Forestry, Faculty of
DSpace
UBCV
Corry, Diego
Master of Science - MSc
University of British Columbia
This thesis investigates the intricate relationship between digital discourse, specifically social media sentiment, and stock price within the Canadian oil and gas industry from 2020-2021. The research comprises two distinct yet complementary studies that collectively shed light on the complex dynamics at play. The first study examines the predictive capacity of non-financial Twitter sentiment on stock prices across nine prominent Canadian oil and gas companies. Employing Granger causality tests, the findings reveal no significant influence of social media sentiment on stock prices within the sample. This contradicts previous studies suggesting a predictive relationship and highlights the potential dominance of market-level sentiment over individual company sentiment, as well as the diminished impact of non-financial discourse on investor decisions. The study bolsters past research which indicates the diminishing power of external stakeholders over firms in the digital era. The second study delves deeper into the relationship between digital discourse and stock price, focusing on Enbridge Inc. and TC Energy Corp. Granger causality tests reveal statistically significant forecasting abilities of Twitter sentiment on stock prices during shortened timeframes surrounding peak Twitter volume dates for these two companies. However, qualitative analysis suggests that stock price movement may be affected more by traditional media coverage than social media sentiment. The findings suggest that the impact of digital discourse on stock price may be company-specific and depend on the nature and intensity of the discourse. Nationally viewed political events or controversies appear to be potential catalysts for movement on stock prices. Additionally, specific RepRisk categories related to environmental, social, and governance (ESG) factors were shared between Enbridge and TC Energy, indicating their potential salience in this relationship.
THE EFFECTS OF SOCIAL MEDIA SENTIMENT ON STOCK PRICE: A STUDY OF THE CANADIAN ENERGY SECTOR by Diego Corry B.A. (with Distinction), The University of British Columbia, 2022 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2024 © Diego Corry, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: The effects of social media sentiment on stock price: a study of the Canadian energy sector_____ Submitted by ___Diego Corry________ in partial fulfillment of the requirements for the the degree of __Master of Science__________________________________________________ in ____Forestry_________________________________________________________________ Examining Committee: Hamish van der Ven, Assistant Professor, Forestry, UBC________________________________ Supervisor Fred Cutler, Associate Professor, Political Science, UBC________________________________ Supervisory Committee Member Rajat Panwar, Professor, Forestry, Oregon State University______________________________ Supervisory Committee Member Gary Bull, Professor, Forestry, UBC________________________________________________ Additional Examiner iii Abstract This thesis investigates the intricate relationship between digital discourse, specifically social media sentiment, and stock price within the Canadian oil and gas industry from 2020-2021. The research comprises two distinct yet complementary studies that collectively shed light on the complex dynamics at play. The first study examines the predictive capacity of non-financial Twitter sentiment on stock prices across nine prominent Canadian oil and gas companies. Employing Granger causality tests, the findings reveal no significant influence of social media sentiment on stock prices within the sample. This contradicts previous studies suggesting a predictive relationship and highlights the potential dominance of market-level sentiment over individual company sentiment, as well as the diminished impact of non-financial discourse on investor decisions. The study bolsters past research which indicates the diminishing power of external stakeholders over firms in the digital era. The second study delves deeper into the relationship between digital discourse and stock price, focusing on Enbridge Inc. and TC Energy Corp. Granger causality tests reveal statistically significant forecasting abilities of Twitter sentiment on stock prices during shortened timeframes surrounding peak Twitter volume dates for these two companies. However, qualitative analysis suggests that stock price movement may be affected more by traditional media coverage than social media sentiment. The findings suggest that the impact of digital discourse on stock price may be company-specific and depend on the nature and intensity of the discourse. Nationally viewed political events or controversies appear to be potential catalysts for movement on stock prices. Additionally, specific RepRisk categories related to environmental, social, and governance (ESG) factors were shared between Enbridge and TC Energy, indicating their potential salience in this relationship. iv Lay Summary This thesis explores the relationship between social media sentiment and stock prices of Canadian oil and gas companies during 2020-2021. The first study found no significant link between Twitter sentiment and stock prices across nine firms, contradicting previous research suggesting that Twitter sentiment affects stock prices. The second study revealed that for Enbridge and TC Energy, Twitter sentiment could predict stock prices during periods of high Twitter activity surrounding these companies. However, this predictive ability was not observed for other firms. Specific environmental, social, and governance factors shared by Enbridge and TC Energy were identified as relevant to this relationship. While negative social media sentiment did not precede negative media coverage, it could follow and adversely impact stock price when certain variables were present. The thesis underscores the complexity of translating digital discourse into organizational outcomes and calls for further research considering socio-economic and cultural factors. v Preface This thesis is the original, independent work of the author, Diego Corry. I identified the research questions, collected and analyzed the data, and wrote the contents of these documents. I received academic guidance, and constant support from my supervisory committee throughout the entire process. vi Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ................................................................................................................................ iv Preface ............................................................................................................................................. v Table of Contents .......................................................................................................................... vi List of Tables ............................................................................................................................... viii List of Figures ............................................................................................................................... ix Acknowledgements ........................................................................................................................ xi Dedication..................................................................................................................................... xii Chapter 1: Introduction ................................................................................................................. 1 1.1 Why Social Media Matters ............................................................................................................... 1 1.2 Context of Canadian Energy Industry ............................................................................................ 2 1.3 Research Overview and Thesis Organization ................................................................................. 4 Chapter 2: Do Tweets matter? ....................................................................................................... 6 2.1 Introduction ....................................................................................................................................... 6 2.2 Existing Scholarship on Social Media and Stock Price.................................................................. 7 2.2.1 Previous Research on Stock Price and Social Media Sentiment ............................................................... 10 2.2.2 This Study’s Intervention .......................................................................................................................... 11 2.3 Research Design and Data .............................................................................................................. 12 2.3.1 Variables & Timeframe ............................................................................................................................. 12 2.3.2 Company Data ........................................................................................................................................... 13 2.3.3 Twitter Data ............................................................................................................................................... 14 2.3.4 Sentiment Analysis .................................................................................................................................... 15 2.3.5 Modelling and Analysis ............................................................................................................................. 19 2.3.6 Limitations ................................................................................................................................................. 20 2.4 Empirical Results ............................................................................................................................ 21 2.4.1 Granger Causality Tests ............................................................................................................................ 21 2.4.2 Potential for Coding Error ......................................................................................................................... 22 2.4.3 Industry-Level Application ....................................................................................................................... 25 2.5 Discussion ......................................................................................................................................... 25 2.5.1 Social Media Stakeholders Exert Limited Influence Over Investors ........................................................ 26 2.5.2 Financial Chatter on Social Media Differs from Other Types of Chatter ................................................. 27 2.5.3 Social Media Sentiment Impacts Firms’ Stock Prices Differently than Markets ...................................... 27 2.6 Conclusions and Future Research ................................................................................................. 28 Chapter 3: Under what conditions do Tweets matter? ............................................................... 31 3.1 Introduction ..................................................................................................................................... 31 3.1.1 Evidence supporting the impact of social media on stock prices .............................................................. 32 3.1.2 Evidence supporting traditional news media ............................................................................................. 33 3.1.3 Evidence supporting specific conditions where media events relate to firm value ................................... 34 vii 3.2 Research Design and Data .............................................................................................................. 34 3.2.1 Variables and Timeframe .......................................................................................................................... 34 3.2.2 Twitter Data ............................................................................................................................................... 35 3.2.3 Qualitative Analysis .................................................................................................................................. 38 3.2.4 Limitations ................................................................................................................................................. 40 3.3 Empirical Results ............................................................................................................................ 41 3.3.1 Granger Causality Tests ............................................................................................................................ 41 3.3.2 Enbridge Background ................................................................................................................................ 42 3.3.3 TC Energy Background ............................................................................................................................. 43 3.4 Discussion ......................................................................................................................................... 45 3.4.1 RepRisk Classifications ............................................................................................................................. 45 3.4.2 Social Media vs. Traditional News ........................................................................................................... 46 3.4.3 Comparisons with the Industry .................................................................................................................. 47 3.5 Conclusions and Further Research................................................................................................ 49 Chapter 4: Summary and Conclusions ....................................................................................... 52 4.1 Key Insights and Results ................................................................................................................. 52 4.2 Strengths and Limitations .............................................................................................................. 53 4.3 Contributions and Future Research .............................................................................................. 55 References .................................................................................................................................... 58 Appendix ....................................................................................................................................... 73 viii List of Tables Table 1: Summary of company market capitalization…………………………………………...11 Table 2: Summary of Tweet volume for each company throughout the entire time frame.……..13 Table 3: Summary of company-level Granger Causality Tests………………….………………20 Table 4: Example of Twitter sentiment scoring………………………………………………….21 Table 5: Example of Tweets referring to Enbridge’s Line 3 Pipeline…………………………...22 Table 6: Industry-level results of Granger Causality Tests……………………………………...23 Table 7: Peak twitter volume dates for each company…………………………………………..35 Table 8: Company-level granger causality tests surrounding peak twitter volume dates – 1-month analysis…………………………………………………………………………………………...38 Table 9: Company-level granger causality tests surrounding peak twitter volume dates – 2-week analysis…………………………………………………………………………………………...39 Table 10: RepRisk thematic coding schema……………………………………………………..41 ix List of Figures Figure 1: Sentiment score and adjusted closing price of Enbridge Inc. across 2020 & 2021...…14 Figure 2: Sentiment score and adjusted closing price of ARC Resources Ltd. across 2020 & 2021………………………………………………………………………………………………15 Figure 3: Sentiment score and adjusted closing price of Canadian Natural Resources Ltd. across 2020 & 2021…………………………………………………...………………………………...15 Figure 4: Sentiment score and adjusted closing price of Cenovus Energy Inc. across 2020 & 2021………………………………………………………………………………………………15 Figure 5: Sentiment score and adjusted closing price of Imperial Oil Ltd. across 2020 & 2021………………………………………………………………………………………………15 Figure 6: Sentiment score and adjusted closing price of Ovintiv Oil Ltd. across 2020 & 2021………………………………………………………………………………………………16 Figure 7: Sentiment score and adjusted closing price of Pembina Pipeline Corp. across 2020 & 2021………………………………………………………………………………………………16 Figure 8: Sentiment score and adjusted closing price of Suncor Energy Inc. across 2020 & 2021………………………………………………………………………………………………16 Figure 9: Sentiment score and adjusted closing price of TC Energy Corp. across 2020 & 2021………………………………………………………………………………………………16 Figure 10: Volume distribution of Tweets for Canadian Natural Resources Ltd.……………… 33 Figure 11: Volume distribution of Tweets for Imperial Oil Ltd………………………………... 33 Figure 12: Volume distribution of Tweets for ARC Resources Ltd……………………………..33 x Figure 13: Volume distribution of Tweets for Enbridge Inc…………………………………… 33 Figure 14: Volume distribution of Tweets for Cenovus Energy Inc…………………………….34 Figure 15: Volume distribution of Tweets for TC Energy Inc…………………………………. 34 Figure 16: Volume distribution of Tweets for Ovintiv Oil Ltd………………………………… 34 Figure 17: Volume distribution of Tweets for Suncor Energy Inc……………………………... 