Publications
16. "Judge Ideology and Opportunistic Insider Trading" (with Kai Wai Hui and Yue Zheng), Journal of Financial and Quantitative Analysis, forthcoming.
Although federal judges are the ultimate arbiters of insider trading enforcement, the role of their political ideology in insider trading is unclear. Using the partisanship of judges’ nominating presidents to measure judge ideology, we first document that liberal judges are associated with heavier penalties in insider trading lawsuits than conservative judges. Next, we find that firms located in circuits with more liberal judges have fewer opportunistic insider sales. Cross-sectional analyses show that this deterrent effect is stronger when managers face a higher risk of insider trading lawsuits. Finally, we find that the SEC considers judges’ ideology when selecting litigation forums.
Although federal judges are the ultimate arbiters of insider trading enforcement, the role of their political ideology in insider trading is unclear. Using the partisanship of judges’ nominating presidents to measure judge ideology, we first document that liberal judges are associated with heavier penalties in insider trading lawsuits than conservative judges. Next, we find that firms located in circuits with more liberal judges have fewer opportunistic insider sales. Cross-sectional analyses show that this deterrent effect is stronger when managers face a higher risk of insider trading lawsuits. Finally, we find that the SEC considers judges’ ideology when selecting litigation forums.
15. "Expropriation Risk and Investment: A Natural Experiment" (with Siddharth M. Bhambhwani and Hui Dong), Journal of Financial and Quantitative Analysis, November 2024, Vol. 59 Iss. 7, pp. 3448-3478.
This paper uses the enactment of China’s 2007 Property Law (the Law), which reduces the risk of expropriation by local governments, as the setting to investigate the importance of property rights protection for private firm investment. Using propensity score matching and a difference-in-differences design, we find that firms facing weaker property rights protection prior to the Law significantly increase their investment and investment efficiency after the Law. Cross-sectional analyses document evidence consistent with a decrease in firms’ perceived expropriation risk as the main mechanism underlying the Law’s effect. Finally, we show that the Law improves local economic outcomes and employment.
This paper uses the enactment of China’s 2007 Property Law (the Law), which reduces the risk of expropriation by local governments, as the setting to investigate the importance of property rights protection for private firm investment. Using propensity score matching and a difference-in-differences design, we find that firms facing weaker property rights protection prior to the Law significantly increase their investment and investment efficiency after the Law. Cross-sectional analyses document evidence consistent with a decrease in firms’ perceived expropriation risk as the main mechanism underlying the Law’s effect. Finally, we show that the Law improves local economic outcomes and employment.
14. "Securities Law Precedents, Litigation Risk and Misreporting" (with Benedikt Franke, Reeyarn Li and Hui Wang), Review of Finance, March 2024, Vol. 28 Iss. 2, pp. 413-445.
In common law systems, firms’ litigation risk depends both on written laws and how courts interpret these laws. Using 321 U.S. circuit court rulings, we introduce a novel measure capturing courts’ attitudes towards defendants in securities lawsuits. Our results confirm that financial misreporting firms in more defendant-friendly circuits face fewer lawsuits. Consistent with lower expected litigation costs, firms in these circuits face less negative market reactions when misreporting is revealed, invest less in preventing misreporting, and are more likely to engage in aggressive misreporting. We conclude that defendant-friendly precedents reduce firms’ legal liability and worsen their financial reporting quality.
In common law systems, firms’ litigation risk depends both on written laws and how courts interpret these laws. Using 321 U.S. circuit court rulings, we introduce a novel measure capturing courts’ attitudes towards defendants in securities lawsuits. Our results confirm that financial misreporting firms in more defendant-friendly circuits face fewer lawsuits. Consistent with lower expected litigation costs, firms in these circuits face less negative market reactions when misreporting is revealed, invest less in preventing misreporting, and are more likely to engage in aggressive misreporting. We conclude that defendant-friendly precedents reduce firms’ legal liability and worsen their financial reporting quality.
13. "Auditing Decentralized Finance" (with Siddharth M. Bhambhwani), British Accounting Review, March 2024, Vol. 56 Iss. 2, 101270.
