Adaptive prediction of stock exchange indices by state space wavelet networks

Brdyś, M. A. ; Borowa, A. ; Idźkowiak, P. ; Brdyś, M. T.

International Journal of Applied Mathematics and Computer Science, Tome 19 (2009) no. 2, pp. 337-348.

Résumé

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.

Détail

Keywords: forecasting, stock exchange, artificial intelligence, state space wavelet network, simulated annealing
Mots-clés : prognozowanie, giełda, sztuczna inteligencja, wyżarzanie symulowane

Bibliographie

[1] Borowa, A., Brdyś, M.A. and Mazur, K. (2007). Modeling of wastewater treatment plant for monitoring and control purposes by state-space wavelet networks, International Journal of Computers, Communications Control II(2): 121-131.

[2] Brdyś, M.A., Grochowski, M., Gminski, T., Konarczak, K. And Drewa, M. (2008). Hierarchical predictive control of integrated wastewater treatment systems, Control Engineering Practice 16(6): 751-767.

[3] Grossmann, A. and Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape, SIAM Journal on Mathematical Analysis 15(4): 723-736.

[4] Hajek, B. (1988). Cooling schedules for optimal annealing, Mathematics of Operations Research 13(2): 311-329.

[5] Jacobson, S.H., Hall, S.N., Mclay, L.A. and Orosz, J.E. (2005). Performance analysis of cyclical simulated annealing algorithms, Methodology and Computing in Applied Probability 7(2): 183-201.

[6] Karafyllidis, I. (1999). A simulator for single-electron tunnel devices and circuits based on simulated annealing, Superlattices and Microstructures 25(4): 567-572.

[7] Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. (1983). Optimization by simulated annealing, Science 220: 671-680.

[8] Khalil, H.K. (2002). Nonlinear Systems, Prentice Hall.

[9] Kulawski, G.J. and Brdys´s, M.A. (2000). Stable adaptive control with recurrent networks, Automatica 36(1): 5-22.

[10] Kuo, R.J., Chen, C.H. and Hwang, Y.C. (2001). An intelligent stock trading support system through integration of genetic algorithm based fuzzy neural network and artificial neural network, Fuzzy Sets and Systems 118(1): 21-45.

[11] Locatelli, M. (2000). Convergence of a simulated annealing algorithm for continuous global optimization, Journal of Global Optimization 18(3): 219-234.

[12] Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E. (1953). Equations of state calculations by fast computing machines, Journal of Chemical Physics 21: 1087-1092.

[13] Mun, J. (2006). Modeling Risk, Applying Monte Carlo Simulation, Real Options Analysis, Forecasting and Optimisation Techniques, John Wiley Sons, Inc.

[14] 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.

[15] 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.

[16] 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.

[17] 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.

[18] 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.

[19] Zamarreno, J.M. and Pastora, V. (1998). State space neural network. Properties and applications, Neural Networks 11(6): 1099-1112.

[20] Zhang, G., Patuwo, B.E. and Hu, M.Y. (1998). Forecasting with artificial neural networks: The state of the art, International Journal of Forecasting 14(1): 35-62.

[21] Zhang, Q. and Beneveniste, A. (1992). Wavelet networks, IEEE Transactions on Neural Networks 3(6): 889-898.

[22] Zhang, Q. (1992). Wavelet network: The radial structure and an efficient initialization procedure, Technical Report LiTHISY-I-1423, Linköping University.