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

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...

This paper examines the cross-sectional properties of stock return forecasts based on Fama-MacBeth regressions using all firms contained in the STOXX Europe 600 index during the September 1999-December 2018 period. Our estimation approach is strictly out-of-sample, mimicking an investor who...

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...

This paper uses a comprehensive set of variables from the five largest Eurozone countries to compare the performance of simple univariate and machine learning-based multivariate models in predicting stock market crashes. The statistical predictive performance of a support vector machine-based...

This paper evaluates the predictive performance of machine learning techniques in estimating time-varying betas of US stocks. Compared to established estimators, tree-based models and neural networks outperform from both a statistical and an economic perspective. Random forests perform the best...