A maximal predictability portfolio using absolute deviation reformulation

Hiroshi Konno, Yuuhei Morita, Rei Yamamoto

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


This paper shows that a large-scale maximal predictability portfolio (MPP) optimization problem can be solved within a practical amount of computational time using absolute deviation instead of squared deviation in the definition of the coefficient of determination. Also, we will show that MPP portfolio outperforms the mean-absolute deviation portfolio using real asset data in Tokyo Stock Exchange.

Original languageEnglish
Pages (from-to)47-60
Number of pages14
JournalComputational Management Science
Issue number1
Publication statusPublished - 2010 Jan
Externally publishedYes


  • 0-1 mixed integer programming
  • Absolute deviation
  • Fractional programming
  • Maximal predictability portfolio
  • Portfolio optimization

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems


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