Neural Network Time Series Forecasting of Financial Markets
Neural Network Time Series Forecasting of Financial Markets
Evolving neural networks through augmenting topologies
Evolutionary Computation
A neural evolutionary approach to financial modeling
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolving a real-world vehicle warning system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
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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).