Competitive Online Strategy Based on Improved Exponential Gradient Expert and Aggregating Method

Yong Zhang, Jiahao Li, Xingyu Yang () and Jianliang Zhang
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Yong Zhang: Guangdong University of Technology
Jiahao Li: Guangdong University of Technology
Xingyu Yang: Guangdong University of Technology
Jianliang Zhang: Guangdong University of Technology

Computational Economics, 2024, vol. 64, issue 2, No 7, 789-814

Abstract: Abstract In recent years, online portfolio selection (OLPS) has received more and more attention from quantitative investment and artificial intelligence communities. This paper first improves a classic OLPS strategy Exponential Gradient (EG) (Helmbold in MF 8:325–347, 1998) by fully exploiting multi-period price information via the $$L_{1}$$ L 1 -median estimator, and further designs a novel strategy named Aggregating Improved Exponential Gradient (AIEG) by using Weak Aggregating Algorithm (WAA) to aggregate an infinite number of Improved EG (IEG) expert advice. The universality of the proposed strategy is proved. This paper empirically evaluates the proposed strategy through a wide range of experiments. Promising empirical results verify that the proposed AIEG strategy performs well in terms of different aspects and can resist reasonable transaction costs.

Keywords: Universal portfolio; $$L_{1}$$ L 1 -median estimator; Online learning; Weak aggregating algorithm; Expert advice; Competitive performance (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10430-2

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