Decentralized finance (DeFi), which executes financial transactions using blockchain without an intermediary, has attracted over US$250 billion in total value locked (TVL) at its peak. However, little is known about how DeFi protocols assure users of the safety of their investments. This paper provides the first empirical evidence on DeFi audit services that check and verify the smart contracts underlying these protocols. Using data on 316 of the largest protocols, we find that those vetted by more smart contract auditors and by higher quality auditors have higher TVL and that these protocols have higher market capitalization (native token values). Using an event study approach, we document that TVL and token values significantly increase after a protocol's first audit, especially those involving a high-quality auditor. We also find that protocols with more auditors and higher audit quality exhibit a smaller drop in TVL and token values after the collapse of the TerraUSD stablecoin, which reduced aggregate DeFi TVL by almost 65%. Overall, our findings suggest that DeFi users and investors perceive audits as providing assurance regarding the safety of their deposits and investments.
Decentralized finance (DeFi), which executes financial transactions using blockchain without an intermediary, has attracted over US$250 billion in total value locked (TVL) at its peak. However, little is known about how DeFi protocols assure users of the safety of their investments. This paper provides the first empirical evidence on DeFi audit services that check and verify the smart contracts underlying these protocols. Using data on 316 of the largest protocols, we find that those vetted by more smart contract auditors and by higher quality auditors have higher TVL and that these protocols have higher market capitalization (native token values). Using an event study approach, we document that TVL and token values significantly increase after a protocol's first audit, especially those involving a high-quality auditor. We also find that protocols with more auditors and higher audit quality exhibit a smaller drop in TVL and token values after the collapse of the TerraUSD stablecoin, which reduced aggregate DeFi TVL by almost 65%. Overall, our findings suggest that DeFi users and investors perceive audits as providing assurance regarding the safety of their deposits and investments.
12. "The Usefulness of Credit Ratings for Accounting Fraud Detection" (with Pepa Kraft and Shiheng Wang), The Accounting Review, November 2023, Vol. 88 Iss. 7, pp. 347-376.
This study examines whether and when credit ratings are useful for accounting fraud prediction. We find that negative rating actions by Standard & Poor’s (S&P), an issuer-paid credit rating agency (CRA), have predictive ability for fraud incremental to fraud prediction models (e.g., F-score) and other market participants. In contrast, rating actions by Egan-Jones Rating Company (EJR), an investor-paid CRA relying on public information, have less predictive ability, which is subsumed by S&P and other market participants. Our results are robust to including firms not covered by EJR, using only rating downgrades, controlling for firm characteristics, and using alternative benchmarks. We also find that the ability of negative S&P rating actions to predict fraud becomes stronger after the 2008–2009 financial crisis. Last, compared to EJR, S&P is quicker to take negative rating actions against fraud firms. In sum, our results suggest that issuer-paid CRAs’ information advantage helps predict accounting fraud.
This study examines whether and when credit ratings are useful for accounting fraud prediction. We find that negative rating actions by Standard & Poor’s (S&P), an issuer-paid credit rating agency (CRA), have predictive ability for fraud incremental to fraud prediction models (e.g., F-score) and other market participants. In contrast, rating actions by Egan-Jones Rating Company (EJR), an investor-paid CRA relying on public information, have less predictive ability, which is subsumed by S&P and other market participants. Our results are robust to including firms not covered by EJR, using only rating downgrades, controlling for firm characteristics, and using alternative benchmarks. We also find that the ability of negative S&P rating actions to predict fraud becomes stronger after the 2008–2009 financial crisis. Last, compared to EJR, S&P is quicker to take negative rating actions against fraud firms. In sum, our results suggest that issuer-paid CRAs’ information advantage helps predict accounting fraud.
11. "Artificial Intelligence and Financial Decision Making" (with Haifeng You), 2023, In: Hilary, G. and R. D. McLean (eds.), Handbook of Financial Decision Making, Edward Elgar Publishing, Cheltenham .
Artificial intelligence (AI), powered by machine learning algorithms, is capable of extracting information efficiently from big data and, therefore, has great potential for improving financial decision-making. In this chapter, we summarize several important applications of AI in this context. First, we review AI algorithms that extract information from unstructured data, with a focus on natural language processing algorithms. Next, we discuss how AI extracts and aggregates information from unstructured and structured data so as to facilitate financial decisions such as investment and FinTech lending. Lastly, we discuss the complementary roles of AI and humans in improving financial decision-making.
