The dissertation analyzes three issues related to corporate environmental performance. In the first essay, I analyze the stock price reactions of the defendant firms and their peer firms to environmental lawsuits. Empirical evidence finds that the defendant and their peer firms experience negative and significant cumulative abnormal returns to the announcement of environmental lawsuits. Additionally, cross-sectional analyses find certain firm characteristics, such as profitability, growth opportunities and leverage can influence the market reaction. Furthermore, if the plaintiffs are government agencies or corporations instead of individual citizens, the defendant and peer firms experience higher negative market reactions. The second essay examines if a firm's environmental, social, and governance (ESG) performance can moderate the negative market response to environmental lawsuits. The results are mixed. The overall sample of the defendant and their peer firms show that ESG performance is not a significant factor in mitigating the negative market response. However, an interesting finding shows, for defendant and peer firms in the environmentally sensitive industries, better ESG ratings help reduce the adverse market reactions. The final essay investigates whether the defendant and peer firms improve their ESG performance in the next two years following the lawsuits. The results indicates that firms generally experience a drop in their ESG ratings in the year the lawsuits are filed. However, post-lawsuit filing years, there is a general trend for the defendant and peer firms in the environmentally sensitive industries to improve their environmental performance.

<div> In the first chapter, I document that online feedback loops, such as search engines, drive customers and revenue to prominent firms, contributing to rising industry concentration. To identify prominent firms online, I measure centrality in a network of firm websites covering more than 100,000 public and private firms. Industries with firms that are more central become more concentrated and central firms increase their market share during the sample period. This appears to be due to firms' ability to meet earnings expectations. Central firms become more profitable and peripheral firms earn negative risk-adjusted returns and underperform earnings forecasts. Evidence from the COVID-19 shutdown, which drove economic activity to the Internet, supports these conclusions. Central firms received the vast majority of the influx of web traffic and had significantly higher returns during the shutdown.</div><div> </div><div> In the second chapter, I create a novel definition of peer groups (web-based peers) for over 100,000 public and private firms by extracting clusters from a network of firm websites. The network is built from unique data on overlapping web traffic. Peer firms are therefore more likely to have similar website users, and by extension, provide similar products or services. Web-based peer groups are related to traditional industry classifications, the preferred choice when defining industries for private firms, but outperform them in standard benchmarking tests. To further demonstrate the benefits of web-based peer groups, I examine IPO waves. IPO activity is closely related to peer-firm IPO activity in the past 12 months, controlling for the overall IPO market and waves within traditional industries. IPO followers earn lower post-IPO returns up to three months after going public, consistent with these firms being lower quality and attempting to benefit from the successful IPOs of their peers.</div>

This dissertation investigates how economically-comparable peer firms can be used to obtain inferences about a company’s accounting quality in two different research settings. The first Chapter examines whether auditor industry specialization, measured using auditor market share by industry, improves audit quality. After matching clients of specialist and non-specialist auditors according to industry, size and performance, there are no significant differences in audit quality between these two groups of auditors. In addition, this Chapter uses two analyses that do not rely primarily on matched samples. First, examining a sample of Arthur Andersen clients that switched auditors in 2002, there is no evidence of industry-specialization effects following the auditor change. Second, using a simulation approach, this study shows that client characteristics, and particularly client size, influence the observed association between auditor industry specialization and audit quality. Overall, these findings do not imply that industry knowledge is not important for auditors, but that the methodology used in extant studies examining this issue may not fully parse out the effects of auditor industry expertise from client characteristics. The second Chapter examines whether account-level expectation models for analytical audit procedures can be enhanced by using information from economically-comparable peer firms. This Chapter assesses the effectiveness of three main types of expectation models, with and without including information from peer firms: heuristic, time-series, and industry cross-sectional models. Information from peer firms improves the accuracy of all models and improves the detection power of time-series and industry cross-sectional models. Comparing between models, one-period heuristic models are generally unreliable, and industry cross-sectional models can be more effective than time-series models. These findings may help auditors of public companies and financial analysts in selecting expectation models and finding peer firms to assess the reasonability of a company’s financial information at the account-level.

This dissertation investigates how economically-comparable peer firms can be used to obtain inferences about a company’s accounting quality in two different research settings. The first Chapter examines whether auditor industry specialization, measured using auditor market share by industry, improves audit quality. After matching clients of specialist and non-specialist auditors according to industry, size and performance, there are no significant differences in audit quality between these two groups of auditors. In addition, this Chapter uses two analyses that do not rely primarily on matched samples. First, examining a sample of Arthur Andersen clients that switched auditors in 2002, there is no evidence of industry-specialization effects following the auditor change. Second, using a simulation approach, this study shows that client characteristics, and particularly client size, influence the observed association between auditor industry specialization and audit quality. Overall, these findings do not imply that industry knowledge is not important for auditors, but that the methodology used in extant studies examining this issue may not fully parse out the effects of auditor industry expertise from client characteristics. The second Chapter examines whether account-level expectation models for analytical audit procedures can be enhanced by using information from economically-comparable peer firms. This Chapter assesses the effectiveness of three main types of expectation models, with and without including information from peer firms: heuristic, time-series, and industry cross-sectional models. Information from peer firms improves the accuracy of all models and improves the detection power of time-series and industry cross-sectional models. Comparing between models, one-period heuristic models are generally unreliable, and industry cross-sectional models can be more effective than time-series models. These findings may help auditors of public companies and financial analysts in selecting expectation models and finding peer firms to assess the reasonability of a company’s financial information at the account-level.