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Two Algorithms with Logarithmic Regret for Online Portfolio Selection

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Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 891))

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Abstract

In online portfolio selection, an online investor needs to distribute her wealth iteratively and hopes to maximize her final wealth. To model the behavior of the prices of assets in a financial market, we consider two measures of the price relative vectors: quadratic variability and deviation. There exist algorithms which achieve good performance in terms of these two measures. However, the theoretical guarantees depend on an additional parameter, which may not be available before the investor chooses her strategies. In this paper, the performances of the algorithms are tested using real stock market data to understand the influence of this additional parameter.

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Author information

Authors and Affiliations

  1. School of Big Data Management, Soochow University, Taipei, Taiwan

    Chia-Jung Lee

Authors
  1. Chia-Jung Lee
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Corresponding author

Correspondence to Chia-Jung Lee .

Editor information

Editors and Affiliations

  1. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Pavel Krömer

  2. School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, China

    Hong Zhang

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Yongquan Liang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

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Lee, CJ. (2019). Two Algorithms with Logarithmic Regret for Online Portfolio Selection. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_45

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