Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management

Paper: arXiv 2402.00515

Abstract

Deep or reinforcement learning (RL) approaches have been adapted as reactive agents to quickly learn and respond with new investment strategies for portfolio management under the highly turbulent financial market environments in recent years. In many cases, due to the very complex correlations among various financial sectors, and the fluctuating trends in different financial markets, a deep or reinforcement learning based agent can be biased in maximising the total returns of the newly formulated investment portfolio while neglecting its potential risks under the turmoil of various market conditions in the global or regional sectors. Accordingly, a multi-agent and self-adaptive framework namely the MASA is proposed in which a sophisticated multi-agent reinforcement learning (RL) approach is adopted through two cooperating and reactive agents to carefully and dynamically balance the trade-off between the overall portfolio returns and their potential risks. Besides, a very flexible and proactive agent as the market observer is integrated into the MASA framework to provide some additional information on the estimated market trends as valuable feedbacks for multi-agent RL approach to quickly adapt to the ever-changing market conditions. The obtained empirical results clearly reveal the potential strengths of our proposed MASA framework based on the multi-agent RL approach against many well-known RL-based approaches on the challenging data sets of the CSI 300, Dow Jones Industrial Average and S&P 500 indexes over the past 10 years. More importantly, our proposed MASA framework shed lights on many possible directions for future investigation.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced concepts from deep reinforcement learning (e.g., TD3, POMDP, multi-agent systems) and operationalizes them into a concrete computational framework, while demonstrating empirical rigor with specific backtest-ready datasets (CSI 300, DJIA, S&P 500), reported hardware (RTX 3090), and comparative performance metrics.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Develop a Multi-Agent, Self-Adaptive Framework<br>(MASA) for Portfolio Risk Management"] --> B["Methodology:<br>Multi-Agent Reinforcement Learning (MARL)"]
  B --> C["Data Input:<br>10-Year Historical Market Indices<br>(CSI 300, Dow Jones, S&P 500)"]
  C --> D["Computational Process:<br>1. Market Observer (Proactive Agent)<br>2. Multi-Agent RL (Cooperating Agents)<br>3. Self-Adaptive Balancing of Risk vs. Return"]
  D --> E["Key Findings:<br>1. Outperforms Baseline RL Approaches<br>2. Effectively Balances Risk-Return Trade-off<br>3. Dynamic Adaptation to Market Turmoil"]

Cited References (63)

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2. On Information and Sufficiency (1951) — link
3. Divergence measures based on the Shannon entropy (1991) — link
4. A brief review of portfolio optimization techniques (2023)
5. A deep learning approach to estimating fill probabilities in a limit order book (2022)
6. AI for Finance (2023)
7. Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis (2020)
8. An optimal least square support vector machine based earnings prediction of blockchain financial products (2020)

0 of 63 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.