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General Information
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2020 Vol.10(5): 139-144 ISSN: 2010-3697
DOI: 10.7763/IJMO.2020.V10.761

Deep Reinforcement Learning for Stock Portfolio Optimization

Le Trung Hieu
Abstract—Stock portfolio optimization is the process of constant re-distribution of money to a pool of various stocks. In this paper, we will formulate the problem such that we can apply Reinforcement Learning for the task properly. To maintain a realistic assumption about the market, we will incorporate transaction cost and risk factor into the state as well. On top of that, we will apply various state-of-the-art Deep Reinforcement Learning algorithms for comparison. Since the action space is continuous, the realistic formulation were tested under a family of state-of-the-art continuous policy gradients algorithms: Deep Deterministic Policy Gradient (DDPG), Generalized Deterministic Policy Gradient (GDPG) and Proximal Policy Optimization (PPO), where the former two perform much better than the last one. Next, we will present the end-to-end solution for the task with Minimum Variance Portfolio Theory for stock subset selection, and Wavelet Transform for extracting multi-frequency data pattern. Observations and hypothesis were discussed about the results, as well as possible future research directions.

Index Terms—Reinforcement learning, stock trading, deep learning, deterministic policy gradient, proximal policy optimization, stock portfolio optimization

Le Trung Hieu is with National University of Singapore, Singapore (email: e0072405@u.nus.edu).

[PDF]

Cite: Le Trung Hieu, "Deep Reinforcement Learning for Stock Portfolio Optimization," International Journal of Modeling and Optimization vol. 10, no. 5, pp. 139-144, 2020.

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