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Multi-factor portfolio optimization: A combined random Forest–AdaBoost model with cost-sensitive learning11This paper was supported by the National Natural Science Foundation of China (Nos. 71,871,071, 72071051); the Natural Science Foundation of Guangdong Province of China (Nos. 2025A1515010937, 2023A1515011354)

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  • Yao, Haixiang
  • Wan, Chunzhuo

Abstract

This paper proposes a machine learning-driven multi-factor investment strategy, denoted as DE-CS-RFA, which integrates the Random Forest-AdaBoost (RFA) ensemble learning model, Cost-Sensitive (CS) learning, and the Differential Evolution algorithm (DE). The model utilizes 110 heterogeneous predictive features as input characteristics, eliminating redundant features via Kendall correlation analysis to enhance computational efficiency while comprehensively capturing market information. Subsequently, the Rank-Sum Ratio comprehensive evaluation method is employed to construct the initial investment universe and to develop an investment strategy based on the model's predicted data. Empirical results demonstrate that RFA outperforms other mainstream machine learning models on multiple evaluation metrics. Moreover, the simulation trading results indicate that the DE-CS-RFA model can effectively capture the market complexity and individual investor differences, enhancing the applicability and effectiveness of the investment strategy. Interpretability analysis further reveals the key factors influencing the stock price trends in the A-share market. Finally, robustness tests confirm that the DE-CS-RFA model can adapt to diverse financial market characteristics, holding potential to promote the widespread application of multi-factor investment strategies in the A-share market.

Suggested Citation

  • Yao, Haixiang & Wan, Chunzhuo, 2025. "Multi-factor portfolio optimization: A combined random Forest–AdaBoost model with cost-sensitive learning11This paper was supported by the National Natural Science Foundation of China (Nos. 71,871,071, 72071051); the Natural Science Foundation of Gua," Pacific-Basin Finance Journal, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:pacfin:v:94:y:2025:i:c:s0927538x25002835
    DOI: 10.1016/j.pacfin.2025.102946
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    1. Kwangwon Ahn & Linxiao Cong & Hanwool Jang & Daniel Sungyeon Kim, 2024. "Business cycle and herding behavior in stock returns: theory and evidence," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-14, December.
    2. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Yin, Anwen, 2020. "Equity premium prediction and optimal portfolio decision with Bagging," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    4. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    5. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
    6. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    7. Richard G. Sloan & Annika Yu Wang, 2025. "Predictable EPS growth and the performance of value investing," Review of Accounting Studies, Springer, vol. 30(1), pages 33-78, March.
    8. Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.
    9. Hurst, Gareth & Docherty, Paul, 2015. "Trend salience, investor behaviours and momentum profitability," Pacific-Basin Finance Journal, Elsevier, vol. 35(PB), pages 471-484.
    10. Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022. "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    11. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    12. Fanyi Wang & Ruobing Zhang & Faraz Ahmed & Syed Mir Muhammed Shah, 2022. "Impact of investment behaviour on financial markets during COVID-19: a case of UK," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 2273-2291, December.
    13. Abbas, Ghulam & Bashir, Usman & Wang, Shouyang & Zebende, Gilney Figueira & Ishfaq, Muhammad, 2019. "The return and volatility nexus among stock market and macroeconomic fundamentals for China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    14. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
    15. Grudniewicz, Jan & Ślepaczuk, Robert, 2023. "Application of machine learning in algorithmic investment strategies on global stock markets," Research in International Business and Finance, Elsevier, vol. 66(C).
    16. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    17. Sami Ben Jabeur & Cheima Gharib & Salma Mefteh-Wali & Wissal Ben Arfi, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Post-Print hal-05238300, HAL.
    18. Baltussen, Guido & Da, Zhi & Lammers, Sten & Martens, Martin, 2021. "Hedging demand and market intraday momentum," Journal of Financial Economics, Elsevier, vol. 142(1), pages 377-403.
    19. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    20. Hanauer, Matthias X. & Jansen, Maarten & Swinkels, Laurens & Zhou, Weili, 2024. "Factor models for Chinese A-shares," International Review of Financial Analysis, Elsevier, vol. 91(C).
    21. Sun, Yucheng & Xu, Wen & Zhang, Chuanhai, 2023. "Identifying latent factors based on high-frequency data," Journal of Econometrics, Elsevier, vol. 233(1), pages 251-270.
    22. Song, Yu & Akagi, Fumio, 2016. "Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock marketAuthor-Name: Qiu, Mingyue," Chaos, Solitons & Fractals, Elsevier, vol. 85(C), pages 1-7.
    23. Jiang, Rui & Wen, Conghua & Zhang, Ruonan & Cui, Yu, 2022. "Investor's herding behavior in Asian equity markets during COVID-19 period," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    24. Sheng, Yankai & Qu, Yuanyu & Ma, Ding, 2024. "Stock price crash prediction based on multimodal data machine learning models," Finance Research Letters, Elsevier, vol. 62(PA).
    25. Wang, Zijun, 2021. "The high volume return premium and economic fundamentals," Journal of Financial Economics, Elsevier, vol. 140(1), pages 325-345.
    26. Davide Pettenuzzo & Riccardo Sabbatucci & Allan Timmermann, 2020. "Cash Flow News and Stock Price Dynamics," Journal of Finance, American Finance Association, vol. 75(4), pages 2221-2270, August.
    27. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    28. Zhi Yang & Zhao Fei & Jing Wang, 2024. "Research on the Correlation between the Exchange Rate of Offshore RMB and the Stock Index Futures," Mathematics, MDPI, vol. 12(5), pages 1-15, February.
    29. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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