29 May 2013 Wavelet neural networks for stock trading
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Abstract
This paper explores the application of a wavelet neural network (WNN), whose hidden layer is comprised of neurons with adjustable wavelets as activation functions, to stock prediction. We discuss some basic rationales behind technical analysis, and based on which, inputs of the prediction system are carefully selected. This system is tested on Istanbul Stock Exchange National 100 Index and compared with traditional neural networks. The results show that the WNN can achieve very good prediction accuracy.
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Tianxing Zheng, Tianxing Zheng, Kamaladdin Fataliyev, Kamaladdin Fataliyev, Lipo Wang, Lipo Wang, "Wavelet neural networks for stock trading", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 87500A (29 May 2013); doi: 10.1117/12.2018040; https://doi.org/10.1117/12.2018040
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