How Do Neural Networks Enhance the Predictability of Central European Stock Returns?
AbstractIn this paper, the author applies neural networks as nonparametric and nonlinear methods to Central European (Czech, Polish, Hungarian, and German) stock market returns modeling. In the first part, he presents the intuition of neural networks and also discusses statistical methods for comparing predictive accuracy, as well as economic significance measures. In the empirical tests, he uses data on the daily and weekly returns of the PX-50, BUX, WIG, and DAX stock exchange indices for the 2000–2006 period. He finds neural networks to have a significantly lower prediction error than the classical models for the daily DAX series and the weekly PX-50 and BUX series. The author also achieves economic significance of the predictions for both the daily and weekly PX-50, BUX, and DAX, with a 60% prediction accuracy.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Charles University Prague, Faculty of Social Sciences in its journal Finance a uver - Czech Journal of Economics and Finance.
Volume (Year): 58 (2008)
Issue (Month): 07-08 (Oktober)
emerging stock markets; predictability of stock returns; neural networks;
Find related papers by JEL classification:
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- John Barkoulas & Nickolaos Travlos, 1998. "Chaos in an emerging capital market? The case of the Athens Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 8(3), pages 231-243.
- Hsieh, David A, 1991. " Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-77, December.
- Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," The Journal of Business, University of Chicago Press, vol. 54(4), pages 513-33, October.
- Diebold, Francis X & Mariano, Roberto S, 1995.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(3), pages 253-63, July.
- Tom Doan, . "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- James M. Hutchinson & Andrew W. Lo & Tomaso Poggio, 1994.
"A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks,"
NBER Working Papers
4718, National Bureau of Economic Research, Inc.
- Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. " A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-89, July.
- Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
- Timotej Jagric & Vita Jagric & Davorin Kracun, 2011. "Does Non-linearity Matter in Retail Credit Risk Modeling?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(4), pages 384-402, August.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lenka Herrmannova).
If references are entirely missing, you can add them using this form.