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Anomalies enhanced: A portfolio rebalancing approach
Author(s) -
Han Yufeng,
Huang Dayong,
Zhou Guofu
Publication year - 2020
Publication title -
financial management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.647
H-Index - 68
eISSN - 1755-053X
pISSN - 0046-3892
DOI - 10.1111/fima.12329
Subject(s) - trading strategy , portfolio , anomaly (physics) , value (mathematics) , financial economics , algorithmic trading , economics , econometrics , computer science , machine learning , physics , condensed matter physics
Many anomalies are based on firm characteristics and rebalanced yearly ignoring any information during the year. In this paper, we provide two dynamic trading strategies to rebalance anomaly portfolios monthly. For eight major anomalies, we find that dynamic trading strategies substantially enhance their economic value with improvements in the Fama and French five‐factor alpha ranging from 0.40% to 0.75% per month. Our findings indicate that many well‐known anomalies can be more profitable than previously thought if managed with our dynamic trading strategies. This much improved performance, which relies on both the anomalies and the trading strategies, raises a new challenge for theoretical explanations.