TY - JOUR
T1 - Zeroing In on the Expected Returns of Anomalies
AU - Chen, Andrew Y.
AU - Velikov, Mihail
N1 - Publisher Copyright:
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - We zero in on the expected returns of long-short portfolios based on 204 stock market anomalies by accounting for i) effective bid-ask spreads, ii) post-publication effects, and iii) the modern era of trading technology that began in the early 2000s. Net of these effects, the average anomaly's expected return is a measly 4 bps per month. The strongest anomalies net, at best, 10 bps after controlling for data mining. Several methods for combining anomalies net around 20 bps. Expected returns are negligible despite cost mitigations that produce impressive net returns in-sample and the omission of additional trading costs, like price impact.
AB - We zero in on the expected returns of long-short portfolios based on 204 stock market anomalies by accounting for i) effective bid-ask spreads, ii) post-publication effects, and iii) the modern era of trading technology that began in the early 2000s. Net of these effects, the average anomaly's expected return is a measly 4 bps per month. The strongest anomalies net, at best, 10 bps after controlling for data mining. Several methods for combining anomalies net around 20 bps. Expected returns are negligible despite cost mitigations that produce impressive net returns in-sample and the omission of additional trading costs, like price impact.
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U2 - 10.1017/S0022109022000874
DO - 10.1017/S0022109022000874
M3 - Article
AN - SCOPUS:85137411345
SN - 0022-1090
VL - 58
SP - 968
EP - 1004
JO - Journal of Financial and Quantitative Analysis
JF - Journal of Financial and Quantitative Analysis
IS - 3
ER -