34 Figure 18: Volume distribution of Tweets for Pembina Pipeline Corp………………………… 35 xi Acknowledgements Firstly, I’d like to thank my supervisor, Hamish van der Ven for his support and direction throughout this entire process. Thank you to my supervisory committee, Fred Cutler and Rajat Panwar, for lending their expertise and knowledge. Thank you to those behind the selection process for the funding opportunities I received – namely, the SSHRC CGS-M Scholarship, the Peter N. Affleck Memorial Graduate Scholarship in Forest Policy, and the Christopher Lee Memorial Scholarship. Thank you to my parents for instilling in me the importance of education and the relentless pursuit of knowledge. You have both provided so much support and wisdom not only through this process, but through everything that led me to this degree. xii Dedication This thesis is dedicated to my family. 1 Chapter 1: Introduction The advent of digital technologies and the proliferation of social media platforms has profoundly reshaped the landscape of communication and information dissemination. In the contemporary business environment, the interplay between digital discourse, particularly social media sentiment, and stock price has emerged as a subject of significant interest and debate. Social media has emerged as a medium where the public can speak directly to firms. In 2021, Burger King attempted to highlight the fact that professional culinary positions are only 20% female – tweeting “Women belong in the kitchen” on International Women’s day. The outcry following this tweet called for boycotts, leaving Burger King interacting directly with social media users for days (Rowan Kelleher, 2021). In another case, Hyatt Hotels hosted a Conservative Political Action Conference in 2021, with many Twitter-users comparing the similarities of the conference’s stage design with Nazi symbolism. The Twitter outrage culminated in the hashtag “#boycottHYATT”, prompting numerous direct responses and apologies from the hotel company (Rowan Kelleher, 2021). These examples show the direct line of communication social media platforms bring to the public-firm relationship. While there has been evidence to both support and refute the presence of this social media activity affecting stock price, there has been no consensus within the debate. This thesis delves into the intricate relationship between these two domains, focusing specifically on the Canadian oil and gas industry during the pivotal period of 2020-2021. 1.1 Why Social Media Matters 2 Social media use has risen astronomically in the past decade. We saw the rate of annual Twitter users increase from 40 million in 2010 to 401 million in 2022 (Iqbal, 2024). This massive shift onto social media platforms such as Twitter (now “X”) has created a new pathway for the dissemination of information that rivals traditional media outlets. It has democratized media and communication by providing the same platform to everyone, allowing for connective power between individuals to rival the reach of multi-national corporations or governments. There have been significant findings on numerous different aspects of the relationship between social media and firms. Scholars have found that social media plays a crucial part in building and measuring organizational legitimacy for firms (Etter et al., 2018). Further, social media has been found to bring greater attention to corporate sustainability issues, thereby instigating institutional change for firms (de Bakker, 2015). Twitter activism by critical stakeholders has been found to influence the decisions of investors in particular industries and companies (Gomez-Carrasco & Michelon, 2017). These are some of the different dimensions measured within the relationship of social media and business. While the aforementioned literature finds that social media can have implications for companies, it is the question of how these implications are quantified that holds particular value. How can the effects of social media activity on a company’s value be measured? How can we separate social media action from other variables that might affect a company’s bottom-line? How does social media relate to traditional or established media? These are questions that need to be asked in order to fully understand the power of social media over the corporate world. 1.2 Context of Canadian Energy Industry 3 The Canadian energy sector is an ideal test case for gauging the impact of social media on stock price. I focus my analysis on Canadian companies in the oil and gas sector due to the particularly heated level of contention surrounding many of the infrastructure projects in development over the past few years. Many of the companies in the chosen sample have active projects in Western Canada, which has been and remains a hotbed for both environmental activism and pro-oil and gas actors. Enbridge’s Line 3 pipeline serves as one example. The project transports crude oil from Alberta, Canada to Wisconsin, US. Although operating since 1968, a replacement program to fix and update the old pipeline network was proposed in 2014 with construction beginning in November of 2020. Opposition to the project sprung up based on concerns regarding the pipelines environmental track record (Morgan, 2021). Opposition from both indigenous bands (mainly Ojibwe) and environmental activists has been significant. There is also an issue of treaty rights, with opposition claiming that the project violates the tribal sovereignty laid out in the American constitution. The hashtags “#StopLine3” and “#ResistLine3” have seen widespread use to express opposition on Twitter. TC Energy’s Coastal Gaslink pipeline serves another example, transporting natural gas from Dawson Creek, BC to Kitimat, BC. The project’s route runs through several First Nation’s territories, most notable being the unceded territory of the Wet’suwet’en near Burns Lake, BC. Construction commenced in 2019, however there was significant opposition to the development of the pipeline from the hereditary chiefs of the Wet’suwet’en who claimed jurisdiction over the unceded land. Numerous protests, roadblocks, and disruptions were created throughout 2020, with further escalation occurring during altercations between the RCMP and protesters. Protests were held across the country in solidarity of the Wet’suwet’en land defenders, reaching levels of virality on social media nationally and internationally. The hashtags “#ShutDownCanada” and 4 “#WetsuwetenStrong” both saw significant use on twitter during the time of the protests (The Wet’suwet’en Conflict Disrupting Canada’s Rail System, 2020). The topic of oil and gas development is constantly and hotly disputed in parliamentary chambers and twitter paragraphs alike. Further, the selected timeframe encompasses the COVID-19 pandemic which left people stuck in their homes spending more time online (Cho et al, 2023). The strict rules on in-person gatherings shifted on-the-ground activism to the internet, heightening levels of online public scrutiny on both businesses and governments (Grant & Smith, 2021). 1.3 Research Overview and Thesis Organization This thesis investigates the relationship between digital discourse, specifically Twitter sentiment, and stock price within the Canadian oil and gas industry from 2020-2021. This thesis is comprised of four chapters, including an introduction, two empirical papers and a conclusion. In lieu of including chapters that review the extant literature and describe my methodology, I cover each of these topics within my empirical chapters with relation to the specific study. Chapter 2 examines the predictive capacity of non-financial Twitter sentiment on stock prices across nine prominent Canadian oil and gas companies. Chapter 3 then delves deeper into the relationship between digital discourse and stock price, and the distinction between traditional media and social media as an impactful force on stock price. This chapter aims to distinguish the effects of traditional and social media by examining two particular cases of interest that show significant forecasting ability on stock price. Chapter 4 concludes the thesis and provides a comprehensive synthesis of this thesis. It covers the main findings and key contributions, limitations, and 5 suggestions for future research. This thesis comprises two distinct yet complementary studies that collectively shed light on the complex dynamics at play. 6 Chapter 2: Do Tweets matter? 2.1 Introduction Should firms be concerned about negative public perception online? And conversely, should activists bother targeting their subjects via social media? These questions are hotly debated as the use of social media continues to grow rapidly – serving as a platform for both corporate and public expression. Advocates of digital activism view it as a cost-effective continuation of in-person activism – removing the need for physical participation and allowing for broader dissemination of key messages and lower barriers to entry for activists in general (Martin & Kracher, 2008). Skeptics claim that the sheer volume of commentary online introduces more chaos and mixed messaging, making digital activism less effective in the modern age (Barnett et al, 2018, Kristofferson et al, 2014). Skeptics further warn that digital activism (or ‘slacktivism’ as it is sometimes pejoratively labeled) is a superficial alternative to traditional activism whose primary purpose is virtue signalling to enhance one’s one image (Aagerup, 2022). This paper explores the interplay of social media discourse and stock price. I examine how negative social media sentiment impacts the stock price of nine Canadian oil and gas companies between 2020 and 2021. Although this industry and time frame saw massive political and environmental controversy, this study finds no significant relationship between negative posting on Twitter and stock price. These findings run contrary to much of the extant literature which does find a relationship between online sentiment and stock price. I argue that the reasons for this disparity lie in the distinction between firm and market responses to negative sentiment, the unique features of the energy sector, and the differing impacts of financial and non-financial 7 chatter on social media. Our study contributes novel conceptual and methodological insights to the burgeoning research agenda on firm and market responses to social media discourse. I proceed as follows. First, I begin by reviewing the literature on the impacts of social media activity on stock price. Second, I describe the methods used to collect and analyze our data. Third, I describe the findings from our statistical analyses. Fourth, I discuss the implications of these findings and their relationship to extant literature. Fifth, I conclude the paper by highlighting the key points and takeaways and outlining some areas for future research. 2.2 Existing Scholarship on Social Media and Stock Price The growing global use of social media has created a new landscape for firms in a number of ways (de Bakker, 2015). First, it has broadened the definition of “stakeholder” (Saxton et al, 2021). Stakeholders are individuals or groups that can affect or be affected by a firm’s activities (Freeman, 2010). While traditionally, stakeholders (or “primary stakeholders”) have been defined as actors that engage in contractual relationships with the firm (i.e., shareholders), secondary stakeholders are defined as those who influence or affect, or are influenced or affected by the firm, while not being contractually engaged with it (Clarkson, 1995). Saxton et al., (2021) further disaggregate stakeholders into the following four categories of stakeholders: (1) enabling stakeholders – those who have some control over the firm, (2) functional stakeholders – those who provide input and receive output from the firm, (3) normative stakeholders – those who share common interests with the firm, and (4) diffused stakeholders – those who have no formal relationship with the firm (Saxton et al, 2021). Within this typology, social media users fall within the category of diffused stakeholders who are capable of affecting firm behaviour. 8 Within this growing category of diffused stakeholders, the often-positive relationship between profit and stakeholder satisfaction is disrupted (Jia & Zhang, 2014, Freeman et al, 2018, Clark & Crawford, 2012, de Bakker & den Hond, 2008). Stakeholders on social media have expansive and divergent interests (Barnett et al, 2018); a feature that makes them difficult to satisfy in service of better performance and sustainable growth (Freeman, 2010). Thus, a key question that confronts modern firms is how seriously should they take concerns of stakeholders on social media? And to what extent are these stakeholders capable of hitting them where it hurts (i.e., on value)? Second, the widespread use of social media raises new questions about the relationship between the news media, corporate reputation, legitimacy, and stock price. Legitimacy theory states that firm behaviour is shaped by current societal boundaries and norms (Deegan, 2002). As companies feel the scrutiny of public pressure, they must act to make sure that external perceptions don’t sour (Pasirayi et al., 2022). Traditionally, the news media (television, print, and radio journalism) has served as a conduit for societal norms and contributed to the (de)legitimization of firms with consequences for stock price (De Bondt & Thaler, 1985; Pérez et al., 2020). More recently, social media has provided an arena that empowers stakeholders to advance agendas through ongoing discourse and exchange about business conduct, allowing the broader public to hold firms accountable for their actions (Lundgaard & Etter, 2023). Two key questions, then, are to what extent does social media play a similar role to traditional media in defining the parameters of legitimate business behaviour and with what consequences for stock price? In previous research, traditional media has been found to shape public sentiment and have a significant impact on a company’s stock price (Chan, 2003, Beaudry and Portier, 2006, Baule and Tallau, 2016, Coyne et al., 2017). The evidence for the impact of social media is far less 9 clear. Extant research sees social media as a venue for ‘everyday talk’ whereby users collectively develop preferences and shared values (Graham et al., 2013; Lundgaard & Etter, 2023). Over time, everyday talk can lead to the emergence of new norms that define the parameters of legitimate business behaviour (de Bakker, 2015). However, the extent to which these new norms affect stock price remains under-researched and unclear. Third, the rise of social media has altered the relationship between activism and stock price. Previous research has established a relationship between offline activism and stock price, whereby activist ‘naming-and-shaming’ campaigns exerted downward pressure on stock price (Bartley and Child, 2011). However, the growing usage of social media as a platform for ‘digital activism’ requires scholars to re-evaluate this relationship. Digital activism can be defined as social activism mediated through digital technologies (George & Leidner, 2019). This definition is broad, encompassing both coordinated social movements and individual autonomous actions. It is the effect of diffuse autonomous action on stock price that is particularly unclear. Contra the logic of collective action that unites traditional activists through a common mobilizing structure, frame, and political opportunity (King, 2008), the logic uniting digital activists is somewhat different. Digital activism is much more diffuse and decentralized, allowing stakeholders to engage with lower participation barriers and frame particular issues in line with their own values. This digital, decentralized, large-scaled, and fluid response has been termed ‘connective action’ insofar as it allows diffuse stakeholders with different agendas to participate in meaningful and significant dialogue with firms (Hodges & Stocking, 2016, Bennett & Segerberg, 2012). While connective action allows activism to spread more widely and quickly than traditional activism, the sheer volume of public commentary can also create an information overload. Some scholars point out that the quantity has clouded the quality and that the ‘cognitive mechanisms that 10 translate voluminous information disclosures into substantive stakeholder actions and, thereafter, changes in firm behaviors have become constraining in the digital age’ (Barnett et al., 2018). Consequently, it remains unclear whether digital activism shares the ability of traditional activism to exert downward pressure on stock price. 2.2.1 Previous Research on Stock Price and Social Media Sentiment To the extent that it has been researched in the past, much of the research identifies a relationship between social media sentiment and stock price. Bollen et al. (2010) find that measurements of collective mood states derived from large-scale Twitter datasets are in fact correlated with the value of the Dow Jones industrial average (DJIA) over time. Further, Smailovic et al. (2013) found that sentiment on Twitter can indicate stock price movements a few days in advance in some blue-chip stocks. Rao & Srivastava (2014) analyzed 4 million tweets, finding that the movement of stock prices and market indices are greatly affected in the short term by Twitter discussions between June 2010 and July 2011. Zhang et al’s (2011) paper concludes that the mood of tweets was negatively correlated with the DJIA, the NASDAQ, and the S&P 500. Aissia & Neffati (2023) arrive at the same conclusion, finding a significant negative relationship between investor sentiment and stock return. Sun et al. (2017) collect data from several microblogs, chat rooms, and web forums, finding that there is a strong correlation between post sentiment and stock prices in China. Importantly, all of the conclusions described thus far have included financial sentiment in their social media datasets. From the findings described above, we can observe this relationship at both market index and firm levels. We also see this relationship observed in both North American and Asian markets. 