Enhancing Markowitz's portfolio selection paradigm with machine learning

Marcos López de Prado (), Joseph Simonian (), Francesco A. Fabozzi () and Frank J. Fabozzi ()
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Marcos López de Prado: Abu Dhabi Investment Authority (ADIA)
Joseph Simonian: Autonomous Investment Technologies
Francesco A. Fabozzi: Yale’s International Center for Finance
Frank J. Fabozzi: Johns Hopkins University

Annals of Operations Research, 2025, vol. 346, issue 1, No 19, 319-340

Abstract: 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.

Keywords: Machine learning; Signal generation; Feature selection; Portfolio optimization; Generative Language models; Natural language processing (search for similar items in EconPapers)
JEL-codes: C6 G11 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-024-06257-1

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