Geometric mean maximization: A note on expected, observed, and simulated performance

Ken Johnston, John Hatem

Research output: Contribution to journalArticlepeer-review

Abstract

This article discusses why the maximization of any portfolio optimization model cannot be isolated from the investor’s risk perception. To allow for differing risk preferences among investors, the efficient frontiers of Sharpe ratio maximization (SRM) and geometric mean maximization (GMM) are the appropriate metrics for making a comparison. The authors demonstrate that, for a given level of risk, the two optimization techniques will choose the same portfolio asset weights. Although GMM provides investors with a different way to approach portfolio optimization (maximizing terminal wealth), it is not a competing portfolio optimization technique to mean–variance maximization but, rather, a complementary one.

Original languageEnglish
Pages (from-to)87-94
Number of pages8
JournalJournal of Investing
Volume30
Issue number4
DOIs
StatePublished - Jun 2021

Scopus Subject Areas

  • Finance
  • Strategy and Management
  • Management of Technology and Innovation

Keywords

  • Performance measurement
  • Portfolio construction
  • Quantitative methods
  • Statistical methods

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