An empirical study of volatility predictions: stock market analysis using neural networks

Fong, B., Fong, A.C.M., Hong, G.Y. and Wong, L. (2005) An empirical study of volatility predictions: stock market analysis using neural networks. In: Deng, X. and Ye, Y. (eds.) Internet and Network Economics. Series: Lecture notes in computer science. Internet and network economics, 3828 (3828). Springer, pp. 473-480. ISBN 9783540309000 (doi: 10.1007/11600930_47)

Abstract

Volatility is one of the major factor that causes uncertainty in short term stock market movement. Empirical studies based on stock market data analysis were conducted to forecast the volatility for the implementation and evaluation of statistical models with neural network analysis. The model for prediction of Stock Exchange short term analysis uses neural networks for digital signal processing of filter bank computation. Our study shows that in the set of four stocks monitored, the model based on moving average analysis provides reasonably accurate volatility forecasts for a range of fifteen to twenty trading days.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Fong, Dr Alvis Cheuk Min
• Authors: Fong, B., Fong, A.C.M., Hong, G.Y., and Wong, L.
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer
• ISSN: 0302-9743
• ISBN: 9783540309000