A maximal predictability portfolio model: Algorithm and performance evaluation

Rei Yamamoto, Daisuke Ishii, Hiroshi Konno

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1095-1109
Number of pages15
JournalInternational Journal of Theoretical and Applied Finance
Volume10
Issue number6
DOIs
Publication statusPublished - 2007 Sep 1
Externally publishedYes

Keywords

  • 0-1 Integer programming
  • Fractional programming
  • Global optimization
  • Maximal predictability portfolio
  • Mean-variance portfolio
  • Portfolio optimization

ASJC Scopus subject areas

  • Finance
  • Economics, Econometrics and Finance(all)

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