanding of expected and actual ground conditions. This impairment would have been coded as an “ore access” issue because the poor ground conditions made access to the ore more complicated and more costly than expected. In the few cases where I identified two unique and separate root causes (lower metal prices and higher operating costs, for example), I split the impairment event and allocated half of the impairment value to each reported cause. The table below gives a brief description of each mining impairment and provides real examples of reasons for impairments from company financial statements and annual reports. Companies generally reported impacts to capital costs and operating costs only with that level of detail, and specifics about the exact reasons for cost increases were typically not disclosed. Also, I did not find any instances of the ‘product grade’ category in this data set of reported impairments. 130 Table 24: Reported reasons for mining impairments Category Description Example Ore grade Lower than expected mineral or metal content in the ore “In the fourth quarter, efforts to improve grade reconciliation in certain areas of Ernesto/Pau-Pique did not achieve the expected levels set by management requiring an update to the life of mine plan projections. This resulted in a further impairment charge of $23.0 million in respect of Ernesto/Pau-Pique in the fourth quarter …” – Yamana Gold, 2014 Consolidated Financial Statements Ore access Ore cannot be accessed due to geotechnical issues “In December 2004, a revised mine plan for the El Valle and Carlés mines has been developed, and is currently under optimization, resulting in a significant increase in the mining costs, primarily due to a lower mining rate and ore grade that are a result of the difficult ground conditions at Boinás East (El Valle mine). … a write-down of $28,387,600, net of grants (refer to note 6), was recorded in the Consolidated statements of operations and deficit for the year ended December 31, 2004 under the caption Write-down of mineral properties.” – Rio Narcea Gold Mines Ltd., Consolidated Financial Statements for the Three Years ended December 31, 2004 Process recovery Mineral or metal recovery lower than expected “… the Company completed an updated metallurgical sampling and analysis program as part of an expansion study for the Masbate Mine along with an updated life-of-mine plan based on 2014 year end reserve and resource estimates. The updated metallurgical study has resulted in 3% lower projected life of mine recoveries … As a result, the Company recorded a net impairment charge of $305.2 million …” - B2Gold Corp., 2014 Consolidated Financial Statements Metal prices Metal price lower than forecast “During the fourth quarter of 2015, the Company recognized a non-cash impairment loss on non-current assets of $228.7 million, primarily due to the decrease in the forecast of the long-term gold price due to declining market prices and revised life-of-131 Category Description Example mine (“LOM”) plans for the El Castillo and La Colorada mines.” – Argonaut Gold, Management Discussion & Analysis for the Year Ended December 31, 2015 Social license Labour dispute, issues with the local or national government “The Company encountered local opposition from the citizens of Esquel who voted not to support the mine development in a non-binding referendum in March 2003. Shortly thereafter the provincial government passed a law that prohibited open-pit mining and the use of cyanide that became effective in 2003. ... the Company currently does not have persuasive evidence, such as issued permits, to maintain the capitalized cost associated with the Esquel project. Therefore, in compliance with accounting guidelines, the Company wrote down the long-lived assets of the Esquel project to their estimated fair value, which management determined to be the real estate value of the property.” – Meridian Gold Inc., 2005 Annual Report Acquisition Overpayment for an acquired mine “Company recorded after-tax impairment charges aggregating … $111.3 million for Chirano. … The impairment charge at Chirano related entirely to goodwill.” – Kinross Gold, 2012 Annual Report Capital costs Higher than forecast mine capital costs “… after 25 years of bringing all our mines into production on time and on budget, we experienced huge cost overruns and associated issues at our massive Pascua-Lama project on the Argentinean-Chilean border ...” – Barrick Gold Corporation, 2013 Annual Report Operating costs Higher than forecast mine operating costs “Based on the pre-feasibility study results, the increases in operating costs since the study was completed in 2008, especially in the cost of electric power, and due to the high costs of maintaining access to the underground workings, Bogoso/Prestea has stopped its development activities at this project. As a result, the carrying value of the property was fully written down as at December 31, 2008 and an impairment charge of $44.6 million recorded in the consolidated 132 Category Description Example statements of operations.” – Golden Star Resources, 2008 Form 10-K Product grade Lower than expected mineral or metal concentrate purity None identified. Results The coding of the 266 impairment events in the data set identified 268 unique reported causes of impairment. The following charts show the relative number and the relative total impairment value for each category of impairment. Figure 12: Total number of impairment events recorded in each impairment category Metal Prices, 143Ore Grade, 35Ore Access, 23Operating Costs, 15Acquisition, 14Social License, 13Process Recovery, 9Capital Costs, 3Product Grade, 0 Other, 13Technical Error Optimism Bias or Principal-Agent Conflict133 Figure 13: Total value of impairment events recorded in each impairment category I provide further detail for each impairment category in the following summary table. The mean and median percentage of assets impaired relates to the portion of impaired assets in the year that the impairment was declared. If 40% of an asset value was impaired, the impairment event reflected a 40% reduction in the mine's asset value at the time of impairment. Metal Prices, 25,152 Ore Grade, 5,070 Ore Access, 8,297 Operating Costs, 6,999 Acquisition, 8,239 Social License, 5,528 Process Recovery, 1,351 Capital Costs, 6,276 Product Grade, 0Other, 1,198 Technical Error Optimism Bias or Principal-Agent Conflict134 Table 25: Impairment category summary statistics Category Count Value ($MM) Mean ($MM) Median ($MM) Mean (% of assets) Median (% of assets) Metal Prices 143 25,152 176 55 39% 34% Ore Grade 35 5,070 145 56 46% 44% Ore Access 23 8,297 361 70 46% 43% Operating Costs 15 6,999 467 127 42% 32% Acquisition 14 8,272 634 203 35% 29% Social License 13 5,528 425 328 55% 50% Process Recovery 9 1,351 150 48 33% 33% Capital Costs 3 6,276 2,092 127 39% 27% Product Grade 0 0 0 0 0% 0% Other 13 1,198 92 34 61% 63% Total 268 68,144 218 58 43% 37% By far, declining metal prices were the most frequently reported reason for impairment and accounted for the largest share of impaired value among all reported reasons for impairment. Commodity prices were on a steady decline from (roughly) 2012 to the end of the study period in 2016, so many projects declared impairments related to metal prices in more than one year. Consolidating the impairments by mining operation reduces the number of metal price impairments to 103, increases the average impairment value to $244MM, and increases the median value to $88M. Impairments attributed to ore grade issues were driven by grade reconciliation problems during mining operations and by infill drilling of orebody areas that had not yet been mined. Grade reconciliation problems occurred when the mined ore grade did not match the grade predicted by the surface or underground exploration drilling used to create the mine’s orebody model. Grade reconciliation problems often resulted in significant changes to the mine plan that reduced the mine's lifetime value when the company had to reclassify areas of the orebody as 135 waste. Impairments also occurred when infill drilling did not confirm the preliminary orebody model. Infill drilling is surface or underground drilling that occurs at a closer physical spacing than initial exploration drilling to improve confidence in an orebody model. Preliminary exploration drilling may be done at 200-meter intervals (for example) to create an initial orebody model. Mining companies will often conduct more detailed drilling at closer spacing (say 50 or 100-meter intervals) to confirm the initial orebody model after mining of better-defined areas has already started. I attributed impairments to reductions in mineral reserve and resource estimates when infill drilling could not confirm the initial orebody model that was already part of the mine's economic model. Impairments attributed to ore access were typically reported directly as geotechnical issues or changes to the mine plan, with further explanations revolving around increased dilution and lower production estimates. Geotechnical problems included pit slope failures (when the designed wall of an open pit excavation fails or the company expects it to fail), underground failures (when a tunnel or open stope excavation collapses, or the company expects it to collapse), and unexpected water ingress in underground mines. Indirectly reported ore access issues included higher dilution and lower production rates than expected. Higher dilution typically occurs in underground mining when the waste rock surrounding the ore is weaker or more fractured than expected and mixes with the ore after blasting and during ore recovery. It may also occur when an ore vein is narrower than expected, and the mechanical equipment used for mining cannot mine the ore as selectively as expected. Lower production estimates were typically reported by underground operations when rock was either harder than anticipated (lowering drilling and mining rates) or weaker and more fractured than expected (requiring more engineering and ground support than expected). 