Average skewness, which is defined as the average of monthly skewness values across firms, performs well at predicting future market returns. This result still holds after controlling for the size or liquidity of the firms or for current business cycle conditions. We also find that average...

We apply four machine learning methods to cross-sectional return prediction for hedge fund selection. We equip the forecast model with a set of idiosyncratic features, which are derived from historical returns of a hedge fund and capture a variety of fund-specific information. Evaluating the...

The experimental approach is applied to explore the value of unidentified historical information in stock-return prediction. Return sequences were randomly drawn cross section and time from historical S&P500 data. Subjects were requested to predict returns or select stocks from 12 preceding...

The rapid growth of electric vehicles, solar roofs, and wind power suggests that the potential growth in green equity investments is an emerging trend. Accordingly, this study measured the predictors of excess equity returns in a portfolio of global green energy producers, from 2010 to 2019....

We quantify disagreement about the economy with ex-ante measures of divergence of opinion among economic forecasters and investigate if economic disagreement has a significant impact on the cross-sectional pricing of individual stocks. We find a significant disagreement premium of 7.2% per...