Detail publikace

Baruník J.: How Does Neural Networks Enhance the Predictability of Central European Stock Returns?

Autor: doc. PhDr. Jozef Baruník Ph.D.,
Typ: Články v impaktovaných časopisech
Rok: 2008
Číslo: 7
ISSN / ISBN: 0015-1920
Publikováno v: Finance a úvěr-Czech Journal of Economics and Finance, 7-8 (58), pp.359-376 PDF
Místo vydání: Prague
Klíčová slova: emerging stock markets, predictability of stock returns, neural networks
JEL kódy: C45, C53, E44
Citace: Baruník J. (2008): How Does Neural Networks Enhance the Predictability of Central European Stock Returns? Czech Journal of Economics and Finance, 7-8 (58):359-376
Granty: GAUK 46108: Nové nelineární teorie kapitálových trhů: fraktální, bifurkační a behaviorální přístup Výzkumný záměr IES (2005-2011) Integrace české ekonomiky do Evropské unie a její rozvoj
Abstrakt: 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.
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