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Double-Adjusted Mutual Fund Performance

The Review of Asset Pricing Studies 2021 11(1), 169-208 open access
Mutual fund returns are significantly related to stock characteristics in the cross-section after controlling for risk via factor models. We develop a new double-adjusted approach that controls for both factor model betas and stock characteristics in one performance measure. The new measure substantially affects performance rankings, with a quarter of funds experiencing a change in their percentile ranking greater than 10. Double-adjusted performance produces strong evidence of persistence in relative performance. Inference based on the new measure often differs, sometimes dramatically, from that based on traditional performance estimates. Received November 22, 2019; editorial decision June 28, 2020; Editor: Jeffrey Pontiff.

Investor Attention and Asset Pricing Anomalies

Review of Finance 2022 26(3), 563-593 open access
We investigate the relationship between investor attention and financial market anomalies. We find that anomaly returns tend to be higher following high-attention days. The result is robust after controlling for the effect of news and in a natural experiment setting in which a stock market regulation and rounding errors generate exogenous variations in attention. An analysis of order imbalances suggests that large traders trade on anomaly signals more aggressively upon observing higher attention. We discuss the extent to which the findings are driven by inattention-driven underreaction, bias amplification, or coordinated arbitrage mechanisms, thereby providing insight into the understanding of anomalies.

Artificial Market Timing in Mutual Funds

Journal of Financial and Quantitative Analysis 2023 58(8), 3450-3481 open access
We document statistically significant relations between mutual fund betas and past market returns driven by fund feedback trading. Against this backdrop, evidence of “artificial” market timing emerges when standard market timing regressions are estimated across periods that span time variation in fund systematic risk levels, as is typical. Artificial timing significantly explains the inverse relation between timing model estimates of market timing and stock selectivity. A fund’s feedback trading relates to its past performance and remains significant after accounting for trading on momentum. Fund flows suggest that investors value feedback trading, which helps hedge downside risk during bear markets.