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 investigates the impact of individual bank fundamental variables on stock market returns using data from a panel of 235 European banks from 1991 to 2005. The sample period marks a significant transition in the European banking sector, characterized by higher competition, lower profit...

We investigate the performance of a sample of German mutual equity funds over the period from 1994 to 2003. Our general finding is that mutual funds, on average, hardly produce excess returns relative to their benchmark that are large enough to cover their expenses. This conclusion is drawn from...

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 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...