Working Papers:

1. Portfolio Manager Ownership and Low-Risk Anomalies
     Abstract

   This paper examines the impact of agency-issue-induced incentive misalignment on the relation between risk (e.g., beta, idiosyncratic volatility or distress risk) and abnormal return in the stock market. Using hand-collected data on portfolio manager ownership of U.S. active mutual funds, I construct a stock-level measure of exposure to incentives-induced trading and show that this measure is associated with the abnormally low returns of high-risk stocks. Across a comprehensive set of strategies that buy high-risk stocks and sell low-risk stocks, negative alphas concentrate only among stocks subject to high incentives-induced trading. This pattern is neither driven by other firm characteristics nor explained by fund performance, and the effect does not extend to other groups of anomaly strategies. The findings are consistent with the conjecture that incentives-induced trading entails excessive risk taking that distorts market efficiency.
   • Presentations: VICIF 2025, University of Missouri 2024.

2. Flow Hedging and Mutual Fund Performance
     Abstract

   This paper studies hedging behavior of active mutual funds against flow volatility and its implications for fund performance. Recent evidence suggests that shocks to the common component of fund flows are a priced risk factor in expected stock returns. I find that nearly half of U.S. active equity funds tilt their portfolios toward stocks with higher exposure to common flows, suggesting that many funds do not hedge against flow risk. A model in which informed managers receive more precise private signals about common flows provides an explanation for this behavior. Using managers' portfolio tilt as a proxy for ability, I confirm the model's main prediction that funds having higher exposure to common flows generate better risk-adjusted performance. These funds also attract higher future flows.
   • Presentations: AFA Poster Session 2024, EFA 2024, SWFA 2024, FMA 2023, NFA Ph.D. Session 2023, SFA 2023.

3. Out-of-Sample Performance of Factor Return Predictors
     Abstract

   In a factor timing context, recent studies have emphasized on developing techniques that reduce the factor dimension and demonstrated return predictability using only a few predictors with specific choice of estimation design. This focus inadvertently neglects the crucial issue of model instability that has been shown to plague the forecasting literature. Using almost a hundred equity factors and a broader set of predictor variables, I find that the forecasting performance of recent factor timing techniques is indeed sensitive to the choice of empirical design. Applying a variety of shrinkage methods on predictors and focusing on forecasting individual factors to better capture the dynamics between factor returns and predictive signals, I document robust evidence of out-of-sample predictability and more stable investment performance for factor timing strategies. The optimal timing portfolio has a 30% higher Sharpe ratio and generates more than twice the economic gains relative to the factor dimension-reduction approach.
   • Presentations: SWFA 2024, University of Missouri 2023.


Note: ^† indicates presentation by co-author.