Adaptive Prediction of Stock Exchange Indices by State Space Wavelet Networks

Brdyś, Mietek; Borowa, Adam; Idźkowiak, Piotr; Brdyś, Marcin

doi:10.2478/v10006-009-0029-z

Abstract

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.

References

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): 121-131.10.15837/ijccc.2007.2.2345
Search in Google Scholar
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): 751-767.10.1016/j.conengprac.2007.01.008
Search in Google Scholar
Grossmann, A. and Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape, SIAM Journal on Mathematical Analysis 15(4): 723-736.10.1137/0515056
Search in Google Scholar
Hajek, B. (1988). Cooling schedules for optimal annealing, Mathematics of Operations Research 13(2): 311-329.10.1287/moor.13.2.311
Search in Google Scholar
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): 183-201.10.1007/s11009-005-1482-2
Search in Google Scholar
Karafyllidis, I. (1999). A simulator for single-electron tunnel devices and circuits based on simulated annealing, Superlattices and Microstructures 25(4): 567-572.10.1006/spmi.1998.0681
Search in Google Scholar
Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. (1983). Optimization by simulated annealing, Science 220: 671-680.10.1126/science.220.4598.671
Search in Google Scholar
Khalil, H.K. (2002). Nonlinear Systems, Prentice Hall.
Search in Google Scholar
Kulawski, G.J. and Brdysś, M.A. (2000). Stable adaptive control with recurrent networks, Automatica 36(1): 5-22.10.1016/S0005-1098(99)00092-8
Search in Google Scholar
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): 21-45.10.1016/S0165-0114(98)00399-6
Search in Google Scholar
Locatelli, M. (2000). Convergence of a simulated annealing algorithm for continuous global optimization, Journal of Global Optimization 18(3): 219-234.
Search in Google Scholar
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: 1087-1092.10.1063/1.1699114
Search in Google Scholar
Mun, J. (2006). Modeling Risk, Applying Monte Carlo Simulation, Real Options Analysis, Forecasting and Optimisation Techniques, John Wiley & Sons, Inc.
Search in Google Scholar
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.
Search in Google Scholar
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.
Search in Google Scholar
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): 900-925.10.1016/j.fss.2007.08.009
Search in Google Scholar
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): 1395-1398.10.1109/81.802844
Search in Google Scholar
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): 453-461.10.1016/j.engappai.2006.10.002
Search in Google Scholar
Zamarreno, J.M. and Pastora, V. (1998). State space neural network. Properties and applications, Neural Networks 11(6): 1099-1112.
Search in Google Scholar
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): 35-62.10.1016/S0169-2070(97)00044-7
Search in Google Scholar
Zhang, Q. and Beneveniste, A. (1992). Wavelet networks, IEEE Transactions on Neural Networks 3(6): 889-898.10.1109/72.16559118276486
Search in Google Scholar
Zhang, Q. (1992). Wavelet network: The radial structure and an efficient initialization procedure, Technical Report LiTHISY-I-1423, Linköping University.
Search in Google Scholar

Adaptive Prediction of Stock Exchange Indices by State Space Wavelet Networks

Abstract

Paradigm

My account