Journal of Heuristics

, Volume 17, Issue 4, pp 373–387 | Cite as

Optimal enough?

Open Access
Article

Abstract

An alleged weakness of heuristic optimisation methods is the stochastic character of their solutions: instead of finding the truly optimal solution, they only provide a stochastic approximation of this optimum. In this paper we look into a particular application, portfolio optimisation. We demonstrate that the randomness of the ‘optimal’ solution obtained from the algorithm can be made so small that for all practical purposes it can be neglected. More importantly, we look at the relevance of the remaining uncertainty in the out-of-sample period. The relationship between in-sample fit and out-of-sample performance is not monotonous, but still, we observe that up to a point better solutions in-sample lead to better solutions out-of-sample. Beyond this point there is no more cause for improving the solution any further: any in-sample improvement leads out-of-sample only to financially meaningless improvements and unpredictable changes (noise) in performance.

Keywords

Optimisation heuristics Portfolio optimisation Threshold accepting 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of EconometricsUniversity of GenevaGeneva 4Switzerland
  2. 2.Department of Econometrics and Swiss Finance InstituteUniversity of GenevaGeneva 4Switzerland

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