Artificial intelligence (AI), powered by machine learning algorithms, is capable of extracting information efficiently from big data and, therefore, has great potential for improving financial decision-making. In this chapter, we summarize several important applications of AI in this context. First, we review AI algorithms that extract information from unstructured data, with a focus on natural language processing algorithms. Next, we discuss how AI extracts and aggregates information from unstructured and structured data so as to facilitate financial decisions such as investment and FinTech lending. Lastly, we discuss the complementary roles of AI and humans in improving financial decision-making.
10. "FinBERT--A Large Language Model Approach to Extracting Information from Financial Text" (with Hui Wang and Yi Yang), Contemporary Accounting Research, Summer 2023, Vol. 40 Iss. 2, pp. 806-841.
We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT’s advantage over other algorithms, and Google’s original bidirectional encoder representations from transformers (BERT) model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18%, compared with FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.
FinBERT Model and Code
FinSent: A browser-based financial sentiment analysis dashboard of U.S. and Hong Kong firms based on FinBERT
We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT’s advantage over other algorithms, and Google’s original bidirectional encoder representations from transformers (BERT) model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18%, compared with FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.
FinBERT Model and Code
FinSent: A browser-based financial sentiment analysis dashboard of U.S. and Hong Kong firms based on FinBERT
9. "The Unintended Benefit of the Risk Factor Mandate of 2005" (with Jianghua Shen and Amy Zang), Review of Accounting Studies, December 2022, Vol. 27 Iss. 4, pp. 1319-1355.
In 2005, the SEC mandated that firms disclose risk factors to provide useful information about firm risk. An unintended effect of the mandate is that mandatory risk factor (RF) disclosure may constitute “meaningful cautionary language” as defined in the Private Securities Litigation Reform Act, and may therefore provide legal protection for forward-looking statements (FLSs). Using both a difference-in-differences design and a two-stage least squares approach, we find that, following the mandate, firms that had not previously disclosed risk factors (late RF disclosers) became more willing to provide qualitative FLSs, particularly positive ones, than other firms. This finding is consistent with our prediction that, for late RF disclosers, the mandate reduces managers’ perceived litigation risk. We also find that these firms experience improvement in their information environment. A path analysis reveals that the mandate improves firms’ information environment not only directly but also indirectly by prompting more disclosure of positive FLSs, illustrating an unintended benefit of the 2005 RF mandate. Cross-sectional tests show that the RF mandate induces a larger increase in positive FLSs for firms whose managers perceive a higher level of benefit from safe harbor protection arising from meaningful cautionary statements.
In 2005, the SEC mandated that firms disclose risk factors to provide useful information about firm risk. An unintended effect of the mandate is that mandatory risk factor (RF) disclosure may constitute “meaningful cautionary language” as defined in the Private Securities Litigation Reform Act, and may therefore provide legal protection for forward-looking statements (FLSs). Using both a difference-in-differences design and a two-stage least squares approach, we find that, following the mandate, firms that had not previously disclosed risk factors (late RF disclosers) became more willing to provide qualitative FLSs, particularly positive ones, than other firms. This finding is consistent with our prediction that, for late RF disclosers, the mandate reduces managers’ perceived litigation risk. We also find that these firms experience improvement in their information environment. A path analysis reveals that the mandate improves firms’ information environment not only directly but also indirectly by prompting more disclosure of positive FLSs, illustrating an unintended benefit of the 2005 RF mandate. Cross-sectional tests show that the RF mandate induces a larger increase in positive FLSs for firms whose managers perceive a higher level of benefit from safe harbor protection arising from meaningful cautionary statements.
8. "Cross-Industry Information Sharing among Colleagues and Analyst Research" (with An-Ping Lin and Amy Zang), Journal of Accounting and Economics, August 2022, Vol. 74 Iss. 1, 101496.
