Research Interests: Delegated Portfolio Management. Market Efficiency and Market Frictions. The Real Impact of Financial Markets (e.g., Short Selling and Financial Globalization). The Influence of Social, Environmental, and Cultural Considerations.
 Charles Cao, Grant Farnsworth, and Hong Zhang, 2021, “The Economics of Hedge Fund Startups: Theory and Evidence,” Journal of Finance, 76-3, 1427-1469.
We extend Berk and Green’s (2004) model to examine how search frictions influence the managerial incentives and organizational structure in the hedge fund industry. When new managers search for accredited investors, they have incentives to acquire skills when encountering low investor demand. Fund families endogenously arise to mitigate search frictions but weaken performance incentives. Empirical analysis based on TASS-HFR-BarclayHedge confirms these predictions.
 Massimo Massa, Chengwei Wang, Hong Zhang, and Jian Zhang, 2020, “Investing in Low-trust Countries: Trust in the Global Mutual Fund Industry,” forthcoming, Journal of Financial and Quantitative Analysis.
Social trust can help mitigate the contracting incompleteness of activeness investment. Trusting investors allow for more active global mutual funds, and managers reciprocate in a trustworthy way: trust-related active share delivers superior performance of 2% per year.
 Jennifer (Jie) Li, Massimo Massa, Hong Zhang, and Jian Zhang, 2019, “Air Pollution, Behavioral Bias, and the Disposition Effect in China,” forthcoming, Journal of Financial Economics.
Air pollution can significantly increase the disposition effect of retail investors. Endogeneity tests based on strong winds-induced pollution dissipation and the Huai-river policy support a causal interpretation. Our results suggest that air pollutions may incur severe indirect (social) costs.
Finalist, the 2020 NBS -ONE Research Impact on Practice Award
 Si Cheng, Massimo Massa, and Hong Zhang, 2019, “The Unexpected Activeness of Passive Investors: A Worldwide Analysis of ETFs,” Review of Asset Pricing Studies 9-2, 296–355.
ETFs are more complicated than low-cost index trackers. Their real investments may deviate from their benchmarks to leverage affiliated banks’ information advantage and help affiliated OEFs. In this regard, ETFs extend banks’ off-balance-sheet and may affect financial stability.
 Yawen Jiao, Massimo Massa, and Hong Zhang, 2016, “Short Selling Meets Hedge Fund 13F: An Anatomy of Informed Demand,” Journal of Financial Economics 122, 544–567.
Short selling and hedge fund holdings are largely two sides of the same coin. We show that the opposite changes in the two sides are likely to be driven by information. This identification allows us to explore the economic source of informed trading.
 Massimo Massa, Yanbo Wang, and Hong Zhang, 2016, “Benchmarking and Embedded Currency Risk,” Journal of Financial and Quantitative Analysis 51: 629-654.
Benchmarking against an international stock index creates an embedded currency risk to mutual funds. We show that global mutual funds manage this risk by concentrating equity investments on fewer “safe” currencies, constraining funds from achieving the best equity allocation.
 Massimo Massa, Wenlan Qian and Weibiao Xu, and Hong Zhang, 2015, “Competition of the Informed: Does Short Selling Affect Insider Trading,” Journal of Financial Economics 118: 268-288.
In the presence of short selling, corporate insiders have incentives to sell more (shares from their existing stakes) and trade faster to preempt the potential competition from short sellers. Hence, short selling can indirectly improve market efficiency by affecting other informed traders.
 Massimo Massa, Bohui Zhang, and Hong Zhang, 2015, “The Invisible Hand of Short Selling: Does Short-Selling Discipline Earnings Management?,” Review of Financial Studies 28: 1701-1736.
We show that short selling has a disciplining role vis-à-vis firm managers forcing them to reduce earnings management. Our findings suggest that the invisible hand of short selling provides an external governance mechanism to discipline managers.
A post of the paper is solicited and featured at Harvard Law School Forum on Corporate Governance and Financial Regulation (the link). The paper also attracts attention from a broad base of readers, ranging from Stephen Bainbridge, the William D. Warren Distinguished Professor of Law at the UCLA School of Law, to “Silicon Investor “, an online discussing board for stock investments.
 Chunmei Lin and Massimo Massa, and Hong Zhang, 2014, “Mutual Funds and Information Diffusion: The Role of Country-Level Governance,” Review of Financial Studies 27: 3343-3387.
When weak institutions jeopardize public information, a market-based corrective mechanism arises to rely on institutional investors to process semipublic information. However, we show that this corrective mechanism creates more problems than it solves in terms of financial stability, suggesting that weak institutions propose a fundamental challenge to the market.
 Matt Spiegel and Hong Zhang, 2013, “Mutual Fund Risk and Market Share Adjusted Fund Flows,” Journal of Financial Economics 108-2: 506-528.
The flow-performance convexity is widely argued to affect managerial incentives. However, we show that the pooling of heterogeneous linear responses in cross-sectional analysis can yield false convexity estimates. Using the alternative specification (market shares), we find no evidence of convexity in the flow-performance relationship.
 Harry Mamaysky, Matt Spiegel, and Hong Zhang, 2008, “Estimating the Dynamics of Mutual Fund Alphas and Betas,” Review of Financial Studies 21(1): 233-264.
We develop a dynamic model, which allows us to use the Kalman filter to track the information processing of mutual fund managers. Mutual fund alphas identified in this way deliver superior performance out of sample.
 Harry Mamaysky, Matt Spiegel, and Hong Zhang, 2007, “Improved Forecasting of Mutual Fund Alphas and Betas,” Review of Finance 11: 359-400 (the lead article).
Traditional OLS models cannot differentiate estimation errors from true skills. A simple backtesting procedure, which screens out false-positive signals, can produce reliable out-of-sample forecasts.