136 Impairments attributed to higher operating costs included increases in operating costs due to higher global demand for materials and labour, increases in local operating costs due to home country inflation or import restrictions, and lower than expected labour productivity. Most impairments related to operating cost increases occurred between 2008-2012 when commodity prices dramatically increased (relative to prices over the rest of this study period). The demand for mining materials and skilled labour was strong through these years as existing operations expanded and new mines started. Operating cost increases due to home country factors were unique to Brazilian and Argentinean operations during this study period. Brazilian inflation reached almost 15% in 2003 and caused significant increases in local operating costs. Argentina instituted several tax, currency, and trade laws in 2013 and 2014, which resulted in impairments related to higher operating costs for some mines in Argentina. Some companies cited lower than expected labour productivity causing operating cost impairments in Romania and the Democratic Republic of the Congo during the study period. When a company purchases assets, the company records any purchase price above the fair asset value as goodwill (The International Financial Reporting Standards Foundation 2020). Some companies reported goodwill impairments without any reported changes to mine revenue or costs. Companies often cited declines in market valuations for mines producing a similar commodity or in a similar geographic area. Recalling the IFRS definition of impairment from the last chapter, an impairment must be declared when the recorded value on an asset on a company’s balance sheet exceeds its market value or value in use. When the reported impairment references a decline in market valuations as the reason for impairment, I assume that the company’s calculation of market value is higher than the value in use and the acquisition took 137 place with the company speculating that the market value or the value in use would increase after the purchase. I attributed these impairments to acquisition overpayment. Impairments attributed to social license to operate mainly consisted of mining permits not issued as expected or issued and then revoked. Sometimes a change in government was cited as a contributing factor, and, in almost all cases related to permitting issues, the companies unsuccessfully challenged the permit rulings in local courts. When national tax changes were broad in scope, I coded them as operating cost increases. However, when they explicitly targeted the mining industry, I coded them as social license issues. There were three instances of a mining-targeted tax increase, and they all occurred in African countries. One instance of a labour dispute caused the temporary shutdown of a mine in Mexico, and one instance of outright nationalization completely impaired a mine in Venezuela. The reported reasons for process recovery impairment mainly were related to new processes not functioning as expected or for lower-than-expected recovery of refractory ore (a catch-all term for ore that cannot be processed by traditional mineral recovery methods). From a qualitative point of view, there were fewer impairments associated with process recovery than I expected. However, there were many instances where companies cited lower recovery or lower metal/mineral production as the reason for impairment. The underlying cause was a lower ore feed grade to the mill compared to the mine model’s predictions, rather than problems specific to the recovery process. I coded the root cause of these impairments as ore grade since the underlying issue was lower than expected ore grade. There were only three impairments attributed to capital cost overruns. Most of the impaired value in this category was the $6 billion impairment of the Pascua Llama mine on Chile and Argentina's border. Given that much of the project forecasting error literature is focused on 138 the topic of capital cost overruns, I expected capital cost overruns to account for much more of the reported number and value of mining impairments. However, it is possible that increases in metal prices during and after the construction of mines hid capital cost overruns through the first half of the study period when most new mines were being constructed. The three reported capital cost impairments all occurred after 2012, when metal prices were declining. I develop this theory further in the following Discussion section. No companies reported impairments related to mineral or metal product grade or purity. I believe this is likely because a mining operation will sacrifice recovery to reach a saleable product grade rather than sacrificing product grade to hit a recovery target. Since most metal and mineral concentrates are commodities, they must achieve a specified commercial quality for minimum quantities of valuable metal and maximum quantities of penalty elements. For example, a copper concentrate with 0.9% arsenic (a penalty element) may be saleable with a high penalty charge, whereas it may not be saleable at all with 1.1% arsenic. Suppose a process plant receives ore that will generate a copper concentrate containing 1.1% arsenic given current copper recovery targets. In that case, the operation will likely sacrifice copper recovery to reduce the concentrate's arsenic levels. I would have coded this intentional reduction in recovery as a process recovery problem. Alternatively, if the exploration drilling did not identify arsenic at the start of the project, I would have coded it as an ore grade issue. Thirteen reported impairments did not fit into any of the theorized categories. I coded them as ‘other.’ This category included seven impairment events related to the write-down of the residual asset value at the end of a mine’s life (total of $202MM), four impairment events related to an accounting change from the value-in-use of the asset to the market value at divestment (total of $227MM), one impairment event related to a shift from US GAAP to IFRS ($40MM), 139 and one impairment event related to an inability to finance the completion of a mine during the 2008 global financial crisis which accounted for the bulk of the impaired value in the ‘other’ category ($730MM). Overall, this analysis shows that companies reported impairments in all theorized categories except for product grade. Mapping these impairments back to the theoretical framework provided by the literature on project forecasting errors in the table below shows that all theorized categories are accounted for except for Technical Error related to mine revenue. As expected, there was no reported distinction between impairments caused by optimism bias and principal-agent conflict. Table 26: Mapping mining impairments to project forecasting error root causes Benefits Costs Revenue Reserves Capital Costs Operating Costs Forecasting Error Causes Technical Error Product grade (0) Ore grade (35) Ore access (23) Process recovery (9) Optimism Bias or Principal-Agent Conflict Metal prices (143) Metal prices (143) Social license (13) Capital costs (3) Operating costs (15) 140 Figure 14: Mining impairments by type of forecasting error (count) Figure 15: Mining impairments by type of forecasting error (value) Technical Error, 67Optimism Bias or Principal-Agent Conflict, 174Technical Error, 14,719 Optimism Bias or Principal-Agent Conflict, 43,955 141 Discussion This section discusses the reported causes of mining firm impairments in the frame of project forecasting errors. It identifies areas for further research to confirm, clarify, improve, and expand on this study's findings. Optimism Bias and Principal-Agent Conflict Metal price declines were the most frequently reported cause of impairment in the optimism bias and principal-agent conflict category. However, I believe that prior increases in metal prices likely hid causes that would have fallen into other impairment categories. As a method of accurately identifying forecasting errors in the mining industry, impairments are significantly limited because they are only required when the overall market value or value in use of a mine falls below the reported asset value. First, there is no requirement to report a forecasting error if it does not cause the market value or value in use of the mine to fall below the recorded value. Second, the impairment test takes both positive and negative forecasting errors into account and only reports impairments when the net calculation is negative within a financial quarter. For example, suppose the development of a mine has a capital cost overrun of $10 million over a two-year development period. If metal price increases over those two years increase the expected discounted cash flow by $20 million over the mine's life, no impairment will be reported. Looking at the chart below, we can see that the development or acquisition of mines appears to be strongly related to increases in metal prices. 142 Figure 16: New mine development or acquisition and metal prices over time If capital cost overruns were declared independently of the overall value in use of the mine, I would expect capital cost impairment reporting to coincide with the completion of mine construction. As we saw from these analysis results, there were only three declarations of capital cost impairments, and they all occurred after 2012. In contrast, the chart below shows that companies declared relatively few impairments of any category during periods of rising commodity prices. This observation provides some evidence that rising metal prices mitigate the financial effects of many types of forecasting errors that companies only reported when required by accounting standards. 