Selected Working Papers
"Securities Law Precedents, Litigation Risk, and Misreporting" (with Benedikt Franke and Reeyarn Li).
We study securities law precedents and their impact on firms’ litigation risk and financial misreporting decisions. Using a sample of 438 circuit court precedents for alleged securities law violations from 1996 to 2018, we measure each circuit court’s leniency by its tendency to dismiss these lawsuits at each point in time and show that leniency affects future lawsuit rulings and firms’ litigation risk. Case-level analyses reveal that district courts heed the circuit court precedents and are more likely to dismiss pending cases when their home circuits dismiss more allegations. In firm-level analyses, we find that shareholders are less likely to sue misreporting firms that reside in more lenient circuits. The effect of precedents on misreporting firms’ litigation risk is more pronounced when managerial intent is difficult to judge and in the presence of sophisticated potential plaintiffs and higher expected lawsuit payoffs. Finally, consistent with lenient precedents lowering expected litigation costs, we show that investors react less negatively to the restatement announcements of firms residing in more lenient circuits and that such firms are more likely to misreport. We conclude that securities law precedents induce considerable heterogeneity in firms’ securities litigation risk, which affects their financial reporting quality.
We study securities law precedents and their impact on firms’ litigation risk and financial misreporting decisions. Using a sample of 438 circuit court precedents for alleged securities law violations from 1996 to 2018, we measure each circuit court’s leniency by its tendency to dismiss these lawsuits at each point in time and show that leniency affects future lawsuit rulings and firms’ litigation risk. Case-level analyses reveal that district courts heed the circuit court precedents and are more likely to dismiss pending cases when their home circuits dismiss more allegations. In firm-level analyses, we find that shareholders are less likely to sue misreporting firms that reside in more lenient circuits. The effect of precedents on misreporting firms’ litigation risk is more pronounced when managerial intent is difficult to judge and in the presence of sophisticated potential plaintiffs and higher expected lawsuit payoffs. Finally, consistent with lenient precedents lowering expected litigation costs, we show that investors react less negatively to the restatement announcements of firms residing in more lenient circuits and that such firms are more likely to misreport. We conclude that securities law precedents induce considerable heterogeneity in firms’ securities litigation risk, which affects their financial reporting quality.
"FinBERT: A Pretrained Language Model for Financial Communications" (with Yi Yang and Mark Christopher Siy Uy).
Contextual pretrained language models, such as BERT (Devlin et al., 2019), have made significant breakthrough in various NLP tasks by training on large scale of unlabeled text re-sources.Financial sector also accumulates large amount of financial communication text.However, there is no pretrained finance specific language models available. In this work,we address the need by pretraining a financial domain specific BERT models, FinBERT, using a large scale of financial communication corpora. Experiments on three financial sentiment classification tasks confirm the advantage of FinBERT over generic domain BERT model. The code and pretrained models are available at this https URL. We hope this will be useful for practitioners and researchers working on financial NLP tasks.
GitHub link for Pre-Trained FinBERT
Contextual pretrained language models, such as BERT (Devlin et al., 2019), have made significant breakthrough in various NLP tasks by training on large scale of unlabeled text re-sources.Financial sector also accumulates large amount of financial communication text.However, there is no pretrained finance specific language models available. In this work,we address the need by pretraining a financial domain specific BERT models, FinBERT, using a large scale of financial communication corpora. Experiments on three financial sentiment classification tasks confirm the advantage of FinBERT over generic domain BERT model. The code and pretrained models are available at this https URL. We hope this will be useful for practitioners and researchers working on financial NLP tasks.
GitHub link for Pre-Trained FinBERT
"Cross-Industry Information Sharing among Colleagues and Analyst Research" (with An-Ping Lin and Amy Zang).
We identify a specific organizational resource in brokerage houses—information sharing among analyst colleagues who cover economically related industries along a supply chain. We hypothesize that this resource benefits analyst research and allows analysts to have a higher level of industry specialization. The impact of this resource depends on colleagues’ information complementarity, which we measure using the level of economic connection between an analyst’s industry and her colleagues’ industries. We perform various empirical analyses to control for brokerage selection effects and find evidence consistent with our predictions. Specifically, we find that analysts with colleagues who are more economically connected have higher earnings forecast accuracy, stock recommendation profitability, and investor recognition, and that analysts and highly connected colleagues tend to issue revision reports contemporaneously. We further show that colleagues’ coverage of downstream (upstream) industries are positively related to the accuracy of only the analyst’s revenue (expense) forecasts. Last, we find that information sharing among colleagues allows analysts, especially less capable ones, to cover a more specialized research portfolio. Overall, our findings align with the theory of the firm that a firm’s organizational structures contribute to employee performance and specialization.
