Machine learning is having a major impact in the development of many fields, including finance, where its domain of application and efficiency impact may be considered limitless. Modern techniques of reinforcement learning have led practitioners and academics to conjecture on the scope of a potential artificial intelligence revolution in portfolio management. In this chapter, we summarize the main strands of machine learning currently used in portfolio decisions and discuss both the current limitations of the algorithms and the dominant conjectures on the future avenues of its extensions.

Machine Learning in Portfolio Decisions

Guidolin, Massimo
2024

Abstract

Machine learning is having a major impact in the development of many fields, including finance, where its domain of application and efficiency impact may be considered limitless. Modern techniques of reinforcement learning have led practitioners and academics to conjecture on the scope of a potential artificial intelligence revolution in portfolio management. In this chapter, we summarize the main strands of machine learning currently used in portfolio decisions and discuss both the current limitations of the algorithms and the dominant conjectures on the future avenues of its extensions.
2024
9781800615205
9781800615212
Marco Corazza (Ca' Foscari University of Venice, Italy), René Garcia (University of Montreal, Canada & SKEMA Business School Canada, Canada), Faisal Shah Khan (Rethinc. Labs, UNC Kenan-Flagler Business School, USA & SKEMA Business School Raleigh, USA), Davide La Torre (SKEMA Business School, France), and Hatem Masri (Applied Science University, Bahrain)
Artificial Intelligence and Beyond for Finance
Guidolin, Massimo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4070469
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