020406080100120140051015202530354045Metal Price Index ($)Mine Development & Acquisition (count)Developed & Acquired Mines Metal Price Index143 Figure 17: Impairment events and metal prices over time While the charts above provide some evidence pointing towards discretionary impairment categorization by mine management, it would require further study to confirm this observation. Estimates of capital costs, operating costs, and metal prices in mine feasibility studies would need to be compared to the actual values to determine when impairments should have been attributed to declines in metal prices rather than other impairment categories. There is also an opportunity to dive much more deeply into commodity price forecasting practices among mining companies, comparing the forecasting behaviour of companies that declared metal price impairments to the forecasting behaviour of companies that did not. As it currently stands in this research, inaccurate metal price forecasting resulted in more than one-third of the total impaired value in this data set. It represents an area of sizeable potential improvement for reducing impairments. However, there may be an incentive for mining company management to blame any forecasting errors on declining metal prices. The explanations for capital cost overruns, 020406080100120140010203040506070Metal Price Index ($)Impairment Events (count)Impairment Events Metal Price Index144 operating cost increases or low mine productivity (for example) would point directly to errors in management forecasting and decision making. On the other hand, metal prices affect most mining operations equally. There is less individual blame for metal price forecasting errors than, for example, the construction of one specific mine going 200% over budget2. A similar case could be made for technical errors (geotechnical and ore grade issues) based on information mining companies could have known ahead of time. The decision to start the mine was taken with a limited amount of imperfect drilling information. It is theoretically likely that misrepresentation during the mine planning and evaluation process could have caused many of this category's impairments. The previous chapter established that the development of mining projects and infrastructure projects share many similarities. The existence of several principal-agent relationships when developing a mine is a relevant similarity for this discussion. The first significant relationship is between financiers (equity investors and debt holders) and mining company management. Financiers want an appropriate risk-adjusted return on their investment. At the same time, mining company management may be more interested in growing the company’s size to increase prestige and command higher compensation while understanding that a job change may shield them from the personal impacts of deciding to develop an underperforming mine. The second significant relationship is between mining company management and consulting firms. Sometimes mining companies will have an internal project team to create feasibility studies and mine designs, but companies often contract this occasional work to arms-length engineering firms. At the early pre-feasibility stage, engineering firms have an incentive to create a financially positive study to secure more detailed and more lucrative feasibility-level studies. At the feasibility or bankable feasibility stage, engineering 2 Metal price forecasts are often based on an ambiguous “industry consensus value” rather than management judgment – although it is clearly a management decision to use this value. 145 companies have an incentive to deliver aggressive capital costs estimates in the hopes of securing the much more lucrative detailed design and construction contract. For open-ended contracts, these aggressive capital cost estimates can result in significant cost overruns. For fixed-price contracts, it can result in barely functioning process plants and the litigation that follows. These principal-agent dynamics are only theories based on my personal experience and observations in the mining industry and would require personal, likely anonymous interviews to verify. Technical errors There were many similarities between the examples given for technical errors in the project forecasting literature and the impairment reasons reported in this data set. Many examples from major projects referenced unexpected subsurface conditions adding cost and time to infrastructure projects (Flyvbjerg 2004). Similarly, most of the mining impairments coded into technical error had to do with unexpected subsurface conditions, either related to the value of the metal in the ore or the properties of the rock below the surface. Process recovery errors may also be analogous to errors described in the major project literature around the expected performance of new technology (e.g., new rail transportation systems and information technology infrastructure). Conclusions This chapter's stated purpose was to investigate the root causes of impairments in the mining industry by leveraging the literature on the root causes of project forecasting errors. It started by reviewing the major project literature that identified three primary root causes of forecasting errors: technical error, optimism bias, and principal-agent conflict. It then created a theoretical framework that connected mining impairments to forecasting errors and to these root causes. This framework identified issues related to product grade, ore grade, ore access, and 146 process recovery corresponding to technical error. It identified problems related to metal prices, social license to operate, capital costs, and operating costs corresponding to optimism bias and principal-agent conflict. This chapter then took a data set consisting of every impairment declared by a TSX-listed mining company from January 1st, 2003 to January 1st, 2016 and coded these impairments into the categories of mine forecast errors. In total, I coded 266 impairment events representing over $68 billion in lost value. The largest category was impairments related to declines in metal prices (143), followed by ore grade (35) and ore access (23). All theoretical categories were accounted for in the coding except for issues related to product grade. Thirteen impairments did not fall into any of the theorized categories. I coded them as ‘other.’ Overall, roughly 30% of coded impairments fell into mining forecast error categories corresponding to technical error and 70% to optimism bias and principal-agent conflict. Overall, viewing mining impairments through the lens of project forecasting errors helped to understand the likely root causes and to suggest a potential path to reduce impairments. A significant limitation in taking mine impairments to represent forecasting errors is that impairments only indirectly represent forecasting errors. An impairment is declared when the total of both positive and negative forecasting errors results in a negative financial impact on the expected lifetime value of a mine, and only when this adverse financial impact requires an adjustment to the recorded book value of the mine. Given this limitation, the results generated by this chapter should be considered highly conservative when applied to estimates of forecasting errors in the mining industry. There are several opportunities for future research. The relative frequency and impact of optimism bias and principal-agent conflict in the mining industry could be parsed through 147 personal interviews with project participants. Mining forecasting errors could be directly investigated through detailed comparisons of feasibility study forecasts and assumptions to actual outcomes. The following chapter goes into much greater detail on the potential application of reference class forecasting to reduce mining industry impairments. 148 Chapter 6: Reducing Mining Impairments Introduction The previous chapter identified that most reported reasons for mining impairments could be explained through forecasting errors. This chapter aims to take the lessons on reducing project forecasting errors from the major project literature and identify how they can help minimize the frequency and value of mining impairments. This chapter starts by explaining the reference class forecasting methodology for improving project forecasts. It first describes the early methods of taking the ‘outside view’ of a forecast and then describes the application of these methods to major project forecasting. I present examples of reference class forecasting for public infrastructure projects from the project management literature. Next, I discuss the application of reference class forecasting to the mining industry. I describe the opportunities, challenges, and barriers to use in the mining industry and present a small case study to demonstrate the technical viability of building a mining reference class using publicly available information. Finally, I identify the role of corporate governance in driving the adoption of reference class forecasting. Mining company boards of directors are ultimately accountable to shareholders for value creation and destruction. The adoption of reference class forecasting can reduce or eliminate many significant, value-destroying events in the mining industry. The chapter ends with simple recommendations and the next steps for adopting reference class forecasting in the mining industry. 149 Reference Class Forecasting The concept of reference class forecasting has evolved, and researchers have modified it to fit different purposes. Kahneman and Tversky first proposed a remedy for forecasting errors by taking the “outside view” and offering a five-step process for creating more realistic predictions (1979b). First, a reference class is selected. A reference class is defined as “a class to which the case at hand can be referred meaningfully and for which the distribution of outcomes is known or can be assessed with reasonable confidence.” The selection of the most appropriate reference class and the basis of comparison (total costs or relative overruns, for example) requires expert judgement. Second, distributional information for the reference class is assessed. Distributional information may be available from past outcomes, but it may also need to be estimated for outcomes that are expected far into the future or that are materially different from the reference class. Third, an intuitive estimate is produced based only on information from the case at hand. It is assumed that this estimate will be inaccurate, and the objective is to regress/correct this estimate through the next two steps. Fourth, the estimate from the previous step is given a qualitative measure of predictability based on the estimate's perceived accuracy. The measure can range from 0 (a completely random guess) to 1 (complete confidence in the estimate). The authors offer that this can be generated from past forecasting experience or estimated based on the forecaster’s intuition of the predictability of the case at hand. For example, if the forecaster has a strong track record of accurate forecasts in the same domain, the measure would be closer to 1. If someone with no knowledge of the reference class or the case at hand produces the estimate, the measure would be closer to 0. They concede that predictability estimates must be made carefully and may be subject to some of the same biases that reference class forecasting is trying to address. Fifth, the intuitive estimate generated in the third step is 150 regressed to the reference class average using the measure of predictability