Work detail

"On the predictibility of Central European stock returns: Do Neural Networks outperform modern econometric techniques?"

Author: PhDr. Jozef Baruník
Year: 2006 - summer
Leaders:
Consultants: prof. Ing. Miloslav Vošvrda CSc.
Work type: Doctoral
Language: English
Pages: 95
Awards and prizes:
Link:
Abstract: In this thesis we apply neural networks as nonparametric and nonlinear methods
to the Central European stock markets returns (Czech, Polish, Hungarian and
German) modelling. In the first two chapters we define prediction task and link
the classical econometric analysis to neural networks. We also present
optimization methods which will be used in the tests, conjugate gradient,
Levenberg-Marquardt, and evolutionary search method. Further on, we present
statistical methods for comparing the predictive accuracy of the non-nested
models, as well as economic significance measures. In the empirical tests we first
show the power of neural networks on Mackey-Glass chaotic time series followed
by real-world data of the daily and weekly returns of mentioned stock exchanges
for the 2000:2006 period. We find neural networks to have significantly lower
prediction error than classical models for daily DAX series, weekly PX50 and BUX
series. The lags of time-series were used, and also cross-country predictability
has been tested, but the results were not significantly different. We also achieved
economic significance of predictions with both daily and weekly PX-50, BUX and
DAX with 60% accuracy of prediction. Finally we use neural network to learn
Black-Scholes model and compared the pricing errors of Black-Scholes and neural
network approach on the European call warrant on CEZ. We find that networks
can be used as alternative pricing method as they were able to approximate the
market price of call warrant with significantly lower error then Black-Scholes
itself. Our last finding was that Levenberg-Marquardt optimization algorithm used
with evolutionary search provides us with significantly lower errors than
conjugate gradient or gradient descent.
Keywords: emerging stock markets, predictability of stock returns, neural
networks, optimization algorithms, derivative pricing using neural networks
JEL classification: C22, C32, C45, C53, E44, G14, G15
Downloadable: Rigorous thesis

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