Optimal Enough?

Journal of Heuristics, Vol. 17, No. 4, pp. 373-387, 2011

18 Pages Posted: 17 Jun 2009 Last revised: 23 Jul 2011

See all articles by Manfred Gilli

Manfred Gilli

University of Geneva - Research Center for Statistics; Swiss Finance Institute

Enrico Schumann


Date Written: March 14, 2010


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. 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, however, there is practically no cause for improving the solution any further, since any in-sample improvement will, out-of-sample, only lead to financially meaningless improvements and unpredictable changes (noise) in performance.

Keywords: Optimisation heuristics, Portfolio Optimisation, Threshold Accepting

JEL Classification: C61, G11

Suggested Citation

Gilli, Manfred and Schumann, Enrico, Optimal Enough? (March 14, 2010). Journal of Heuristics, Vol. 17, No. 4, pp. 373-387, 2011 , Available at SSRN: https://ssrn.com/abstract=1420058

Manfred Gilli

University of Geneva - Research Center for Statistics ( email )

+41223798222 (Phone)
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HOME PAGE: http://www.unige.ch/ses/metri/gilli/

Swiss Finance Institute ( email )

c/o University of Geneva
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CH-1211 Geneva 4

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