from the fourth step. For example, suppose a project was estimated to cost $100MM, the reference class average was $150MM, and experience shows a 70% correlation between predictions and outcomes for the forecaster. In that case, the regressed estimate would be 150 - 0.7(150 - 100) = 115MM. The method's outcome is a regressed estimate that combines information from a single case estimate and distributional information from a selected reference class. The spirit of the method is to use past, empirical information to mitigate the effects of systematic and predictable biases that cause forecasting errors. Borrowing from Kahneman and Tversky’s conceptual methodology, Flyvbjerg coined the term “reference class forecasting” (Pinto 2013) and introduced the reference class methodology to project management practice in 2004. Flyvbjerg looked at ways to mitigate the effects of optimism bias on capital expenditures in transportation planning through a study commissioned by the British Department of Transportation (Flyvbjerg 2004). Simplifying the methodology of Kahneman and Tversky (1979), Flyvbjerg uses a three-step process: (1) identify a relevant reference class of past projects; (2) establish a probability distribution for the past projects; (3) place the specific project at an appropriate point in the reference class. In the 2004 study, he created reference classes based on project type (Roads, Rail, and Bridge & Tunnel) by drawing on prior research that had identified meaningful statistical differences in cost overruns among those categories (Flyvbjerg et al. 2003). Flyvbjerg treated each reference class as statistically different and each project within a reference class as statistically similar. Next, he collected data from completed projects within the UK, Europe, and North America to populate the three reference classes: 172 road projects, 46 rail projects, and 34 bridge & tunnel projects. The data included the original project cost estimate at the time of project approval and the final cost. 151 Using this information, he was able to calculate the cost overrun for each project. He compiled the project cost overruns into a distribution for each reference class. I show a hypothetical distribution of project cost overruns based on data from the 2004 study in the graph below. Figure 18: Hypothetical cost overrun data Finally, Flyvbjerg provides guidance on placing future capital cost estimates at an appropriate point in the reference class distribution. Using the distributional cost overrun information, one can choose an appropriate cost escalation factor to add to the initial estimate based on the project owner's risk tolerance. For example, a project owner without access to additional funds may add an escalation factor at the 90th percentile of cost overruns, meaning that there is only a 10% chance of a higher cost overrun based on the reference class data. On the other hand, a project owner with an adequate budget for an extensive portfolio of projects may select a cost escalation factor at the 50th percentile of cost overruns and expect the capital cost 152 deviations for all projects in the portfolio to balance out. I show a hypothetical example of a cost escalation factor curve in the graph below (based again data from on Flyvbjerg’s 2004 study). Figure 19: Hypothetical cost estimate escalation factors From this graph, a willingness to accept no more than a 10% chance of a capital cost overrun would result in adding 65% to the projected budget, while a desire to take no more than a 50% chance of a capital cost overrun would result in adding 15% to the capital cost estimate. For consistency with the reference class information, project evaluators should add these escalation factors to the forecast that exists when the project is coming forward for approval. Flyvbjerg notes that the selection of a risk level may be impacted by optimism bias (2004). With that said, Flyvbjerg’s simplified method eliminates the need for an evaluator to assess or estimate the predictive skill of the forecaster. Instead, it allows for a transparent look into the distribution of past outcomes and forces the evaluator to make a judgement about acceptable financial risk. The first practical application of reference class forecasting was the Edinburgh Tram Line 2 project (Flyvbjerg 2006). Using cost escalation factors from the UK Department of Transport, 0102030405060700 10 20 30 40 50 60Required Uplift (%)Chance of Cost Overrun (%)153 a formal review increased the budget estimate from an estimated cost of £320MM (including contingency) to £357MM with a 50% risk of cost overrun and £400M with a 20% risk of cost overrun. (In the end, Edinburgh Tram Lines 1 and 2 were combined for construction at an estimated cost of £498M in 2007 and ultimately ended up costing £776M by its completion in 2014 (Scotsman 2007; Swanson 2020)). The academic literature has subsequently documented several instances of reference class forecasting in the real world. Examples include hydroelectric projects in Africa (Awojobi and Jenkins 2016), Hong Kong roadwork projects (Flyvbjerg et al. 2016), and building construction (Batselier and Vanhoucke 2016). The UK Department of Transportation (DoT) has now been using reference class forecasting for over ten years. Projects that applied reference class forecasting have shown reduced cost overruns compared to projects that did not use it. The UK DoT has cancelled several significant projects after the application of reference class forecasting. The American Planning Association has now officially endorsed reference class forecasting. It has been made mandatory by the Departments of Transport in the UK and Denmark. Individual public projects in Sweden, Norway, Switzerland, the Netherlands, South Africa, Australia, the United States, and other countries have also applied reference class forecasting (Flyvbjerg et al. 2016). Despite wide adoption of reference class forecasting, some challenges and risks remain. The most significant challenge is the selection of an appropriate reference class. The population must be large enough to be statistically meaningful but narrow enough to accurately represent the current project’s potential forecasting errors. Forecasters must mitigate this risk by using expert knowledge in the forecasting domain combined with enough statistical skill to populate the reference class appropriately. Related to the construction of appropriate reference classes, it is worth noting that the academic literature has only separated reference classes by 154 project type (e.g., road, rail, buildings) and has not directly studied the effects of project size or value. Existing studies have normalized project values by using percentage distributions of cost estimating error without explicitly testing the impact of project value on the percentage of estimating error. Further, there is an opportunity for optimism bias to creep back into the process when determining an acceptable level of risk of project cost overrun. Can a company or lender tolerate a 50% risk of overrun? 20%? 70%? In some cases, the acceptable risk may be apparent based on the amount of money raised or financing available. Still, it is more likely to be a qualitative decision where project approvers could massage the risk tolerance to fit existing (anchored) expectations about project budgets. Finally, for all its positive features, reference class forecasting still cannot solve the problem of inaccurate forecasts when there are misaligned interests and the desire for accurate forecasts is low (Flyvbjerg 2006). If there are opportunities for project benefits to accrue to one party while another is bearing costs, the incentive for inaccurate and unrealistic forecasts will persist. This incentive can only be managed or policed through awareness and strong project oversight. I will address this point in more detail as I discuss how the mining industry can benefit from applying reference class forecasting to mine project forecasting. Mine Project Forecasting Most mining projects follow a typical path from ore discovery to project approval based on recommendations from industry associations worldwide, like the CIM guidelines published in 2014 (CIM Standing Committee on Reserve Definitions 2014). Once a prospective ore deposit has been discovered, a mining company will conduct or commission a preliminary economic assessment or scoping study to create a very high-level estimate of project economics. The purpose of this study is to determine whether further exploration or development is warranted. 155 The capital cost estimate is in the order of ±50% based on the level of detail, assumptions, and factors for estimating. The next stage is a pre-feasibility study. A company will commission this study to turn mineral resources into mineral reserves (the definition of “mineral reserves” is the portion of a mineral resource that the company can mine economically). The pre-feasibility study identifies the mining method and recovery process for the ore deposit to forecast project costs and revenues with a target accuracy of ±25% using fewer assumptions and factors than the preliminary economic assessment. The final stage of mine project forecasting is the feasibility study. It is the most comprehensive and detailed technical and economic evaluation of the project. At this point, the target accuracy is ±15%, and the mining company will typically use this report for the financing and development decisions. The accuracy numbers quoted here (±50%, ±25%, and ±15%) represent qualitative levels of estimating effort, and they do not necessarily correspond to actual project costs and benefits after development. It is most useful to think of these numbers as a way to communicate the amount of work, detail and cost that has gone into an estimate between the forecasters and the project owners. While this is the typical method for forecasting mining project costs and benefits, has reference class forecasting been used to evaluate a mining project forecast? Through this research, I have collected over 1,500 public documents (annual reports, annual information forms, financial statements, etc.) from over 100 public companies involved in the mining industry. One thousand one hundred eleven of these documents included the word “forecast” or “forecasting.” None contained the term “reference class.” Having been personally involved in the mining industry for over 15 years, I can attest to having never heard the term “reference class forecasting” before