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Online Portfolio Selection Strategy Based on Combining Experts’ Advice

Author

Listed:
  • Yong Zhang

    (Guangdong University of Technology)

  • Xingyu Yang

    (Guangdong University of Technology)

Abstract

The weak aggregating algorithm (WAA) developed from learning and prediction with expert advice makes decisions by considering all the experts’ advice, and each expert’s weight is updated according to his performance in previous periods. In this paper, we apply the WAA to the online portfolio selection problem. We first consider a simple case in which the expert advice is the strategy for investing in one stock; for this case, we obtain a portfolio selection strategy WAAS and prove that the WAAS can identify the best stock. We also discuss a more complicated case in which constant rebalanced portfolios are considered as expert advice, and obtain a corresponding portfolio selection strategy WAAC. The theoretical result shows that the cumulative gain that WAAC achieves is as large as that of the best constant rebalanced portfolio. Numerical analysis shows that the cumulative gains of our proposed strategies are as large as those of the best expert advice.

Suggested Citation

  • Yong Zhang & Xingyu Yang, 2017. "Online Portfolio Selection Strategy Based on Combining Experts’ Advice," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 141-159, June.
    • Handle: RePEc:kap:compec:v:50:y:2017:i:1:d:10.1007_s10614-016-9585-0
    DOI: 10.1007/s10614-016-9585-0
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    References listed on IDEAS

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    1. Erik Ordentlich & Thomas M. Cover, 1998. "The Cost of Achieving the Best Portfolio in Hindsight," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 960-982, November.
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    Cited by:

    1. Xingyu Yang & Jin’an He & Hong Lin & Yong Zhang, 2020. "Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 231-251, January.
    2. Xiao-Yang Liu & Hongyang Yang & Jiechao Gao & Christina Dan Wang, 2021. "FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance," Papers 2111.09395, arXiv.org.
    3. MohammadAmin Fazli & Mahdi Lashkari & Hamed Taherkhani & Jafar Habibi, 2022. "A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management," Papers 2212.14477, arXiv.org.
    4. Xiao-Yang Liu & Zhuoran Xiong & Shan Zhong & Hongyang Yang & Anwar Walid, 2018. "Practical Deep Reinforcement Learning Approach for Stock Trading," Papers 1811.07522, arXiv.org, revised Jul 2022.
    5. Yong Zhang & Jingting Wu & Wenxiong Lin & Muyu Hou, 2022. "Competitive analysis for two-option online leasing problem under sharing economy," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 670-689, August.
    6. Xiao-Yang Liu & Hongyang Yang & Qian Chen & Runjia Zhang & Liuqing Yang & Bowen Xiao & Christina Dan Wang, 2020. "FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance," Papers 2011.09607, arXiv.org, revised Mar 2022.

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