Jinfei Sheng 

Assistant Professor of Finance

Merage School of Business

University of California, Irvine 

Research Interests: 

Empirical Asset Pricing, Behavioral Finance, AI & FinTech, Textual Analysis

Curriculum Vitae | Google Scholar


Macro News and Micro News: Complements or Substitutes?, Journal of Financial Economics, 145 (2022), 1006-1024.

(with David Hirshleifer)     JFE    NBER    Internet Appendix

Abstract: We study how the arrival of macro-news affects the stock market’s ability to incorporate the information in firm-level earnings announcements. Existing theories suggest that macro and firm-level earnings news are attention substitutes; macro-news announcements crowd out firm-level attention, causing less efficient processing of firm-level earnings announcements. We find the opposite: the sensitivity of announcement returns to earnings news is 17% stronger, and post-earnings announcement drift 71% weaker, on macro-news days. This suggests a complementary relationship between macro and micro news that is consistent with either investor attention or information transmission channels.

Macroeconomic Attention and Announcement Risk Premia, Review of Financial Studies, 35 (2022), 5057–5093. 

(with Adlai Fisher and Charles Martineau)      RFS    Internet Appendix   MAI Data

Abstract: We construct macroeconomic attention indices (MAI), new measures of attention to different macroeconomic risks including monetary policy and employment. Individual MAI increase several days before a related announcement, on average. MAI also respond to changes in macroeconomic fundamentals, with bad news raising attention more than good news. Across announcements, attention predicts announcement risk premia and implied volatility changes with large economic magnitudes. Our findings support theories of endogenous attention and announcement risk premia while demonstrating future research directions, including that announcements can raise new concerns. Macroeconomic announcements are important not only for contents and timing, but also attention. 

 Cheaper Is Not Better: On the 'Superior' Performance of High-Fee Mutual Funds, Review of Asset Pricing Studies, 13 (2023), 375–404.

(with Mikhail Simutin and Terry Zhang)     RAPS     Internet Appendix

Abstract: In contrast with theoretical predictions, high-fee active equity funds generate worse net-of-expenses performance. We show that this fee-performance puzzle is driven by the preference of high-fee funds for stocks with low operating profitability and high investment rates, characteristics associated with low expected returns. After controlling for exposures to profitability and investment factors, high-fee funds significantly outperform low-fee funds before expenses and achieve similarly poor net-of-fees performance. In resolving the fee-performance puzzle, our findings provide support to the theoretical prediction that skilled managers extract rents by charging high fees, and challenge the common advice to prefer low-fee funds over high-fee counterparts. 

Do Investors Affect Financial Analysts’ Behavior? Evidence from Short Sellers, Financial Management, 52 (2023), 199-224. 

(with Yun Ke, Kin Lo, and Jenny Zhang)       FM

Abstract: We examine how short sellers affect financial analysts’ forecast behavior using a natural experiment that relaxes short-sale constraints. We find that increased ease of short selling improves analyst earnings forecast quality by reducing forecast bias and increasing forecast accuracy. The improvements can be explained by both the disciplining pressure from short sellers and increased price efficiency from incorporating information in a timely manner. Although it is well documented that financial analysts can affect investors, our paper provides novel evidence on how sophisticated investors, short sellers, can affect analysts.

Partisan Return Gap: The Polarized Stock Market in the Time of a Pandemic, Management Science, Forthcoming. 

(with Zheng Sun and Wanyi Wang)         MS       Internet Appendix  

Abstract: Using two proxies for investors’ political affiliation, we document sharp differences in stock returns between firms likely dominated by Democratic investors (blue stocks) and those dominated by Republican investors (red stocks) during the COVID pandemic. Red stocks have 20 basis points higher risk-adjusted returns than blue stocks on COVID news days (Partisan Return Gap). Lockdown policies, COVID cases, industry and firm fundamentals only explain at most 40% of the return gap. Polarized political beliefs about COVID, revealed through people’s social distancing behaviors, contribute to about 40% of the return gap beyond the fundamental channel. Our paper provides partisanship as a novel aspect in understanding abnormal stock returns during the pandemic.

Working Papers

Do Mutual Funds Walk the Talk? Evidence from Fund Risk Disclosure, 2023 (R&R)

(with Nan Xu and Lu Zheng)

Abstract: We examine the accuracy of mutual fund risk disclosure using the Risk Coverage Ratio (RCR), comparing the explanatory power of risks disclosed by a fund to that of all risks disclosed by all funds. Excluding market risk, the average fund RCR is 55%; RCR drops to 26% when we exclude information contained in fund names. RCR is positively related to flows from institutional investors. However, RCR correlates with lower future fund performance, suggesting costs associated with revealing private information. Notably, 56% of disclosed risks are insignificant, indicating overdisclosure. Funds improve the informativeness of risk disclosure after receiving SEC comment letters.

