Wavelet Neural Networks Prediction of Central European Stock Markets

0311416 - ÚTIA 2009 RIV SK eng C - Konferenční příspěvek (zahraniční konf.)
Vácha, Lukáš - Baruník, Jozef
Wavelet Neural Networks Prediction of Central European Stock Markets.
[Vlnové neurálních sítě předvídající centrální evropský trh s cennými papíry.]
Quantitative Methods in Economics: Multiple Criteria Decision making XIV. Bratislava: University of Economics in Bratislava, 2008 - (Reiff, S.), s. 291-297. ISBN 978-80-8078-217-7.
[Quantitative Methods in Economics: Multiplie Criteria Decision Making XIV. Tatranská Lomnica (SK), 05.07.2008-07.07.2008]
Grant CEP: GA ČR GP402/08/P207; GA ČR(CZ) GA402/06/1417
Výzkumný záměr: CEZ:AV0Z10750506
Klíčová slova: neural networks * hard threshold denoising * wavelets
Kód oboru RIV: AH - Ekonomie

In this paper we apply neural network with denoising layer method for forecasting of Central European Stock Exchanges, namely Prague, Budapest and Warsaw. Hard threshold denoising with Daubechies 6 wavelet filter and three level decomposition is used to denoise the stock index returns, and two-layer feed-forward neural network with Levenberg-Marquardt learning algorithm is used for modeling. The results show that wavelet network structure is able to approximate the underlying process of considered stock markets better that multilayered neural network architecture without using wavelets. Further on we discuss the impact of structural changes of the market on forecasting accuracy, and we find that for certain periods the one-step-ahead prediction accuracy of the direction of the stock index can reach 60% to 70%.

Vlnové neurálních sítě předvídající centrální evropský trh s cennými papíry
Trvalý link: http://hdl.handle.net/11104/0163034