We identify a specific organizational resource in brokerage houses—information sharing among analyst colleagues who cover economically related industries along a supply chain. After controlling for brokerage selection effects, we show evidence consistent with the benefit of this resource to analyst research performance. Specifically, we find that analysts whose colleagues cover more economically connected industries have better research performance, especially when their colleagues produce higher-quality research. We further show that colleagues’ coverage of downstream (upstream) industries is positively related to the accuracy of only the analyst’s revenue (expense) forecasts and that analysts and their highly connected colleagues tend to issue earnings forecast revisions contemporaneously. Last, we find that analysts with economically connected colleagues tend to have a higher level of industry specialization. Overall, our findings suggest that analysts rely on organizational resources to produce high-quality research. Hence, a portion of their performance and reputation is not transferable across employers.
We identify a specific organizational resource in brokerage houses—information sharing among analyst colleagues who cover economically related industries along a supply chain. After controlling for brokerage selection effects, we show evidence consistent with the benefit of this resource to analyst research performance. Specifically, we find that analysts whose colleagues cover more economically connected industries have better research performance, especially when their colleagues produce higher-quality research. We further show that colleagues’ coverage of downstream (upstream) industries is positively related to the accuracy of only the analyst’s revenue (expense) forecasts and that analysts and their highly connected colleagues tend to issue earnings forecast revisions contemporaneously. Last, we find that analysts with economically connected colleagues tend to have a higher level of industry specialization. Overall, our findings suggest that analysts rely on organizational resources to produce high-quality research. Hence, a portion of their performance and reputation is not transferable across employers.
7. "The Long-Term Consequences of Short-Term Incentives" (with Alex Edmans and Vivian Fang), Journal of Accounting Research, June 2022, Vol. 60 Iss. 3, pp. 1007-1046.
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. A potential driver of the negative M&A returns is subsequent 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. A potential driver of the negative M&A returns is subsequent 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.
6. "Federal Judge Ideology: A New Measure of Ex-Ante Litigation Risk" (with Kai Wai Hui and Reeyarn Li), Journal of Accounting Research, May 2019, Vol. 57 Iss. 2, pp. 431-489.
Drawing on the political theory of judicial decision making, our paper proposes a new and parsimonious ex ante litigation risk measure: federal judge ideology. We find that judge ideology complements existing measures of litigation risk based on industry membership and firm characteristics. Firms in liberal circuits (the third quartile in ideology) are 33.5% more likely to be sued in securities class action lawsuits than those in conservative circuits (the first quartile in ideology). This result is stronger after the U.S. Supreme Court’s ruling in the Tellabs case. We next show that the effect of judge ideology on litigation risk is greater for firms with more sophisticated shareholders and with higher expected litigation costs. Furthermore, judicial appointments affect litigation risk and the value of firms in the circuit, highlighting the economic consequences of political appointments of judges. Finally, using our new measure, we document that litigation risk deters managers from providing long-term earnings guidance, a result that existing measures of litigation risk cannot show.
Online Appendix to Huang, Hui and Li (2019)
Email me for judge ideology data updated to 2021
Federal Judge Ideology: Background and Other Data Source
Drawing on the political theory of judicial decision making, our paper proposes a new and parsimonious ex ante litigation risk measure: federal judge ideology. We find that judge ideology complements existing measures of litigation risk based on industry membership and firm characteristics. Firms in liberal circuits (the third quartile in ideology) are 33.5% more likely to be sued in securities class action lawsuits than those in conservative circuits (the first quartile in ideology). This result is stronger after the U.S. Supreme Court’s ruling in the Tellabs case. We next show that the effect of judge ideology on litigation risk is greater for firms with more sophisticated shareholders and with higher expected litigation costs. Furthermore, judicial appointments affect litigation risk and the value of firms in the circuit, highlighting the economic consequences of political appointments of judges. Finally, using our new measure, we document that litigation risk deters managers from providing long-term earnings guidance, a result that existing measures of litigation risk cannot show.
Online Appendix to Huang, Hui and Li (2019)
Email me for judge ideology data updated to 2021
Federal Judge Ideology: Background and Other Data Source
5. "Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach" (with Reuven Lehavy, Amy Zang, and Rong Zheng), Management Science, June 2018, Vol. 64, Iss. 6, pp. 2833-2855.