 Asset Pricing in the Information Age: Employee Expectations and Stock Returns, 2023 New Version

Abstract: Using a novel dataset of online employee reviews, I study the value of employee expectations through the lens of the stock market. Firms with a more positive employee expectation tend to earn higher future returns, delivering annualized abnormal returns ranging from 8% to 11%. The forecasting power of employee expectations can be explained by their links to forward-looking information about firms’ fundamentals. Additional evidence suggests that hedge funds may actively trade on this information, resulting in a decay in the forecasting power over longer holding horizons. This paper highlights the importance of labor in asset pricing from an information perspective.

 How Do Investors Value Technology in Cryptocurrency: Evidence from Textual Analysis, 2024 New Version

(with Yukun Liu and Wanyi Wang) 

Abstract: This paper examines how investors evaluate new technologies from the perspective of cryptocurrencies. Employing a machine learning method, we construct a novel Tech Index from whitepapers to capture technology sophistication of cryptocurrency. While cryptocurrencies with higher Tech Indexes are more likely to enjoy early success and a high first-day listing price, they tend to earn significantly lower subsequent returns after being listed. The return reversal is stronger when market sentiment is higher. Moreover, cryptocurrencies with higher Tech Index earn lower returns after the unexpected Luna Crash and FTX scandal. Overall, these findings suggest investors overreact to cryptocurrency technology.

 Generative AI and Asset Management, 2024 New

(with Zheng Sun, Baozhong Yang, Alan Zhang) 

Abstract: This paper proposes a novel measure of the reliance on generative AI of investment companies and utilizes it to study the adoption and implications of generative AI tools in the asset management industry, particularly hedge fund companies. We document a sharp increase in the use of generative AI by hedge fund companies after ChatGPT was introduced in 2022. In a difference-indifferences test, we find that hedge fund companies adopting generative AI produce superior raw and risk-adjusted returns relative to nonadopters, with a gain of 3 to 5% in annualized abnormal returns. We further identify this effect by exploiting ChatGPT outages as exogenous shocks. The outperformance originates from investment in AI talent, and more from firm policy and performance information than from macroeconomic information. Unlike hedge funds, non-hedge fund companies do not produce significant returns from their adoption. Large and more active hedge fund companies adopt the technology early and achieve higher returns than others, indicating that utilizing generative AI effectively as an investment tool may require a combination of other resources, such as data and expertise. Overall, our findings suggest that generative AI may exacerbate existing disparities among investors rather than mitigate them, further widening the gap between market participants.

 Trading in Twilight: Sleep, Mental Alertness, and Stock Market Trading, 2024 New

(with Hee-Seo Han, David Hirshleifer, and Zheng Sun) 

Abstract: We study here how mental alertness affects investor trading behavior and profitability. To capture exogenous variation in mental alertness, we focus on sources of sleep deprivation, which has been identified as a substantial public health problem with economic consequences. We use household-level stock trading panel data from a large discount brokerage. Using a regression discontinuity design based on time zones, we find a large negative and causal relationship between sleep disruption, proxied by local sunset time, and trading performance. Investors who are on the later sunset side of timezone borders on average trade less profitably--2 basis points lower daily abnormal returns over the next 250 days (5% per annum). Further evidence indicates that   inattention to new information and greater extrapolation of extreme past returns contribute to the effect. Overall, our research demonstrates that mental alertness has a substantial influence on investor investment behavior and performance.

Employee Disagreement, 2024 New

Abstract: This paper studies the relationship between disagreement of the employee crowd and stock returns, utilizing online forecasts regarding their employers’ business outlook. Stocks with high disagreement tend to earn lower future returns. A trading strategy based on employee disagreement delivers an annualized abnormal return of -8% to -9%. This return predictability is attributed to the fundamental channel, as firms characterized by high employee disagreement tend to demonstrate lower profitability and less employee synergy. This information is incorporated into stock price gradually over time as this predictive power is sustained for two months. Overall, disagreement among online crowds is important for understanding stock returns.

