IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i8p1317-d1636803.html
   My bibliography  Save this article

Global Cross-Market Trading Optimization Using Iterative Combined Algorithm: A Multi-Asset Approach with Stocks and Cryptocurrencies

Author

Listed:
  • Kansuda Pankwaen

    (Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Sukrit Thongkairat

    (Independent Researcher, Chiang Mai 50200, Thailand)

  • Worrawat Saijai

    (Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

This study presents an advanced adaptive trading framework that integrates Deep Reinforcement Learning (DRL) with the Iterative Model Combining Algorithm (IMCA) to overcome the critical limitations of static ensemble methods in global portfolio optimization. Using a diverse cross-market dataset of 39 stocks from the US, Australia, Europe, Thailand, and one cryptocurrency (BTC-USD), the research rigorously evaluates models’ adaptability under volatile market conditions. Volatile market conditions—such as COVID-19, SVB crisis, and the 2022 crypto crash—are captured via volatility metrics (e.g., drawdown), with DRL models like PPO/TD3 adapting through dynamic reward signals. This cross-asset integration is particularly critical, as it captures the complex dynamics and correlations between traditional financial markets and emerging digital assets. Although DRL models like PPO and TD3 outperform traditional strategies, they remain vulnerable to market drawdowns and high volatility. IMCA significantly surpasses these models, achieving the highest cumulative return of 29.52% and a superior Sharpe ratio of 0.829 by dynamically recalibrating model weights in response to real-time market dynamics. This study addresses a substantial research gap, highlighting the failure of traditional ensemble models—reliant on static weightings—to adapt to evolving financial conditions, resulting in suboptimal risk-adjusted returns. IMCA offers a dynamic, data-driven approach that continuously optimizes portfolio strategies across fluctuating market regimes, demonstrating its scalability and robustness across diverse asset classes and regional markets, and providing an empirical framework for adaptive portfolio management. Policy recommendations underscore the need for financial institutions to adopt AI-driven adaptive models like IMCA to enhance portfolio resilience, profitability, and responsiveness in uncertain markets.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:8:p:1317-:d:1636803
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/8/1317/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/8/1317/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jean-Michel Sahut & Petr Hajek & Vladimir Olej & Lubica Hikkerova, 2025. "The role of news-based sentiment in forecasting crude oil price during the Covid-19 pandemic," Annals of Operations Research, Springer, vol. 345(2), pages 861-884, February.
    2. Gang Huang & Xiaohua Zhou & Qingyang Song, 2024. "A Deep Reinforcement Learning Framework for Dynamic Portfolio Optimization: Evidence from China's Stock Market," Papers 2412.18563, arXiv.org, revised Feb 2025.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    4. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Emilia Calefariu Giol & Oana Panazan & Catalin Gheorghe, 2025. "Cyber, Geopolitical, and Financial Risks in Rare Earth Markets: Drivers of Market Volatility," Risks, MDPI, vol. 13(3), pages 1-27, February.
    7. Koutmos, Gregory & Booth, G Geoffrey, 1995. "Asymmetric volatility transmission in international stock markets," Journal of International Money and Finance, Elsevier, vol. 14(6), pages 747-762, December.
    8. Chung I Lu, 2023. "Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation," Papers 2307.07694, arXiv.org, revised Aug 2025.
    9. Maria Soledad Martinez Peria & Mr. Giovanni Majnoni & Mr. Matthew T Jones & Mr. Winfrid Blaschke, 2001. "Stress Testing of Financial Systems: An Overview of Issues, Methodologies, and FSAP Experiences," IMF Working Papers 2001/088, International Monetary Fund.
    10. David Reifschneider, 2024. "US monetary policy and the recent surge in inflation," Working Paper Series WP24-13, Peterson Institute for International Economics.
