A Combined Algorithm Approach for Optimizing Portfolio Performance in Automated Trading: A Study of SET50 Stocks
Sukrit Thongkairat, Woraphon Yamaka
Source abstract
This study investigates portfolio optimization for SET50 stocks using Deep Reinforcement Learning (DRL) algorithms to address market volatility. Five DRL algorithms—Advantage Actor–Critic (A2C), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Soft Actor–Critic (SAC), and Twin Delayed DDPG (TD3)—were evaluated for their effectiveness in managing risk and optimizing returns. We propose an Iterative Model Combining Algorithm (IMCA) that dynamically adjusts model weights based on market conditions to enhance performance. Our results demonstrate that IMCA consistently outperformed traditional strategies, including the Minimum Variance model. IMCA achieved a cumulative return of 14.20% and a Sharpe Ratio of 0.220, compared to the Minimum Variance model’s return of −4.35% and Sharpe Ratio of 0.018. This research highlights the adaptability and robustness of DRL algorithms for portfolio management, particularly in emerging markets like Thailand. It underscores the advantages of dynamic, data-driven strategies over static approaches.
Evidence graph
No public relationships recorded yet.
Integrity note: This page is a factual metadata record created by deterministic ingestion. It is not a claim that the work moves a mathematical frontier or has been independently verified.