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A Maximal Predictability Portfolio Model: Algorithm And Performance Evaluation

Author

Listed:
  • REI YAMAMOTO

    (Department of Industrial and Systems Engineering, Chuo University and Mitsubishi UFJ Trust Investment Technology Institute Co., Ltd., Japan)

  • DAISUKE ISHII

    (Department of Industrial and Systems Engineering, Chuo University, Japan)

  • HIROSHI KONNO

    (Department of Industrial and Systems Engineering, Chuo University, Japan)

Abstract

The purpose of this paper is to show that an algorithm recently proposed by authors can in fact solve a maximal predictability portfolio (MPP) optimization problem, which is a hard nonconvex fractional programming optimization. Also, we will compare MPP with standard mean-variance portfolio (MVP) and show that MPP outperforms MVP and index. We believe that this paper is of interest to researchers and practitioners in the field of portfolio optimization.

Suggested Citation

  • Rei Yamamoto & Daisuke Ishii & Hiroshi Konno, 2007. "A Maximal Predictability Portfolio Model: Algorithm And Performance Evaluation," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(06), pages 1095-1109.
    • Handle: RePEc:wsi:ijtafx:v:10:y:2007:i:06:n:s0219024907004561
    DOI: 10.1142/S0219024907004561
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    Citations

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    Cited by:

    1. Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
    2. Michael Pinelis & David Ruppert, 2023. "Maximizing Portfolio Predictability with Machine Learning," Papers 2311.01985, arXiv.org.
    3. Philippe Goulet Coulombe & Maximilian Gobel, 2023. "Maximally Machine-Learnable Portfolios," Working Papers 23-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2023.
    4. Hiroshi Konno & Yuuhei Morita & Rei Yamamoto, 2010. "A maximal predictability portfolio using absolute deviation reformulation," Computational Management Science, Springer, vol. 7(1), pages 47-60, January.

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