A maximal predictability portfolio using dynamic factor selection strategy

Hiroshi Konno; Yoshihiro Takaya; Rei Yamamoto

doi:10.1142/S0219024910005802

A maximal predictability portfolio using dynamic factor selection strategy

Hiroshi Konno, Yoshihiro Takaya, Rei Yamamoto

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	355-366
Number of pages	12
Journal	International Journal of Theoretical and Applied Finance
Volume	13
Issue number	3
DOIs	https://doi.org/10.1142/S0219024910005802
Publication status	Published - 2010 May
Externally published	Yes

Keywords

01 integer programming
Maximal predictability portfolio
absolute deviation
factor model
fractional programming
nonconvex minimization problem

ASJC Scopus subject areas

Finance
Economics, Econometrics and Finance(all)

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10.1142/S0219024910005802

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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.",

keywords = "01 integer programming, Maximal predictability portfolio, absolute deviation, factor model, fractional programming, nonconvex minimization problem",

author = "Hiroshi Konno and Yoshihiro Takaya and Rei Yamamoto",

note = "Funding Information: This research was supported in part by the Grant-in-Aid for Scientific Research (B)18310109 of the MEXT.",

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T1 - A maximal predictability portfolio using dynamic factor selection strategy

AU - Konno, Hiroshi

AU - Takaya, Yoshihiro

AU - Yamamoto, Rei

N1 - Funding Information: This research was supported in part by the Grant-in-Aid for Scientific Research (B)18310109 of the MEXT.

PY - 2010/5

Y1 - 2010/5

N2 - 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.

AB - 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.

KW - 01 integer programming

KW - Maximal predictability portfolio

KW - absolute deviation

KW - factor model

KW - fractional programming

KW - nonconvex minimization problem

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JO - International Journal of Theoretical and Applied Finance

JF - International Journal of Theoretical and Applied Finance

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ER -

A maximal predictability portfolio using dynamic factor selection strategy

Abstract

Keywords

ASJC Scopus subject areas

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