Journal of Computational Physics
Optimization heuristics in econometrics: applications of threshold accepting
Optimization heuristics in econometrics: applications of threshold accepting
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Portfolio Management with Heuristic Optimization (Advances in Computational Management Science)
Portfolio Management with Heuristic Optimization (Advances in Computational Management Science)
The convergence of estimators based on heuristics: theory and application to a GARCH model
Computational Statistics
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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.