Machine Learning in Portfolio Decisions

Manuela Pedio, Massimo Guidolin and Giulia Panzeri

No 24233, BAFFI CAREFIN Working Papers from BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy

Abstract: Machine learning is significantly shaping the advancement of various fields, and among them, notably, finance, where its range of applications and efficiency impacts are seemingly boundless. Contemporary techniques, particularly in reinforcement learning, have prompted both practitioners and academics to contemplate the potential of an artificial intelligence revolution in portfolio management. In this paper, we provide an overview of the primary methods in machine learning currently utilized in portfolio decision-making. We delve into discussions surrounding the existing limitations of machine learning algorithms and explore prevailing hypotheses regarding their future expansions. Specifically, we categorize and analyze the applications of machine learning in systematic trading strategies, portfolio weight optimization, smart beta and passive investment strategies, textual analysis, and trade execution, each separately surveyed for a comprehensive understanding.

Keywords: Machine learning; portfolio choice; artificial intelligence; neural language processing; stock return predictions, market timing, mean-variance asset allocation. (search for similar items in EconPapers)
JEL-codes: C45 C61 G10 G11 G17 (search for similar items in EconPapers)
Pages: 100
Date: 2024
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-fmk
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Chapter: Machine Learning in Portfolio Decisions (2024)
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