How Do Neural Networks Enhance the Predictability of Central European Stock Returns?
Year: 2008 Volume: 58 Issue: 7 -8 Pages: 359-376
Abstract: In this paper, we apply neural networks as nonparametric and nonlinear methods to Central European (Czech, Polish, Hungarian, and German) stock market returns modeling. In the first part, we present the intuition of neural networks and we also discuss statistical methods for comparing predictive accuracy, as well as economic significance measures. In the empirical tests, we use data on the daily and weekly returns of the PX-50, BUX, WIG, and DAX stock exchange indices for the 2000–2006 period. We find 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. We also achieve economic significance of the predictions for both the daily and weekly PX-50, BUX, and DAX, with a 60% prediction accuracy.
JEL classification: C45, C53, E44
Keywords: emerging stock markets, predictability of stock returns, neural networks
RePEc: http://ideas.repec.org/a/fau/fauart/v58y2008i7-8p359-376.html
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