Capitalizing on Retail Investor Sentiment: Evidence from FinTech ETFs, 2023

(with Bong Ko and Zheng Sun)

Abstract: This paper studies how professional money managers respond to retail investors' sentiment toward new technology through the lens of FinTech ETFs. Retail investor sentiment about FinTech, measured by Google Search Volume, has tripled from 2015 to 2022. Using a hand-collected dataset, we find that the number of FinTech ETFs surges when investor sentiment toward FinTech is high. On average, 34% of FinTech ETFs engage in “FinTech Washing," as evidenced by the low fraction of FinTech stocks in their portfolios and low risk-exposure to the FinTech space. Despite their high fees (68 bps per annum), FinTech ETFs yield annual excess returns of -13%, significant even after accounting for various risk factors. The investor base, primarily comprised of retail investors, is not aware of the FinTech washing and appears unresponsive to unsatisfactory performance. Their underperformance can be attributed to market timing and the activeness of funds. The disappointing performance cannot be explained by low-quality fund families launching those ETFs. Overall, the evidence suggests that asset managers capitalize on the retail investors' sentiment toward the FinTech space by offering misrepresented and low-quality ETFs, which carries significant policy and welfare implications for both regulators and investors.

Abstract: Interest rates have declined dramatically over the past 30 years. At the same time the birth rate has declined, and life expectancy has increased. Demographic changes leading to an older population have been proposed as an explanation for the decline in rates. However, this conjecture is difficult to test because demographics change slowly over time, and are correlated with other country characteristics. We show that in a cross-section of U.S. MSAs, the relationship between interest rates and demographics is only partially consistent with the above conjecture, and with existing models, which predict a negative association between age and interest rates. This association is, indeed, negative for lending rates, but positive for deposit rates. We rationalize this pattern by extending an OLG model where the banking sector is not perfectly competitive. 

 How Does Soft Information Affect External Firm Financing? Evidence from Online Employee Ratings, 2020

(with Thomas Chemmanur and Harshit Rajaiya)

Abstract: We analyze how employees’ online ratings of firms’ affect their corporate financing and investment policies. We hypothesize that, while employees are unlikely to have access to inside information, their ratings, being driven by their day-to-day interactions with their employers, are likely to be correlated with long-run firm value and performance. This means that employee ratings are likely to affect the external financing behavior of firms in a setting where potential equity investors have access to online employee ratings (and firm insiders are aware of such access). We develop and test hypotheses based on the above assumptions using a large sample of around 1.1 million employee ratings from the Glassdoor website covering a sample of 2842 public firms. We find that firms with higher average online employee rating realizations are associated with algebraically greater abnormal stock returns upon an equity issue announcement; a greater propensity to have positive abnormal stock returns upon such an announcement; a greater propensity to issue equity rather than debt to raise external financing; higher annual investment expenditures; greater participation by institutional investors in their equity offerings (SEOs); and better long-run post-SEO operating performance. We demonstrate causality by making use of a difference-in-differences (DID) methodology relying on the staggered implementation of laws protecting the First Amendment Rights of citizens (anti-SLAPP laws) across US states. 

 The Real Effects of Government Intervention: Firm-level Evidence from TARP, 2021

Abstract: This paper investigates the real and financial effects of the largest government intervention in US history, the Troubled Asset Relief Program (TARP), on individual firms. Firms borrowing from banks that participate in TARP increase long-term debt and have more cash holdings and working capital after the Program compared to firms borrowing from banks that do not participate in TARP. But, there is no significant impact of TARP on corporate investment, employment, or R&D. We conclude that TARP exerts significant influence on firms’ liquidity and financial decisions, yet its impact on firms’ real activities is limited. 

Working in progress

 Investor Attention to Mutual Fund Disclosure and Information Efficiency

(with Yichun He, Qiguang Wang) 

Abstract: Mutual fund disclosures contain substantial value-related information for the stock market. Both institutional and retail investors have strong incentives to acquire information from these disclosures. This paper studies how attention to mutual funds disclosure affects information efficiency in the stock market, using EDGAR server log data that tracks downloads of mutual fund filings. We find attention to fund disclosures improves information efficiency. The sensitivity of immediate return response to earnings surprise is stronger for stocks that are included in the portfolios of mutual funds with heightened investor attention. The subsequent post-earnings announcement drift (PEAD) is attenuated. The effect is significant in both institutional attention and retail attention. The effect can be explained by the increased attention to stocks and information transmission from funds to investors. Overall, these findings suggest that attention towards fund dis-closure matters for market efficiency.

 Attention to Mutual Fund Disclosure

(with Yichun He and Lu Zheng)

Abstract: We examine what drives attention to mutual fund disclosures, as well as its implication for fund future flow and performance. 

 FOMC Announcements, Short Selling, and Anomalies

(with Zhi Da, Xi Dong and Yushui Shi)

Abstract: We examine short selling activities around FOMC announcements and its implications for anomalies