11 However, the science of social media sentiment and stock price is far from settled and a dissenting narrative exists. Yilmaz et al. (2022) examine the effects of social media activities on stock prices of the energy sector specifically, taking 20 energy stocks from the S&P 500, finding Twitter sentiment to have no significant impact on the returns of the companies. Brown (2012) finds that sentiment found within Twitter messages does not appear to have any correlation with the S&P 500 Index on a daily or weekly basis. Wang et al. (2015) examined approximately 90,000 posts on the financial social media site “Stock Twits” in the US between 2009 and 2014, discovering low correlation between sentiment and stock performance. Nofer & Hinz (2015) examine a sample of 100 million Tweets from users in Germany between 2011 and 2013, exhibiting an insignificant relationship between Twitter sentiment and the German stock market. Finally, Ranco et al. (2015) find a relatively low correlation between social media sentiment and the DJIA index over a 15-month period but find stronger correlations during specifically measured events. 2.2.2 This Study’s Intervention The variety of findings laid out in the preceding section show that there is little consensus within the academic community on whether online sentiment has significant or lasting effects on the value of firms. I make several novel interventions into this field of research. First, there is a lack of industry specific studies or those that take a closer look at the relationship between stakeholder sentiment and stock price within a given subset of industry specific companies. Much of the extant research tracks the impact of social media sentiment on broader market indices. The few that have focused on specific industries employ a literature synthesis approach (Colaço and Simão, 2011). In focusing on the Canadian energy sector, I take a more surgical 12 approach to unpacking the relationship between social media and stock price. Second, I explicitly focus on the effect of non-financial chatter on social media. Removing online rumours about financial opportunities and risks allows us to isolate the effect of public pressure more specifically. This approach thereby allows us to gain better analytic leverage over the broader questions seeded earlier in this section about the role of social media in conditioning societal expectations of firms and the influence of digital activism on stock price. 2.3 Research Design and Data 2.3.1 Variables & Timeframe The overarching goal of this study is to measure the effect of Twitter sentiment on stock price across nine firms in the Canadian energy section. Twitter sentiment (independent variable) is operationalized as the daily average sentiment score of all relevant tweets mentioning a company. Stock price (dependent variable) is operationalized as the daily closing stock price of the firm. Two control variables are used to isolate stock price movement from broader Canadian market and industry movement. To account for the former, Ishares Core S&P/TSX Capped Composite Index Exchange Traded Fund ($XIC.TO) was employed as it replicates the performance of the Standard & Poor’s/Toronto Stock Exchange composite index, accounting for general movements across the Canadian market. To account for the latter, Horizons S&P/TSX Capped Energy Index Exchange Traded Fund ($HXE.TO) was employed to replicate the performance of the Canadian energy industry (Ranco et al, 2015). The timeframe of this research spans two years, from January 2020 to December 2021. This period was chosen due its financial and political volatility within the Canadian oil and gas sector. The Wet’Suwet’en Coastal Gas Link protests occurred during this time, sparking massive 13 dialogue online not only towards the owner of Coastal Gas Link, TC Energy, but the entire oil and gas industry in Canada (Eisenberg, 2022). The conflict highlighted the topics of indigenous sovereignty, human rights, modern colonialism, and corporate authority, receiving international media coverage and criticism (Criminalization of Wet’suwet’en Land Defenders, 2023). This was all in the context of a period that saw the “Global Climate Strike” and “Fridays for Futures” youth movements in full force, calling for total and immediate divestment from fossil fuels (Russel, 2020). Further, the sudden halt of international supply chains due to the COVID-19 pandemic exposed the public to what could happen if there was immediate action on climate change (Rasmussen, 2021). This period is therefore a most-likely case insofar as if one were to expect a relationship between negative Twitter sentiment and stock price, it would most likely occur in this period. 2.3.2 Company Data The companies analyzed in this paper are the 9 largest public Canadian oil and gas firms measured by market capitalization (total value of a firm’s outstanding shares owned by stockholders), represented in Table 1 (Dang et al, 2018). All financial data was obtained from Yahoo! Finance, which included daily data for the opening price, closing price, adjusted closing price, and trading volume of each firm throughout the chosen timeframe. Adjusted closing price was chosen as the final measurement for company value as it better reflects the price of the firm’s stock price after accounting for all applicable splits and dividend distributions (Carl, 2019). Private firms were excluded due to the lack of available public financial data. Company Ticker (TSX) Market Capitalization (CAD) Suncor Energy Inc. SU 55.99 B Pembina Pipeline Corp. PPL 24.89 B 14 Enbridge Inc. ENB 99.35 B ARC Resources Ltd ARX 12.00 B Cenovus Energy Inc. CVE 40.25 B Canadian Natural Resources Ltd. CNQ 89.09 B Imperial Oil Ltd. IMO 41.51 B Ovintiv Inc. (Encana) OVV 15.26 B TC Energy Corp. TRP 53.21 B Table 1. 2.3.3 Twitter Data To measure the concept of digital discourse, the social media platform Twitter (or ‘X’ as it is now termed) was selected due to its popular use for public and corporate commentary (Tsai & Wang, 2021). In order to scrape historical Twitter data, I used a third-party service named “Tweetbinder”. Tweetbinder is a company that allows users to enter parameters such as timeframe, hashtags, and usernames, generating a CSV file containing all matching tweets. For this research, the search parameters require a mention of any of the company names or their corresponding Twitter handles. I eliminated any tweets containing the “$” character, as this is a common symbol used to identify stock tickers. This was done to eliminate any financial chatter, as the focus of this research is social sentiment rather than financial sentiment. The total number of tweets (N) in the given timeframe was 196,215. Significant data cleaning was required to retain appropriate Twitter data.1 This cleaning also included removing all tweets from company Twitter accounts. The total number of tweets for each company after data cleaning is shown in Table 2. 1 Originally the data set consisted of 10 companies, however the twitter data obtained for Tourmaline Oil Corporation contained overwhelming erroneous tweets. These mostly referred to the crystal “Tourmaline”, leaving the relevant sample size insufficient to analyze. 15 Company Number of Tweets (N) Suncor Energy Inc. 18,995 Pembina Pipeline Corp. 644 Enbridge Inc. 55,295 ARC Resources Ltd 610 Cenovus Energy Inc. 4,468 Canadian Natural Resources Ltd. 1,964 Imperial Oil Ltd. 6,931 Ovintiv Inc. (Encana) 655 TC Energy Corp. 18,683 Table 2. 2.3.4 Sentiment Analysis The Valence Aware Dictionary Sentiment Reasoner (VADER) package within the statistical analysis software “R” is used to code each tweet. With the goal of transforming tweet text into a sentiment score that is representative of the tweet’s positivity or negativity, VADER is chosen due to its lexicon and rule-based approach specifically made for social media text (Pano & Kashef, 2020). The VADER package transforms each tweet into a vector of sentiment scores with negative, neutral, positive, and compound polarities. The aforementioned scores are then standardized to be between 0 and 1, where the compound polarity is an aggregate measure of overall sentiment in that body of text (Hutto & Gilbert, 2014). Once all of the company datasets are run through the VADER analysis, each tweet then has an associated sentiment score between -1 and 1. Since there are often several tweets on one day, an average daily sentiment score is created for the sake of compatibility with the stock price time series. This is done by taking the mean of all scores on a given day (Tsai & Wang, 2021, Yaqub et al., 2017). Another issue with regards to compatibility of our stock price time-series is the closure of markets on weekends and holidays. To address this, a forward fill function (or “last observation carried forward”) is used in 16 R to carry forward the previous stock price through weekends and holidays (Hendahewa et al., 2021). Thus, if the adjusted closing price on a Friday was $20, that same price would be assigned to the adjusted closing prices of the following Saturday and Sunday. An example of the adjusted closing stock price and daily sentiment score time series’ is visualized in table 3 below for the firm with the most Tweet observations: Enbridge Inc. Figure 1: Sentiment score and adjusted closing price of Enbridge Inc. across 2020 & 2021 17 Figure 2. Figure 3. Figure 4. Figure 5. 18 Figure 6. Figure 7. Figure 8. Figure 9. 19 2.3.5 Modelling and Analysis The relationship between twitter sentiment and stock price of the chosen firms is explored via Granger causality analysis, a statistical technique used to test the forecasting abilities of one time series for the subsequent values of another series. This involves using F-tests to examine whether lagged information on a variable Y provides any statistically significant information about a variable X in the presence of lagged X (Niburski & Niburski, 2020, Li et al, 2019). This technique is chosen due its prevalent use in similar research (Rao & Srivstava, 2014, Bollen et al, 2011, Aissia & Naffati, 2023, Smailovic et al, 2013, Sun et al, 2017). Before running a Granger causality test on each company’s financial and sentiment time-series, unit root tests for all time-series are conducted to ensure stationarity among all data. In this case the Augmented Dickey-Fuller test is used to test for non-stationarity, revealing the stock price time series to be non-stationary and the social sentiment time-series to be stationary (p-value of ADF tests are greater than 0.05) (Chiou-Wei et al., 2008, Li et al., 2019). In order to address the non-stationarity of the stock price time series, the data sets are transformed using the first-difference approach to obtain stationary time-series (McCabe & Tremayne, 1995). After differencing all nine daily stock price datasets, the p-value of the ADF tests are all equal to 0.01, allowing me to reject the alternative of non-stationarity and move on to granger testing. Subsequently, a granger causality test is run on each company, allowing me to see the relationship between social media sentiment and stock price movement across a large sample size of the Canadian Energy industry. Two control variables are included in the regressions to control for overall market movement and Canadian industry-specific movement as described in section 4.1. Introducing control variables into a bivariate Granger causality test creates a complexity issue as it then becomes a multivariate Granger causality test. To avoid this, I regress 20 the dependent variable on the aforementioned controls, saving the residuals, and then using those residuals as the dependent variable for the Granger causality analyses. These residuals are effectively the day-over-day change in each firm’s stock price, with the overall market and energy sector market trends removed. Granger causality analyses require a lag to be chosen for the effect of one time-series on the other. Although there are methods such as the “AIC” (Aikake Information Criterion) to determine the optimal lag use, this study uses a maximum lag of 4 days. This lag length has been selected due to the consistent use of these lags in similar studies (Cao, 2017, Bollen et al, 2011, Smailovic et al, 2013). For each analysis, lags are run from 1 day through 4 days to ensure robustness. 2.3.6 Limitations The findings of this study may not be applicable to other industries, regions, or timeframes. For example, completing the same analysis within the renewable energy sector of Germany may yield very different results. The analysis relies on Twitter data obtained from Tweetbinder, which may not capture all relevant Tweets regarding the sample companies. Additionally, the use of the VADER sentiment package introduces potential biases and limitations inherent to the rules and structuring of the package. While efforts are made to control for broader market movements and industry-specific factors using exchange-traded funds, stock price movements are complex, and the control variables might not perfectly isolate the effects of online sentiment on company value. Further, the relationship quite possibly could be confounded by other unknown variables. Granger causality analysis provides insights into potential relationships between variables, but it does not establish causation definitively. Other factors not accounted for in this study could 21 influence both digital discourse and company value, leading to spurious correlations. Further, traditional bivariate Granger Causality tests lose forecasting performance when nonlinearity is introduced (Hiemstra & Jones, 1994). The lag selection for the Granger causality analyses is based on previous studies, but different lag lengths could yield different results. The optimal lag length may vary, and the use of an information criterion method may be favorable. Despite efforts to clean the Twitter data, some irrelevant or noisy data may still have influenced the sentiment analysis results. The study focuses on the nine largest public Canadian oil and gas firms by market capitalization, potentially excluding smaller firms or private companies that could exhibit different relationships between digital discourse and company value. Lastly, the oil and gas industry is heavily influenced by macroeconomic factors such as commodity prices that might be more disconnected from public opinion (Ramos & Veiga, 2011). Social media sentiment could potentially have a larger effect in other industries with less dependence on external market fluctuations. 2.4 Empirical Results 2.4.1 Granger Causality Tests After running Granger causality tests on the 9 companies in the sample, the findings show no statistically significant variation between Twitter sentiment and stock price on any of the four lags. I am unable to reject the null hypothesis for all nine firms. In lay terms, negative social media sentiment does not appear to exert downward pressure on stock price in our sample. However, there are a few reasons to interpret this finding cautiously. 22 Granger Causality Tests p-values Company Lag = 1 Lag = 2 Lag = 3 Lag = 4 Suncor Energy Inc. 0.8951 0.7012 0.868 0.5695 Pembina Pipeline Corp. 0.8756 0.286 0.4684 0.6152 Enbridge Inc. 0.6422 0.7304 0.8314 0.9163 ARC Resources Ltd 0.7439 0.6938 0.8471 0.7078 Cenovus Energy Inc. 0.6032 0.4483 0.559 0.4568 Canadian Natural Resources Ltd. 0.8627 0.892 0.6899 0.5539 Imperial Oil Ltd. 0.1748 0.3344 0.4887 0.6837 Ovintiv Inc. (Encana) 0.4148 0.4709 0.6414 0.438 TC Energy Corp. 0.8408 0.9424 0.9593 0.9339 Table 3:* Significance threshold of 5% 2.4.2 Potential for Coding Error In order to understand the scoring of the tweets and calculation of the daily sentiment score, some examples are provided below in table 5, representing a spectrum of tweets based on their sentiment. Upon initial observation, there are some issues with the sentiment scoring. Table 5 has two examples of negative, neutral and positive tweets. Tweet no. 1 & 2 both have objectively negative sentiment, warranting their significantly negative sentiment scores. Our neutral examples provide slightly more dissonance (no. 3 & 4). Tweet no. 3 simply states a length of time before asking others to re-tweet the message if they believe “Enbridge line 5 needs to go”. Although the language in this tweet is fairly neutral, the intention is to draw attention to stopping Enbridge’s line 5 extension – an objectively counter-Enbridge sentiment. If we then consider tweet no. 4, we see an objectively neutral tweets, simply stating that a project will cost more than anticipated. Lastly, the positively coded tweets (no. 5 & 6) show some error in the sentiment coding. Tweet no. 5 is objectively positive, thanking Enbridge for its economic contributions to 23 Alberta, Canada. Tweet no. 6 has a similar sentiment score, but contains anti-Enbridge sentiment, characterized by its use of “#stopline3 before Enbridge wreaks havoc on us all”. The inconsistency of the VADER sentiment package shown in table 5 is a limitation in the accuracy of adjudicating sentiment of tweets correctly. The exact amount of error is unknown. No. Tweet Sentiment Score Sentiment 1. @kellybarnhill @Enbridge But the Mayor/town fought Enbridge for 4.5 years and seems to be committed. The complete 180 from fighting for the town to draconian gag orders/signing rights away tells us differently. I fucking hate this. I hate it so much. We are heartbroken but will still fight. 