Prague Economic Papers 2011, 20(3):232-249 | DOI: 10.18267/j.pep.398

Exchange Rate Predictions in International Financial Management by Enhanced GMDH Algorithm

Josef Taušer1, Petr Buryan2
1 University of Economics, Prague (tauser@vse.cz).
2 Czech Technical University, Prague (buryan@labe.felk.cvut.cz).

Exchange rate forecasting is an important financial problem that is receiving increasing attention nowadays especially because of its difficulty and host of practical applications in globalising world of today. The paper presents an enhanced MIA-GMDH-type network, discusses its design methodology and carries out some numerical experiments in the field of exchange rate forecasting. The method presented in this paper is an enhancement of self-organizing polynomial Group Method of Data Handling (GMDH) with several specific improved features - coefficient rounding and thresholding schemes and semi-randomized selection approach to pruning. The experiments carried out include exchange rate prediction and hedging case study where the predictions were used for financial management decision simulation of a virtual company. The results indicate, that the method shows promising potential of self-organizing network methodology. This implies that the proposed modelling approaches can be used as a feasible solution for exchange rate forecasting in financial management.

Keywords: GMDH, self-organizing polynomial networks, time series analysis, exchange rate prediction, FX hedging
JEL classification: F37, G17

Published: January 1, 2011  Show citation

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Taušer, J., & Buryan, P. (2011). Exchange Rate Predictions in International Financial Management by Enhanced GMDH Algorithm. Prague Economic Papers20(3), 232-249. doi: 10.18267/j.pep.398
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References

  1. Buryan, P. (2006), "Time Series Analysis by Means of Enhanced GMDH Algorithm." Dissertation Thesis, CTU Prague.
  2. Buryan, P. Onwubolu, G. C., Lemke, F. (2007), "Modeling Tool Wear In End-Milling Using Enhanced Gmdh Learning Networks." International Journal Of Advanced Manufacturing Technology, Springer Verlag 2007, Doi 10.1007/S00170-007-1296-1. Go to original source...
  3. Iba, H., de Garis, H., Sato, T. (1994), "Genetic Programming Using a Minimum Description Length Principle," in Advances in Genetic Programming, Kinnear, K. E. Jr. (ed), Cambridge: MIT, pp. 265-284.
  4. Ivakhnenko, A. G. (1971), "Polynomial Theory of Complex Systems." IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-1, No. 4, pp. 364-378. Go to original source...
  5. Kordík P., Šnorek M., Genyk-Berezovskyj M. (2004), "Hybrid Inductive Models: Deterministic Crowding Employed." Proceedings of the International Joint Conference on Neural Networks; Piscataway: IEEE, pp. 2343-2346; ISBN 0-7803-8360-5. Go to original source...
  6. Lai K. K., Yu L., Wang S. (2007), Foreign-Exchange-Rate Forecasting with Artificial Neural Networks. Springer Verlag, ISBN: 978-0-387-71719-7.
  7. Madala, H. R., Ivakhnenko A. G. (1994), Inductive Learning Algorithms for Complex Systems Modeling. CRC Press, Boca Raton.
  8. Madura, J. (2006), International Corporate Finance. 8th Edition. Thomson South-Western. ISBN 0-324-32382-4.
  9. Mentzel, S. M. (1998), Real Exchange Rate Movements. Springer Verlag, ISBN: 978-3-7908-1081-3. Go to original source...
  10. Mueller, J. A., Lemke, F., Ivakhnenko A. G. (1998), "GMDH algorithm for complex systems modelling." Mathematical Modelling of Systems, No. 4. Go to original source...
  11. Park, H. S., Park, B. J., Kim, H. K., Oh, S. K. (2004), "Self-organizing Polynomial Neural Networks Based on Genetically Optimized Multi-layer Perceptron Architecture." International Journal of Control, Automation, and Systems, 2(4), pp. 423-434.
  12. Taušer, J. (2007), Měnový kurz v mezinárodním podnikání. VŠE Praha, ISBN 978-80-245-1165-8.
  13. Žamberský, P. (2003), Ekonomie měnového kurzu I. Praha : Nakladatelství Oeconomica, ISBN 80-245-0637-8.

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