Below I list recent working papers in themes
Please refer to my SSRN Author Page and Google Scholar Page for more working papers.
(Inspired by the figure on the left, plotted by my daughter when she was in primary school.)
Theme 1: Delegated Portfolio Management and Demand-side Frictions
My first research theme considers the investor side of the financial market. I am particularly interested in how delegated portfolio management affects investor welfare and market efficiency. My early papers (2007 RoF; 2008 RFS; 2013 JFE) examine fund incentives and strategies. More recently, I explore the economic grounds affecting the dual efficiency of security prices and delegated portfolio management, e.g., search frictions (2020 JF), social trust (2020 JFQA), leverage, and strategic complementarity.
“Data Specialists and Market Efficiency,” with Massimo Massa and Yijun Zhou, working paper 2023. (SSRN)
In the age of big data, investors need to process ever more complicated, multidimensional data to decipher different aspects of a firm. How do investors deal with multidimensional data? Using trading concentration across news categories as a proxy for data specialization, we find more informed institutional investors tend to specialize in subsets of firm aspects (i.e., data specialists) instead of processing all available data. Such data specialization, however, may hamper market efficiency.
“Hedge Fund Leverage, Delegated Portfolio Management, and Asset Prices,” with Charles Cao, Grant Farnsworth, and Yijun Zhou, working paper 2023. (SSRN coming soon)
We extend Berk and Green (2004) to model hedge funds as an institution with leverage advantage. Consistent with our model, we observe that: 1) higher leverage allows hedge funds to reap more economic rents (fees), 2) hedge funds simultaneously adjust leverage and holding beta during adverse funding conditions, and 3) a leverage-tightness factor constructed from hedge fund holding betas can significantly predict the cross-section of asset returns. The 14th Annual Hedge Fund Research Conference.
Theme 2: Strategic Firms and Risk Factors
My second research theme investigates the firm (supply) side of the market, with a particular focus on how strategic firms may affect market efficiency and asset prices. For example, strategic business groups can alter firm risk via within-group asset allocations, as has been shown in a series of cases I developed when examining the revolution of corporate treasury operations in the new century. This theme also dates back to my Ph.D. thesis, in which I explored “anomalies” associated with strategic firm investments that can generate non-normally distributed cash flows.
“An Anatomy of Characteristics in Dynamic Trading,” with Matthew Spiegel and Tao Huang, working paper, 2021. (SSRN)
We propose testing the joint and marginal power of characteristics in predicting stock returns via their contribution to optimal dynamic trading (e.g., Kyle 1985). Applying dynamic trading to a sample of 147 characteristics confirms their joint power in delivering significant out-of-sample returns (23% annually). However, most characteristics (88%) fail to supply independent information—removing them can further enhance returns to 33.7%. Our analysis also reveals a leading role of Fama-French-Carhart factors as informative characteristics. 2021 CICF.
“Does the Community Reinvestment Act (CRA) Crowd Out Corporate Lending?,” with Massimo Massa, Ruichang Lu, and Wenlan Qian, ongoing project, 2023.
The CRA aims to promote mortgage lending by banks to mid to low-income borrowers. Could it crowd out corporate lending? Our findings suggest the opposite: relationship and investment-grade firms receive more loans from CRA-regulated banks. CRA-induced loans are larger and cheaper ex ante but carry a moderately higher distress risk ex post. Our results suggest bank-level risk constraints may motivate banks to (over)supply credit to high-quality corporate clients in order to offset the CRA-induced risk from mortgage lending.
Theme 3: The Mutual Influence between the Market and Society
Why do we need financial markets in the first place? To understand this broad question, I examine how market-based mechanisms can affect the real economy and human society. I show that short selling can serve as an “invisible hand” to discipline firm incentives and gauge information flow (2015 RFS; 2015 JFE; 2016 JFE). The reverse question is how social considerations (e.g., ESG), institutions, and norms may influence firms and investors in financial markets. My research (2014 RFS) indicates that a country’s weak institutions can pose a fundamental challenge to its financial market. My more recent papers examine the impact of air pollution (2019 JFE), social trust (2020 JFQA), and social norms. I am particularly interested in how behavioral bias may affect different aspects of green finance (e.g., investors, regulators, etc.).
