Detail publikace

Vacha L., Barunik J.: Wavelet Neural Networks Prediction of Central European Stock Markets

Autor: doc. PhDr. Jozef Baruník Ph.D.,
Mgr. Lukáš Vácha Ph.D.,
Typ: Články ve sborníku
Rok: 2008
Číslo: 0
ISSN / ISBN: 978-80-8078-217-7
Publikováno v: Quantitative Methods in Economics proceedings
Místo vydání:
Klíčová slova: neural networks, hard threshold denoising, time series prediction, wavelets
JEL kódy:
Citace:
Granty: GAUK 46108: Nové nelineární teorie kapitálových trhů: fraktální, bifurkační a behaviorální přístup
Abstrakt: 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%.

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ČSOB
Deloitte
McKinsey & Company

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