This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Faculty of Economics VSB Technical University of Ostrava Sokolska Trida 33 701 21 Ostrava 1 Czech Republic

Faculty of Management Science and Informatics University of Zilina Univerzitna 8215 1 010 26 Zilina Slovakia

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Bollerslev T. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics. 1986;31(3):307–327. doi: 10.1016/0304-4076(86)90063-1. DOI

White H. Economic prediction using neural networks: the case of IBM daily stock returns. Proceedings of the 2nd IEEE Annual International Conference on Neural Networks; July 1988; San Diego, Calif, USA. IEEE; pp. 451–458. DOI

Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks. 1989;2(5):359–366. doi: 10.1016/0893-6080(89)90020-8. DOI

Hornik K. Some new results on neural network approximation. Neural Networks. 1993;6(8):1069–1072. doi: 10.1016/s0893-6080(09)80018-x. DOI

Maciel L. S., Ballini R. Design a Neural Network for Time Series Financial Forecasting: Accuracy and Robustness Analysis. 2008, http://www.cse.unr.edu/~harryt/CS773C/Project/895-1697-1-PB.pdf.

Darbellay G. A., Slama M. Forecasting the short-term demand for electricity: do neural networks stand a better chance? International Journal of Forecasting. 2000;16(1):71–83. doi: 10.1016/s0169-2070(99)00045-x. DOI

Zhang G., Patuwo B. E., Hu M. Y. Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting. 1998;14(1):35–62. doi: 10.1016/s0169-2070(97)00044-7. DOI

Anderson J. A., Rosenfeld E. Neurocomputing: Foundations of Research. MIT Press; 1988. A collection of articles summarizing the state-of-the-art as of 1988.

Hecht-Nielsen R. Neurocomputing. Reading, Mass, USA: Addison-Wesley; 1990.

Hertz J., Krogh A., Palmer R. G. Introduction to the Theory of Neural Computation. Westview Press; 1991.

Hiemstra C., Jones J. D. Testing for linear and nonlinear Granger causality in the stock price-volume relation. The Journal of Finance. 1994;49(5):1639–1664. doi: 10.1111/j.1540-6261.1994.tb04776.x. DOI

McCulloch W. S., Pitts W. A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics. 1943;5(4):115–133. PubMed

Minsky M., Papert S. Perceptrons: An Introduction to Computational Geometry. MIT Press; 1969.

Orr M. J. L. Edinburgh, Scotland: Center for Cognitive Science, University of Edinburgh; 1996. Introduction to radial basis function networks.

Buhmann M. D. Radial Basis Functions: Theory and Implementations. Cambridge University Press; 2003. DOI

Ahmed N. K., Atiya A. F., El Gayar N., El-Shishiny H. An empirical comparison of machine learning models for time series forecasting. Econometric Reviews. 2010;29(5-6):594–621. doi: 10.1080/07474938.2010.481556. DOI

Marcek D., Falat L. Volatility forecasting in financial risk management with statistical models and ARCH-RBF neural networks. Journal of Risk Analysis and Crisis Response. 2014;4(2):77–95. doi: 10.2991/jrarc.2014.4.2.4. DOI

Marcek D., Marcek M. Neural Networks and Their Applications. Žilina, Slovakia: EDIS-ZU; 2006.

de Gooijer J. G., Hyndman R. J. 25 years of time series forecasting. International Journal of Forecasting. 2006;22(3):443–473. doi: 10.1016/j.ijforecast.2006.01.001. DOI

Hill T., Marquez L., O'Connor M., Remus W. Artificial neural network models for forecasting and decision making. International Journal of Forecasting. 1994;10(1):5–15. doi: 10.1016/0169-2070(94)90045-0. DOI

Park C. H. O., Irwin S. H. AgMAS Project Research Report. 2004-04. Champaign, Ill, USA: University of Illinois at Urbana-Champaign; 2004. The profitability of technical analysis: a review.

Chou Y. Statistical Analysis: With Business and Economic Applications. Holt, Rinehart and Winston; 1975. (Quantitative Methods Series).

Dacco R., Satchell S. Why do regime-switching models forecast so badly? Journal of Forecasting. 1999;18(1):1–16. doi: 10.1002/(sici)1099-131x(199901)18:160;1::aid-for68562;3.0.co;2-b. DOI

Yang Y. Combining forecasting procedures: some theoretical results. Econometric Theory. 2004;20(1):176–222. doi: 10.1017/S0266466604201086. DOI

Clemen R. T. Combining forecasts: a review and annotated bibliography. International Journal of Forecasting. 1989;5(4):559–583. doi: 10.1016/0169-2070(89)90012-5. DOI

Hornik K., Stinchcombe M., White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks. 1990;3(5):551–560. doi: 10.1016/0893-6080(90)90005-6. DOI

Leshno M., Lin V. Y., Pinkus A., Schocken S. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Networks. 1993;6(6):861–867. doi: 10.1016/s0893-6080(05)80131-5. DOI

Bryson A. E., Ho Y. C. Applied Optimal Control: Optimization, Estimation, and Control. Blaisdell Publishing Company; 1969.

Montana D. J., Davis L. Training feedforward neural networks using genetic algorithms. Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI '89); 1989; San Francisco, Calif, USA. Morgan Kaufmann Publishers; pp. 762–767.

Dharmistha M. Genetic algorithm based weights optimization of artificial neural network. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2013;1(3):206–211.

Holland J. H. Adaptation in Natural and Artificial Systems. University of Michigan Press; 1975.

Davis L. Genetic Algorithms and Simulated Annealing. London, UK: Pitman; 1987.

Whitley D. Applying Genetic Algorithms to Neural Network Problems. International Neural Network Society; 1988.

Kohonen T. Self-Organizing Maps. Berlin, Germany: Springer; 1995. DOI

MacQueen J. B. Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability; 1967; Berkeley, Calif, USA. University of California Press; pp. 281–297.

Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems. 1989;2(4):303–314. doi: 10.1007/BF02551274. DOI

Box G. E. P., Jenkins G. M. Time Series Analysis: Forecasting and Control. San Francisco, Calif, USA: Holden-Day; 1976.

Heider D., Verheyen J., Hoffmann D. Predicting Bevirimat resistance of HIV-1 from genotype. BMC Bioinformatics. 2010;11, article 37 doi: 10.1186/1471-2105-11-37. PubMed DOI PMC

Dickey D. A., Fuller W. A. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association. 1979;74(366):427–431.

Phillips P. C. B., Perron P. Testing for a unit root in time series regression. Biometrika. 1988;75(2):335–346. doi: 10.1093/biomet/75.2.335. DOI

Kwiatkowski D., Phillips P. C. B., Schmidt P., Shin Y. Testing the null hypothesis of stationarity against the alternative of a unit root; How sure are we that economic time series have a unit root? Journal of Econometrics. 1992;54(1–3):159–178. doi: 10.1016/0304-4076(92)90104-y. DOI

Elliott G., Rothenberg T. J., Stock J. H. Efficient tests for an autoregressive unit root. Econometrica. 1996;64(4):813–836. doi: 10.2307/2171846. DOI

Engle R. F. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica. 1982;50(4):987–1008.

Akaike H. Statistical predictor identification. Annals of the Institute of Statistical Mathematics. 1970;22:203–217. doi: 10.1007/BF02506337. DOI

Schwarz G. Estimating the dimension of a model. The Annals of Statistics. 1978;6(2):461–464. doi: 10.1214/aos/1176344136. DOI