Many anomalies are based on firm characteristics and are rebalanced yearly, ignoring any information during the year. In this paper, we provide dynamic trading strategies to rebalance the anomaly portfolios monthly. For eight major anomalies, we find that these dynamic trading strategies substantially enhance their economic importance, with improvements in the Fama and French (2015) five-factor risk-adjusted abnormal return ranging from 0.40% to 0.75% per month. The results are robust to a number of controls. Our findings indicate that many well known anomalies are more profitable than previously thought, yielding new challenges for their theoretical explanations.

Keywords: Anomaly, low frequency information, volatility timing, technical analysis

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