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 \(L_{1}\)-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.
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Data Availability
The data that analyzed during this study are available upon reasonable request.
Code Availability
The code that used in this study can be disclosed upon reasonable request.
Notes
Yahoo Finance: http://finance.yahoo.com; Google Finance: http://www.google.com/finance; Choice Database: http://choice.eastmoney.com.
Gábor collected till 2006 (http://www.cs.bme.hu/\(\sim \)oti/portfolio/).
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Funding
This research was supported by National Natural Science Foundation of China (72371080), Guangdong Basic and Applied Basic Research Foundation (2023A1515012840), Special Project of Guangzhou Basic and Applied Basic Research (SL2024A04J02640), and Humanities and Social Science Foundation of the Ministry of Education of China (No. 21YJA630117).
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Zhang, Y., Li, J., Yang, X. et al. Competitive Online Strategy Based on Improved Exponential Gradient Expert and Aggregating Method. Comput Econ (2023). https://doi.org/10.1007/s10614-023-10430-2
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DOI: https://doi.org/10.1007/s10614-023-10430-2