A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting
Expert Systems with Applications: An International Journal
Grey system theory-based models in time series prediction
Expert Systems with Applications: An International Journal
Spurious valleys in the error surface of recurrent networks: analysis and avoidance
IEEE Transactions on Neural Networks
Single-step ahead prediction based on the principle of concatenation using grey predictors
Expert Systems with Applications: An International Journal
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We propose a new methodology to aid in designing a portfolio of investment over multiple stock markets. It is our hypothesis that financial stock market trends may be predicted better over a set of markets instead of any one single market. A selection criteria is proposed in this paper to make this choice effectively. This criteria is based upon the observed backpropagation and recurrent neural networks prediction accuracy, and the overall change recorded in the previous year. The results obtained when using data for four consecutive years over five international stock markets supports our claim. Backpropagation networks use gradient descent to learn spatial relationships. On the other hand, recurrent networks are capable of capturing spatiotemporal information from training data.This paper analyzes application of recurrent networks to the stock market return prediction problem in contrast with backpropagation networks. On the basis of the results observed during these experiments it follows that the effect of learning temporal information was not substantial on the prediction accuracy for the stock market returns.