Skip to main content

Hybrid Neural Systems in Exchange Rate Prediction

  • Chapter
Natural Computing in Computational Finance

Part of the book series: Studies in Computational Intelligence ((SCI,volume 100))

  • 1014 Accesses

Summary

In this chapter, a new hierarchical hybrid wavelet — artificial neural network strategy for exchange rate prediction is introduced. The wavelet analysis (the Mallat’s pyramid algorithm) is utilised for separating signal components of various frequencies and then separate neural perceptrons perform prediction for each separate signal component. The strategy was tested for predicting the US dollar/Polish zloty average exchange rate. The achieved accuracy of prediction of value alterations direction is equal to 90%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aboufadel E, Schlicker S (1999) Discovering Wavelets, Wiley, Chichester

    MATH  Google Scholar 

  2. Andreou AS, Pavlides G, Karytinos A, (2000) Nonlinear time-series analysis on the Greek exchange-rate market Int. Jour. Bifurcation and Chaos 10:1729-1758

    Article  Google Scholar 

  3. Battle G (1987) A block spin construction of ondelettes. Part I: Lemarie functions, Commun. Math. Phys. 110:601-615

    Article  MathSciNet  Google Scholar 

  4. Bernhard W, Leblang D (2002) Democratic processes, political risk, and foreign exchange markets, American Journal of Political Science 46:316-333

    Article  Google Scholar 

  5. Cheung YW, Chinn MD (2001) Currency traders and exchange rate dynamics: a survey of the US market, Journal of International Money and Finance 20:439-471

    Article  Google Scholar 

  6. Cybenko G (1989) Approximation by Superposition of a Sigmoidal Function, Mathematics of Control, Signals and Systems 2:303-314.

    Article  MATH  MathSciNet  Google Scholar 

  7. Fernández-Rodriguez F, Sosvilla-Rivero S, Andrada-Félix J (1999) Exchange-rate forecasts with simultaneous nearest-neighbour methods: evidence from the EMS, Int. Jour. of Forecasting 15:383-392

    Article  Google Scholar 

  8. Franklin P (1928) A set of continues orthogonal functions, Math. Ann. 100:522-29

    Article  MATH  MathSciNet  Google Scholar 

  9. Haar A (1910) Zur Theorie der orthogonalen Funktionensysteme, Math. Ann. 69:331-371

    Article  MATH  MathSciNet  Google Scholar 

  10. Hajto P (2002) A neural economic time series prediction with the use of the wavelet analysis, Schedae Informaticae 11:115-132

    Google Scholar 

  11. Hecht-Nielsen R (1987) Kolmogorov's Mapping Neural Network Existence Theorem, Proceedings of the International Conference on Neural Networks, Part III, IEEE, New York

    Google Scholar 

  12. Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley Publ., Reading

    Google Scholar 

  13. Hertz J, Krogh A, Palmer RG (1991) Introduction to the Theory of Neural Computation. Addison-Wesley Publ., Massachusetts

    Google Scholar 

  14. Hornik K, (1991) Approximation Capabilities of Multilayer Feedforward Networks, Neural Networks 4:251-258.

    Article  Google Scholar 

  15. Hornik K, (1993) Some New Results on Neural Network Approximation, Neural Networks 6:1069-1072

    Article  Google Scholar 

  16. Hornik K, Stinchcombe M, White H (1989) Mulitilayer Feedforward Networks Are Universal Approximators, Neural Networks 2:359-366

    Article  Google Scholar 

  17. Huang W, Lai KK, Nakamori Y, Wang S (2004) Forecasting foreign exchange rates with artificial Neural networks: a review, Int. Jour. of Information Technology and Decision Making 3:145-165

    Article  Google Scholar 

  18. Jensen A, Cour-Harbo A (2001) Ripples in Mathematics. The Discrete Wavelet Transform. Springer-Verlag, Berlin Heidelberg

