Selected Working Papers
"FinBERT: A Deep Learning Approach to Extracting Textual Information" (with Hui Wang and Yi Yang).
In this paper, we develop FinBERT, a state-of-the-art deep learning algorithm that incorporates the contextual relations between words in the finance domain. First, using a researcher-labeled analyst report sample, we document that FinBERT significantly outperforms the Loughran and McDonald (LM) dictionary, the naïve Bayes, and Word2Vec in sentiment classification, primarily because of its ability to uncover sentiment in sentences that other algorithms mislabel as neutral. Next, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 32% compared with FinBERT. Our results also indicate that FinBERT’s greater accuracy is especially relevant when empirical tests may suffer from low power, such as with small samples. Last, textual sentiments summarized by FinBERT can better predict future earnings than the LM dictionary, especially after 2011, consistent with firms’ strategic disclosures reducing the information content of textual sentiments measured with LM dictionary. Our results have implications for academic researchers, investment professionals, and financial market regulators who want to extract insights from financial texts.
GitHub link for Pre-Trained FinBERT
In this paper, we develop FinBERT, a state-of-the-art deep learning algorithm that incorporates the contextual relations between words in the finance domain. First, using a researcher-labeled analyst report sample, we document that FinBERT significantly outperforms the Loughran and McDonald (LM) dictionary, the naïve Bayes, and Word2Vec in sentiment classification, primarily because of its ability to uncover sentiment in sentences that other algorithms mislabel as neutral. Next, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 32% compared with FinBERT. Our results also indicate that FinBERT’s greater accuracy is especially relevant when empirical tests may suffer from low power, such as with small samples. Last, textual sentiments summarized by FinBERT can better predict future earnings than the LM dictionary, especially after 2011, consistent with firms’ strategic disclosures reducing the information content of textual sentiments measured with LM dictionary. Our results have implications for academic researchers, investment professionals, and financial market regulators who want to extract insights from financial texts.
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.
"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.
"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 that commit accounting fraud before the fraud is publicly revealed and investigates the economic effects of such rating actions. After controlling for firms’ economic fundamentals and other sources of information, we find that Standard & Poor’s (S&P), an issuer-paid rating agency, takes negative rating actions against fraud firms, including downgrades and negative credit watches, as early as four quarters before the revelation of fraud. Egan-Jones, an investor-paid rating agency with no access to management and material nonpublic information, is slower than S&P to downgrade fraud firms, especially when information uncertainty is high and when fraud firms are subject to private SEC investigations. Last, we find that S&P’s negative rating actions against fraud firms are informative to the market and are associated with a shorter fraud duration. Taken together, our results suggest that credit rating agencies use their information advantage to detect accounting fraud.
This study examines whether and when credit rating agencies take negative rating actions against issuers that commit accounting fraud before the fraud is publicly revealed and investigates the economic effects of such rating actions. After controlling for firms’ economic fundamentals and other sources of information, we find that Standard & Poor’s (S&P), an issuer-paid rating agency, takes negative rating actions against fraud firms, including downgrades and negative credit watches, as early as four quarters before the revelation of fraud. Egan-Jones, an investor-paid rating agency with no access to management and material nonpublic information, is slower than S&P to downgrade fraud firms, especially when information uncertainty is high and when fraud firms are subject to private SEC investigations. Last, we find that S&P’s negative rating actions against fraud firms are informative to the market and are associated with a shorter fraud duration. Taken together, our results suggest that credit rating agencies use their information advantage to detect accounting fraud.
"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.
