Does reinforcement learning outperform deep learning and traditional portfolio optimization models in frontier and developed financial markets?

Vu Minh Ngo, Huan Huu Nguyen and Phuc Van Nguyen

Research in International Business and Finance, 2023, vol. 65, issue C

Abstract: Advancements in machine learning have opened up a wide range of new possibilities for using advanced computer algorithms, such as reinforcement learning in portfolio risk management. However, very little evidence has been provided on the superior performance of reinforcement learning models over traditional optimization models following the mean-variance framework in different financial market settings. This study uses two experiments with data from the Vietnamese and U.S. securities markets to justify whether advanced machine learning models could outperform traditional portfolios' cumulative returns while optimizing the Sharpe ratio. The results suggest that reinforcement learning consistently outperforms the established methods and benchmarks in both experiments, even when using a very similar degree of diversification in portfolio construction and the same input data. This study confirms the ability of reinforcement learning to provide dynamic responses to market conditions and redefine the risk-return standard in the financial system.

Keywords: reinforcement learning; machine learning; portfolio construction models; Sharpe ratio; mean-variance models (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0275531923000624
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:65:y:2023:i:c:s0275531923000624

DOI: 10.1016/j.ribaf.2023.101936

Access Statistics for this article

Research in International Business and Finance is currently edited by T. Lagoarde Segot

More articles in Research in International Business and Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().