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Performance analysis of log-optimal portfolio strategies with transaction costs

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  • Mih�ly Ormos
  • Andr�s Urb�n

Abstract

In this paper we introduce an empirical approximation of the log-optimal investment strategy that guarantees an almost optimal growth rate of investments. The proposed strategy also considers the effects of portfolio rearrangement costs on growth optimality and recommends a suboptimal portfolio for discrete investment periods. We do not assume any parametric structure for the market process, only a first-order Markov property. The model introduced is based on kernel-based agents' (experts') approximation of the maximum theoretical growth rate with transaction costs. Although the optimal solution is theoretically a complex Bellman programming problem, our suboptimal empirical result appears to be attractive for Dow Jones 30 shares. The paper presents a performance analysis where the return of the empirical log-optimal portfolio is compared with passive portfolio counterparts compiled from similar components using the CAPM, the three-factor model and the four-factor model. The proposed methods, in the presence of transaction costs, provide a significant positive abnormal return compared with the preceding equilibrium models, and is even a survivorship bias-free setup.

Suggested Citation

  • Mih�ly Ormos & Andr�s Urb�n, 2013. "Performance analysis of log-optimal portfolio strategies with transaction costs," Quantitative Finance, Taylor & Francis Journals, vol. 13(10), pages 1587-1597, October.
    • Handle: RePEc:taf:quantf:v:13:y:2013:i:10:p:1587-1597
    DOI: 10.1080/14697688.2011.570368
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    References listed on IDEAS

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    Cited by:

    1. Zhenhan Huang & Fumihide Tanaka, 2021. "MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management," Papers 2102.03502, arXiv.org, revised Feb 2022.
    2. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    3. Farzan Soleymani & Eric Paquet, 2021. "Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocket," Papers 2105.08664, arXiv.org.
    4. Jinyang Li, 2024. "A Deep Reinforcement Learning Framework For Financial Portfolio Management," Papers 2409.08426, arXiv.org.
    5. Yunan Ye & Hengzhi Pei & Boxin Wang & Pin-Yu Chen & Yada Zhu & Jun Xiao & Bo Li, 2020. "Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States," Papers 2002.05780, arXiv.org.
    6. Yinheng Li & Junhao Wang & Yijie Cao, 2019. "A General Framework on Enhancing Portfolio Management with Reinforcement Learning," Papers 1911.11880, arXiv.org, revised Oct 2023.
    7. Yifeng Guo & Xingyu Fu & Yuyan Shi & Mingwen Liu, 2018. "Robust Log-Optimal Strategy with Reinforcement Learning," Papers 1805.00205, arXiv.org.

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