Modeling and trading the Greek stock market with artificial intelligence models

Abstract

The main motivation for this thesis is to introduce some new methodologies for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index. Specifically, we use some alternative computational methodologies named Evolutionary Support Vector Machine (ESVM), Gene Expression programming, Genetic Programming Algorithms and 2 hybrid combinations of linear and no linear models for modeling and trading the ASE20 Greek stock index using as inputs previous values of the ASE20 index and of four other financial indices. For comparison purposes, the trading performance of the ESVM stock predictor, Gene Expression Programming, Genetic Programming Algorithms and the 2 Hybrid combination methodologies have been benchmarked with four traditional strategies (a nave strategy, a Buy and Hold strategy, a MACD and an ARMA models), and a Multilayer Pereceptron (MLP) neural network model. As it turns out, the proposed methodologies produced a higher trading performance in terms of annualized return and information ratio, while providing information about the relationship between the ASE20 index and other foreign indices.