Individual environmental variables may contain information obscured in aggregate environmental scores for return forecasting. We apply machine learning methods to granular environmental variables and find that a long-short portfolio that longs stocks with high forecasted returns and sells stocks...

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...

We find that expected return is related to trading volume positively among underpriced stocks but negatively among overpriced stocks. As such, trading volume amplifies mispricing. Our results are robust to alternative mispricing and trading volume measures, alternative portfolio formation...

This paper examines how options traders trade daily stock market mispricing measured by short-term past return and put-call option volatility spread. Anomaly return is 7.31 basis points per day when customer option traders trade along with the anomaly signal and is insignificant when they trade...