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A maximal predictability portfolio using absolute deviation reformulation

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  • Hiroshi Konno
  • Yuuhei Morita
  • Rei Yamamoto

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  • 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.
    • Handle: RePEc:spr:comgts:v:7:y:2010:i:1:p:47-60
    DOI: 10.1007/s10287-008-0075-2
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    References listed on IDEAS

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    1. Andrew W. Lo & Jiang Wang, 2006. "Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model," Journal of Finance, American Finance Association, vol. 61(6), pages 2805-2840, December.
    2. Lo, Andrew W. & Mackinlay, A. Craig, 1997. "Maximizing Predictability In The Stock And Bond Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 1(1), pages 102-134, January.
    3. H. Konno & K. Tsuchiya & R. Yamamoto, 2007. "Minimization of the Ratio of Functions Defined as Sums of the Absolute Values," Journal of Optimization Theory and Applications, Springer, vol. 135(3), pages 399-410, December.
    4. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    5. 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.
    6. R. Yamamoto & H. Konno, 2007. "An Efficient Algorithm for Solving Convex–Convex Quadratic Fractional Programs," Journal of Optimization Theory and Applications, Springer, vol. 133(2), pages 241-255, May.
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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.

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