Deep reinforcement learning in stock portfolios

Research Article
Open access

Deep reinforcement learning in stock portfolios

Yue Quan 1*
  • 1 Boston University, 1 Silber Way Boston, MA 02215    
  • *corresponding author yuequan@bu.edu
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230866
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

This paper investigates stock portfolios by application of Deep Reinforcement Learning (DRL) Models to achieve an optimal tactical asset allocation. The research problem is described as an optimization scenario that seeks to maximize the portfolio risk adjusted returns for a given portfolio asset allocation. The problem is set up with an initial capital investment which is invested in a set of assets. The initial strategic allocation is determined, which in our case is the equal weight allocation, and all of the capital is invested in the set of assets. At each point in time, the assets are reallocated according to the allocation which will increase the portfolio value. Two DRL models are implemented. The performance of the DRL models is compared with the uniform weights portfolio. The results show that, generally, two DRL models have higher cumulative returns.

Keywords:

Deep Reinforcement Learning, Portfolio, Uniform Weights, Cumulative Return, Tactical Asset Allocation.

Quan,Y. (2023). Deep reinforcement learning in stock portfolios. Applied and Computational Engineering,6,482-489.
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References

[1]. Duryea, E. , Ganger, M. , & Wei, H. . (2016). Deep Reinforcement Learning with Double Qlearning.

[2]. Zhixiong, X. U. , Cao, L. , Zhang, Y. , Chen, X. , & Chenxi, L. I. . (2019). Research on deep reinforcement learning algorithm based on dynamic fusion target. Computer Engineering and Applications.

[3]. Neuneier, R., 1996. Optimal asset allocation using adaptive dynamic programming. In Advances in Neural Information Processing Systems. pp. 952-958.

[4]. Yang, H., Liu, X., Zhong, S. and Walid, A., 2020. Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. SSRN Electronic Journal.

[5]. Chakravorty, G., Awasthi, A., Da Silva, B. and Singhal, M., 2018. Deep learning based global tactical asset allocation. SSRN Electronic Journal.

[6]. Obeidat, S., Shapiro, D., Lemay, M., MacPherson, M.K. and Bolic, M., 2018. Adaptive portfolio asset allocation optimization with deep learning. International Journal on Advances in Intelligent Systems, 11(1), pp.25-34.

[7]. Taghian, M. , Asadi, A. , & Safabakhsh, R. . (2022). Learning financial asset-specific trading rules via deep reinforcement learning. Expert Systems with Application (Jun.), pp. 195.

[8]. Hirsa, A. , Osterrieder, J. , Hadji-Misheva, B. , & Posth, J. A. . (2021). Deep reinforcement learning on a multi-asset environment for trading. arXiv e-prints.

Cite this article

Quan,Y. (2023). Deep reinforcement learning in stock portfolios. Applied and Computational Engineering,6,482-489.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Duryea, E. , Ganger, M. , & Wei, H. . (2016). Deep Reinforcement Learning with Double Qlearning.

[2]. Zhixiong, X. U. , Cao, L. , Zhang, Y. , Chen, X. , & Chenxi, L. I. . (2019). Research on deep reinforcement learning algorithm based on dynamic fusion target. Computer Engineering and Applications.

[3]. Neuneier, R., 1996. Optimal asset allocation using adaptive dynamic programming. In Advances in Neural Information Processing Systems. pp. 952-958.

[4]. Yang, H., Liu, X., Zhong, S. and Walid, A., 2020. Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. SSRN Electronic Journal.

[5]. Chakravorty, G., Awasthi, A., Da Silva, B. and Singhal, M., 2018. Deep learning based global tactical asset allocation. SSRN Electronic Journal.

[6]. Obeidat, S., Shapiro, D., Lemay, M., MacPherson, M.K. and Bolic, M., 2018. Adaptive portfolio asset allocation optimization with deep learning. International Journal on Advances in Intelligent Systems, 11(1), pp.25-34.

[7]. Taghian, M. , Asadi, A. , & Safabakhsh, R. . (2022). Learning financial asset-specific trading rules via deep reinforcement learning. Expert Systems with Application (Jun.), pp. 195.

[8]. Hirsa, A. , Osterrieder, J. , Hadji-Misheva, B. , & Posth, J. A. . (2021). Deep reinforcement learning on a multi-asset environment for trading. arXiv e-prints.

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