A Summary of My Research: [Download]
“Private Company Valuations by Mutual Funds”, with Vikas Agarwal, Brad Barber, Allaudeen Hameed, and Ayako Yasuda
“Catering through Globalization: Cross-border Expansion and Misallocation in the Global Mutual Fund Industry“, with Massimo Massa and Hong Zhang
“Financial Globalization vs. Income Inequality: The Surprising Role of Delegated Portfolio Flows in Taming the Top 1%“, with Massimo Massa and Hong Zhang
“What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique“, with Ruichang Lu and Xiaojun Zhang
“Tax Evasion and Market Efficiency: Evidence from the FATCA and Offshore Mutual Funds“, with Massimo Massa and Hong Zhang
“Integrating Factor Models”, with Doron Avramov, Lior Metzker, and Stefan Voigt
“Sustainable Investing with ESG Rating Uncertainty“, with Doron Avramov, Abraham Lioui, and Andrea Tarelli, Journal of Financial Economics, forthcoming.
Abstract: This paper analyzes the asset pricing and portfolio implications of an important barrier to sustainable investing—uncertainty about the corporate ESG profile. In equilibrium, the market premium increases and demand for stocks declines under ESG uncertainty. In addition, the CAPM alpha and effective beta both rise with ESG uncertainty and the negative ESG-alpha relation weakens. Employing the standard deviation of ESG ratings from six major providers as a proxy for ESG uncertainty, we provide supporting evidence for the model predictions. Our findings help reconcile the mixed evidence on the cross-sectional ESG-alpha relation and suggest that ESG uncertainty affects the risk-return trade-off, social impact, and economic welfare.
“Machine Learning versus Economic Restrictions: Evidence from Stock Return Predictability”, with Doron Avramov and Lior Metzker, Management Science, accepted.
Abstract: This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs due to high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk.
“Investor Heterogeneity and Liquidity”, with Kalok Chan and Allaudeen Hameed, Journal of Financial and Quantitative Analysis, accepted.
Abstract: Fund flows are more correlated among funds with similar investment horizon, consistent with correlated demand for liquidity. We find that stocks held by institutions with more heterogeneous investment horizon are more liquid and have lower volatility of liquidity. Identification tests confirm the improvement in stock liquidity holds when the increase in investor heterogeneity arises from an exogenous shock due to the 2003 tax reform. Additionally, extreme flow-induced trading by institutional funds has a bigger price impact when stocks have a less heterogeneous investor base. Moreover, the premium associated with stock illiquidity is concentrated in stocks with low investor heterogeneity.
Abstract: We propose a new measure of fund investment skill, active fund overpricing (AFO), encapsulating the fund’s active share of investments, the direction of fund active bets with regard to mispriced stocks, and the dispersion of mispriced stocks in the fund’s investment opportunity set. We find that fund activeness is not sufficient for outperformance: high (low) AFO funds taking active bets on the wrong (right) side of stock mispricing achieve inferior (superior) fund performance. However, high AFO funds receive higher flows during periods of high investor sentiment, when the performance-flow relation becomes weaker.
“The Unexpected Activeness of Passive Investors: A Worldwide Analysis of ETFs”, with Massimo Massa and Hong Zhang, 2019, Review of Asset Pricing Studies 9(2), 296─355. [Published Version]
Abstract: The global ETF industry provides more complicated investment vehicles than low-cost index trackers. Instead, we find that the real investments of ETFs may deviate from their benchmarks to leverage informational advantages (which leads to a surprising stock-selection ability) and to help affiliated OEFs through cross-trading. These effects are more prevalent in ETFs domiciled in Europe. Moreover, ETF flows seem to respond to additional risk. These results have important normative implications for consumer protection and financial stability.
“Short-Term Reversals: The Effects of Past Returns and Institutional Exits”, with Allaudeen Hameed, Avanidhar Subrahmanyam, and Sheridan Titman, 2017, Journal of Financial and Quantitative Analysis 52(1), 143─173. [Published Version]
Abstract: Price declines over the previous quarter lead to stronger reversals across the subsequent 2 months. We explain this finding based on the dual notions that liquidity provision can influence reversals and that agents who act as de facto liquidity providers may be less active in past losers. Supporting these observations, we find that active institutions participate less in losing stocks and that the magnitude of monthly return reversals fluctuates with changes in the number of active institutional investors. Thus, we argue that fluctuations in liquidity provision with past return performance account for the link between return reversals and past returns.
Abstract: This paper implements momentum among a host of market anomalies. Our investment universe consists of the 15 top (long-leg) and 15 bottom (short-leg) anomaly portfolios. The proposed active strategy buys (sells short) a subset of the top (bottom) anomaly portfolios based on past one-month return. The evidence shows statistically strong and economically meaningful persistence in anomaly payoffs. Our strategy consistently outperforms a naive benchmark that equal weights anomalies and yields an abnormal monthly return ranging between 1.273% and 1.471%. The persistence is robust to the post-2000 period, and various other considerations, and is stronger following episodes of high investor sentiment.
Abstract: A basic intuition is that arbitrage is easier when markets are most liquid. Surprisingly, we find that momentum profits are markedly larger in liquid market states. This finding is not explained by variation in liquidity risk, time-varying exposure to risk factors, or changes in macroeconomic condition, cross-sectional return dispersion, and investor sentiment. The predictive performance of aggregate market illiquidity for momentum profits uniformly exceeds that of market return and market volatility states. While momentum strategies have been unconditionally unprofitable in the United States, in Japan, and in the Eurozone countries in the last decade, they are substantial following liquid market states.