Wavelet neural networks for stock trading
Date of Issue2013
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering (11th : 2013 : Baltimore, USA)
School of Electrical and Electronic Engineering
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.
DRNTU::Engineering::Electrical and electronic engineering
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE). This paper was published in Proceeding of SPIE, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI and is made available as an electronic reprint (preprint) with permission of SPIE. The paper can be found at the following official DOI: [http://dx.doi.org/10.1117/12.2018040]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.