In this paper, we provide insights on the prediction of asset returns via novel machine learning methodologies. Machine learning clustering-enhanced classification and regression techniques to predict future asset return movements are proposed and compared. Numerical experiments show good...

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

"This book explores fractal structure and long-term memory of the financial markets to predict prices of financial assets and financial crisis. It identifies the criteria to select financial assets for investment and the creation of a randomized algorithm of R/S-analysis, which allows to give a...