“Policy Uncertainty Reduces Green Investment,” with Mengyu Wang and Jeffery Wurgler, working paper, 2023. (SSRN). R&R at Journal of Financial Economics.
We find that Chinese firms reduce green investment as the uncertainty of supportive subsidies rises. This effect is identified from weather-driven fluctuations in air pollution that lead to fluctuations in subsidy allocations: Firms in cities where weather-driven subsidy uncertainty is high engage in less green R&D investment and hire fewer associated technical employees. More broadly, the results suggest that economic policy uncertainty may originate not only from political and macroeconomic shocks but from behavioral mechanisms that link policy to salient recent conditions. 2023 NBER Chinese Economy Working Group Meeting.
“A Social Norm Perspective of Information Manipulation in China,” co-authored with Zhe Li, Massimo Massa, and Nianhang Xu, working paper, 2023. R&R at Journal of Financial and Quantitative Analysis.
We examine whether information manipulation by firms may be influenced by social norms in China. Consistent with this notion, we find that China’s leading social norms related to alcohol consumption and social drinking enhance earnings manipulation. An analysis of toxic alcohol scandals supports a causal interpretation.
“The Boundaries of the Law: Can US Private Enforcement Discipline Foreign Firms?,” co-authored with Massimo Massa, Xiaoqiao Wang, and Bohui Zhang, working paper 2022. R&R at Journal of International Business Studies.
We find that a US class-action lawsuit against a foreign firm cross-listed in the US negatively affects the value of its non-US-listed industry peers in its home country. Foreign peer firms subsequently improve their governance practices and financial policies to restore shareholder value. Our findings suggest that private enforcement in the US has a worldwide influence.
Theme 4: AI, FinTech, and New Developments in the Market
This theme explores new developments in the market, such as social media, social networks, machine learning, and blockchain technologies. I am particularly interested in the recent AI progress and new developments that have a “blue ocean” flavor.
“Borrowing from Friends of Friends: Indirect Social Networks and Bank Loans,” co-authored with Sterling Huang, Bo Li, Massimo Massa, and Siyuan Yang, working paper 2022. R&R at Management Science.
We examine how indirect connections (i.e., friends of friends), an important yet understudied feature of social networks, may affect bank loan contracts. We find that indirect connections-initiated new loans exhibit significantly lower spreads. Bank monitoring, loan quality, and firm investments are also negatively affected, suggesting that indirect connections may give rise to a favoritism treatment by banks in the extensive margin (i.e., issuing new loans).
“Predictive Crypto Crashes,” co-authored with Jennifer Li, and Li Liao, and Siyuan Yang, working paper 2023. (SSRN version coming soon)
Bubbles and crashes are hallmarks of cryptocurrencies. Can such bubbles/crashes be predicted in the cross-section of cryptocurrencies? We document that consecutive large price runups and price drawdowns, a leading asset pricing implication of slow-moving capital, can help predict crypto-crashes and negative returns. This effect may arise when investors reduce demand after experiencing inelasticity, leading to predictive price declines. Analysis using ICOs as a source of Ethereum blockchain congestion supports this mechanism.
“A Tale of Two Zoos: Machine Learning Insights on Retail Investors” with Pulak Ghosh, Huahao, Lu, and Jian Zhang, working paper, 2023. (SSRN version coming soon)
We employ various machine learning models to analyze the returns for millions of retail investors in India. We observe that Neural Networks outperform other machine learning and OLS models in uniquely predicting both good and bad out-of-sample performance. Behavioral biases exert a more significant influence on their returns than holding-weighted firm characteristics.