We analyze the performance of a comprehensive set of equity premium forecasting strategies. All strategies were found to outperform the mean in previous academic publications. However, using a multiple testing framework to account for data snooping, our findings support Welch and Goyal (2008) in...

This paper evaluates the performance of machine learning methods in forecasting stock returns. Compared to a linear benchmark model, interactions and non-linear effects help improve predictive performance. But machine learning models must be adequately trained and tuned to overcome the high...

Stock investments have become increasingly international, but only recently a deeper theoretical understanding of the forces influencing global stock market returns has been gained from empirical studies. This is a crucial issue for asset managers in order to control the risks and exposures of...

We document high economy-wide correlations between the Equity Risk Premium (ERP) and the aggregate volume (rho=-0.69) and value (rho=-0.75) of patenting activity by public firms in the United States over the 1977-2018 period, contradicting Schumpeter's (1939) opportunity-costs hypothesis of...

This paper examines the predictive performance of machine learning methods in estimating the illiquidity of U.S. corporate bonds. We compare the predictive performance of machine learning-based estimators (linear regressions, tree-based models, and neural networks) to that of the most commonly...