Stock Trend Prediction With Neural Network Techniques

This thesis presents a study and implementation of stock trend prediction using neural network techniques. The multilayer-perceptron (MLP) and radial basis function network (RBF) are compared with the new neural network technique, Support Vector Machine(SVM). In this study the stock trend is defined...

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主要作者: Mohd Haris Lye Abdullah
格式: Thesis
出版: 2003
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總結:This thesis presents a study and implementation of stock trend prediction using neural network techniques. The multilayer-perceptron (MLP) and radial basis function network (RBF) are compared with the new neural network technique, Support Vector Machine(SVM). In this study the stock trend is defined as the maximum excess return from the stock index closing level observed within the next 10 days ahead.