Turning point identification and Bayesian forecasting of a volatile time series
Computers and Industrial Engineering
Genetic Programming Prediction of Stock Prices
Computational Economics
Towards the Genetic Synthesisof Neural Networks
Proceedings of the 3rd International Conference on Genetic Algorithms
Toward More Powerful Recombinations
Proceedings of the 6th International Conference on Genetic Algorithms
Neuron Reordering For Better Neuro-genetic Hybrids
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolving artificial neural networks to combine financial forecasts
IEEE Transactions on Evolutionary Computation
Financial prediction and trading strategies using neurofuzzyapproaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Controlling chaos by GA-based reinforcement learning neural network
IEEE Transactions on Neural Networks
Financial volatility trading using recurrent neural networks
IEEE Transactions on Neural Networks
Evaluation approach to stock trading system using evolutionary computation
Expert Systems with Applications: An International Journal
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We propose a neuro-genetic daily stock prediction model. Traditional indicators of stock prediction are utilized to produce useful input features of neural networks. The genetic algorithm optimizes the neural networks under a 2D encoding and crossover. To reduce the time in processing mass data, a parallel genetic algorithm was used on a Linux cluster system. It showed notable improvement on the average over the buy-and-hold strategy. We also observed that some companies were more predictable than others.