Working Papers:

  1. Flow Hedging and Mutual Fund Performance, 2023.

    This paper studies risk-taking behavior of active mutual funds with respect to flow risk and its consequences on 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 rational model in which informed managers receive more precise private signals about common flows provides an explanation for this behavior. Using a holdings-based measure of flow risk management, I confirm the model’s main prediction that skilled funds have higher exposure to common flows: funds in the top decile of the measure outperform those in the bottom decile by 5% annually in the data. Overall, the paper identifies a new aspect of flow risk management among active equity funds.
    • Presentations: SFA 2023 (scheduled), FMA 2023 (scheduled), NFA Student Session 2023 (scheduled).

  2. Out-of-Sample Performance of Factor Return Predictors, 2023.

    Using a comprehensive set of 92 equity factors, I re-evaluate the performance of variables that have been shown from prior studies to be good predictors of factor returns. I find that most variables do not provide systematic evidence in favor of factor return predictability, as judged by their poor out-of-sample performance. Model instabilities appear to be the primary reason for the weak performance of individual variables. I overcome these limitations by exploring a variety of shrinkage techniques that combine signals from all predictors. Results show that this approach offers more favorable and conclusive evidence for predictability. The shrinkage approach typically leads to significant economic gains for factor timing strategies in real time.
    • Presentations: University of Missouri 2023 (scheduled).

  3. The Up Side of Being Down: Depression and Crowdsourced Forecasts, 2020. R&R
    with Sima Jannati and Sarah Khalaf .

    Using earnings forecasts from Estimize, we test whether crowdsourced financial judgments are affected by persistent mild depression. We find that a 1-standard-deviation increase in the segment of the U.S. population with depression leads to a 0.25% increase in users' forecast accuracy. This effect is robust to alternative measures and is distinct from the influence of temporary seasonal depression or other sentiment measures on decision-making. Reduced optimism and slow processing of information are two mechanisms that explain our findings. Overall, we contribute to the literature by linking depression to crowdsourced financial evaluations.
    • Presentations: SWFA 2021†, World Finance Conference 2021†, University of Missouri 2020.

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