2/2 -0.983 Negative 2. @LtGovFlanagan HEY PEGGY! WAKE THE FUCK UP! Stop selling out the people of Minnesota! STOP THE Enbridge pipeline! Why are you so determined to destroy our planet? You are a horrible democrat and should be ASHAMED. SHAME ON YOU! #SHAME #SHAMEONYOU #Stopenbridge #nopipeline -0.981 Negative 3. 89 more days. Re-tweet if you think .@Enbridge #Line5 needs to go. #May12 0 Neutral 4. Enbridge says Line 3 replacement will cost $1 billion more than expected https://t.co/1zqAA4EDXJ https://t.co/5nmDiV1CcJ 0 Neutral 5. @jkenney @Enbridge Nice to have some good news - thanks for celebrating something positive. Hope we have more wins for Alberta economy/lives. Keep up the good work on behalf of Albertans. 0.977 Positive 6. okay. yes. Thank you! that‚Äôs good. We agree & appreciate that stance. Also btw @UnRestrictMN has excellent points that go beyond our simply not emulating Texas. We can do better. Also please #StopLine3before Enbridge wreks havok on us all. Pretty please? Thank you! #mndfl https://t.co/DGzgA69dmg 0.983 Positive Table 4. By taking a closer look at the data, we can examine what events were associated with particular days leading to negative daily sentiment scores. For example, in the Enbridge Inc. dataset, July 1st of 2021 had one of the most negative daily sentiment scores (-0.313). This daily 24 score was created by averaging the sentiment score of 41 individual tweets. A sample of ten of these tweets is found in table 5 – with the majority of tweets focusing on the “#StopLine3” hashtag. This hashtag refers to Enbridge’s Line 3 pipeline expansion that would transport petroleum products from Alberta, Canada to Wisconsin, US. @Enbridge when are you going to stop being too üêì üí© to recognize you're trespassing on protected Indigenous lands? What's it like to ignore US treaties for corporate greed?#stopline3 @duluthnews I'm still trying to clear my throat. #StopLine3 tell @Enbridge and @RepPeteStauber take their toxic tar sands and shove them up their pipeline! . @Enbridge permit included protection for protestors that they @DHSgov & Local police are now violating #StopLine3 https://t.co/dWyJ8hY6Bg Hey @EPA @Enbridge is trying to destroy the habitat of the endangered Higgins mussels. #StopLine3 https://t.co/O9a2SC0gea .@Enbridge privatized police force are now illegally detaining and arresting American citizens on their own land #StopLine3 https://t.co/MRtDGaMPmi In April The Canadian corporation @Enbridge had purchased HALF A MILLION DOLLARS worth of harassment of indigenous people and water rights protectors including disrupting religious ceremonies#StopLine3 https://t.co/guPhQIGcs2 @Enbridge leave indigenous land and people alone. It‚Äôs not a matter of if the pipeline you are building will explode it will be when. Greedy bastards. #stopline3 Will the @EPA or @InteriorDept do a thing about the violations a foreign co - @Enbridge @enbridgegas - have committed? Bribery of law enforcement endangered species wetlands‚Ä¶ #waterislife #EndTheTarSands https://t.co/SPQy9XgUVe @MNCommerce determined @Enbridge Line 3 will add as much GHGs as FIFTY new coal plants causing $287 BILLION in climate damage by 2050. Stop your WHINING and #STOPLINE3 instead!!! @MnPCA @mndnr @POTUS @SecDebHaaland @USACEHQ @Gina_McCarthy https://t.co/8H4BzzFROq Lampsilis Higginsii is an endangered creature that Enbridge threatens@EPA@Enbridge@OurOcean@JoeBiden@KamalaHarris https://t.co/iEhv2D7A2w Table 5. As one of the lowest daily sentiment scores in the Enbridge dataset, I expected there to be some reflection of this sentiment in the stock price during the same period. By examining table 3 (section 4.4), we see no obvious similarities at first glance in stock price movement and sentiment score during this period. This seems to suggest that stock prices are insulated from even the nadirs of Twitter sentiment. 25 2.4.3 Industry-Level Application In order to extend this analysis beyond the firm-level, I replicated our analysis at the industry level to see if the results aligned with the previous literature mentioned in section 2.2. This was done by compiling each company’s daily sentiment score into a new time-series. That is, for each day, all nine companies’ sentiment scores were averaged to create a new daily industry sentiment score. Thus, the new independent variable is industry daily sentiment score and the new dependent variable is the Horizons S&P/TSX Capped Energy Index Exchange Traded Fund ($HXE.TO). Market movements are accounted for using the same methods as the initial analysis. After controlling for the effect of market movement (XIC.TO) on HXE.TO, the new time series (consisting of the residuals of HXE controlled for movements of XIC) is then made stationary via the first differencing technique before running Granger causality tests at lags 1 through 4 days. Granger Causality Tests p-values ETF Lag = 1 Lag = 2 Lag = 3 Lag = 4 HXE.TO 0.4965 0.6121 0.4081 0.2463 Table 6. * Significance threshold of 5% As shown in table 6, I can again conclude that there is no statistically significant variation between sentiment on Twitter and industry value. All lags showed p-values greater than 0.05, leaving us unable to reject the null hypothesis for the industry ETF. 2.5 Discussion Our findings show that social media sentiment has no significant forecasting ability on the stock price of firms in the Canadian energy sector. This finding offers a novel contribution to 26 the literature by contradicting studies that observe a relationship at the market-level and adding new evidence to scholarship skeptical of social media’s effect on stock price. 2.5.1 Social Media Stakeholders Exert Limited Influence Over Investors In section 2, I reviewed the voluminous literature examining how the news media and digital activists can set the parameters for appropriate firm behaviour, and consequently, have influence over stock price. Entire cottage industries have arisen to monitor reputational risk for firms from bad press (i.e. the RepRisk service). However, the evidence presented in this study implies that social media differs in important ways from traditional media and activism. As Barnett et al. (2020) argue, social media confronts investors and firms with a cacophony of often conflicting concerns and demands. The veracity of many of these concerns is difficult to ascertain and the sheer volume of information makes it easy for investors to selectively ignore some of the white noise. Measures like sentiment analysis only tell us whether the language of a social media post is positive, negative, or neutral. It does not tell us to what extent there are overlapping concerns amongst social media users or the intensity of their grievances. Consequently, it may be the case that negative Twitter sentiment reflects a spectrum of divergent concerns of dubious veracity – none of which would lead an investor to see a credible risk or withdraw their money. Our analysis supports Barnett et al.’s (2020) interpretation of the limitations of social media for influencing firm behaviour, however I would extend these conclusions to investor behaviour as well. 27 2.5.2 Financial Chatter on Social Media Differs from Other Types of Chatter A large amount of research in this field comes from datasets that include financial sentiment on social media platforms (Coyne et al, 2017, Oh & Sheng, 2011, Zhang et al, 2011, Sun et al, 2017, Smailovic et al, 2013, Wu et al, 2022, Koukaras et al, 2022). These studies included data from either finance-specific social media platforms, or Twitter data that includes or specifically focuses on financial posts. In this study, I purposefully screened out tweets that were focused on the stock price of firms in order to measure non-financial sentiment. A key implication, and a reason that our findings differ to those of previous studies, is that investors may be less influenced by non-financial sentiment. This would suggest that general digital discourse – or “everyday talk” – has little bearing on investor decisions within the 2-year time frame chosen for this study (Lundgaard and Etter, 2023). This is an important conceptual distinction since it implies that not all social media sentiment should be taken equally. 2.5.3 Social Media Sentiment Impacts Firms’ Stock Prices Differently than Markets This research focuses on measuring the effects of social media sentiment on stock price at a firm level, specific to a particular industry. By contrast, most of the previous literature focuses on market level financial data (i.e., NASDAQ-100, S&P 500) or “Blue Chip” companies with dominant market share (i.e., Microsoft, Google, Apple) (Rao & Srivastava, 2014, Zhang et al, 2011, Koukaras et al, 2022). This begs the question of whether sentiment toward individual firms may be less influential than sentiment about the overall industry or market. Investors and algorithms parsing social media may pay more attention to overarching themes rather than single companies. Yet, as shown in section 4.3, this was not the case for the Canadian oil and gas industry. Even at an industry-wide level across the chosen timeframe, social media sentiment 28 showed no significant forecasting ability on industry value as measured by the chosen exchange traded fund. This conclusion aligns with Yilmaz et al.’s (2022) lack of findings in the relationship between Twitter sentiments and industry value based on the analysis of 20 S&P 500 firms in the energy industry. With that said, it is worth considering how oil and gas firms may differ empirically from other types of firms. Oil and gas firms may be insulated from public pressure by low expectations for ethical behaviour. Consequently, negative sentiment toward oil and gas companies may be somewhat normalized, resulting in diminished market response to social media discourse compared to industries with higher ethical standards. In such cases, the impact of social media sentiment on stock prices within the oil and gas sector might be dampened, as investors and market participants factor existing negative perceptions into their decisions to some extent (Panwar et al., 2023). 2.6 Conclusions and Future Research This study examined the relationship between social media sentiment and stock price for nine major Canadian oil and gas companies from 2020-2021. Granger causality tests found no significant predictive ability of social media sentiment on stock prices across any of the firms in the sample. This contradicts previous research that has found social media sentiment to be significantly correlated with stock market behavior. Our findings underscore the important distinctions that separate digital from non-digital activities. Principally, while social media has expanded the scope of stakeholders who can directly influence a firm’s value, it has not necessarily rendered these stakeholders more influential. 29 I make three important contributions to the literature. First, I find that negative social media sentiment has little bearing on investor decisions regarding specific firms, at least within the context of the Canadian energy sector. Part of the reason for this may be that negative sentiment can take a variety of forms and may not present any overarching concerns for investors. Amidst the cacophony of white noise on social media, investors may choose to either ignore the deluge of information or be confused by the litany of differing values and perspectives it represents. Second, I highlight the need to conceptually disaggregate financial sentiment from other types of social media sentiment. There is a distinction between investors reacting to rumors and news about future financial projections and investors reacting to broader changes in the social media discourse surrounding a firm. Treating this discourse as distinct has important implications for how scholars think about legitimacy as conditioning market behaviour. Third, I emphasize the need to treat firms and market indices as conceptually distinct. Whereas markets may react to overarching negative sentiment that exists in the zeitgeist, it is not clear that firms behave the same way in response to more acute negative sentiment involving their social media accounts. On this point, I also emphasize the need to separately theorize different industries, since expectations of corporate conduct may vary by sector, and thus, so too may investor decisions about when or when not to pay attention to social media data. This study opens up a number of areas for future inquiry. First, scholarship on social media sentiment needs to more broadly examine the nature and theme of negative sentiment. In addition to broader distinctions between positive and negative, scholars should strive to unpack themes and trends of negative social media posts. This will lend insight into whether negative sentiment clustered around a particular issue or event is more influential than diffuse negative 30 sentiment. Second, there is a need for cross-sectoral comparison to understand how different industries respond to social media sentiment. It may be that investors in consumer-facing firms are more sensitive to social media sentiment than investors in the energy sector. Third, future research might vary the temporal scale by focusing on particular events that may have that created significant changes in sentiment score. It would be valuable to statistically analyze these events – reducing the timeframe to a short time before and after the specific event to see if there is any greater statistical significance. Overall, this study augments our understanding of how digital discourse translates affects stock price within specific contexts. While social media broadens the array of stakeholder voices, its influence on investor assessments of stock price remain indeterminate. More work is needed to disentangle these complex relationships. 31 Chapter 3: Under what conditions do Tweets matter? 3.1 Introduction Under what conditions does digital discourse affect company value in the Canadian energy industry? This study builds on the findings of chapter one, delving further into the relationship of social media sentiment and stock price. It draws from similar literature outlined in the previous chapter. Although no significance is found in our initial study, scholars have found significant relationships between similar variables when focusing on shorter timeframes surrounding particularly catalytic events (Hamraoui & Boubaker, 2022, Rao & Srivastava, 2014, Mao et al, 2013, Teti et al., 2019, Nisar & Yeung, 2018, Gabrovsek et al, 2017). The prevalence of these conclusions sparked my curiosity to examine the effects of Twitter sentiment on stock price within the Canadian oil and gas sector at shorter lengths. In other words, now that we have found that the impacts of digital discourse on stock price are contingent on other factors, under what conditions are they important to a company’s share value? This chapter explores whether particular news events may have caused a spike in Twitter activity with regards to the company under scrutiny. Although determining causality is difficult, it is important to examine the relationship between traditional news media and social media. Is social media simply reacting to traditional news articles? Or does social media drive the traditional news cycle? This is an important question in determining the causal mechanism of what drives changes to stock prices. Previous literature has found evidence to support both side of this debate, with the findings summarized here. 32 3.1.1 Evidence supporting the impact of social media on stock prices The efficient market hypothesis states that new information may change market expectations and thus move a firm’s stock price (Fama, 1970). Since the inception of this theory by Eugene Fama in 1965, the way news is spread has changed significantly. How individuals consume media has shifted drastically in the past decade alone. An increasing body of research indicates that social media is overtaking print and televised media as a primary news source, particularly among younger demographics (Shearer 2018; Suciu 2022). Traditional media platforms have been criticized for falling behind the rapid news cycle of social media and engaging in superficial and cost-cutting journalism by focusing on social media trends (Broersma and Graham 2012). With regard to social media’s predictive abilities on stock movement, measuring the sentiment from social media exclusively proved a successful indicator of stock movement across 18 stocks over a one-year span (Nguyen et al., 2015). Further, traditional news media has been found to only reflect the development of stock movement rather than shape it (Sheufele et al, 2011). Scholars have also found a lack of correlation when examining non-financial traditional news sources and their relationship with stock price movement (Smith & O’Hare, 2022). Elsewhere, Twitter sentiment analysis has shown a high percentage prediction rate with exchange market trends, with the authors concluding that the efficient market hypothesis (EMH) can hold true for social media platforms (Komariah et al., 2016, Papaioannou et al., 2013). This corpus of research leads us to our first hypothesis: H1: Negative Twitter sentiment precedes negative media coverage and is sufficient to adversely impact stock price. 