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UBC Theses and Dissertations

How to avoid the worst financial outcomes in the mining industry Gillis, Andrew


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.

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