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This paper introduces a novel hybrid method for predicting the directional movement of financial assets with an application to the ASE20 Greek stock index. An alternative computational methodology named Evolutionary Support Vector Machine (ESVM) Stock Predictor is used for modeling and trading the ASE20 Greek stock index, extending the pool of the examined inputs to include autoregressive inputs and moving averages of the ASE20 index and other four financial indices. The proposed hybrid methoddoi:10.1080/1351847x.2015.1040167 fatcat:dfevrcy3ybhq7a744ismhg23gi