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.
Keywords
- Neural Network
- Stock Market
- Error Forecast
- Probabilistic Neural Network
- General Regression Neural Network
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fama, E.: The behaviour of stock market prices. Journal of Business, 34–105 (1965)
Kim, J., Moon, J.Y.: Designing towards emotional usability in customer interfaces trustworthiness of cyber-banking system interfaces. Interacting with Computers, 1–29 (1998)
Saad, E.W., Prokhorov, D.V., Wunsch, D.C.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks, 1456–1470 (1998)
Wang, L.: A General Design For Temporal Sequence Processing Using Any Arbitrary Associative Neural Network, Artificial Intelligence - Sowing the seeds to the future. In: Proceedings of the 7th Australian Joint Conference on Artificial Intelligence. World Scientific, Singapore (1994)
Fong, B., Rapajic, P.B., Hong, G.Y., Fong, A.C.M.: On performance of an equalization algorithm based on space and time diversity for wireless multimedia services to home users. IEEE Transactions on Consumer Electronics, 597–601 (2003)
Bollerslev, T.: Generalised autoregressive heteroscedasticity. Journal of Econometrics, 307–327 (1986)
Trippi, R.R., Turban, E.: Neural Networks in Finance and Investing. McGraw-Hill, New York (1996)
Alexander, C.: Market Models: A Guide to Financial Data Analysis. John Wiley, United Kingdom (2001)
Psaltis, D., Sideris, A., Yamamura, A.: A multilayered neural network controller. IEEE Control Systems Magazine, 19–21 (1988)
Borah, D.K., Rapajic, P.B.: Optimal adaptive multiuser detection in unknown multipath channels. IEEE Journal on Selected Areas in Communications, 1115–1127 (2001)
Lisboa, P.: Neural Netowrks: Current Applications. Chapman & Hall, Boca Raton (1992)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 318–362. MIT Press, Cambridge (1986)
Specht, D.: Probabilistic neural networks and general regression neural networks. McGraw-Hill, New York (1996)
Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 987–1007 (1982)
Grassberger, P., Badii, R., Politi, A.: Scaling laws for invariant measures on hyperbolic and nonhyperbolic attractors. J. Stat. Phys., 135 (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fong, B., Fong, A.C.M., Hong, G.Y., Wong, L. (2005). An Empirical Study of Volatility Predictions: Stock Market Analysis Using Neural Networks. In: Deng, X., Ye, Y. (eds) Internet and Network Economics. WINE 2005. Lecture Notes in Computer Science, vol 3828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11600930_47
Download citation
DOI: https://doi.org/10.1007/11600930_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30900-0
Online ISBN: 978-3-540-32293-1
eBook Packages: Computer ScienceComputer Science (R0)