A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management

• URL: http://arxiv.org/abs/2102.03502v2
• Date: Tue, 9 Feb 2021 16:19:01 GMT
• Title: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management
• Authors: Zhenhan Huang, Fumihide Tanaka
• Abstract summary: Financial Portfolio Management is one of the most applicable problems in Reinforcement Learning (RL) MSPM is a novel Multi-agent Reinforcement learning-based system with a modularized and scalable architecture for portfolio management. Experiments on 8-year U.S. stock markets data prove the effectiveness of MSPM in profits accumulation by its outperformance over existing benchmarks.
• Score: 7.6146285961466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Financial Portfolio Management is one of the most applicable problems in Reinforcement Learning (RL) by its sequential decision-making nature. Existing RL-based approaches, while inspiring, often lack scalability, reusability, or profundity of intake information to accommodate the ever-changing capital markets. In this paper, we design and develop MSPM, a novel Multi-agent Reinforcement learning-based system with a modularized and scalable architecture for portfolio management. MSPM involves two asynchronously updated units: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM possesses its reusability to have connections to multiple SAMs. A SAM is responsible for the assets reallocation of a portfolio using profound information from the EAMs connected. With the elaborate architecture and the multi-step condensation of the volatile market information, MSPM aims to provide a customizable, stable, and dedicated solution to portfolio management that existing approaches do not. We also tackle data-shortage issue of newly-listed stocks by transfer learning, and validate the necessity of EAM. Experiments on 8-year U.S. stock markets data prove the effectiveness of MSPM in profits accumulation by its outperformance over existing benchmarks.

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