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2009 | 19 | 2 | 337-348
Tytuł artykułu

Adaptive prediction of stock exchange indices by state space wavelet networks

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper considers the forecasting of the Warsaw Stock Exchange price index WIG20 by applying a state space wavelet network model of the index price. The approach can be applied to the development of tools for predicting changes of other economic indicators, especially stock exchange indices. The paper presents a general state space wavelet network model and the underlying principles. The model is applied to produce one session ahead and five sessions ahead adaptive predictors of the WIG20 index prices. The predictors are validated based on real data records to produce promising results. The state space wavelet network model may also be used as a forecasting tool for a wide range of economic and non-economic indicators, such as goods and row materials prices, electricity/fuel consumption or currency exchange rates.
Rocznik
Tom
19
Numer
2
Strony
337-348
Opis fizyczny
Daty
wydano
2009
otrzymano
2008-05-09
poprawiono
2008-09-14
Twórcy
  • School of Electronic, Electrical and Computer Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
  • Department of Control Systems Engineering, Gdańsk University of Technology, ul. G. Narutowicza 11/12, 80-233 Gdańsk, Poland
autor
  • Department of Control Systems Engineering, Gdańsk University of Technology, ul. G. Narutowicza 11/12, 80-233 Gdańsk, Poland
  • School of Electronic, Electrical and Computer Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
  • Philips Lightning Poland BU CLE/IPLC GLS 10NC & CFL-i Burners Assistant Industrial Engineer
  • Corporate Banking & MIB Division, Financial Markets Department, Planning, Controlling & Support, Bank Pekao SA, ul. Grzybowska 53/57, 00-950 Warsaw, Poland
Bibliografia
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  • Locatelli, M. (2000). Convergence of a simulated annealing algorithm for continuous global optimization, Journal of Global Optimization 18(3): 219-234.
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  • Nguyen, D.T. and Brdyś, M.A. (2006). Dynamic neural network identification and control under unmeasurable plant states, Proceedings of the International Control Conference UKAC 2006, Glasgow, UK.
  • Qi, R. and Brdyś, M.A. (2005). Adaptive fuzzy modelling and control for discrete-time nonlinear uncertain systems, Proceedings of the American Control Conference, ACC 2005, Portland, OR, USA.
  • Qi, R. and Brdyś, M.A. (2008). Stable indirect adaptive control based on discrete-time T-S fuzzy model, Fuzzy Sets and Systems 159(8): 900-925.
  • Sanchez, E.N. and Perez, J.P. (1999). Input-to-state stability analysis for dynamic neural networks, IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications 46(11): 1395-1398.
  • Tsang, P.M., Kwok, P., Choy, S.O., Kwan, R., Ng, S.C., Mak, J., Tsang, J., Koong, K. and Wong. T. (2007). Design and implementation of NN5 for Hong Kong stock price forecasting, Engineering Application of Artificial Inteligence 20(4): 453-461.
  • Zamarreno, J.M. and Pastora, V. (1998). State space neural network. Properties and applications, Neural Networks 11(6): 1099-1112.
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv19i2p337bwm
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