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On Data-Driven Log-Optimal Portfolio: A Sliding Window Approach

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  • Pei-Ting Wang
  • Chung-Han Hsieh

Abstract

In this paper, we propose a data-driven sliding window approach to solve a log-optimal portfolio problem. In contrast to many of the existing papers, this approach leads to a trading strategy with time-varying portfolio weights rather than fixed constant weights. We show, by conducting various empirical studies, that the approach possesses a superior trading performance to the classical log-optimal portfolio in the sense of having a higher cumulative rate of returns.

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  • Pei-Ting Wang & Chung-Han Hsieh, 2022. "On Data-Driven Log-Optimal Portfolio: A Sliding Window Approach," Papers 2206.12148, arXiv.org.
    • Handle: RePEc:arx:papers:2206.12148
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    File URL: http://arxiv.org/pdf/2206.12148
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    Cited by:

    1. Chung-Han Hsieh, 2023. "On Data-Driven Drawdown Control with Restart Mechanism in Trading," Papers 2303.02613, arXiv.org.
    2. Chung-Han Hsieh & Jie-Ling Lu, 2024. "On Accelerating Large-Scale Robust Portfolio Optimization," Papers 2408.07879, arXiv.org.
    3. Hsieh, Chung-Han, 2024. "On solving robust log-optimal portfolio: A supporting hyperplane approximation approach," European Journal of Operational Research, Elsevier, vol. 313(3), pages 1129-1139.
    4. Bruno Gav{s}perov & Marko {DJ}urasevi'c & Domagoj Jakobovic, 2024. "Finding Near-Optimal Portfolios With Quality-Diversity," Papers 2402.16118, arXiv.org.

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