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.
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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
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