Title: Dynamic density forecasting using machine learning
Authors: Lubos Hanus - UTIA AV CR, v.v.i (Czech Republic) [presenting]
Jozef Barunik - UTIA AV CR vvi (Czech Republic)
Abstract: The use of machine learning techniques is proposed to describe and forecast the conditional probability distribution of asset returns. We redefine the problem of forecasting of conditional probabilities looking from a different perspective than traditional ordered binary choice models. Using deep learning methods, we offer a better description of asset returns distribution. The study on the most liquid U.S. stocks shows that predictive performance of machine learning methods is promising out-of-sample. We provide a comparison of machine learning methods to the unordered and order binary choice models used by the literature.