33 3.1.2 Evidence supporting traditional news media There is an opposing body of research suggesting that traditional news media still reigns supreme (van der Ven, 2024). In this theory, social media trails behind traditional journalistic news coverage and functions primarily as an amplification tool rather than a direct source of influence. To some degree, this observation indicates a notable parallel with prior research on social movements, where newspaper coverage has frequently been identified as an influential source (Rohlinger and Earl 2017). More specifically to the relationship of news and stock price, traditional news coverage has long been touted as a driver of stock price (Chan, 2003). Over a two-year span, traditional media coverage has been found to have a predictive effect on stock price fluctuation when including sentiment and emotionality (Strycharz et al., 2017). On a larger scale, using a dataset of 24 million news articles referring to 206 major stocks in the US S&P stock index, particular themes were found to impact and influence the stock market (Hisano et al, 2013). At a four-week timeframe, sentiment analysis of traditional news sources was found to predict stock price trends at a percentage of 67% across 15 companies (Chowdhury et al., 2014). With regards to ESG controversies, news related to ESG has been found to have a predictive relationship with market reactions (Serafeim & Yoon, 2023). On the same note, ESG related news has been found to only have a significant impact when negative, yielding no impact when positive (Capelle-Blancard & Petit, 2019). Building on the conclusions described in this section, the second hypothesis is as follows: H2: Negative Twitter sentiment follows negative media coverage and is an intervening variable in adversely impacting stock price. 34 3.1.3 Evidence supporting specific conditions where media events relate to firm value Looking beyond media in general having an impact on firm value, several scholars have found that there are specific conditions required for media events to have a real impact. For example, ESG controversies in particular have been found to only impact firm value when media reach is high (Mendiratta et la., 2023). The same study found that media coverage can enhance firm value when the severity of the controversy is high, leading to ‘sensemaking intensification’ of the event. Another study found that corporate social responsibility news reports positively affect firm value, depending on the ‘word of mouth’ level surrounding the event (Seok et al., 2020). Further, scholars have found that negative ESG-related news events are correlated with a drop in firm value, but this drop is mitigated depending on the particular sector’s reputation (Capelle-Blancard & Petit, 2017). According to the array of conditions described in this section where media events correlate with firm value, our third hypothesis is as follows: H3: Negative Twitter sentiment is only impactful on stock price when specific variables are present. By quantitatively examining the themes of the articles published about the companies in this study, this paper aims to disentangle the ‘chicken-and-egg relationship’ of traditional news sources and social media. 3.2 Research Design and Data 3.2.1 Variables and Timeframe Digital discourse (independent variable) is measured by public sentiment on Twitter. Company value (dependent variable) is measured by the closing stock prices of the firm. The measurements and data used in this study are similar to those used in chapter 1, however the time 35 frame is shortened to analyses of one month and two weeks, respectively. Additionally, I introduce a qualitative examination of news articles related to the firms of interest and compare them to each company’s peak Twitter volume date. These articles are taken from Proquest’s “Canadian New stream” database and Lexis Nexis’ “Nexus Uni” database, which keep a comprehensive collection of Canadian and American news articles respectively, dating back to the 1970’s. As mentioned, there are two timeframes used for each of the nine companies in this analysis. These timeframes are chosen based on the date within the company dataset that has the highest volume of Twitter activity. The first is a one-month time frame, including fifteen days on each side of the peak Twitter volume date. The second is a two-week timeframe with the peak volume date centered as well. For example, within Enbridge’s entire data set (including tweets from January 2020 to December 2021), the day with the highest Twitter volume is May 12th, 2021. This day consists of 701 tweets referring to Enbridge Inc. Therefore, the thirty-day time frame includes tweets from April 27th, 2021 to May 27th, 2021. 3.2.2 Twitter Data The method of Twitter data collection used in this paper is the same as chapter one of this thesis. The Twitter scraping software “Tweetbinder” is used by inputting particular parameters relevant to the respective companies in the dataset. The volume distribution of Tweets across the original time frame of January 2020 to December 2021 can be seen across all companies in figures one through nine. The Twitter data is cleaned to remove tweets referring to financial news and stock chatter. This is done by filtering out tweets that contain stock ticker symbols and words beginning with “$” (i.e., $ENB for Enbridge). Because of the filtering of financial chatter, I presume that the volume peaks might indicate political or social controversy towards the 36 companies in question rather than reaction to earnings reports or activity of the company’s stock price as many other studies find (Smailovic et al, 2013). Figure 1 Figure 2 37 Figure 5 Figure 6 38 Figure 9 Company Peak Twitter Volume Date No. of Tweets Suncor Energy Inc. October 2nd, 2020 398 Pembina Pipeline Corp. June 17th, 2021 52 Enbridge Inc. May 12th, 2021 701 ARC Resources Ltd February 11th, 2021 52 Cenovus Energy Inc. October 25th, 2020 385 Canadian Natural Resources Ltd. May 7th, 2021 72 Imperial Oil Ltd. December 28th, 2021 368 Ovintiv Inc. (Encana) March 9th, 2020 28 TC Energy Corp. March 31st, 2020 1596 Table 1 3.2.3 Qualitative Analysis Since the relationship between traditional news media and social media is not clear, this paper will examine both types of media with the hopes of contributing to this ongoing debate in the 39 context of the Canadian energy industry. In order to analyse the events associated with each company’s peak Twitter volume, I used the Proquest database “Canadian Newsstream” to examine Canadian news sources. For American media coverage, I use the Lexis Nexis database titled “Nexis Uni”. These databases provide full-text format articles from all major news outlets dating back to the 1970’s, sortable by day, month, and year. In order to find news articles corresponding to each company on its peak Twitter volume date, I simply enter the company name as a search term and then enter the specific date of interest. In order to capture a better idea of traditional news sentiment, I broaden the range of news articles to one day before and one day after the peak Twitter volume date. Further, I filter the search results to only include “Blogs, Podcasts, and Websites” which include digital versions of printed news articles. Finally, I filter to only include national level articles, with the aim of obtaining the most influential news sources. After obtaining the search results, I examine the first ten articles from both Canadian News stream and Nexis Uni to check for major events. I then code the article content to the RepRisk ESG controversy classification schema (Li & Wu, 2020). RepRisk is a platform that measures a firm’s reputational risk starting from the news and information the media provides. This platform is chosen due to it being the only provider that systematically analyzes adverse ESG and business data that may have reputational or financial impacts on a company (Dai et al., 2021). The core research scope is comprised of 28 ESG issues selected in accordance with key international standards such as the World Bank Group Environmental, Health, and Safety Guidelines, the IFC performance standards, the Equator Principles, and the OECD Guidelines for Multinational Enterprises (RepRisk AG, 2021). Since I was unable to attain access to the service due to lack of funding, I chose to manually code the articles according to the RepRisk schema (Beccheti et al., 2023). The schema categorizes issue 40 into three separate issues – environmental, social, and governance issues. Each category is comprised of several different sub-issues. For example, “Climate change, GHG emissions, and global pollution” fall under the broader category of environmental issues. By coding each company’s articles into the RepRisk news classification schema, I can more clearly define the issues that may play a part in stock price movement. The RepRisk coding for each company is shown in table 4. 3.2.4 Limitations This study reduces the given timeframes from the initial two-year period down to smaller increments. These smaller increments are chosen according to the greatest amount of Twitter activity within the company dataset on a given day, which may introduce selection bias. The chosen timeframes, although centered around notable Twitter activity, may not fully capture the nuanced dynamics of public discourse and its impact on stock market behaviors. The use of Twitter sentiment as a proxy for digital discourse may not fully capture the diversity of public opinions or sentiments towards the selected companies. Twitter users represent a specific demographic and may not be representative of the broader population. The reliance on Twitter data introduces potential biases and challenges in data accuracy. Despite efforts to filter out financial chatter, the methodology for identifying and excluding irrelevant tweets may not eliminate all noise from the dataset. The accuracy and reliability of the VADER sentiment analysis tool used to gauge public sentiment on Twitter could also impact the validity of the findings. The study's design, while identifying the forecasting abilities between digital discourse and company value, does not establish causation. Other external factors and variables not 41 accounted for in this analysis may influence both Twitter activity and stock prices. Therefore, attributing changes in company value solely to shifts in public sentiment on Twitter may oversimplify a complex relationship. Further, the qualitative analysis conducted using the Proquest database "Canadian Newsstream" may be subject to limitations inherent in the scope and availability of archived news articles. The database's coverage and accessibility may not encompass all relevant news sources or adequately represent the full spectrum of events impacting company perceptions and market dynamics. The findings of this study may be specific to the context of the selected companies and the Canadian market. Extrapolating these findings to different industries, markets, or geographical regions should be done cautiously, considering the unique socio-economic and cultural factors that shape digital discourse and investor behavior. 3.3 Empirical Results 3.3.1 Granger Causality Tests After running Granger causality tests on all 9 companies in the sample at 1-month, and two-week time frames, I find two companies (Enbridge and TC Energy) with statistical significance at the 1-month timeframe. Only Enbridge remains significant at the two-week frame. The rest of the companies show p-values above 0.1, failing to meet both significance thresholds (5% and 10%). Thus, I conclude that there are no significant forecasting abilities of digital discourse on these company’s stock prices. These conclusions leave us with the question of what differentiates Enbridge and TC Energy from the rest of these companies? Granger Causality Tests – 1-month period with centered peak volume date p-values 42 Company Lag = 1 Lag = 2 Lag = 3 Lag = 4 Suncor Energy Inc. 0.6638 0.8923 0.5206 0.1655 Pembina Pipeline Corp. 0.4674 0.641 0.7791 0.4152 Enbridge Inc. 0.912 0.0493* 0.1548 0.3395 ARC Resources Ltd 0.5459 0.9135 0.9824 0.9596 Cenovus Energy Inc. 0.865 0.5678 0.7652 0.8584 Canadian Natural Resources Ltd. 0.804 0.9212 0.8679 0.3718 Imperial Oil Ltd. 0.1465 0.3328 0.5522 0.5375 Ovintiv Inc. (Encana) 0.4936 0.5999 0.7291 0.9007 TC Energy Corp. 0.03894* 0.06098** 0.02728* 0.03652* Table 2: * Significance threshold of 5%, ** Significance threshold of 10% Granger Causality Tests – 2-week period with centered peak volume date p-values Company Lag = 1 Lag = 2 Lag = 3 Lag = 4 Suncor Energy Inc. 0.1988 0.3018 0.4173 0.4643 Pembina Pipeline Corp. 0.8841 0.9639 0.9944 0.5077 Enbridge Inc. 0.6765 0.6879 0.7031 0.01504* ARC Resources Ltd 0.5754 0.8074 0.7134 0.6926 Cenovus Energy Inc. 0.6471 0.6758 0.1548 0.1676 Canadian Natural Resources Ltd. 0.8252 0.8032 0.9104 0.4359 Imperial Oil Ltd. 0.1465 0.3328 0.5522 0.5375 Ovintiv Inc. (Encana) 0.8251 0.2322 0.1999 0.384 TC Energy Corp. 0.1309 0.6701 0.2895 0.5941 Table 3: * Significance threshold of 5% 3.3.2 Enbridge Background Enbridge Inc. shows statistical significance at one lag in both analyses. That is, regardless of the time frame chosen, digital discourse about Enbridge has significant forecasting abilities on the 43 company’s stock price at a given lag (2-day lag for one-month analysis, four-day lag for two-week analysis). In November 2020, the governor of Michigan Gretchen Whitmer condemned the operations of the Enbridge Line 5 pipeline on Michigan public land. This was due to several controversies related to environmental safety and its partial location running through the Straights of Mackinac in Lake Michigan and Lake Huron (McCarten, 2021). The governor later threatened to seize the profits of the pipeline if Enbridge continued its operations past May 12th, 2021. On the given deadline date, numerous publications were released stating that Enbridge refused to comply with the government of Michigan’s orders (Morgan, 2021, Pingue & Saikali, 2021, Varcoe, 2021) Line 5 has been a consistent source of physical protesting, with a strong opposition presence of First Nation and environmental NGO’s advocating for its cease of operations (Williams, 2021). This oppositional sentiment was echoed on social media platforms such as Twitter, causing a spike in public commentary on the platform as shown in table 4. 3.3.3 TC Energy Background TC Energy shows statistical significance at the one-month analyses, with significance at every lag. On March 31st, 2020, TC Energy Corporation declared that is would proceed with the construction of the Keystone XL pipeline, having secured a $1 billion investment from the government of Alberta. This announcement came just days after the government of Alberta announced the layoffs of tens of thousands of Albertan education professionals (Cryderman, 2020). The pipeline, which would increase the amount of crude oil transported from Alberta to Texas, was riddled with issues from its proposal in 2008 to the announcement of its construction 44 in 2020. Although it would eventually be cancelled by the US government in 2021, the very conversation of its construction drew heated opposition from environmental groups, landowners, First Nations, and small landowners (Denchak, 2022). This opposition came in the form of physical protest and digital activism throughout the pipeline’s era of conception. Company: TC Energy Enbridge Imperial ARC Resources Suncor Ovintiv Cenovus Canadian Natural Pembina Issue Type RepRisk Issue Environmental Issues Animal mistreatment 0 0 0 0 0 0 0 Climate change, GHG emissions, and global pollution 6 8 0 0 0 0 0 0 0 Impacts on landscapes, ecosystems, and biodiversity 6 10 0 0 0 0 0 0 0 Local pollution 6 9 0 0 0 0 0 0 0 Overuse and wasting of resources 0 0 0 0 0 0 0 0 0 Waste issues 0 0 0 0 0 0 0 0 0 Social Issues Child labor 0 0 0 0 0 0 0 0 0 Discrimination in employment 0 0 0 0 0 0 0 0 0 Forced labor 0 0 0 0 0 0 0 0 0 Freedom of association and collective bargaining 0 0 0 0 0 0 0 0 0 Human rights abuses, corporate complicity 0 0 0 0 0 0 0 0 0 Impacts on communities 9 15 0 0 0 0 0 0 0 Local participation issues 7 2 0 0 0 0 0 0 0 Occupational health and safety issues 0 0 0 0 0 0 0 0 0 Poor employment conditions 0 0 0 0 0 0 0 0 0 Social discrimination 0 0 0 0 0 0 0 0 0 Governance Issues Anti-competitive practices 0 0 0 0 0 0 0 0 0 45 Corruption, bribery, extortion, money laundering 0 0 0 0 0 0 0 0 0 Executive compensation issues 0 0 0 0 0 0 0 0 0 Fraud 0 0 0 0 0 0 0 0 0 Misleading communication 0 0 0 0 0 0 0 0 0 Tax evasion 0 0 0 0 0 0 0 0 0 Totals: 34 44 0 0 0 0 0 0 0 Table 4 3.4 Discussion 3.4.1 RepRisk Classifications Table 4 shows the manual coding results of each company’s top news on the selected peak Twitter volume dates. Enbridge and TC Energy are the only companies to be flagged in the RepRisk coding schema, each in five issues within the environmental and social issue categories. The most flagged ESG issue for each company is “Impacts on communities”, followed by “Impacts on Landscapes”, and subsequently “Local Pollution”. The similarity in ESG issues as per the RepRisk schema between Enbridge and TC Energy shows the parallels in the events discussed in the news and on social media. In total, Enbridge has 44 positive thematic matches to the RepRisk schema, while TC Energy has 34. Enbridge’s greater quantity of positive matches is in line with its significant p-values (less than 5%) at both timeframe analyses. TC Energy has a sum of 34 positive RepRisk matches throughout its news coverage, which falls in line with level of significance at the one-month analysis. The rest of the companies show 0 matches with the RepRisk schema due to their mainly financial news. This lack of positive matching aligns with the widespread lack of forecasting abilities shown for these companies at both levels of analysis, as shown in tables two 46 and three. These findings align with our third hypothesis, which states that negative Twitter sentiment is only impactful on stock price when specific variables are present. In this case, it appears that those conditions necessitate a significant environmental or social issue that is covered at a national level. 