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Researchers have known for some time that non-linearity exists in the financial markets and that neural networks can be used to forecast market returns. In this article, we present a novel stock market prediction system which focuses on forecasting the relative tendency growth between different stocks and indices rather than purely predicting their values. This research utilizes artificial neural...
The paper compares the performances between the back propagation (BP) neural network and the radial basis function (RBF) neural network in chaotic time series prediction with the Logistic equation, and the results show that the RBF neural network is better than the BP neural network. Further we apply the RBF neural network to predict the Shanghai Composite index that is chaotic according to the phase...
Stock markets are affected by many uncertainties and interrelated economic and political factors at both local and global levels; determining the set of relevant factors for making accurate predictions is a complicated task. This paper analyzes relevant literature on the Stock Exchange of Thailand (SET), according to the categories of techniques used. The research proposes an approach of soft computing...
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