Minimax Optimization and Multi-Agent Deep Reinforcement Learning for Intelligent Stock Market Forecasting and Decision-Making
Keywords:
Deep learning Deep reinforcement learning Forecasting High-frequency trading Machine learning Multi-agent Predictive model Stock market VolatileAbstract
This study presents a comprehensive survey of recent advancements in stock market forecasting frameworks and techniques. Various machine learning, deep learning, deep reinforcement learning (DRL), and hybrid models applied to stock markets across regions such as the United States, China, Europe, and India have been analyzed. The survey identifies critical research gaps and challenges in stock forecasting, including handling diverse stock profiles, managing class imbalance, addressing model drift, and improving predictive accuracy. To address these challenges, a novel three-phase methodology is proposed, incorporating a Multi-Agent Deep Reinforcement Learning (MA-DRL) model. The proposed framework aims to deliver superior forecasting performance, enhance profitability, effectively manage stock volatility, and achieve robust performance under dynamic market conditions.
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