embarking on this research. Informal conversations with a few other people knowledgeable about the mining industry corroborated the observation that reference class 156 forecasting has either never been used for a mining project or has not been publicly reported by a mining company. There are several plausible explanations about why the mining industry has not used reference class forecasting. The first significant barrier may be access to the information required to construct appropriate reference classes. The second significant barrier may be a lack of incentive for mining project participants to adopt the practice. Information Availability To test for the availability of information to build a comprehensive mining project reference class, I need forecasted and actual data for a mine's benefits and costs. The table below is a simplified version of the previous chapter's theoretical mining benefit and cost framework. I have rolled up more detailed elements into higher-level metrics that mining companies typically report. For example, I have rolled up all operating costs to the overall cost per tonne or cost per metal unit produced. I have removed the social license component for simplicity, but it is reasonable to assume that a reference class related to host-country factors could be developed. Table 27: Simplified mining operation benefits and costs Benefits Costs Revenue Reserves Capital Costs Operating Costs - Metal prices - Production rates - Ore reserves - Process recovery - Development cost, or - Acquisition cost plus initial capital costs - Cost per tonne, or - Cost per metal unit produced In this framework, the benefits comprise revenue and reserves. Revenue consists of the metal or mineral concentrate produced and the value for this product. Reserves consist of the reported mineral ore reserves in the ground. The process recovery represents the percentage of the ore reserves that a company can turn into a saleable product. The costs comprise capital costs and operating costs. Capital costs include development cost to first revenue or commercial production for a newly developed mine or acquisition costs plus initial capital costs for an 157 acquired mine. I express operating costs in either a cost per tonne of ore processed or a cost per unit of metal produced (e.g., ounces of gold, pounds of copper). I can review the public National Instrument 43-101 filing for a proposed mine to test for the availability of this forecasted information at the point a company decides to proceed with the development of a mining project. The objective of the 43-101 – Technical Report is to “provide a summary of material scientific and technical information concerning mineral exploration, development, and production activities on a mineral property that is material to [a mining company]” (Ontario Securities Commission 2011). These reports contain both technical and financial assumptions used in the economic feasibility evaluation for the proposed mine. As a proof of concept, I collected information from the 43-101 technical report for Agnico-Eagle’s Pinos Altos mine in Mexico (Doucet et al. 2007). Agnico-Eagle publicly reported the decision to proceed with the mine's construction in August 2007 (Agnico-Eagle Mines Limited 2007). The company released the feasibility study upon which the decision was based in September of 2007. The following table summarizes the forecasted cost and benefit parameters from the 43-101 report. The mine was expected to continue until 2019, but I only report the forecasted production data to January 1st, 2016 to coincide with the research study period. 158 Table 28: Pinos Altos forecasted mine parameters Parameter Forecasted Value Metal Price US$529/oz Au US$9.59/oz Ag Production (to 2016) 1,222k oz Au 16,444k oz Ag Ore reserves 2,224k oz Au 65,654k oz Ag Process recovery 94.9% Au recovery 48% Ag recovery Capital cost US$216.5 million Operating costs US$38.94 per tonne Taking the information reported for the Pinos Altos mine as of January 1st, 2016, I can compare the forecasted values from 2007 to the mine's actual values from 2009 to the start of 2016. I took the actual metal price values from the commodity price data collected earlier in this research project. I collected production, ore reserve, process recovery, capital cost, and operating cost information from Agnico-Eagle annual reports. Agnico-Eagle did not report any transparent total or summary of the Pinos Altos capital cost. I needed to combine capital spend data over several annual reports to determine the total project capital cost. The following table compares the forecasted values to the actual values. 159 Table 29: Pinos Altos forecasted and actual mine parameters Parameter Forecasted Value Actual Value (2015) Difference Metal Price US$529/oz Au US$9.59/oz Ag US$1,082/oz Au US14/oz Au +104% +46% Production (to 2016) 1,222k oz Au 16,444k oz Ag 1,042k oz Au 11,843k oz Ag -15% -28% Ore reserves 2,224k oz Au 65,654k oz Ag 2,543k oz Au 49,374k oz Ag +14% -25% Process recovery 94.9% Au recovery 48% Ag recovery 94% Au recovery 43% Ag recovery -1% -10% Capital cost US$216.5 million US$356 million (at Nov 2009 commercial production) +64% Operating costs US$38.94 per tonne US$45 per tonne +16% We can see from the forecasted versus actual values that all the cost factors and some benefit factors varied negatively compared to the forecast; however, there was a positive and significant variance in the metal price forecast. It is worth noting that, despite a 64% ($140 million) underestimation of project capital costs, there was no impairment declared for the Pinos Altos mine during the study period. This data supports the proposition made in the previous chapter that increases in metal prices can hide forecasting errors by financially offsetting the errors and preventing an impairment declaration. If the metal price at the start of 2016 had been equal to or less than the study estimate of US$595/oz, the project economics would have certainly been below expectations given the lower production and higher costs compared to the forecast. It also does not appear that Agnico-Eagle was eager to disclose the capital cost overrun for Pinos Altos. To identify the capital cost forecasting error, I needed to add multiple years of project cost information from several Annual Reports and compare this to the initial Technical Report. I did not find any clear and transparent disclosures of the capital cost overrun in Agnico-160 Eagle’s public filings. Despite the work required to gather this information, it was all publicly available through SEDAR filings. Although the information for mining operations is much more scattered than government-funded public infrastructure projects, this exercise demonstrates no fundamental technical or data barrier to constructing reference class forecasts for mining projects. I believe the lack of reference class forecasting in the mining industry may have more to do with a lack of incentive than a technical barrier. Incentives The first incentive gap may be related to a general lack of innovation in the mining industry. Despite recent pushes for innovation, the mining industry is notoriously conservative when it comes to adopting new technology and methods – most mining processes in use today look indistinguishable from those used almost 100 years ago. It may be the case that there is no internal push to innovate or change how mining projects are estimated and forecast. While Kahneman and Tversky developed the concept of using distributional information from past events in 1979, the practical application to project management did not occur until Flyvbjerg analyzed British transportation infrastructure projects in 2004 – 25 years later. I would be surprised if mining industry participants have actively tried to seek out better forecasting methods. The second potential incentive gap has to do with the misaligned interests at two significant points along the project forecasting and decision path. The first point (as mentioned earlier) is the relationship between the engineering firm and the project owner. As Flyvbjerg (2004) identified, if there is an opportunity for a much larger follow-on contract with the project's development, the engineering firm has a strong incentive to provide an aggressively low-cost estimate. The second point is the relationship between company management and 161 project financers. Developing a project gives management valuable experience, and it expands management’s financial and physical sphere of control. These factors give management a strong incentive to push projects forward aggressively and downplay the risks. On the other hand, when projects fail to deliver the promised economic benefit, the debt and equity financers bear the cost. Ultimately it is the project financiers who will have to defend against these conflicts of interest. As I bring this research back to the beginning and look at the problem through the frame of a long-term investor in the mining industry, the board of directors is ultimately responsible for protecting investors from the economic failures that lead to poor market performance. The Role of Corporate Governance Canadian corporate law stipulates that directors have a fiduciary duty to act in the corporation's best interests. In many instances, this means acting in the best interests of long-term shareholders (Institute of Corporate Directors 2014). Further, the board of directors' responsibility is to identify and manage the corporation's principal risks; researchers and regulators have advocated for more proactive risk oversight at the board level (Brown, Steen, and Foreman 2009). This research project has demonstrated that asset impairments pose a material threat to shareholders of Canadian mining firms. Therefore, boards of Canadian mining companies must ensure that measures are in place to manage the risk of significant asset impairments. The use of reference class forecasting is a promising method for addressing the risks of forecasting errors at the project approval stage. The experience from 10 years of applying reference class forecasting to public transportation projects in the UK has already shown it to be an effective method to account for project cost overrun risk (Flyvbjerg et al. 2016). Mining companies can apply the same principles to project evaluation, both for capital cost estimating and revenue forecasting. 