Enhancing Markowitz's portfolio selection paradigm with machine learning

Abstract

In this paper we describe the integration of machine learning (ML) techniques into the framework of Markowitz's portfolio selection and show how they can help advance the robust mathematical strategies necessary for modern financial markets. By combining traditional econometrics with cutting-edge ML methodologies, we show how to enhance portfolio management processes including alpha generation, risk management, and optimization of risk metrics like conditional value at risk. ML's capacity to handle vast and complex datasets allows for more dynamic and informed decision-making in portfolio construction. Moreover, we discuss the practical applications of these techniques in real-world portfolio management, highlighting both the potential enhancements and the challenges faced by portfolio managers in implementing ML strategies.