In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The...

We use supervised learning to identify factors that predict the cross-section of returns and maximum drawdown for stocks in the US equity market. Our data run from January 1970 to December 2019 and our analysis includes ordinary least squares, penalized linear regressions, tree-based models, and...

Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While...

Opponents of the efficient markets hypothesis argue that predictability reflects the psychological factors and “fads” of irrational investors in a speculative market. In that, conventional time series analysis often fails to give an accurate forecast for financial processes due to inherent...

We present a training algorithm for multilayer perceptrons which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix which is computed from the example inputs and their target outputs. For large networks the novel procedure...

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in...