This study examines analyst information intermediary roles using a textual analysis of analyst reports and corporate disclosures. We employ a topic modeling methodology from computational linguistic research to compare the thematic content of a large sample of analyst reports issued promptly after earnings conference calls with the content of the calls themselves. We show that analysts discuss exclusive topics beyond those from conference calls and interpret topics from conference calls. In addition, we find that investors place a greater value on new information in analyst reports when managers face greater incentives to withhold value-relevant information. Analyst interpretation is particularly valuable when the processing costs of conference call information increase. Finally, we document that investors react to analyst report content that simply repeats managers’ discussion. Overall, our study shows that analysts play the information intermediary roles by discovering information beyond corporate disclosures and by clarifying and confirming corporate disclosures.
Internet Appendix to Huang, Lehavy, Zang and Zheng (2018)
Java package to implement Topic Modeling (LDA) algorithm used in the paper
Another Java based package (MALLET)
Python package to implement LDA
List of high frequency phrases that constitute specific financial/technical terms (see Appendix B.2 for details)
This study examines analyst information intermediary roles using a textual analysis of analyst reports and corporate disclosures. We employ a topic modeling methodology from computational linguistic research to compare the thematic content of a large sample of analyst reports issued promptly after earnings conference calls with the content of the calls themselves. We show that analysts discuss exclusive topics beyond those from conference calls and interpret topics from conference calls. In addition, we find that investors place a greater value on new information in analyst reports when managers face greater incentives to withhold value-relevant information. Analyst interpretation is particularly valuable when the processing costs of conference call information increase. Finally, we document that investors react to analyst report content that simply repeats managers’ discussion. Overall, our study shows that analysts play the information intermediary roles by discovering information beyond corporate disclosures and by clarifying and confirming corporate disclosures.
Internet Appendix to Huang, Lehavy, Zang and Zheng (2018)
Java package to implement Topic Modeling (LDA) algorithm used in the paper
Another Java based package (MALLET)
Python package to implement LDA
List of high frequency phrases that constitute specific financial/technical terms (see Appendix B.2 for details)
4. "Imperfect Accounting and Reporting Bias" (with Vivian Fang and Wenyu Wang), Journal of Accounting Research, September 2017, Vol. 55, Iss. 4, pp. 919-962.
Errors and bias are both inherent features of accounting. In theory, while errors discourage bias by lowering the value relevance of accounting, they can also facilitate bias by providing camouflage. Consistent with theory, we find a hump-shaped relation between a firm’s propensity to engage in intentional misstatement and the prevalence of unintentional misstatements in the firm’s industry for the whole economy and a majority of the industries. The result is robust to using firms’ number of items in financial statements and exposure to complex accounting rules as alternative proxies for errors and to using the restatement amount in net income to quantify the magnitude of bias and errors. To directly test for the two effects of errors, we show that, when errors are more prevalent, the market reacts less to firms’ earnings surprises and bias is more difficult to detect. Our results highlight the imperfectness of accounting, advance understanding of firms’ reporting incentives, and shed light on accounting standard setting.
Errors and bias are both inherent features of accounting. In theory, while errors discourage bias by lowering the value relevance of accounting, they can also facilitate bias by providing camouflage. Consistent with theory, we find a hump-shaped relation between a firm’s propensity to engage in intentional misstatement and the prevalence of unintentional misstatements in the firm’s industry for the whole economy and a majority of the industries. The result is robust to using firms’ number of items in financial statements and exposure to complex accounting rules as alternative proxies for errors and to using the restatement amount in net income to quantify the magnitude of bias and errors. To directly test for the two effects of errors, we show that, when errors are more prevalent, the market reacts less to firms’ earnings surprises and bias is more difficult to detect. Our results highlight the imperfectness of accounting, advance understanding of firms’ reporting incentives, and shed light on accounting standard setting.
3. "Short Selling and Earnings Management: A Controlled Experiment" (with Vivian Fang and Jonathan Karpoff), Journal of Finance, June 2016, Vol. 71, Iss. 3, pp. 1251-1294.
During 2005 to 2007, the SEC ordered a pilot program in which one-third of the Russell 3000 index were arbitrarily chosen as pilot stocks and exempted from short-sale price tests. Pilot firms’ discretionary accruals and likelihood of marginally beating earnings targets decrease during this period, and revert to pre-experiment levels when the program ends. After the program starts, pilot firms are more likely to be caught for fraud initiated before the program, and their stock returns better incorporate earnings information. These results indicate that short selling, or its prospect, curbs earnings management, helps detect fraud, and improves price efficiency.