We identify a specific organizational resource in brokerage houses—information sharing among analyst colleagues who cover economically related industries along a supply chain. We hypothesize that this resource benefits analyst research and allows analysts to have a higher level of industry specialization. The impact of this resource depends on colleagues’ information complementarity, which we measure using the level of economic connection between an analyst’s industry and her colleagues’ industries. We perform various empirical analyses to control for brokerage selection effects and find evidence consistent with our predictions. Specifically, we find that analysts with colleagues who are more economically connected have higher earnings forecast accuracy, stock recommendation profitability, and investor recognition, and that analysts and highly connected colleagues tend to issue revision reports contemporaneously. We further show that colleagues’ coverage of downstream (upstream) industries are positively related to the accuracy of only the analyst’s revenue (expense) forecasts. Last, we find that information sharing among colleagues allows analysts, especially less capable ones, to cover a more specialized research portfolio. Overall, our findings align with the theory of the firm that a firm’s organizational structures contribute to employee performance and specialization.
"Credit Rating Agencies and Accounting Fraud Detection" (with Pepa Kraft and Shiheng Wang).
This study examines whether and when credit rating agencies take negative rating actions against issuers committing accounting fraud before the fraud is publicly revealed as well as the economic impacts of such rating actions. We find that Standard & Poor’s (S&P), an issuer-paid rating agency, downgrades fraud firms and puts them on negative credit watch as early as four quarters prior to public fraud revelation, controlling for firms’ economic fundamentals and performance. Furthermore, Egan-Jones, an investor-paid rating agency without access to management and private information, is much less timely than S&P in downgrading fraud firms, especially when information uncertainty is high. In addition, S&P distinguishes fraud firms from similar-looking non-fraud firms even when the former appears financially healthy, whereas Egan-Jones does not. This further supports that S&P’s private information advantage facilitates fraud detection. Finally, S&P’s negative rating actions against fraud firms inform the market and are associated with management turnover and shorter fraud duration.
This study examines whether and when credit rating agencies take negative rating actions against issuers committing accounting fraud before the fraud is publicly revealed as well as the economic impacts of such rating actions. We find that Standard & Poor’s (S&P), an issuer-paid rating agency, downgrades fraud firms and puts them on negative credit watch as early as four quarters prior to public fraud revelation, controlling for firms’ economic fundamentals and performance. Furthermore, Egan-Jones, an investor-paid rating agency without access to management and private information, is much less timely than S&P in downgrading fraud firms, especially when information uncertainty is high. In addition, S&P distinguishes fraud firms from similar-looking non-fraud firms even when the former appears financially healthy, whereas Egan-Jones does not. This further supports that S&P’s private information advantage facilitates fraud detection. Finally, S&P’s negative rating actions against fraud firms inform the market and are associated with management turnover and shorter fraud duration.
"The Long-Term Consequences of Short-Term Incentives" (with Alex Edmans and Vivian Fang).
This paper studies the long-term consequences of actions induced by vesting equity, a measure of short-term concerns. Vesting equity is positively associated with the probability of a firm repurchasing shares, the amount of shares repurchased, and the probability of the firm announcing a merger or acquisition (M&A). However, it is also associated with more negative long-term returns over the 2-3 years following repurchases and 4 years following M&A, as well as future M&A goodwill impairment. These results are inconsistent with CEOs buying underpriced stock or companies to maximize long-run shareholder value, but consistent with these actions being used to boost the short-term stock price and thus equity sale proceeds. CEOs sell their own stock shortly after using company money to buy the firm’s stock, also inconsistent with repurchases being motivated by undervaluation.
This paper studies the long-term consequences of actions induced by vesting equity, a measure of short-term concerns. Vesting equity is positively associated with the probability of a firm repurchasing shares, the amount of shares repurchased, and the probability of the firm announcing a merger or acquisition (M&A). However, it is also associated with more negative long-term returns over the 2-3 years following repurchases and 4 years following M&A, as well as future M&A goodwill impairment. These results are inconsistent with CEOs buying underpriced stock or companies to maximize long-run shareholder value, but consistent with these actions being used to boost the short-term stock price and thus equity sale proceeds. CEOs sell their own stock shortly after using company money to buy the firm’s stock, also inconsistent with repurchases being motivated by undervaluation.
"Judge Ideology and Corporate Tax Planning" (with Travis Chow, Kai Wai Hui and Terry Shevlin).
We investigate whether and how the federal judiciary affects corporate tax planning. We find that firms engage in less aggressive tax planning when Circuit Court and Tax Court judges are more liberal. This effect is economically significant and robust across various measures of tax planning. We further detail specific tax planning tactics in response to liberal judge ideology, such as shifting less income overseas, conducting more foreign tax planning, and acquiring more auditor-provided tax services. Firms also avoid liberal judges through forum shopping. Finally, we show that IRS enforcement complements the judge ideology effect. Overall, we are the first to demonstrate the judicial branch as a key determinant of corporate tax planning, which contributes to a more complete understanding of tax enforcement.
We investigate whether and how the federal judiciary affects corporate tax planning. We find that firms engage in less aggressive tax planning when Circuit Court and Tax Court judges are more liberal. This effect is economically significant and robust across various measures of tax planning. We further detail specific tax planning tactics in response to liberal judge ideology, such as shifting less income overseas, conducting more foreign tax planning, and acquiring more auditor-provided tax services. Firms also avoid liberal judges through forum shopping. Finally, we show that IRS enforcement complements the judge ideology effect. Overall, we are the first to demonstrate the judicial branch as a key determinant of corporate tax planning, which contributes to a more complete understanding of tax enforcement.