162 Constructing a distribution of commodity price behaviour for revenue forecasting, rather than assuming a single price with a ±10% sensitivity analysis, could provide a much more realistic forecast that includes a quantitative assessment of commodity price risk. The first step in adopting reference class forecasting is for corporate boards to mandate reference class cost overrun factors for mining projects that are put forward for approval. Adding these factors would not disrupt the current project forecasting process in any way; companies could still retain engineering firms to provide detailed estimates and forecasts for feasibility studies. However, project approvers would then apply Flyvbjerg’s three-step regression process with reference class data (Flyvbjerg 2004). This practice would give companies much better guidance on realistic project economics and the amount of cash reserves required at a specific level of cost overrun risk before starting development. The next step in maturity is for companies to use reference class forecasting directly and at each stage of project study: scoping study, prefeasibility study, and feasibility study. References classes could be created for projects at each forecasting stage to better address the uncertainty and risk tolerance in each development stage. Finally, with a large enough data set of completed projects, reference class forecasting could be used to directly estimate project costs and benefits, completely bypassing the traditional ‘inside view’ method of mine forecasting. This direct reference class estimation method can be faster, less expensive, and more accurate than traditional forecasting practices. Conclusions This chapter's stated purpose was to offer recommendations to reduce asset impairments in the mining industry. This chapter started by identifying reference class forecasting as a valuable tool for reducing the mine project forecasting errors that eventually lead to impairments. Reference class forecasting compares cost and benefit forecasts to actual outcomes from 163 completed projects and uses this information to adjust the forecasts of proposed projects. The literature around reference class forecasting describes widespread use in European public transportation projects. Over the past ten years, the UK Department of Transportation’s experience has shown that reference class forecasting has reduced project cost overruns compared to projects that did not use the methodology. This chapter then identified that reference class forecasting is uncommon (if not wholly missing) in the mining industry and discussed some possible barriers to use. It presented a small case study that demonstrated the data to construct reference classes for mining projects is publicly available. It then identified the principal obstacle to adopting reference class forecasting as a lack of incentive among industry members due to principal-agent conflicts during mine planning and forecasting. This chapter then identified that the adoption of reference class forecasting would likely need to be driven through corporate governance mechanisms to bypass the risks of psychological bias and conflicts of interest through the mine forecasting process. Mining company boards of directors are accountable for the creation and destruction of shareholder value, and reference class forecasting can reduce or eliminate some of the most significant value-destroying events in the mining industry. This chapter ends with some simple recommendations for the adoption of reference class forecasting. Forecasters can construct reference classes with publicly available information and apply cost overrun factors to projects that are already following the traditional forecasting and review process. 164 Conclusion This dissertation began by observing that the mining industry has generated low shareholder returns for many decades and asked if anything could be done to improve returns. Were some mining companies generating much higher returns, and were some generating much lower returns than the industry average? If so, are there lessons that could be learned from these companies to improve the industry's performance in general? The goal of this dissertation was to answer these broad questions and ultimately provide recommendations to mining company management and mining industry investors to improve shareholder returns over the long run. The existing literature did not leave us with much established knowledge about the performance of mining companies. Researchers performed these studies using either a small number of firms for analysis, short study periods during rising commodity prices, or both. Further, the researchers conducted no investigations to parse factors under management control (like mineral reserve growth or the type of commodity produced) and factors that are not (like commodity prices or market valuations). This research study used a dataset of 105 Canadian mining firms over 13 years to establish more reliable mining firm performance knowledge, understand the variance in performance, identify performance factors inside and outside the control of company management, and recommend practices to improve mining firm performance. The first chapter tested existing mining firm performance studies using a more extensive data set over a longer period. The data set included every metal mining company listed on the Toronto Stock Exchange (TSX) between January 1st, 2003 and January 1st, 2016, with a minimum of 3 continuous years of operation – a total of 105 companies and almost 900 firm-year observations. I repeated the analyses of Shapiro et al. (2007), Camus et al. (2009), and 165 MacDiarmid et al. (2018) using this larger data set. My analysis did not support the conclusions of Shapiro et al. (strategic position is related to return on assets) and Camus et al. (mineral reserve growth plus production is related to shareholder return). However, my analysis did support the findings of MacDiarmid et al. (company revenue and commodity prices are related to enterprise value). The first chapter concluded that commodity price plays a significant role in mining company performance. The results from studies using shorter periods during rising commodity price environments may not be generalizable to mining companies' long-run performance over different commodity price environments. The purpose of the second chapter was to identify the most appropriate measure of performance from the point of view of a long-term investor in the mining industry and determine if factors other than commodity price may have a significant effect on performance. I reviewed the business literature on firm performance. I chose annualized total shareholder return (aTSR) as the most appropriate performance measure for a long-term mining company shareholder. Next, I performed an analysis that found commodity prices play a significant role in explaining mining company returns on an annual basis; however, the explanatory power of commodity prices fell by almost half when looking at shareholder returns over the long run. Further, I identified significant dispersion in individual firms' long-run returns, both on an absolute and commodity-adjusted basis. Both results pointed to the existence of long-term, firm-level effects that significantly impact mining company returns. The third chapter's purpose was to identify the firm-level factors that affect mining company performance over the long run. First, I developed a theoretical framework that identified mineral reserve growth, mineral asset impairments, commodity prices, and production costs as the drivers of mining company returns. Factors representing mineral reserve growth, 166 mineral asset impairments, and production costs were tested against aTSR using Canadian mining firms (the previous chapter had already analyzed the effect of commodity prices). The analysis found that asset impairments and changes in production costs both had a statistically significant effect on long-run company returns. A multivariable linear regression model that included factors related to impairments, production costs, and commodity prices explained almost 60% of the variance in company returns over the long run. The impairment factor had an effect size one order of magnitude larger than production costs and commodity prices. Overall, this chapter concluded that mineral asset impairments are a significant driver of mining company returns over the long run. The purpose of the fourth chapter was to identify an existing research frame that could help explain the reasons behind mining impairments. I reviewed the business literature on asset impairments and discovered that the mining industry is typically removed from impairment studies due to unique statutory reporting requirements. Next, major project forecasting errors were identified as a possible analogue to mining impairments and the project management literature on this topic was reviewed. I proposed that impairments were linked to forecasting errors and then performed an analysis comparing mining impairments and major project forecasting errors. The investigation found that the financial scale of mining projects and major projects is similar. The factors known to increase project forecasting errors also increase mining impairments (project location, entry into new markets, and new technology). The chapter concluded by accepting major project forecasting errors as an analogue to mining firm impairments from both a theoretical and empirical perspective. The fifth chapter aimed to investigate the root causes of mining impairments through the lens of project forecasting errors. First, I reviewed the literature on the root causes of project 167 forecasting errors. I identified three principal root causes: technical error, optimism bias, and principal-agent conflict. Next, I developed a theoretical framework to categorize mining impairments by the type of forecasting errors they represent. Then I mapped these errors to the root causes from the project management literature. I created a data set of every mine impairment declared by a TSX-listed mining company from January 1st, 2003, to January 1st, 2016. I coded these impairments into the categories of mine forecast errors. In total, mining companies recorded over $68 billion of impairments during the study period. The most significant reported cause of impairment was declining metal prices, followed by ore grade and ore access issues. Roughly 30% of the coded impairments corresponded to forecasting errors caused by technical error, and 70% corresponded to forecasting errors causes by optimism bias and principal-agent conflict. The chapter concluded that the existing literature on project forecasting errors helped to clarify the causes of mining firm impairments and identified reference class forecasting as a possible way to reduce impairments. The sixth chapter's purpose was to offer recommendations to reduce asset impairments in the mining industry. This chapter took the methods and lessons on reducing forecasting errors from the project management literature and applied them to mining projects. First, I discussed the concept of reference class forecasting and then reviewed the literature on its application to public infrastructure projects. I presented a small case study demonstrating the feasibility of generating reference classes for mining projects. I then identified some of the potential barriers to adoption. The chapter concluded by identifying that mining company boards of directors, financiers, and investors will need to drive the adoption of reference class forecasting in the industry to improve mining companies' long-term returns. 