    11. Yuxuan Dong & Helai Huang & Gongquan Zhang & Jieling Jin, 2024. "Adaptive Transit Signal Priority Control for Traffic Safety and Efficiency Optimization: A Multi-Objective Deep Reinforcement Learning Framework," Mathematics, MDPI, vol. 12(24), pages 1-23, December.
    12. Shahzad, Syed Jawad Hussain & Raza, Naveed & Balcilar, Mehmet & Ali, Sajid & Shahbaz, Muhammad, 2017. "Can economic policy uncertainty and investors sentiment predict commodities returns and volatility?," Resources Policy, Elsevier, vol. 53(C), pages 208-218.
    13. Philip R. Lane, 2012. "The European Sovereign Debt Crisis," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 49-68, Summer.
    14. 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.
    15. Marco Sorge, 2004. "Stress-testing financial systems: an overview of current methodologies," BIS Working Papers 165, Bank for International Settlements.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pompella Maurizio & Dicanio Antonio, 2016. "Bank Vulnerability and Financial Soundness Testing: The Bank Resilience Index," Ekonomika (Economics), Sciendo, vol. 95(3), pages 52-63, December.
    2. Demetrio Lacava & Luca Scaffidi Domianello, 2021. "The Incidence of Spillover Effects during the Unconventional Monetary Policies Era," JRFM, MDPI, vol. 14(6), pages 1-18, May.
    3. Chang Liu & Raja Nassar, 2019. "Stress Testing for Retail Mortgages Based on Probability Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 433-455, January.
    4. Ataurima Arellano, Miguel & Rodríguez, Gabriel, 2020. "Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    5. Randolph & Xiao Qin & Tan Gee Kwang, 2004. "Unit Root Tests with Markov-Switching," Econometric Society 2004 Australasian Meetings 145, Econometric Society.
    6. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    7. Poshakwale, Sunil S. & Aquino, Katty Pérez, 2008. "The dynamics of volatility transmission and information flow between ADRs and their underlying stocks," Global Finance Journal, Elsevier, vol. 19(2), pages 187-201.
    8. Mark Coppejans & Donna Gilleskie & Holger Sieg & Koleman Strumpf, 2007. "Consumer Demand under Price Uncertainty: Empirical Evidence from the Market for Cigarettes," The Review of Economics and Statistics, MIT Press, vol. 89(3), pages 510-521, August.
    9. Martin Èihák, 2005. "Stress Testing of Banking Systems (in English)," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 55(9-10), pages 418-440, September.
    10. Alexandre Carbonneau & Fr'ed'eric Godin, 2021. "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures," Papers 2107.11340, arXiv.org.
    11. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2021. "The impact of Euro through time: Exchange rate dynamics under different regimes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1375-1408, January.
    12. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    13. Hematizadeh, Roksana & Tajaddini, Reza, 2024. "A state-dependent international CAPM for partially integrated markets: Using local and US risk factors," Global Finance Journal, Elsevier, vol. 62(C).
    14. Baele, Lieven, 2005. "Volatility Spillover Effects in European Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(2), pages 373-401, June.
    15. Salvatore Perdichizzi & Matteo Cotugno & Giuseppe Torluccio, 2022. "Is the ECB’s conventional monetary policy state‐dependent? An event study approach," Manchester School, University of Manchester, vol. 90(2), pages 213-236, March.
    16. Nicholas Vinden & Raeid Saqur & Zining Zhu & Frank Rudzicz, 2025. "Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework," Papers 2502.16023, arXiv.org.
    17. Stella C. Dong & James R. Finlay, 2025. "Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning," Papers 2506.13113, arXiv.org.
    18. Kosater, Peter & Mosler, Karl, 2006. "Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices," Applied Energy, Elsevier, vol. 83(9), pages 943-958, September.
    19. Steeley, James M., 2006. "Volatility transmission between stock and bond markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(1), pages 71-86, February.
    20. Wang, Steven Shuye & Firth, Michael, 2004. "Do bears and bulls swim across oceans? Market information transmission between greater China and the rest of the world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(3), pages 235-254, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:8:p:1317-:d:1636803. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.