List of pilot stocks published SEC's release No. 50104
Data and Codes to replicate our principal findings (README)
During 2005 to 2007, the SEC ordered a pilot program in which one-third of the Russell 3000 index were arbitrarily chosen as pilot stocks and exempted from short-sale price tests. Pilot firms’ discretionary accruals and likelihood of marginally beating earnings targets decrease during this period, and revert to pre-experiment levels when the program ends. After the program starts, pilot firms are more likely to be caught for fraud initiated before the program, and their stock returns better incorporate earnings information. These results indicate that short selling, or its prospect, curbs earnings management, helps detect fraud, and improves price efficiency.
List of pilot stocks published SEC's release No. 50104
Data and Codes to replicate our principal findings (README)
2. "Evidence on the Information Content of Text in Analyst Reports" (with Amy Zang and Rong Zheng), The Accounting Review, November 2014, Vol. 89, No. 6, pp. 2151-2180.
We document that textual discussions in a sample of 363,952 analyst reports provide information to investors beyond that in the contemporaneously released earnings forecasts, stock recommendations, and target prices, and also assist investors in interpreting these signals. Cross-sectionally, we find that investors react more strongly to negative than to positive text, suggesting that analysts are especially important in propagating bad news. Additional evidence indicates that analyst report text is more useful when it places more emphasis on nonfinancial topics, is written more assertively and concisely, and when the perceived validity of other information signals in the same report is low. Finally, analyst report text is shown to have predictive value for future earnings growth in the subsequent five years.
Weka: Data mining software to implement the naive Bayes algorithm
Using FinBERT trained on the 10,000 sentence from analyst reports to classify sentiments (FinBERT paper)
We document that textual discussions in a sample of 363,952 analyst reports provide information to investors beyond that in the contemporaneously released earnings forecasts, stock recommendations, and target prices, and also assist investors in interpreting these signals. Cross-sectionally, we find that investors react more strongly to negative than to positive text, suggesting that analysts are especially important in propagating bad news. Additional evidence indicates that analyst report text is more useful when it places more emphasis on nonfinancial topics, is written more assertively and concisely, and when the perceived validity of other information signals in the same report is low. Finally, analyst report text is shown to have predictive value for future earnings growth in the subsequent five years.
Weka: Data mining software to implement the naive Bayes algorithm
Using FinBERT trained on the 10,000 sentence from analyst reports to classify sentiments (FinBERT paper)
1. "CEO Reputation and Earnings Quality" (with Jennifer Francis, Shivaram Rajgopal and Amy Zang), Contemporary Accounting Research, Spring 2008, Vol. 25, Iss. 1, pp. 109-147.
We examine the relation between CEO reputation and measures of the firm’s earnings quality. Using press coverage (media counts) to proxy for CEO reputation, we find that more reputed CEOs are associated with poorer earnings quality. This finding is inconsistent with an efficient contracting view, which predicts that reputed CEOs take actions that result in good earnings quality. This seemingly counterintuitive result is, however, consistent with two other theories: a rent extraction hypothesis (which predicts that reputed managers are more likely to use their discretion to manipulate earnings in order to manage labor and stock market perceptions) and a matching hypothesis (which predicts that selection on the part of firms gives rise to a demand for reputed CEOs for firms where earnings quality is inherently poor). Further analyses provide little support for the rent extraction explanation and some support for the matching explanation.
We examine the relation between CEO reputation and measures of the firm’s earnings quality. Using press coverage (media counts) to proxy for CEO reputation, we find that more reputed CEOs are associated with poorer earnings quality. This finding is inconsistent with an efficient contracting view, which predicts that reputed CEOs take actions that result in good earnings quality. This seemingly counterintuitive result is, however, consistent with two other theories: a rent extraction hypothesis (which predicts that reputed managers are more likely to use their discretion to manipulate earnings in order to manage labor and stock market perceptions) and a matching hypothesis (which predicts that selection on the part of firms gives rise to a demand for reputed CEOs for firms where earnings quality is inherently poor). Further analyses provide little support for the rent extraction explanation and some support for the matching explanation.
