Machine Learning in Portfolio Decisions

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

JEL Classification: C61, G10, G11, G17