Published and Accepted Papers:

  1. 1. Nguyen, Du, 2026, Informational Advantages and Flow Hedging in Mutual Funds, Journal of Empirical Finance
    · SSRN     · Journal

    This paper studies heterogeneity in flow-hedging behavior among active mutual funds. While recent evidence shows that the aggregate mutual fund industry hedges against common flow risk by tilting toward low-flow-beta stocks, I find that nearly half of U.S. active equity funds tilt their portfolios toward high-flow-beta stocks. To rationalize this finding, I propose an information-based model in which managers with more precise private signals about future flow shocks perceive lower uncertainty and hedge less aggressively. Empirically, funds with higher flow risk exposure are associated with proxies for informational advantages, and their flows predict aggregate industry and common flows. Moreover, funds that hedge against flow risk the least outperform those that hedge the most over 3% per year, suggesting that hedging against flow beta is costly.

  2. 2. Jannati, Sima, Sarah Khalaf, and Du Nguyen, 2025, The Up Side of Being Down: Depression and Crowdsourced Forecasts, Journal of Banking and Finance
    · SSRN     · Journal

    This study examines the role of non-severe depression as a psychological anchor against overoptimism. Using earnings forecasts from Estimize, we find that an increase in the proportion of the U.S. population with depression is associated with improved forecast accuracy among users. This effect is concentrated among forecasts that are optimistic and analysts who take longer time to issue forecasts, highlighting reduced optimism and slow information processing as economic mechanisms that explain our results. We also show that this effect is distinct from the influence of temporary seasonal depression or other sentiment measures on decision-making. Overall, our research establishes a link between depression and crowdsourced financial evaluations.
  3. Media Coverage: St. Louis Business Journal (October 2022)

Working Papers:

  1. Portfolio Manager Ownership and Low-Risk Anomalies

    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. Out-of-Sample Performance of Factor Return Predictors

    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.

    3. Note: † indicates presentation by co-author.