3.4.2 Social Media vs. Traditional News This study posits that digital discourse affects company value via their stock price. The logic holds that if widespread negativity towards the stock is evident, investors will withdraw their capital from the company’s stock to avert the risk associated with a decline in poor corporate social performance. The results of the Granger causality tests indicate that digital discourse, as measured by social media activity, has significant forecasting abilities on the stock prices of Enbridge and TC Energy, but not the other companies in the sample. This suggests that the impact of digital discourse on stock prices may be company-specific and dependent on factors such as the nature and intensity of the discourse. To further explore the differences between social media and traditional news coverage, a comparison of the volume and sentiment of the discourse surrounding the Enbridge Line 5 and TC Energy Keystone XL pipeline issues was conducted. For the Enbridge case, the social media data showed a clear spike in volume of tweets on May 12, 2021 which had a daily average sentiment score of -0.003 – neutral but negative. This suggests that the digital discourse was critical of Enbridge's handling of the Line 5 pipeline controversy. In contrast, the traditional news coverage, while extensive, did not exhibit the same level of negativity. Many articles took a more balanced or even sympathetic tone towards Enbridge's position. 47 The discrepancy between traditional news sentiment and social media sentiment portrays a reaction from the public to the news articles published by traditional media outlets. This reaction is amplificatory, as it is still the results of numerous traditional news articles being published, followed by an increase in digital discourse activity. The question remains as to whether social media is simply a reflection of what investors are feeling themselves, or whether investors look to reactionary discourse after news release to inform their financial decisions. For the TC Energy case, the daily average sentiment score for March 31st, 2020 is 0.1362, a relatively neutral yet positive score. We can interpret this score as a balanced representation on Twitter between proponents and opponents of the Keystone XL pipeline project. Traditional news coverage tended to present the issue in a more neutral manner, often simply reporting on the government's decision to proceed with construction. There was a lack of opinion pieces or editorials compared to the Enbridge case. Our second hypothesis states that negative Twitter sentiment follows negative media coverage and is an intervening variable in adversely impacting stock price. For both cases, we see significant traditional news covering controversial events associated with the companies in question. We subsequently see a massive jump in Twitter activity for each company. In this case, it seems the Twitter activity is reactionary in nature to the news being released by traditional news sources at a national level. Thus, we accept our second hypothesis. However, further research is necessary to examine the amplificatory effects of social media reaction to news events. Investigating whether similar significant forecasting abilities hold when there is an absence of largescale news coverage on an event should aid in answering this question. 3.4.3 Comparisons with the Industry 48 Both of the peak Twitter volume dates for Enbridge and TC Energy represents specifically politically controversial events that fall within the criteria for environmental and social issues according to the RepRisk coding schema. For Enbridge, this represents a legal standoff with the Michigan state government. For TC Energy, this represents a significant amount of funding being allocated from the Albertan government that was taken from other industries, warranting partisan criticism and the progression of a tumultuous pipeline construction. Is a nationally viewed political event classified as an environmental or social issue the requisite that allows for significant forecasting ability of digital discourse on stock price? The cases of Enbridge and TC Energy might suggest this, but deeper analysis is necessary. To explore this further, the other seven companies peak volume dates are examined qualitatively below. On February 11th, 2021, 52 tweets were made in the dataset referring to ARC Resources, the most in a single day during the original two-year timespan for the company. This came as a result of a merger announced between ARC and Seven Generations Energy Ltd. The deal created the largest oil producer in the Montney shale gas formation which spans from northern British Columbia to southern Alberta. Imperial Oil saw a peak twitter date on December 28th, 2021 with 368 tweets. The database used returned no results when searching for articles on this date. Canadian Natural Resources Ltd. Saw its peak Twitter date on May 7th, 2021 with 72 tweets. The articles were published with mixed themes. These discuss the company’s stock movement, and its executive’s involvement in carbon capture technology. Pembina’s peak twitter volume occurred on June 17th, 2021 with a tweet count of 52. All articles published referred to the announcement of a joint venture with TC Energy to develop a carbon transportation and sequestration grid with the capacity to transport more than 20 million tonnes of CO2 annually. 49 Suncor saw its peak twitter volume at 398 tweets on October 2nd, 2020. The articles published on this date referred to the company’s announcement of a 15% staff reduction, equal to approximately 2,000 workers being terminated. This came in the midst of the COVID-19 pandemic, which saw the price of oil drop. Ovintiv Inc. saw a peak twitter date on March 9th, 2020 with a volume of 28 tweets. 3 articles were published on this date. Two articles referred to its stock decline of 72% in the wake of an industry wide value slide. The third article referred to an announcement the company made of its resilience in the fall out of the stock price movement. Cenovus’ peak Twitter date occurred on October 25th, 2020, with a volume of 385 tweets. This date corresponded with the announcement of its acquisition of Husky Energy Inc. at a cost of $3.8 billion. The events shown for each company on their peak Twitter date describe financial, economic, and technological announcements. None of these events could be construed as nationally viewed political events. Nor did any of these articles match the RepRisk schema for ESG issues as shown in table four. Enbridge’s Line 5 and TC Energy’s Keystone XL involved both provincial and national governments of Canada and the US, while spurring environmental protest and economic support from both sides of the political spectrum. Further, the aforementioned companies showed a significant number of positive matches to the RepRisk ESG coding schema. The multi-faceted and controversial nature of these events within the energy sector serves as an explanation for the statistical significance of digital discourse’s forecasting abilities on the stock price of these companies. 3.5 Conclusions and Further Research 50 The findings of this study provide insights into the relationship between digital discourse and stock price in the Canadian energy industry. The Granger causality tests revealed significant forecasting abilities of Twitter sentiment on the stock prices of Enbridge Inc. and TC Energy Corp. during shortened timeframes surrounding their peak Twitter volume dates. However, no such significance was observed for the other seven companies in the sample. The qualitative analysis highlighted the contrasting dynamics between social media discourse and traditional news coverage, while emphasizing the complex amplificatory relationship between the two media sources. In the case of Enbridge's Line 5 pipeline controversy, social media data exhibited negative sentiment, while traditional media coverage tended to be more balanced or sympathetic towards the company's position. Conversely, for TC Energy's Keystone XL pipeline announcement, both social media and traditional news coverage maintained a relatively neutral stance. These findings suggest that the impact of digital discourse on stock price may be company-specific and depend on the nature and intensity of the discourse. Nationally viewed political events or controversies appear to be a potential catalyst for significant forecasting abilities of digital discourse on stock prices, as shown by the Enbridge and TC Energy cases. Further, 5 RepRisk categories (Climate change/GHG emissions/global pollution, Impacts on landscapes/ecosystems/biodiversity, Local pollution, Impacts on communities, and Local participation issues) were shared between Enbridge and TC Energy, which should be examined further in different context as particularly salient to this relationship. The first hypothesis of this study stated that negative Twitter sentiment precedes negative media coverage sufficient to adversely impact stock price. I did not find supporting evidence, thus hypothesis one was rejected. Based off of alignment of peak Twitter volume dates with nationally viewed controversies, my conclusions confirmed my second hypothesis stating that 51 negative Twitter sentiment follows negative media coverage and is an intervening variable in adversely impacting stock price. Based off of Enbridge and TC Energy’s specific RepRisk classifications, I was able to confirm my third hypothesis stating that negative Twitter sentiment is only impactful on stock price when specific variables are present. The selection of timeframes based on peak Twitter activity may introduce bias, and the use of Twitter sentiment as a proxy for digital discourse may sufficiently represent the diversity of public opinions. Additionally, granger causality does not establish causation, it simply reflects the statistical predictive power of one time-series on another (Stern, 2011). External factors not accounted for in this study could influence both dependent and independent variables in this study. Future research should explore the generalizability of these findings across different industries and geographical regions, while accounting for socio-economic and cultural factors that might shape digital discourse and investor behavior. Furthermore, investigating the interplay between traditional news media, social media, and investor sentiment could provide valuable insights into the dynamics of information dissemination and decision-making in financial markets. Finally, further investigation of the RepRisk ESG coding schema in different contexts should be done to solidify it as a classification standard for further studies. 52 Chapter 4: Summary and Conclusions This thesis explored the complex relationship between digital discourse, specifically social media sentiment, and stock price within the context of the Canadian oil and gas industry from 2020-2021. Through two distinct yet complementary studies, it has shed light on the dynamics at play and contributed to the ongoing debate surrounding the impact of social media on corporate performance. It has also lent new insight to the relationship between traditional news media and social media with respect to impacting stock prices. 4.1 Key Insights and Results The first objective of this thesis was to analyse the predictive capacity of non-financial Twitter sentiment on stock prices across nine prominent Canadian oil and gas companies (Chapter 2). My data was collected through data scraping which yielded more than 180,000 tweets. They were then processed through a natural language processing code to create a sentiment analysis, and then analyzed via granger causality to test for forecasting ability. The results of the granger tests revealed no significant influence of social media sentiment on stock prices within the sample. This finding contradicts previous studies suggesting a predictive relationship. It also found a lack of significance in market-level sentiment over individual company sentiment, as well as diminished impact of non-financial discourse on investor decisions. The study resonates with Barnett's theory, indicating minimal adaptation of firms to digital era pressures (Barnett, 2018). My second objective was to delve deeper into the relationship between digital discourse and stock price, focusing on reducing timeframes for analysis to come before and after particularly controversial dates (Chapter 3). Granger causality tests revealed significant 53 forecasting abilities of Twitter sentiment on stock prices during shortened timeframes surrounding peak Twitter volume dates for two companies: Enbridge Inc. and TC Energy Corp. Qualitative analysis highlighted social media’s amplifying effect on traditional news, acting as an intervening variable. The findings from the Chapter 3 suggest that the impact of digital discourse on stock price may be company-specific and depend on the nature and intensity of the discourse. Nationally viewed political events or controversies appear to be potential catalysts for significant forecasting abilities of digital discourse on stock prices. In the case of Enbridge Inc., the Line 3 pipeline garnered news coverage at national levels from American and Canadian sources. It also involved state and federal governments, local populations, issues of land rights, and environmental concerns. Similarly, TC Energy’s Keystone XL pipeline involved provincial and federal governments, issues of land rights, environmental concerns, and news coverage at national levels. Additionally, specific RepRisk categories related to environmental, social, and governance (ESG) factors were shared between Enbridge and TC Energy, indicating their potential salience in this relationship. Specifically, five RepRisk categories were found to be of particular importance: Climate change/GHG emissions/global pollution, impacts on landscapes/ecosystems/biodiversity, local pollution, impacts on communities, and local participation issues. 4.2 Strengths and Limitations This thesis applied both quantitative and qualitative methods. It combined data scraping from Twitter with a natural language processing procedure to undergo sentiment analysis on over 180,000 tweets over a two-year period. It introduced econometric analysis to compare the 54 forecasting abilities of the sentiment analysis product to the adjusted closing stock prices of nine public companies operating in Canada. These quantitative methods were introduced to a hitherto unstudied geographic region and industry, yielding novel results. Further, the qualitative analysis relied upon news network databases from both the United States and Canada to examine traditional news media coverage of each company. The resulting news articles were then introduced to a private-sector ESG controversy categorization schema that shed light on commonalities between the companies in the dataset. This combination of qualitative and quantitative methods applied to a new geographic area and industry provides a robust and novel way of addressing the broader question of whether social media matters for stock price. However, there are some limitations worth acknowledging. Firstly, the findings of this thesis may not be applicable to other industries or regions. The cross-border nature of pipeline infrastructure introduces unique geopolitical tensions that are not necessarily replicable in other geographical settings beyond the US and Canada. US-Canada pipelines create an economic dynamic that requires multi-level governmental cooperation and negotiation, as shown by the cases of Enbridge and TC Energy. Further, the issue of unceded and ceded indigenous land introduces a unique legal context that may not be found in other regions. The analysis of both Chapter 2 and 3 relies on Twitter data obtained through third-party data scraping, which may not capture all relevant Tweets regarding the companies in question. This is due to potential limits inherent in the Twitter API which restricts the amount of historical data available. Further, it may not capture Tweets from private accounts due to privacy restrictions (Dilmegani, 2024). It is important to emphasize that the Granger causality tests used in both chapters do not establish definitive causation – only a statistical significance in forecasting ability. Further event studies and larger sample sizes will help contribute in establishing causality. 55 The VADER sentiment package used to create a sentiment analysis in Chapter 2 introduces potential biases and limitations inherent to the structure of the R package. Sentiment analysis techniques such as the VADER package may misinterpret human use of sarcasm and irony, yielding inaccurate sentiment scores (Ducret et al., 2021). Further, the efforts made in Chapter 2 to control for broader market movements and industry-specific factors may not have perfectly isolated the effects of online sentiment on company value. In Chapter 3 the chosen timeframes were centered around notable Twitter activity. These timeframes may not fully capture the intricate nature of public discourse and its impact on stock market behaviors. Using Twitter sentiment as a proxy for digital discourse may not fully capture the diversity of public discourse relevant to the selected companies. Further, focusing only on data from Twitter instead of other social media platforms such as Tiktok or Instagram limits my ability to capture total sentiment expressed by the public on social media. 