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A Combined Algorithm Approach for Optimizing Portfolio Performance in Automated Trading: A Study of SET50 Stocks

Author

Listed:
  • Sukrit Thongkairat

    (Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
    These authors contributed equally to this work.)

  • Woraphon Yamaka

    (Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Sukrit Thongkairat & Woraphon Yamaka, 2025. "A Combined Algorithm Approach for Optimizing Portfolio Performance in Automated Trading: A Study of SET50 Stocks," Mathematics, MDPI, vol. 13(3), pages 1-25, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:461-:d:1580111
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    References listed on IDEAS

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    1. Vaughn Gambeta & Roy Kwon, 2020. "Risk Return Trade-Off in Relaxed Risk Parity Portfolio Optimization," JRFM, MDPI, vol. 13(10), pages 1-28, October.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Kansuda Pankwaen & Sukrit Thongkairat & Worrawat Saijai, 2025. "Global Cross-Market Trading Optimization Using Iterative Combined Algorithm: A Multi-Asset Approach with Stocks and Cryptocurrencies," Mathematics, MDPI, vol. 13(8), pages 1-27, April.

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