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Performance of portfolio selection using the MARCOS model with machine learning-based prediction

Author

Listed:
  • Ge, Zhipeng
  • Wu, Yinze
  • Deng, Zhijuan
  • Du, Puliang
  • Zheng, Andi

Abstract

The Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) is effective for portfolio selection but is limited by its reliance on historical criteria, restricting its application in dynamic markets. This study investigates the potential of machine learning to enhance the performance of portfolio selection using the MARCOS model. Experimental results from the Chinese stock market reveal that the Support Vector Regression method, which disregards ambiguous data, shows substantial improvement in performance and maintains stable results. The XGBoost and AdaBoost methods, which take abnormal data into account, can yield more benefits but at the cost of increased risk. Conversely, the Random Forest method, treating data indiscriminately, performs unsatisfactorily. This research has significant practical implications for investors aiming to optimize their portfolios and achieve superior investment outcomes.

Suggested Citation

  • Ge, Zhipeng & Wu, Yinze & Deng, Zhijuan & Du, Puliang & Zheng, Andi, 2025. "Performance of portfolio selection using the MARCOS model with machine learning-based prediction," Finance Research Letters, Elsevier, vol. 86(PA).
    • Handle: RePEc:eee:finlet:v:86:y:2025:i:pa:s154461232501596x
    DOI: 10.1016/j.frl.2025.108342
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    References listed on IDEAS

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    1. Yu, Pengrui & Ge, Zhipeng & Gong, Xiaomin & Cao, Xiao, 2024. "Dynamic portfolio optimization with the MARCOS approach under uncertainty," International Review of Financial Analysis, Elsevier, vol. 96(PA).
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    5. Zhao, Yonggan, 2007. "A dynamic model of active portfolio management with benchmark orientation," Journal of Banking & Finance, Elsevier, vol. 31(11), pages 3336-3356, November.
    6. Li, Shicheng & Huang, Xiaoyong & Cheng, Zhonghou & Zou, Wei & Yi, Yugen, 2023. "AE-ACG: A novel deep learning-based method for stock price movement prediction," Finance Research Letters, Elsevier, vol. 58(PA).
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