Jinfei Sheng

Assistant Professor of Finance

University of California, Irvine

Merage School of Business


Research Interests:

Empirical Asset Pricing, FinTech, Machine Learning, Textual Analysis, Labor Finance, Financial Intermediation.


Curriculum Vitae

Publications

Macro News and Micro News: Complements or Substitutes?, Journal of Financial Economics, Forthcoming.

(with David Hirshleifer) JFE NBER

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.

Keywords: Macro news, earnings announcements, market efficiency, investor attention, complementary relationship

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.

Working Papers

Partisan Return Gap: The Polarized Stock Market in the Time of a Pandemic, 2021, R&R at Management Science

(with Zheng Sun and Wanyi Wang)

Abstract: We document sharp differences in stock returns between firms headquartered in counties dominated by Democratic voters (blue stocks) and those in counties dominated by Republican voters (red stocks) during the COVID pandemic. Red stocks have about 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 15% of the return gap. The majority of the return gap is likely due to polarized political beliefs. Overall, our paper provides partisanship as a novel aspect in understanding abnormal stock returns during the pandemic.

Cheaper Is Not Better: On the 'Superior' Performance of High-Fee Mutual Funds, 2021, R&R at Review of Asset Pricing Studies

(with Mikhail Simutin and Terry Zhang)

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 Mutual Funds Walk the Talk? A Textual Analysis of Risk Disclosure by Mutual Funds, 2022

(with Nan Xu and Lu Zheng)

  • Conferences: CICF, MFA, China Fintech Research Conference, Future Finance Information Webinar, Workshop “Leveraging the latest advances in Natural Language Processing” , New Zealand Finance Meeting

Abstract: Using textual analysis, we examine whether risk disclosures in funds’ summary prospectus reflect funds’ investment risks. We document the risks disclosed by funds and how they relate to academic risk factors. We then evaluate the relevance, conciseness and order of the risk disclosures. We find that disclosed risks explain about 50% of variations in fund returns; funds tend to overdisclose by reporting insignificant risks; the order of disclosure does not imply importance. We also find that funds improve their disclosure relevance after receiving SEC comment letters. We further explore the implications of risk disclosure for flow, risk taking and performance.

Asset Pricing in the Information Age: Employee Expectations and Stock Returns, 2022

  • Conferences: FIRS, AFA Poster, Tel Aviv Finance Conference, "Machine Learning and Finance: The New Empirical Asset Pricing" at University of Chicago Booth, NFA | Media coverage: SeekingAlpha

Abstract: This paper studies the value of employees' expectations to stock markets, using a novel dataset of over one million employee reviews. Employees' beliefs about their employers' business prospects predict future stock returns, delivering an annualized abnormal return of 8% to 11%. The forecasting power of employee expectations is novel relative to existing return predictors such as analyst forecasts, insider trading, and Amazon reviews. Furthermore, employee expectations predict future trading activities by hedge funds and corporate insiders. Overall, this paper highlights the importance of crowd-sourced data in understanding stock returns and market efficiency in the information age.

Technology and Cryptocurrency Valuation: Evidence from Machine Learning, 2021

(with Yukun Liu and Wanyi Wang)

  • Conferences: Conference on Financial Economics and Accounting (NYU), AFA Poster, CARF Research Workshop on FinTech, Future of Financial Information Conference, Shanghai-Edinburgh Fintech Conference, Miami Research Conference on Machine Learning and Business, Hong Kong Conference for Fintech, AI, and Big Data in Business, UWA Blockchain and Cryptocurrency Conference, Global AI Finance Research Conference | Media coverages: Forbes, Duke FinReg Blog

Abstract: This paper studies the role of technological sophistication in Initial Coin Offering (ICO) successes and valuations. Using various machine learning methods, we construct technology indexes from ICO whitepapers to capture technological sophistication for all cryptocurrencies. We find that the cryptocurrencies with high technology indexes are more likely to succeed and less likely to be delisted subsequently. Moreover, the technology indexes strongly and positively predict the long-run performances of the ICOs. Overall, the results suggest that technological sophistication is an important determinant of cryptocurrency valuations.

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.

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.

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

  • Conferences: AEA Poster, NFA, Econometric Society Summer Meeting.

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.

Abstract: We examine how short sellers affect financial analysts’ forecast behaviors using a natural experiment that relaxes short-sale constraints. We find that increased ease of short selling improves analyst earnings forecast quality by reducing the forecast bias and increasing the 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. While it is well-documented that financial analysts can affect investors, our paper provides novel evidence on how sophisticated investors–short sellers–can affect analysts’ behaviors.