The main motivation for this paper is to introduce a novel hybrid method for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index.

Specifically, we use an alternative computational methodology named evolutionary support vector machine ESVM stock predictor for modeling and trading the ASE20 Greek stock index extending the universe of the examined inputs to include autoregressive inputs and moving averages of the ASE20 index and other four financial indices.

The proposed hybrid method consists of a combination of genetic algorithms with support vector machines modified to uncover effective short-term trading models and overcome the limitations of existing methods.

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Andreas Karathanasopoulos Suliman S Olayan School of Business, American University of Beirut, Beirut, Lebanon Correspondence andreas. Received 23 Oct Log in via your institution Shibboleth OpenAthens.

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