168 In summary, this research project has made contributions to the mining firm performance literature, the business performance literature, and the project forecasting literature. It has contributed to the mining firm performance literature by identifying commodity prices as a principal driver of annual mining firm returns, and asset impairments as a principal driver of mining firm returns over the long run. The business performance literature typically considers only annual panel data to assess performance over multi-year periods. This research has contributed to the business performance literature by identifying different annual and long-run performance factors; this finding may have implications for the identification of long-run performance factors in other industries. It also conducts a quantitative study of non-discretionary asset impairment causes, while the business literature has only studied the causes of discretionary impairments. By identifying significant gaps between the expected and realized benefits and costs of mining projects, this research adds findings from the mining industry to support the literature on project forecasting errors, which has primarily studied public infrastructure projects. It also provides the first quantitative assessment of the relative frequency and financial impact of forecasting error root causes. However, this research is limited because impairments are only a crude approximation of forecasting errors. Impairment reporting hides negative forecasting errors that are balanced by positive errors (metal price increases, for example) and hides any errors that do not reduce the asset's book value. For this reason, impairments should be considered a very conservative indication of total forecasting errors. The precision of the impairment research is limited because the reported reasons for impairments are up to company management's discretion. For reasons discussed in the earlier chapters, it is likely that metal price impairments were overreported as an impairment cause and reduced the reported frequency of other causes (capital cost overruns, for example). A 169 much clearer picture of forecasting errors and their corresponding causes could be developed through direct research on this topic. Further, collecting only publicly available information limited this research study in uncovering any root causes of impairments that had to do with principal-agent conflicts and misaligned incentives, which have been observed in the project management literature through personal interviews. There are several areas of potential research following this project. First, business research studies have an opportunity to investigate long-run performance factors that only appear over multi-year periods. Most studies of performance cited in this dissertation used annual panel data to identify performance factors. This research has shown that compiling annual data can hide the impact of significant factors or events that occur with a less-than-annual frequency and have effects that span several fiscal years, at least in the mining industry. Second, this data set could be used to add to the business literature around the timing of impairment disclosures and stock price movements. I reviewed the literature on this topic as part of this research study, but a direct response was outside the current project's scope. Third, there is a vast opportunity to investigate mine forecasting errors directly. Most information is publicly available, and analyses of the causes of forecasting errors could be done directly without relying on the impairment explanations reported by mining company management. Also, there is an opportunity to collect data on project-specific forecasting errors followed by (likely anonymous) personal interviews to quantitatively identify the relative impact of forecasting errors caused by optimism bias and principal-agent conflict. Data collected on project-specific forecasting errors could lead to the third broad area of potential future research: applying reference class forecasting to the mining industry. Reference classes could be created, back-tested against out-of-sample projects, and forecasting error estimates could be made for newly proposed projects. 170 There are several practical contributions from this research project. First, it should now be apparent that mining companies should do what they can to avoid impairments (if this was not obvious already), and investors should avoid mining companies that they believe will trigger impairments. Second, this research has shown that there are many factors that mining industry investors pay attention to which do not have a significant relationship to company returns. Reviewing over 1,000 company reports over this research project, I can confidently say that management communication places a lot of emphasis on increasing mineral reserves and increasing metal production. This research study found that neither has a significant relationship to mining company returns over the long run. 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Table 32: Mining companies included in MacDiarmid et al. (2018) study Anglo American plc Rio Tinto Plc CVRD-Vale BHP Billiton 184 Appendix B Table 33: Market exchange listings of all data set companies Company TSX NYSE/ AMEX ASX LSE/ AIM Other AGNICO EAGLE MINES x x ALACER GOLD CORP x x ALAMOS GOLD INC x x ALEXCO RESOURCE x x TIMMINS GOLD CORP x x AMERICAS SILVER CORP x ANACONDA MINING INC x AREHADA MINING LTD x ARGONAUT GOLD INC x AURA MINERALS x AURICO GOLD INC x x AURIZON MINES x x AVION GOLD CORP x AVNEL GOLD MINING LTD x B2GOLD CORP x x BANRO CORP x x BARRICK GOLD CRP x x x BEMA GOLD CORP x x BRIGUS GOLD CORP x CALEDONIA MINING CORP PLC x x NASDAQ CAMBIOR INC x x CANICKEL MINING LTD x CAPITAL GOLD CORP x x CAPSTONE MINING x CENTERRA GOLD INC x CENTRAL SUN MINING INC x x CHINA GOLD INTERNATIONAL x HKSE CLAUDE RESOURCES x CREW GOLD CORP x Oslo CROCODILE GOLD CORP x CRYSTALLEX INTL CORP x x DETOUR GOLD CORP x 185 Company TSX NYSE/ AMEX ASX LSE/ AIM Other DUNDEE PRECIOUS x DYNACOR GOLD MINES INC x EASTERN PLATINUM x JSE ELDORADO GOLD x x x ENDEAVOUR MINING x ENDEAVOUR SILVER x x EUROPEAN GOLDFIELDS x x EXCELLON RESOURCES INC x FIRST MAJESTIC SILVER CORP x x BMV, Borse FIRST QUANTUM x x x Lusaka FORTUNA SILVER x x Borse GLAMIS GOLD LTD x x GOLDCORP INC x x GOLDEN STAR RESOURCES x x Ghana GOLDGROUP MINING INC x GRAN COLOMBIA GOLD x GREAT BASIN GOLD x x JSE GREAT PANTHER SILVER x x HIGH RIVER GOLD x IAMGOLD CORP x x IBERIAN MINERALS x INMET MINING CORP x JAGUAR MINING INC x KATANGA MINING LTD x KINROSS GOLD x x KIRKLAND LAKE GOLD x LA MANCHA RESOURCES INC x Borse LAKE SHORE GOLD CORP x x LUNA GOLD CORP x LUNDIN MINING x Oslo MALAGA INC x MANDALAY RESOURCES CORP x MCEWEN MINING x x MERIDIAN GOLD x x MINEFINDERS CORP LTD x x 186 Company TSX NYSE/ AMEX ASX LSE/ AIM Other NEVSUN RESOURCES x x NEW DAWN MINING CORP x NEW GOLD INC x x NEWMONT MINING CORP x x x NORTH AMERICAN PALLADIUM x x NORTHERN SUN MINING CORP x NORTHGATE MINERALS x x OCEANAGOLD CORP x x NZX OROSUR MINING INC x x ORVANA MINERALS x OSISKO MINING CORP x Borse PAN AMERICAN SILVER x NASDAQ PLACER DOME INC x x PRIMERO MINING x x QMX GOLD CORP x RED BACK MINING x REVETTMINING INC x x RICHMONT MINES x x RIO NARCEA GOLD x x ROCA MINES INC x RUSORO MINING LTD x SAN GOLD CORP x SEMAFO INC x NASDAQ SIERRA METALS INC x Lima SILVERCORP METAL x x SSR MINING INC x NASDAQ ST ANDREW GOLD x STARCORE INTERNATIONAL MINES x STERLITE GOLD LTD x TALISON LITHIUM x x TASEKO MINES x x TECK RESOURCES x x TERANGA GOLD x x THOMPSON CREEK M x x TVI PACIFIC INC x 187 Company TSX NYSE/ AMEX ASX LSE/ AIM Other WESDOME GOLD MIN x WHEATON RIVER x x YAMANA GOLD INC x x x 188 Appendix C Table 34: Status of all data set mine and mining project occurrences Mine Company Status Agbaou Endeavour Mining Developed Aguablanca Lundin Mining Acquired Aguablanca Rio Narcea Gold Developed Aguas Tenidas Iberian Minerals Developed Ahafo Newmont Mining Developed Akyem Newmont Mining Developed Alamo Dorado Pan American Silver Developed Alumbrera Goldcorp Acquired Alumbrera Yamana Acquired Amapari Goldcorp Acquired Amapari Wheaton River Project Aquarius St Andrew Project Aranzazu Aura Minerals Acquired Ararat plant Sterlite Acquired Aurizona Luna Gold Developed Australian Operations Alacer Gold Acquired Back River Dundee Project Batu Hijau Newmont Mining Developed Bella Vista Central Sun Developed Berezitovy High River Gold Developed Bisha Nevsun Developed Bissa High River Gold Constructing Bjorkdal Mandalay Acquired Black Fox Primero Acquired Black Fox Brigus Gold Acquired Blackwater project New Gold Project Boddington Newmont Mining Developed Bogoso/Presta Golden Star Acquired Bolanitos/Guanajuato Endeavour Silver Acquired Bolivar Sierra Metals Developed Boroo Centerra Gold Developed Bucko Canickel Mining Developed Buckreef IAMGOLD Project 189 Mine Company Status Bulawayo New Dawn Acquired Bulyanhulu Barrick Gold Acquired Burnstone Great Basin Developed Buryatzoloto High River Gold Acquired Buzwagi Barrick Gold Developed Bwana/Lonshi First Quantum Developed C1 Santa Luz Yamana Developed Caballo Blanco Goldgroup Mining Project Camp Caiman IAMGOLD Project Camp Caiman Cambior Project Canadian Malartic Agnico Eagle Acquired Canadian Malartic Yamana Acquired Candelaria Lundin Mining Acquired Carlin Newmont Mining Developed Cayeli First Quantum Acquired Cayeli Inmet Mining Developed Caylloma