Constrained Portfolio Management Using Action Space Decomposition for Reinforcement Learning
MCML Authors
Niklas Strauß
Dr.
Matthias Schubert
Prof. Dr.
Principal Investigator
Thomas Seidl
Prof. Dr.
Director
Abstract
Niklas Strauß
Dr.
Matthias Schubert
Prof. Dr.
Principal Investigator
Thomas Seidl
Prof. Dr.
Director
Abstract
Financial portfolio managers typically face multi-period optimization tasks such as short-selling or investing at least a particular portion of the portfolio in a specific industry sector. A common approach to tackle these problems is to use constrained Markov decision process (CMDP) methods, which may suffer from sample inefficiency, hyperparameter tuning, and lack of guarantees for constraint violations. In this paper, we propose Action Space Decomposition Based Optimization (ADBO) for optimizing a more straightforward surrogate task that allows actions to be mapped back to the original task. We examine our method on two real-world data portfolio construction tasks. The results show that our new approach consistently outperforms state-of-the-art benchmark approaches for general CMDPs.
inproceedings
PAKDD 2023
27th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Osaka, Japan, May 25-28, 2023.
Authors
D. Winkel • N. Strauß • M. Schubert • Y. Ma • T. SeidlLinks
DOIResearch Area
BibTeXKey: WSS+23