A Primer on Deep Reinforcement Learning for Finance
31 Pages Posted: 3 Jan 2023 Last revised: 13 Jan 2023
Date Written: January 2, 2023
Abstract
Deep reinforcement learning (DRL) is a powerful and emerging technique for solving complex decision-making problems by learning from experience and interaction with an environment. In the field of finance, DRL has the potential to revolutionize the way we optimize portfolios, manage risk, and execute trades, by leveraging the vast amounts of data available and the ability of neural networks to learn and adapt. However, applying DRL to finance problems also poses several challenges, such as dealing with high-dimensional state spaces, long-term dependencies, and limited data. In this paper, we review the key concepts and algorithms of DRL, and we describe the opportunities and challenges of applying DRL to finance problems, such as portfolio optimization, risk management, and algorithmic trading. We also present several case studies and examples of how DRL has been applied to finance problems, and we discuss the evaluation and potential future directions of DRL in finance. Our aim is to provide a comprehensive overview of DRL in finance and to highlight the potential and limitations of this promising field of research and application.
Keywords: Deep reinforcement learning, finance, portfolio optimization, risk management, algorithmic trading
JEL Classification: G40
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