Abstract
In this paper, we attempt to make accurate predictions of the movement of the stock market with the aid of an evolutionary artificial neural network (EANN). To facilitate this objective we constructed an EANN for multi-objective optimization (MOO) that was trained with macro-economic data and its effect on market performance. Experiments were conducted with EANNs that updated connection weights through genetic operators (crossover and mutation) and/or with the aid of back-propagation. The results showed that the optimal performance was achieved under natural complexification of the EANN and that back-propagation tended to over fit the data. The results also suggested that EANNs trained with multi-objectives were more robust than that of a single optimization approach. The MOO approach produced superior investment returns during training and testing over a single objective optimization (SOO).
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