Financial Prediction with Neuro-fuzzy Systems
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
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Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Expert Systems with Applications: An International Journal
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INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
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Expert Systems with Applications: An International Journal
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ACM Transactions on Intelligent Systems and Technology (TIST)
Expert Systems with Applications: An International Journal
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Applied Soft Computing
International Journal of Business Intelligence and Data Mining
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
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AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
lp-norm multikernel learning approach for stock market price forecasting
Computational Intelligence and Neuroscience
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In this paper, we propose a hybrid neurogenetic system for stock trading. A recurrent neural network (NN) having one hidden layer is used for the prediction model. The input features are generated from a number of technical indicators being used by financial experts. The genetic algorithm (GA) optimizes the NN's weights under a 2-D encoding and crossover. We devised a context-based ensemble method of NNs which dynamically changes on the basis of the test day's context. To reduce the time in processing mass data, we parallelized the GA on a Linux cluster system using message passing interface. We tested the proposed method with 36 companies in NYSE and NASDAQ for 13 years from 1992 to 2004. The neurogenetic hybrid showed notable improvement on the average over the buy-and-hold strategy and the context-based ensemble further improved the results. We also observed that some companies were more predictable than others, which implies that the proposed neurogenetic hybrid can be used for financial portfolio construction