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A Maximal Predictability Portfolio Using Dynamic Factor Selection Strategy

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Author Info

  • HIROSHI KONNO

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

  • YOSHIHIRO TAKAYA

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

  • REI YAMAMOTO

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

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    Abstract

    In this paper, we will propose a practical method for improving the performance of a maximal predictability portfolio (MPP) model proposed by Lo and MacKinlay and later extended by the authors. We will employ an alternative version of MPP using absolute deviation instead of variance as a measure of fitting and apply a dynamic strategy for choosing the set of factors which fits best to the market data. It will be shown that this approach leads to a significantly better performance than the standard MPP and the index.

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    Bibliographic Info

    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal International Journal of Theoretical and Applied Finance.

    Volume (Year): 13 (2010)
    Issue (Month): 03 ()
    Pages: 355-366

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    Handle: RePEc:wsi:ijtafx:v:13:y:2010:i:03:p:355-366

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    Related research

    Keywords: Maximal predictability portfolio; factor model; nonconvex minimization problem; fractional programming; absolute deviation; 0–1 integer programming;

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