4.3 Contributions and Future Research This thesis makes three main contributions. Firstly, the previous academic literature fails to address the relationship of digital discourse and stock price in the specific context of the North American oil and gas industry. The findings presented in this thesis provide a preliminary step in filling this void, with the insights being useful for businesses and activist groups alike. By building on this research, further guidance can be provided to businesses on how to deal with public perception on social media. That is, firms should not concentrate significant resources on correcting negative sentiment from the public when it is not concerning nationally viewed political events or controversies. Although it is important to maintain positive relations with all 56 stakeholders, a firm should consider these implications when evaluating negative sentiment events. This research also provides valuable guidance to individual and organizational activists seeking to make changes from the outside on business behaviour. Although there may be value in disseminating information to the public about a firm’s practices, if one seeks to affect the value of a firm, they should organize their efforts under the conditions of a nationally viewed political event or controversy to achieve maximum results. These conditions will provide the most opportune situation to do so. Secondly, the findings provide an outline of the causal mechanisms between ESG controversies, news media, social media, and stock prices of companies. When a news story is published at a national level covering a firm-related political event or controversy, it then garners attention and coverage on social media platforms, thereby influencing investor decisions and affecting the stock price of the firm in question. By qualitatively analyzing each company’s key news events, social sentiment, and stock price change, I provide specific conditions that should be analyzed further as pertinent to business performance and public perception. Lastly, the findings of this thesis show that, contra previous literature, social media does not appear to have a widespread impact on stock price. The results show that in order for social media to have an impact, there are very specific contextual conditions required for it to manifest in a company’s value. Rather than a blanket effect, these results suggest that highly specific and contextual conditions must be present, that may not apply across industries or firms. Overall, this thesis contributes to the ongoing academic discourse by providing empirical evidence and insights into the nuanced interplay between social media sentiment and stock price 57 within the Canadian oil and gas industry. While the findings do not conclusively establish a universal predictive relationship, they highlight the potential for digital discourse to influence stock price under specific circumstances and for certain companies. The research also underscores the importance of considering the amplificatory relationship between social media discourse and traditional news coverage, as well as the potential relevance of ESG factors in shaping the impact of digital discourse on stock price. Future research should explore the generalizability of these findings across different industries and geographical contexts, as well as delve deeper into the specific mechanisms through which digital discourse influences stock price. Additionally, the development of more sophisticated analytical techniques and the integration of alternative data sources could further enhance our understanding of this relationship. Further qualitative research should include more comprehensive analysis of traditional news sources, which might provide better insights into the significance of traditional news media on stock price. Conducting first-hand interviews with firm executives may provide insights into how firms actually react to significant media events. As digital technologies continue to evolve and social media platforms become increasingly ingrained in our daily lives, the interplay between digital discourse and stock price will likely remain a subject of significant interest and debate. This thesis contributes to this ongoing discourse and paves the way for further exploration and understanding of this complex and evolving phenomenon. 58 References Aagerup, U. (2022). The ethics of conspicuous virtue signaling: when brand co-creation on social media turns negative. In Research Handbook on Brand Co-Creation (pp. 303–315). Edward Elgar Publishing. https://www.elgaronline.com/edcollchap/edcoll/9781839105418/9781839105418.00030.xml Aissia, D. B., & Neffati, N. (2023). Investor sentiment metrics and stock market returns: a study of the causality relationship using VAR models. American Journal of Finance and Accounting, 7(2), 90–124. https://doi.org/10.1504/AJFA.2023.134710 Aouadi, A., & Marsat, S. (2018). Do ESG Controversies Matter for Firm Value? Evidence from International Data. Journal of Business Ethics, 151(4), 1027–1047. https://doi.org/10.1007/s10551-016-3213-8 Barnett, M., Henriques, I., & Husted, B. (2018). The Rise and Stall of Stakeholder Influence: How the Digital Age Limits Social Control. Academy of Management Perspectives, 34. https://doi.org/10.5465/amp.2017.0080 Bartley, T., & Child, C. (2011). Movements, Markets and Fields: The Effects of Anti-Sweatshop Campaigns on U.S. Firms, 1993-2000. Social Forces, 90(2), 425–451. https://doi.org/10.1093/sf/sor010 Baule, R., & Tallau, C. (2016). Stock Returns Following Large Price Changes and News Releases - Evidence from Germany. Credit and Capital Markets, 49(1), 57–91. https://doi.org/10.3790/ccm.49.1.57 Beaudry, P., & Portier, F. (2006). Stock Prices, News, and Economic Fluctuations. The American Economic Review, 96(4), 1293–1307. https://doi.org/10.1257/aer.96.4.1293 59 Becchetti, L., Cucinelli, D., Ielasi, F., & Rossolini, M. (2023). Corporate social irresponsibility: The relationship between ESG misconduct and the cost of equity. International Review of Financial Analysis, 89, 102833. https://doi.org/10.1016/j.irfa.2023.102833 Bennett, W. L., & Segerberg, A. (2012). The Logic of Connective Action. Information, Communication & Society, 15(5), 739–768. https://doi.org/10.1080/1369118X.2012.670661 Bollen, J., Mao, H., & Zeng, X.-J. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. https://doi.org/10.1016/j.jocs.2010.12.007 Broersma, M., & Graham, T. (2012). Social Media as Beat: Tweets as a news source during the 2010 British and Dutch elections. Journalism Practice, 6(3), 403–419. https://doi.org/10.1080/17512786.2012.663626 Brown, E. (2012, August 19). Bulls, Bears…and Birds? Studying the Correlation between Twitter Sentiment and the S&P500. https://doi.org/10.13140/RG.2.1.3556.9766 Cao, G. (2017). The Impacts of Information on Stock PricesAssessed by Social Media Sentiment. 2017 IEEE International Conference on Internet of Things (IThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 1–8. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.8 Capelle-Blancard, G., & Petit, A. (2019). Every Little Helps? ESG News and Stock Market Reaction. Journal of Business Ethics, 157(2), 543–565. https://doi.org/10.1007/s10551-017-3667-3 Carl, S. (2019). Prediction of Stock Prices using Financial Analysis. International Journal of Innovative Science, Engineering & Technology, 6(12), 8-19. https://ijiset.com/articlesv6/articlesv6s12.html. 60 Chan, W. S. (2003). Stock price reaction to news and no-news: drift and reversal after headlines. Journal of Financial Economics, 70(2), 223–260. https://doi.org/10.1016/S0304-405X(03)00146-6 Chiou-Wei, S. Z., Chen, C.-F., & Zhu, Z. (2008). Economic growth and energy consumption revisited — Evidence from linear and nonlinear Granger causality. Energy Economics, 30(6), 3063–3076. https://doi.org/10.1016/j.eneco.2008.02.002 Cho, H., Li, P., Ngien, A., Tan, M. G., Chen, A., & Nekmat, E. (2023). The bright and dark sides of social media use during COVID-19 lockdown: Contrasting social media effects through social liability vs. social support. Computers in Human Behavior, 146, 107795. https://doi.org/10.1016/j.chb.2023.107795 Chowdhury, S., Routh, S., & Chakrabarti, S. (2014). News Analytics and Sentiment Analysis to Predict Stock Price Trends. https://www.semanticscholar.org/paper/News-Analytics-and-Sentiment-Analysis-to-Predict-Chowdhury-Routh/57abadf88a1470daebe9e54a02b845bfb54866c3 Clark, C. E., & Crawford, E. P. (2012). Influencing Climate Change Policy: The Effect of Shareholder Pressure and Firm Environmental Performance. Business & Society, 51(1), 148–175. https://doi.org/10.1177/0007650311427594 Clarkson, M. B. E. (1995). A Stakeholder Framework for Analyzing and Evaluating Corporate Social Performance. The Academy of Management Review, 20(1), 92–117. https://doi.org/10.2307/258888 Colaço, R., & Simão, J. (2018). Disclosure of corporate social responsibility in the forestry sector of the Congo Basin. Forest Policy and Economics, 92, 136–147. https://doi.org/10.1016/j.forpol.2018.04.012 61 Coyne, S., Madiraju, P., & Coelho, J. (2017). Forecasting Stock Prices Using Social Media Analysis. 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), 1031–1038. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.169 Criminalization of Wet’suwet’en land defenders. (2023, March 1). Amnesty International. https://www.amnesty.org/en/latest/news/2023/03/criminalization-wetsuweten-land-defenders/ Cryderman, K. (2020, March 31). The UCP’s Keystone XL pipeline investment shows where Alberta’s priorities lie: There are several potential benefits to the province’s support, but the optics are awful at a time when thousands of jobs in the education system could be affected. The Globe and Mail (Online). https://www.proquest.com/canadiannews/docview/2384615955/citation/6DBF56744A304090PQ/1 Dai, R., Liang, H., & Ng, L. (2021). Socially responsible corporate customers. Journal of Financial Economics, 142(2), 598–626. https://doi.org/10.1016/j.jfineco.2020.01.003 Dang, C., (Frank) Li, Z., & Yang, C. (2018). Measuring firm size in empirical corporate finance. Journal of Banking & Finance, 86, 159–176. https://doi.org/10.1016/j.jbankfin.2017.09.006 de Bakker, F. G. A. (2015). Online Activism, CSR and Institutional Change. In J. Uldam & A. Vestergaard (Eds.), Civic Engagement and Social Media: Political Participation Beyond Protest (pp. 23–43). Palgrave Macmillan UK. https://doi.org/10.1057/9781137434166_2 de Bakker, F. G. A., & den Hond, F. (2008). Introducing the Politics of Stakeholder Influence: A Review Essay. Business & Society, 47(1), 8–20. https://doi.org/10.1177/0007650307306637 62 De Bondt, W. F. M., & Thaler, R. (1985). Does the Stock Market Overreact? The Journal of Finance, 40(3), 793–805. https://doi.org/10.1111/j.1540-6261.1985.tb05004.x Deegan, C. (2002). The Legitimising Effect of Social and Environmental Disclosures – A Theoretical Foundation. Accounting, Auditing & Accountability Journal, 15, 282–311. https://doi.org/10.1108/09513570210435852 Denchak, M. (2022, March 15). The Keystone XL Pipeline: Everything You Need To Know. NRDC. https://www.nrdc.org/stories/what-keystone-xl-pipeline Dilmegani, C. (2024). X (Twitter) Web Scraping: Legality, Methods & Use Cases 2024. AIMultiple: High Tech Use Cases & Tools to Grow Your Business. https://research.aimultiple.com/twitter-web-scraping/ Ducret, M., L, K., C, M., A, F., & Peng, J. (2021). You Don’t Say....Linguistic Features in Sarcasm Detection. CLIC-IT 2021: Seventh Italian Conference on Computational Linguistics Bologna. https://par.nsf.gov/biblio/10463476-you-dont-say-linguistic-features-sarcasm-detection Eisenberg, A. (2022). Decolonizing Authority: The Conflict on Wet’suwet’en Territory. Canadian Journal of Political Science, 55(1), 40–58. https://doi.org/10.1017/S0008423921000858 Etter, M., Colleoni, E., Illia, L., Meggiorin, K., & D’Eugenio, A. (2018). Measuring Organizational Legitimacy in Social Media: Assessing Citizens’ Judgments With Sentiment Analysis. Business & Society, 57(1), 60–97. https://doi.org/10.1177/0007650316683926 Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486 Freeman, R. E. (2010). Strategic Management: A Stakeholder Approach. Cambridge University Press. 63 Freeman, R. E., Harrison, J. S., & Zyglidopoulos, S. (2018). Stakeholder Theory: Concepts and Strategies. Elements in Organization Theory. https://doi.org/10.1017/9781108539500 Gabrovšek, P., Aleksovski, D., Mozetič, I., & Grčar, M. (2017). Twitter sentiment around the Earnings Announcement events. PLOS ONE, 12(2), e0173151. https://doi.org/10.1371/journal.pone.0173151 George, J. J., & Leidner, D. E. (2019). From clicktivism to hacktivism: Understanding digital activism. Information and Organization, 29(3), 100249. https://doi.org/10.1016/j.infoandorg.2019.04.001 Gomez-Carrasco, P., & Michelon, G. (2017). The Power of Stakeholders’ Voice: The Effects of Social Media Activism on Stock Markets. Business Strategy and the Environment, 26(6), 855–872. https://doi.org/10.1002/bse.1973 Graham, T., & Wright, S. (2013). Discursive Equality and Everyday Talk Online: The Impact of “Superparticipants.” Journal of Computer-Mediated Communication, 19. https://doi.org/10.1111/jcc4.12016 Grant, P. R., & Smith, H. J. (2021). Activism in the time of COVID-19. Group Processes & Intergroup Relations, 24(2), 297–305. https://doi.org/10.1177/1368430220985208 Hamraoui, I., & Boubaker, A. (2022). Impact of Twitter sentiment on stock price returns. Social Network Analysis and Mining, 12(1), 28. https://doi.org/10.1007/s13278-021-00856-7 Hendahewa, D. N., Perera, A. A. D. S. N. A., De Meraal, T. D. N., Indikadulle, I. K. P. H., Perera, S. D., & Rathnayake, H. M. S. C. (2021). Comparative Analysis between Supervised Machine Learning and Time Series Models for stock price prediction. 2021 International Conference on Data Analytics for Business and Industry (ICDABI), 354–358. https://doi.org/10.1109/ICDABI53623.2021.9655888 64 Hiemstra, C., & Jones, J. D. (1994). Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation. The Journal of Finance, 49(5), 1639–1664. https://doi.org/10.1111/j.1540-6261.1994.tb04776.x Hisano, R., Sornette, D., Mizuno, T., Ohnishi, T., & Watanabe, T. (2013). High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility. PLOS ONE, 8(6), e64846. https://doi.org/10.1371/journal.pone.0064846 Hodges, H. E., & Stocking, G. (2016). A pipeline of tweets: environmental movements’ use of Twitter in response to the Keystone XL pipeline. Environmental Politics, 25(2), 223–247. https://doi.org/10.1080/09644016.2015.1105177 Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550 Iqbal, M. (n.d.). Twitter Revenue and Usage Statistics (2024). Business of Apps. Retrieved May 31, 2024, from https://www.businessofapps.com/data/twitter-statistics/ Jia, M., & Zhang, Z. (2014). How Does the Stock Market Value Corporate Social Performance? When Behavioral Theories Interact with Stakeholder Theory. Journal of Business Ethics, 125(3), 433–465. https://doi.org/10.1007/s10551-013-1924-7 Kelleher, S. R. (n.d.). How A Nazi Symbol At CPAC Turned Into A Massive Hyatt Public Relations Disaster. Forbes. Retrieved May 31, 2024, from https://www.forbes.com/sites/suzannerowankelleher/2021/03/01/how-hosting-cpac-turned-into-a-massive-hyatt-public-relations-disaster/ Kelleher, S. R. (n.d.). ‘Women Belong In The Kitchen’: Burger King’s International Women’s Day Tweet Goes Down In Flames. Forbes. Retrieved May 31, 2024, from 65 https://www.forbes.com/sites/suzannerowankelleher/2021/03/09/women-belong-in-the-kitchen-burger-kings-international-womens-day-tweet-goes-up-in-flames/ Kheiriddin, T. (n.d.). Tasha Kheiriddin: Killing Line 5 will only make people realize how much we need fossil fuels | National Post. Retrieved May 7, 2024, from https://nationalpost.com/opinion/tasha-kheiriddin-killing-line-5-will-only-make-people-realize-how-much-we-need-fossil-fuels King, B. G. (2008). A Political Mediation Model of Corporate Response to Social Movement Activism. Administrative Science Quarterly, 53(3), 395–421. https://www.jstor.org/stable/27749273 Komariah, K. S., Machbub, C., Prihatmanto, A. S., & Sin, B.-K. (2016). A Study on Efficient Market Hypothesis to Predict Exchange Rate Trends Using Sentiment Analysis of Twitter Data. Journal of Korea Multimedia Society, 19(7), 1107–1115. https://doi.org/10.9717/kmms.2016.19.7.1107 Koukaras, P., Nousi, C., & Tjortjis, C. (2022). Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning. Telecom, 3(2), 358–378. https://doi.org/10.3390/telecom3020019 Kristofferson, K., White, K., & Peloza, J. (2014). The Nature of Slacktivism: How the Social Observability of an Initial Act of Token Support Affects Subsequent Prosocial Action. Journal of Consumer Research, 40(6), 1149–1166. https://doi.org/10.1086/674137 Li, J., & Wu, D. (Andrew). (2020). Do Corporate Social Responsibility Engagements Lead to Real Environmental, Social, and Governance Impact? Management Science, 66(6), 2564–2588. https://doi.org/10.1287/mnsc.2019.3324 66 Li, S., Zhang, H., & Yuan, D. (2019). Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests. Energy Economics, 84, 104494. https://doi.org/10.1016/j.eneco.2019.104494 Lundgaard, D., & Etter, M. (2023). Everyday Talk on Twitter: Informal Deliberation About (Ir-)responsible Business Conduct in Social Media Arenas. Business & Society, 62(6), 1201–1247. https://doi.org/10.1177/00076503221139838 Mao, Y., Wei, W., & Wang, B. (2013). Twitter volume spikes: analysis and application in stock trading. Proceedings of the 7th Workshop on Social Network Mining and Analysis, 1–9. https://doi.org/10.1145/2501025.2501039 Martin, K. D., & Kracher, B. (2008). A Conceptual Framework for Online Business Protest Tactics and Criteria for Their Effectiveness. Business & Society, 47(3), 291–311. https://doi.org/10.1177/0007650307299218 McCabe, B. P. M., & Tremayne, A. R. (1995). Testing a Time Series for Difference Stationarity. The Annals of Statistics, 23(3), 1015–1028. https://doi.org/10.1214/aos/1176324634 McCarten, J. (2021, May 12). Canada makes case to keep Line 5 pipeline running. Waterloo Region Record, A.7. https://www.proquest.com/canadiannews/docview/2525490395/citation/88B00BDFC49C4AF9PQ/1 Mendiratta, A., Singh, S., Yadav, S. S., & Mahajan, A. (2023). When do ESG controversies reduce firm value in India? Global Finance Journal, 55, 100809. https://doi.org/10.1016/j.gfj.2023.100809 Morgan, G. (2021, May 12). Line 5 shutdown could “harm” U.S. ties; Michigan governor Whitmer threatens to seize Enbridge profits. National Post, FP.1. 67 https://www.proquest.com/canadiannews/docview/2774503599/citation/B5D1D59F54714322PQ/1 Nguyen, T. H., Shirai, K., & Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603–9611. https://doi.org/10.1016/j.eswa.2015.07.052 Niburski, K., & Niburski, O. (2020). Impact of Trump’s Promotion of Unproven COVID-19 Treatments and Subsequent Internet Trends: Observational Study. Journal of Medical Internet Research, 22(11), N.PAG-N.PAG. https://doi.org/10.2196/20044 Nisar, T. M., & Yeung, M. (2018). Twitter as a tool for forecasting stock market movements: A short-window event study. The Journal of Finance and Data Science, 4(2), 101–119. https://doi.org/10.1016/j.jfds.2017.11.002 Nofer, M., & Hinz, O. (2015). Using Twitter to Predict the Stock Market - Where is the Mood Effect? Business & Information Systems Engineering, 57(4), 229–242. https://aisel.aisnet.org/bise/vol57/iss4/2 Oh, C., & Sheng, O. (2011). Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement. 4. Pano, T., & Kashef, R. (2020). A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19. Big Data and Cognitive Computing, 4(4), 33. https://doi.org/10.3390/bdcc4040033 Panwar, R., Pandey, V., Suddaby, R., & Vidal, N. G. (2023). Did India’s CSR Mandate Enhance or Diminish Firm Value? Business & Society, 62(2), 401–433. https://doi.org/10.1177/00076503221085962 68 Papaioannou, P., Russo, L., Papaioannou, G., & Siettos, C. I. (2013). Can social microblogging be used to forecast intraday exchange rates? NETNOMICS: Economic Research and Electronic Networking, 14(1), 47–68. https://doi.org/10.1007/s11066-013-9079-3 Pasirayi, S., Fennell, P. B., & Follmer, K. B. (2022). #Activism: Investor Reactions to Corporate Sociopolitical Activism. Business & Society, 00076503221110457. https://doi.org/10.1177/00076503221110457 Pérez, A., López-Gutiérrez, C., García-De Los Salmones, M. D. M., & San-Martín, P. (2020). Stakeholder salience, positive CSR news and the market value of banks. Spanish Journal of Finance and Accounting / Revista Española de Financiación y Contabilidad, 49(4), 483–502. https://doi.org/10.1080/02102412.2019.1681718 Pingue, L., & Saikali, A. (2021, May 12). Evening Update: It’s deadline day for Enbridge’s Line 5 - now what?: Also: Questions mount about the future of the Oxford-AstraZeneca vaccine in Canada as supply issues and blood-clot concerns prompt provinces to press pause. The Globe and Mail (Online). https://www.proquest.com/canadiannews/docview/2525746690/citation/8FC244AC0044476EPQ/1 Ramos, S. B., & Veiga, H. (2011). Risk factors in oil and gas industry returns: International evidence. Energy Economics, 33(3), 525–542. https://doi.org/10.1016/j.eneco.2010.10.005 Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015). The Effects of Twitter Sentiment on Stock Price Returns. PLOS ONE, 10(9), e0138441. https://doi.org/10.1371/journal.pone.0138441 Rao, T., & Srivastava, S. (2014). Twitter Sentiment Analysis: How to Hedge Your Bets in the Stock Markets. In F. Can, T. Özyer, & F. Polat (Eds.), State of the Art Applications of Social 69 Network Analysis (pp. 227–247). Springer International Publishing. https://doi.org/10.1007/978-3-319-05912-9_11 Rasmussen, C. (2021, November 9). Emission Reductions From Pandemic Had Unexpected Effects on Atmosphere. Climate Change: Vital Signs of the Planet. https://climate.nasa.gov/news/3129/emission-reductions-from-pandemic-had-unexpected-effects-on-atmosphere RepRisk | Approach. (n.d.). Retrieved May 21, 2024, from https://www.reprisk.com/approach Rohlinger, D., & Earl, J. (2017). The Past, Present, and Future of Media and Social Movements Studies: Introduction to the Special Issue on Media and Social Movements (pp. 1–20). https://doi.org/10.1108/S2050-206020170000014004 Russel, R. (2020, September 25). Climate strikers are back on the streets – DW – 09/25/2020. Dw.Com. https://www.dw.com/en/climate-change-environment-greta-thunberg-merkel-global-strike-climate-action-luisa-neubauer/a-54618006 Saxton, G. D., Ren, C., & Guo, C. (2021). Responding to Diffused Stakeholders on Social Media: Connective Power and Firm Reactions to CSR-Related Twitter Messages. Journal of Business Ethics, 172(2), 229–252. https://doi.org/10.1007/s10551-020-04472-x Scheufele, B., Haas, A., & Brosius, H.-B. (2011). Mirror or Molder? A Study of Media Coverage, Stock Prices, and Trading Volumes in Germany. Journal of Communication, 61(1), 48–70. https://doi.org/10.1111/j.1460-2466.2010.01526.x Seok, J., Lee, Y., & Kim, B.-D. (2020). Impact of CSR news reports on firm value. Asia Pacific Journal of Marketing and Logistics, 32(3), 644–663. https://doi.org/10.1108/APJML-06-2019-0352 70 Serafeim, G., & Yoon, A. (2023). Stock price reactions to ESG news: the role of ESG ratings and disagreement. Review of Accounting Studies, 28(3), 1500–1530. https://doi.org/10.1007/s11142-022-09675-3 Shearer, E. (2018, December 10). Social media outpaces print newspapers in the U.S. as a news source. Pew Research Center. https://www.pewresearch.org/short-reads/2018/12/10/social-media-outpaces-print-newspapers-in-the-u-s-as-a-news-source/ Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2013). Predictive Sentiment Analysis of Tweets: A Stock Market Application. In A. Holzinger & G. Pasi (Eds.), Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data (pp. 77–88). Springer. https://doi.org/10.1007/978-3-642-39146-0_8 Smith, S., & O’Hare, A. (2022). Comparing traditional news and social media with stock price movements; which comes first, the news or the price change? Journal of Big Data, 9(1), 47. https://doi.org/10.1186/s40537-022-00591-6 Stern, D. I. (2011). From Correlation to Granger Causality (SSRN Scholarly Paper No. 1959624). https://doi.org/10.2139/ssrn.1959624 Strycharz, J., Strauss, N., & Trilling, D. (2018). The Role of Media Coverage in Explaining Stock Market Fluctuations: Insights for Strategic Financial Communication. International Journal of Strategic Communication. https://www.tandfonline.com/doi/abs/10.1080/1553118X.2017.1378220 Suciu, P. (n.d.). Teens Increasingly Rely On Social Media For News - But They Don’t Trust It. Forbes. Retrieved May 24, 2024, from https://www.forbes.com/sites/petersuciu/2022/07/22/teens-increasing-rely-on-social-media-for-newsbut-they-dont-trust-it/ 71 Sun, T., Wang, J., Zhang, P., Cao, Y., Liu, B., & Wang, D. (2017). Predicting Stock Price Returns Using Microblog Sentiment for Chinese Stock Market. 87–96. https://doi.org/10.1109/BIGCOM.2017.59 Teti, E., Dallocchio, M., & Aniasi, A. (2019). The relationship between twitter and stock prices. Evidence from the US technology industry. Technological Forecasting and Social Change, 149, 119747. https://doi.org/10.1016/j.techfore.2019.119747 Tsai, M. H., & Wang, Y. (2021). Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19. International Journal of Environmental Research and Public Health, 18(12), 6272. https://doi.org/10.3390/ijerph18126272 Van der Ven, H. 2024. “Does Clicktivism Change Corporate Environmental Practices? Evidence from #Buttergate”. [Unpublished Manuscript]. Faculty of Forestry, University of British Columbia. Varcoe, C. (2021, May 12). Canada wades into line 5 legal scrap, warns U.S. of impact; Brief details why Michigan shouldn’t unilaterally shut down Enbridge pipeline so vital to our economy. Edmonton Journal, A.7. https://www.proquest.com/canadiannews/docview/2525908062/citation/4C80A1205F794D9CPQ/1 Wang, G., Wang, T., Wang, B., Sambasivan, D., Zhang, Z., Zheng, H., & Zhao, B. Y. (2015). Crowds on Wall Street: Extracting Value from Collaborative Investing Platforms. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 17–30. https://doi.org/10.1145/2675133.2675144 72 Williams, N. (2021). Enbridge briefly shut Line 5 after protesters tampered with pipeline | Reuters. https://www.reuters.com/business/energy/enbridge-briefly-shut-line-5-after-protesters-tampered-with-pipeline-2021-10-20/ Wu, S., Liu, Y., Zou, Z., & Weng, T.-H. (2022). S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis. Connection Science, 34(1), 44–62. https://doi.org/10.1080/09540091.2021.1940101 X/Twitter: number of users worldwide 2024. (n.d.). Statista. Retrieved February 29, 2024, from https://www.statista.com/statistics/303681/twitter-users-worldwide/ Yaqub, U., Chun, S. A., Atluri, V., & Vaidya, J. (2017). Sentiment based Analysis of Tweets during the US Presidential Elections. Proceedings of the 18th Annual International Conference on Digital Government Research, 1–10. https://doi.org/10.1145/3085228.3085285 Yilmaz, E. S., Ozpolat, A., & Destek, M. A. (2022). Do Twitter sentiments really effective on energy stocks? Evidence from the intercompany dependency. Environmental Science and Pollution Research, 29(52), 78757–78767. https://doi.org/10.1007/s11356-022-21269-9 Zhang, X., Fuehres, H., & Gloor, P. (2011). Predicting Stock Market Indicators Through Twitter – “I Hope it is Not as Bad as I Fear. Procedia - Social and Behavioral Sciences, 26, 55–62. https://doi.org/10.1016/j.sbspro.2011.10.562 73 Appendix R Commands 1. Create sentiment scores for Tweets # Set working directory setwd("/Users/diegocorry/Desktop") # Load required libraries library(tidytext) library(dplyr) library(vader) library(readxl) library(tidytext) # Read in the data set tweets <- read_excel("ARC Tweets.xlsx") data <- tweets vader_df(data) sentiment_scores <- vader_df(data) data_with_sentiment <- cbind(data, sentiment_scores) write.csv(data_with_sentiment, "data_with_sentiment.csv", row.names = FALSE) 2. Aggregates sentiment scores by date setwd("/Users/diegocorry/Desktop") library(tidytext) library(dplyr) library(vader) library(readxl) library(tidytext) Company_data <- read_excel("TC Energy - Combined Sheet.xlsx") aggregated_data <- Company_data %>% group_by(`Tweet Date`) %>% summarise(Average_Score = mean(compound, na.rm = TRUE)) write.csv(aggregated_data, "daily_sentiment_scores.csv", row.names = FALSE) 74 3. Merge data frame to create separate frames for stocks and sentiment scores setwd("/Users/diegocorry/Desktop") library(tidytext) library(dplyr) library(vader) library(readxl) library(tidytext) file_path1 <- "TC Energy_average_daily_sentiment_scores.xlsx" file_path2 <- "TRP.TO.xlsx" file_path3 <- "All Dates.xlsx" data1 <- read_xlsx(file_path1) data2 <- read_xlsx(file_path2) data3 <- read_xlsx(file_path3) Sentiment_Scores <- data1 Stock_Prices <- data2 All_Dates <- data3 rm(data1) rm(data2) rm(data3) Stock_Prices$Date <- as.Date(Stock_Prices$Date) Sentiment_Scores$Date <- as.Date(Sentiment_Scores$Date) All_Dates$Date <- as.Date(All_Dates$Date) date_sequence <- seq(min(All_Dates$Date), max(All_Dates$Date), by = "day") merged_data <- data.frame(Date = date_sequence) merged_data <- merge(merged_data, Sentiment_Scores, by = "Date", all.x = TRUE) merged_data <- merge(merged_data, Stock_Prices, by = "Date", all.x = TRUE) merged_data$Open <- NULL merged_data$High <- NULL merged_data$Low <- NULL merged_data$Close <- NULL write.csv(merged_data, file = "merged_data.csv", row.names = FALSE) 4. Forward fill stock price data to fill missing points setwd("/Users/diegocorry/Desktop") library(tidytext) 75 library(dplyr) library(vader) library(readxl) library(tidytext) install.packages("zoo") library(zoo) Company_data <- read_excel("TC Energy_filled_data.xlsx") # Convert "NA" values in 'Adj Close' column to missing values Company_data$`Adj Close`[Company_data$`Adj Close` == "NA"] <- NA # Forward fill missing values in 'Adj Close' column Company_data$`Adj Close`[-1] <- na.locf(Company_data$`Adj Close`[-1]) # Convert "NA" values in 'Average_Score' column to missing values Company_data$`Average_Score`[Company_data$`Average_Score` == "NA"] <- NA # Forward fill missing values in 'Average_Score' column Company_data$`Average_Score`[-1] <- na.locf(Company_data$`Average_Score`[-1]) write.csv(Company_data, file = "TC Energy_filled_data.csv", row.names = FALSE) 5. Turn control variable data frames to include all dates setwd("/Users/diegocorry/Desktop") library(dplyr) library(readxl) library(tidyr) # Load your data file_path1 <- "All_Dates.xlsx" file_path2 <- "HXE.TO.xlsx" file_path3 <- "XIC.TO.xlsx" data1 <- read_xlsx(file_path1) data2 <- read_xlsx(file_path2) data3 <- read_xlsx(file_path3) # Rename the columns for consistency colnames(data1) <- "Date" colnames(data2) <- c("Date", "HXE.TO") colnames(data3) <- c("Date", "XIC.TO") 76 # Convert Date columns to Date format data1$Date <- as.Date(data1$Date) data2$Date <- as.Date(data2$Date) data3$Date <- as.Date(data3$Date) # Create a sequence of dates from min to max date_sequence <- seq(min(data3$Date), max(data3$Date), by = "day") # Create a data frame with the complete sequence of dates merged_data <- data.frame(Date = date_sequence) # Left join Sentiment_Scores to the merged_data merged_data <- merge(merged_data, data1, by = "Date", all.x = TRUE) # Left join Stock_Prices to the merged_data merged_data <- merge(merged_data, data2, by = "Date", all.x = TRUE) merged_data <- merge(merged_data, data3, by = "Date", all.x = TRUE) #forward fill the data merged_data <- merged_data %>% arrange(Date) %>% fill(HXE.TO, XIC.TO) # Save the merged data frame write.csv(merged_data, "Merged_Control_Data.csv", row.names = FALSE) 6. Merge control variables with existing data sets setwd("/Users/diegocorry/Desktop") library(dplyr) library(readxl) # Load your data file_path1 <- "TC Energy_filled_data_with%.xlsx" file_path2 <- "Merged_Control_Data.xlsx" data1 <- read_xlsx(file_path1) data2 <- read_xlsx(file_path2) # Rename the columns for consistency colnames(data1) <- c("Date", "Average_Score", "Adj.Close", "Volume", "Percentage_Change") colnames(data2) <- c("Date", "HXE.TO", "XIC.TO") # Convert Date columns to Date format 77 data1$Date <- as.Date(data1$Date) data2$Date <- as.Date(data2$Date) # Create a sequence of dates from min to max date_sequence <- seq(min(data2$Date), max(data2$Date), by = "day") # Create a data frame with the complete sequence of dates merged_data <- data.frame(Date = date_sequence) # Left join Sentiment_Scores to the merged_data merged_data <- merge(merged_data, data1, by = "Date", all.x = TRUE) # Left join Stock_Prices to the merged_data merged_data <- merge(merged_data, data2, by = "Date", all.x = TRUE) # Save the merged data frame write.csv(merged_data, "TC Energy_Regression_Data.csv", row.names = FALSE) 7. Stationarity Test setwd("/Users/diegocorry/Desktop") install.packages ("aTSA") library(tseries) Company_data <- read.csv("Enbridge_Regression_Data.csv") adf.test(Company_data$Average_Score) 8. Create Residuals of Interaction between stock prices and control ETF’s install.packages("lmtest") install.packages("vars") setwd("/Users/diegocorry/Desktop") Company_data <- read.csv("Enbridge_Regression_Data.csv") Company_data$Volume <- NULL Company_data$Percentage_Change <- NULL model <- lm(Adj.Close ~ XIC.TO * HXE.TO, data = Company_data) Company_data$Residuals<-model$resid adf.test(Company_data$Residuals) 78 9. Differencing to remove stationarity and granger causality testing #Differencing to remove non-stationarity from Stock time series install.packages("lmtest") install.packages("vars") setwd("/Users/diegocorry/Desktop") Company_data <- read.csv("TC Energy_Regression_Data.csv") Company_data$Volume <- NULL Company_data$Percentage_Change <- NULL model <- lm(Adj.Close ~ XIC.TO * HXE.TO, data = Company_data) Company_data$Residuals<-model$resid plot(Company_data$Residuals, ylab = expression(paste(nabla, "(", nabla^2,"CO"[2], ")"))) library(tseries) ## difference the data Residuals_d1 <- diff(Company_data$Residuals, differences = 1) Company_data <- Company_data[-(1:1), ] ## plot the differenced data Company_data$Residuals_d1 <- Residuals_d1 plot(Company_data$Residuals_d1, ylab = expression(paste(nabla^2, "CO"[2]))) adf.test(Company_data$Residuals_d1) library(lmtest) library(vars) # Using the $ operator Company_data$Volume <- NULL Company_data$Percentage_Change <- NULL Company_data$HXE.TO <- NULL Company_data$XIC.TO <- NULL Company_data$Date <- as.Date(Company_data$Date) # Convert other columns to numeric numeric_columns <- c("Average_Score", "Residuals_d1") Company_data[numeric_columns] <- sapply(Company_data[numeric_columns], as.numeric) 79 # Handle missing values Company_data <- na.omit(Company_data) # Assuming Average_Score, Adj.Close, HXE.TO, TXE.TO are your variables ts_data <- ts(data.frame(Company_data$Average_Score, Company_data$Residuals_d1), frequency = 1) var_model <- VAR(ts_data, p = 4) # Set an appropriate lag order # Specify the null hypothesis (H0: lagged values of y1 do not Granger cause y2) granger_test_result <- causality(var_model, cause = "Company_data.Average_Score") print(granger_test_result) summary(var_model)
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NoDerivatives 4.0 International
Graduate
Ven, Hamish van der
The effects of social media sentiment on stock price : a study of the Canadian energy sector
Text