Fortuna Silver Acquired Cerattepe Inmet Mining Project Cerro Bayo Mandalay Acquired Cerro Blanco Goldcorp Project Cerro Casale Barrick Gold Project Cerro Casale Kinross Gold Project Cerro Colorado Goldgroup Mining Acquired Cerro del Gallo Primero Project Cerro Moro Yamana Project Cerro Negro Goldcorp Developed Cerro San Pedro New Gold Acquired Certej Eldorado Gold Acquired Certej Project Europen Goldfields Project Chapada Yamana Developed Chelopech Dundee Acquired Chirano Kinross Gold Acquired Chirano Red Back Developed Choco Mine Rusoro Acquired Cobre Panama First Quantum Project Cobre Panama Inmet Mining Constructing 190 Mine Company Status Condestable Iberian Minerals Acquired Conga Newmont Mining Project Cortez Barrick Gold Acquired Costerfield Mandalay Acquired Cote Gold IAMGOLD Project Cowal Barrick Gold Developed Cozamin Capstone Mining Acquired Cripple Creek Newmont Mining Acquired Crixas Kinross Gold Acquired Crocodile River Eastern Platinum Acquired Cusi Sierra Metals Developed Damang IAMGOLD Acquired Del Toro First Majestic Developed Detour Lake Detour Gold Developed Dolores Pan American Silver Acquired Dolores Minefinders Developed Don Mario Orvana Developed Donlin Creek Barrick Gold Project Doyon Cambior Acquired Doyon IAMGOLD Acquired Dynacor mill Dynacor Acquired Eagle Lundin Mining Acquired El Cajon Americas Silver Project El Castillo Argonaut Gold Acquired El Chanate Alamos Gold Acquired El Chanate Aurico Gold Acquired El Chanate Capital Gold Developed El Cubo Endeavour Silver Acquired El Cubo Aurico Gold Acquired El Gallo McEwen Mining Developed El Limon Goldcorp Project El Limon Wheaton River Project El Morro Goldcorp Project El Morro New Gold Project El Penon Yamana Acquired El Penon Meridian Gold Developed 191 Mine Company Status El Sauzal Goldcorp Acquired El Sauzal Glamis Gold Developed El Valle-Boinas Rio Narcea Gold Developed El Valle-Boinas Orvana Acquired Eleonore Goldcorp Developed Endako Thompson Creek Acquired Ernesto/Pau-a-Pique Yamana Developed Eskay Creek Barrick Gold Acquired Esquel Meridian Gold Project Essekane IAMGOLD Acquired Fekola B2Gold Constructing Florence Taseko Project Fort Hills Teck Resources Project Fort Knox Kinross Gold Acquired Fosterville Crocodile Gold Acquired Fosterville Northgate Minerals Acquired Frog's Leg La Mancha Developed Frontier First Quantum Developed Fruta del Norte Kinross Gold Project Galmoy Lundin Mining Acquired Gatsuurt Centerra Gold Project Gold Bar McEwen Mining Project Goldex Agnico Eagle Developed Goldstrike Barrick Gold Developed Gramalote B2Gold Project Greenbushes Talison Acquired Gualcamayo Yamana Developed Guanacevi Endeavour Silver Acquired Guanajuato Great Panther Acquired Guelb Moghrein First Quantum Developed Gurupi Jaguar Project Gweru New Dawn Acquired Hammond Reef Osisko Mining Project Hassai La Mancha Acquired Hemlo Barrick Gold Acquired Heva-Hosco Aurizon Project 192 Mine Company Status Hinge San Gold Developed Hollister Great Basin Developed Holt/Holloway/Hilsop St Andrew Acquired Hope Bay Newmont Mining Project Huaron Pan American Silver Acquired Isidora Rusoro Acquired Ity La Mancha Acquired Ity Endeavour Mining Acquired Jabal Sayid Barrick Gold Developed Jacobina Yamana Acquired Jeronimo Yamana Project Julietta Bema Gold Developed Julietta Kinross Gold Acquired Kadoma New Dawn Acquired Kalgoorlie Barrick Gold Acquired Kalgoorlie Newmont Mining Acquired Kamoto Katanga Mining Acquired Kansanshi First Quantum Developed Kapan Dundee Acquired Kaymaz Eldorado Gold Project Kemess North Northgate Minerals Project Kemess South Northgate Minerals Acquired Kennedy's Vale Eastern Platinum Project Keno Hill Alexco Resources Developed Kettle River Kinross Gold Developed Kevitsa First Quantum Developed Kiaka B2Gold Project Kiniero Semafo Developed Kittila Agnico Eagle Developed Kolwezi First Quantum Project Krumovgrad Dundee Project Kubaka Kinross Gold Acquired Kumtor Centerra Gold Acquired Kupol Kinross Gold Acquired Kupol Bema Gold Project Kutcho Capstone Mining Project 193 Mine Company Status La Coipa Kinross Gold Acquired La Colorada Argonaut Gold Developed La Colorada Pan American Silver Acquired La Encantada First Majestic Acquired La Guirarra First Majestic Acquired La Herradura Newmont Mining Developed La India Agnico Eagle Developed La Luz First Majestic Project La Parrilla First Majestic Acquired Lac des Iles North American Palladium Developed Lac Herbin QMX Gold Acquired Lac Pelletier QMX Gold Project Lagunas Norte / Alto Chicama Barrick Gold Developed Lapa Agnico Eagle Developed LaRonde Agnico Eagle Acquired Las Cristinas Crystallex Project Las Cruces First Quantum Acquired Las Cruces Inmet Mining Developed LEFA Crew Gold Acquired Libertad B2Gold Acquired Libertad Central Sun Acquired Limon mine B2Gold Acquired Limon Central Sun Acquired Lobo Marte Kinross Gold Project Long Canyon Newmont Mining Project Los Azules McEwen Mining Project Los Filos Goldcorp Developed Los Filos Wheaton River Project Luismin / San Dimas Goldcorp Acquired Lumwana Barrick Gold Acquired Macassa Kirkland Lake Acquired Maco Crew Gold Acquired Malartic Osisko Mining Developed Mana Semafo Developed Manantial Espejo Pan American Silver Developed Mareesburg Eastern Platinum Project 194 Mine Company Status Maricunga/Refugio Kinross Gold Acquired Marigold Glamis Gold Acquired Marigold Goldcorp Acquired Marigold SSR Mining Acquired Marlin Glamis Gold Developed Marlin Goldcorp Acquired Marmato Gran Colombian Gold Project Masbate B2Gold Acquired Max Roca Mines Developed McWatters Northern Sun Developed Meadowbank Agnico Eagle Acquired Meghradzor Sterlite Acquired Meliadine Agnico Eagle Project Mercedes Yamana Developed Mesquite New Gold Acquired Minera Florida Yamana Acquired Minera Florida Meridian Gold Acquired San Jose McEwen Mining Acquired Minto Capstone Mining Acquired Morococha Pan American Silver Acquired Mount Milligan Thompson Creek Developed Mulatos Alamos Gold Developed Mupane IAMGOLD Acquired Musselwhite Goldcorp Acquired Musselwhite Kinross Gold Acquired Nalunaq/Nugget Pond Crew Gold Developed Namoya Banro Corp Developed Natougou Semafo Project Navidad Pan American Silver Project Neves-Corvo Lundin Mining Acquired New Afton New Gold Developed New Britannia High River Gold Developed Nickel concentrator Northern Sun Developed Niobec IAMGOLD Acquired Niobec Cambior Acquired Nixon Fork St Andrew Acquired 195 Mine Company Status Northern Territory Crocodile Gold Developed Nzema Endeavour Mining Acquired Ocampo Aurico Gold Developed Ok Tedi Inmet Mining Developed Oksut Centerra Gold Project Olympias Europen Goldfields Project Omai Bauxite Cambior Acquired Omai Gold IAMGOLD Acquired Omai Gold Cambior Developed Otjikoto B2Gold Developed Pajingo Newmont Mining Acquired Paracatu Kinross Gold Acquired Pascua Llama Barrick Gold Constructing Pasto Bueno Malaga Acquired Peak Goldcorp Acquired Peak New Gold Acquired Penasquito Goldcorp Developed Perama Hill Eldorado Gold Project Petrex Bema Gold Acquired Phoenix Newmont Mining Developed Pierina Barrick Gold Developed Pilar Yamana Developed Pine Cove Anaconda Mining Developed Pinos Altos Agnico Eagle Developed Pinto Valley Capstone Mining Acquired Pirquitas SSR Mining Developed Platosa Excellon Developed Plutonic Barrick Gold Acquired Porcupine Goldcorp Acquired Porcupine Kinross Gold Acquired Porgera Barrick Gold Acquired Pueblo Viejo Barrick Gold Developed Pueblo Viejo Goldcorp Developed Pyhasalmi First Quantum Acquired Pyhasalmi Inmet Mining Acquired Quiruvilca Pan American Silver Acquired 196 Mine Company Status Rainy River New Gold Project Rand Glamis Gold Developed Ravensthorpe First Quantum Acquired Red Lake Goldcorp Acquired Redstone Northern Sun Acquired Refugio Bema Gold Developed Rice Lake San Gold Acquired Rooipoort Caledonia Mining Project Rosebel IAMGOLD Acquired Rosebel Cambior Developed Round Mountain Barrick Gold Acquired Round Mountain Kinross Gold Acquired Sabodala Terenga Gold Acquired Sadiola IAMGOLD Developed Samira Hill Semafo Developed San Andres Aura Minerals Acquired San Dimas Primero Acquired San Francisco Alio Gold Acquired San Gregorio Crystallex Acquired San Gregorio Orosur Mining Acquired San Jose Fortuna Silver Developed San Martin Goldcorp Acquired San Martin First Majestic Acquired San Martin Starcore Acquired San Martin Glamis Gold Developed San Rafael Americas Silver Project San Vicente Pan American Silver Acquired Santa Elena First Majestic Acquired Santo Domingo Capstone Mining Project Sao Bento Eldorado Gold Acquired Sao Vicente/Francisco Aura Minerals Acquired Sapes Eldorado Gold Project Seabee Claude Resources Developed Sedibelo Barrick Gold Project Segovia Gran Colombian Gold Acquired Sentinel/Trident First Quantum Developed 197 Mine Company Status Serrote Aura Minerals Project Skouries Europen Goldfields Project Sleeping Giant IAMGOLD Acquired Sleeping Giant Cambior Acquired Snow Lake QMX Gold Acquired Spitzkop Eastern Platinum Project Stawell Crocodile Gold Acquired Stawell Northgate Minerals Acquired Storliden Lundin Mining Acquired Tabakoto Endeavour Mining Acquired Taltal Anaconda Mining Project Tanami Newmont Mining Acquired Taparko-Bouroum High River Gold Developed Tarkwa IAMGOLD Acquired Tartan Lake Claude Resources Project Tasiast Kinross Gold Acquired Tasiast Red Back Acquired Tasiast Rio Narcea Gold Constructing Taylor St Andrew Project Tenke Fungurume Lundin Mining Acquired Thompson Creek Thompson Creek Acquired Timmins CGU Lake Shore Gold Acquired TJS Eldorado Gold Acquired Tocantinzinho Eldorado Gold Project Topia Great Panther Acquired Troilus Inmet Mining Developed Troy Revett Mining Acquired Tulawaka Barrick Gold Developed Turkey Operations Alacer Gold Developed Turquoise Ridge Barrick Gold Acquired Twin Creeks Newmont Mining Acquired Veladero Barrick Gold Developed Vezza / Sleeping Giant North American Palladium Acquired Vila Nova Eldorado Gold Developed Waihi Newmont Mining Acquired Wassa Golden Star Developed 198 Mine Company Status Wharf Goldcorp Acquired White Foil La Mancha Developed Yanacocha Newmont Mining Developed Yatela IAMGOLD Developed Yauricocha Sierra Metals Acquired Youga Endeavour Mining Acquired Young Davidson Aurico Gold Acquired Young Davidson Alamos Gold Acquired Young Davidson Northgate Minerals Project Zaldivar Barrick Gold Acquired Zarafshan Newmont Mining Acquired Zinkgruvan Lundin Mining Acquired Zod Sterlite Acquired
Thesis/Dissertation
eng
Mining Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Steen, John Thomas, 1971-
How to avoid the worst financial outcomes in the mining industry
Text