Discussing the article: "Neural Networks in Trading: A Multi-Agent Self-Adaptive Model (MASA)"
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Check out the new article: Neural Networks in Trading: A Multi-Agent Self-Adaptive Model (MASA).
Computer technologies are becoming an integral part of financial analytics, offering innovative approaches to solving complex problems. In recent years, reinforcement learning (RL) has proven its effectiveness in dynamic portfolio management under the conditions of turbulent financial markets. However, existing methods often concentrate on maximizing returns while paying insufficient attention to risk management—particularly under uncertainty caused by pandemics, natural disasters, and regional conflicts.
To address this limitation, the study "Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management" introduces MASA (Multi-Agent and Self-Adaptive). MASA integrates two interacting agents: the first optimizes returns using the TD3 algorithm, while the second minimizes risks through evolutionary algorithms or other optimization methods. In addition, MASA incorporates a market observer that leverages deep neural networks to analyze market trends and provide feedback.
The authors tested MASA on data from the CSI 300, Dow Jones Industrial Average (DJIA), and S&P 500 indices over the past 10 years. Their results demonstrate that MASA outperforms traditional RL-based approaches in portfolio management.
Author: Dmitriy Gizlyk