Stock portfolio selection with full-scale optimization and differential evolution

Björn Hagströmer*, Jane M. Binner

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Full-Scale Optimization (FSO) is a utility maximization approach to portfolio choice problems that has theoretical appeal but that suffers from computational burden in large scale problems. We apply the heuristic technique differential evolution to solve FSO-type asset selection problems of 97 assets under complex utility functions rendering rough utility search surfaces. We show that this problem is computationally feasible and that solutions retrieved with random starting values are converging to one optimum. Furthermore, the study constitutes the first FSO application to stock portfolio optimization. The results indicate that when investors are loss averse, FSO improves stock portfolio performance compared to Mean Variance (MV) portfolios. This finding widens the scope of applicability of FSO, but it is also stressed that out-of-sample success will always be dependent on the forecasting ability of the input return distributions.

Original languageEnglish
Pages (from-to)1559-1571
Number of pages13
JournalApplied Financial Economics
Volume19
Issue number19
DOIs
Publication statusPublished - 2009

Fingerprint Dive into the research topics of 'Stock portfolio selection with full-scale optimization and differential evolution'. Together they form a unique fingerprint.

  • Cite this