Computational Economics

, Volume 34, Issue 2, pp 173–193 | Cite as

Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm

  • Massimiliano Kaucic


This article introduces an automated procedure to simultaneously select variables and detect outliers in a dynamic linear model using information criteria as objective functions and diagnostic tests as constraints for the distributional properties of errors. A robust scaling method is considered to take into account the sensitiveness of estimates to abnormal data. A genetic algorithm is developed to these purposes. Two examples are presented where models are designed to produce short-term forecasts for the excess returns of the MSCI Europe Energy sector on the MSCI Europe index and a recursive estimation-window is used to shed light on their predictability performances. In the first application the data-set is obtained by a reduction procedure from a very large number of leading macro indicators and financial variables stacked at various lags, while in the second the complete set of 1-month lagged variables is considered. Results show a promising capability to predict excess sector returns through the selection, using the proposed methodology, of most valuable predictors.


Genetic algorithm Penalty function method Model selection Excess return Information criteria 

JEL Classification

C32 C52 C53 C61 C63 


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Copyright information

© Springer Science+Business Media, LLC. 2009

Authors and Affiliations

  1. 1.Dipartimento di Economia e Tecnica AziendaleUniversity of TriesteTriesteItaly

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