Modern Probabilistic Machine Learning and Control Methods for Portfolio Optimization

Abstract

Many recent theoretical developments in the field of machine learning and control have rapidlyexpanded its relevance to a wide variety of applications. In particular, a variety of portfoliooptimization problems have recently been considered as a promising application domain formachine learning and control methods. In highly uncertain and stochastic environments,portfolio optimization can be formulated as optimal decision-making problems, and for thesetypes of problems, approaches based on probabilistic machine learning and control methodsare particularly pertinent. In this paper, we consider probabilistic machine learning and controlbased solutions to a couple of portfolio optimization problems. Simulation results show thatthese solutions work well when applied to real financial market data.