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Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method

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  • Xingyu Yang

    (Guangdong University of Technology)

  • Jin’an He

    (Guangdong University of Technology)

  • Hong Lin

    (Guangdong University of Technology)

  • Yong Zhang

    (Guangdong University of Technology)

Abstract

Online portfolio selection is one of the fundamental problems in the field of computational finance. Although existing online portfolio strategies have been shown to achieve good performance, we always have to set the values for different parameters of online portfolio strategies, where the optimal values can only be known in hindsight. To tackle the limits of existing strategies, we present a new online portfolio strategy based on the online learning character of Weak Aggregating Algorithm (WAA). Firstly, we consider a number of Exponential Gradient (EG$$(\eta )$$(η)) strategies of different values of parameter $$\eta $$η as experts, and then determine the next portfolio by using the WAA to aggregate the experts’ advice. Furthermore, we theoretically prove that our strategy asymptotically achieves the same increasing rate as the best EG$$(\eta )$$(η) expert. We prove our strategy, as EG$$(\eta )$$(η) strategies, is universal. We present numerical analysis by using actual stock data from the American and Chinese markets, and the results show that it has good performance.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:55:y:2020:i:1:d:10.1007_s10614-019-09890-2
    DOI: 10.1007/s10614-019-09890-2
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    References listed on IDEAS

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    2. Ameer Tamoor Khan & Xinwei Cao & Shuai Li, 2023. "Using Quadratic Interpolated Beetle Antennae Search for Higher Dimensional Portfolio Selection Under Cardinality Constraints," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1413-1435, December.
    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. 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.

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