    MATH  Google Scholar 

  19. Kurkova V (1992) Kolmogorov's Theorem and Multilayer Neural Networks, Neural Networks 5:501-506

    Article  Google Scholar 

  20. Lee VCS, Wong HT (2007) A multivariate neuro-fuzzy system for foreign currency risk management decission making, Neurocomputing 70:942-951

    Google Scholar 

  21. Lemarie PG (1998) Ondelettes à localisation exponentielle, J. Math. Pures Appl 67:227-236

    MathSciNet  Google Scholar 

  22. Lin K (1997) The ABC's of BDS, Journal of Computational Intelligence in Finance 5:23-26

    Google Scholar 

  23. Lula P (1999) Feedforeward neural networks for economic phenomena modelling. Wydawnictwo Akademii Ekonomicznej w Krakowie, Kraków (in Polish)

    Google Scholar 

  24. Mallat S (1989) Multiresolution approximation and wavelet orthonormal bases of L2 (R), Trans. Am. Math. Soc. 315:69-88

    Article  MATH  MathSciNet  Google Scholar 

  25. McCulloch WS, Pitts W (1943) A logical calculus for the ideas immanent in nervous activity, Bull. of Math. Biophysics 5:115-133

    MATH  MathSciNet  Google Scholar 

  26. Mizuno T, Kurihara S, Takayasu M, Takayasu H (2003) Analysis of high-resolution foreign exchange data of USD-JPY for 13 years, Physica A 324:296-302

    Article  MATH  Google Scholar 

  27. Muniandy SV, Lim SC, Murugan R (2001) Inhomogeneous scalling behaviors in Malaysian foreign currency exchange rates, Physica A 301:407-428

    Article  MATH  Google Scholar 

  28. Ohira T, Sazuka N, Marumo K, Shimizu T, Takayasu M, Takayasu H (2002) cit Predictability of currency market exchange, Physica A308:368-374

    Article  MATH  Google Scholar 

  29. Panda C, Narasimhan V (2007) Forecasting exchange rate better with artificial neural network, Jour. Policy Modeling 29:227-236

    Article  Google Scholar 

  30. Pesaran MH, Timmermann A (1992) A simple non-parametric test of predictive performance, Jour. Business and Economic Statistics 10:461-465

    Article  Google Scholar 

  31. Sazuka N, Ohira T, Marumo K, Shimizu T, Takayasu M, Takayasu H (2003) A dynamical stucture of high frequency currency exchange market, Physica A 324:366-371

    Article  MATH  Google Scholar 

  32. Schauder MJ (1928) Einige Eigenschaften der Haarschen Orthogonalsysteme, Math. Zeit. 28:317-320

    Article  MATH  MathSciNet  Google Scholar 

  33. Shin T, Han I (2000) Optimal signal multi-resolution by genetic algorithms to support artificial neural networks for exchange-rate forecasting, Expert Systems with Applications 18:257-269

    Article  Google Scholar 

  34. Str ömberg JO (1983) A modified Franklin system and higher order spline systems on Rn as unconditional bases for Hardy spaces, Proc. Conference in Harmonic Analysis in Honor of A. Zygmund, vol. II, Wadsworth, Belmont 475-493

    Google Scholar 

  35. Steiner M, Wittkemper HG (1995) Neural Networks as an Alternative Stock Market Model. In: Refenes APN (ed) Neural Networks in the Capital Markets. Wiley, Chichester

    Google Scholar 

  36. Tsibouris G, Zeidenberg M (1995) Testing the Efficient Markets Hypothesis with Gradient Descent Algorithm. In: Refenes APN (ed) Neural Networks in the Capital Markets. Wiley, Chichester

    Google Scholar 

  37. Walker JS (1997) Fourier analysis and wavelet analysis, Notices of the AMS 44:658-670

    MATH  Google Scholar 

  38. White H (1988) Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns, Proceedings of the IEEE International Conference of Neural Networks, San Diego

    Google Scholar 

  39. Wojtaszczyk P (1987) A Mathematical Introduction to Wavelets. Cambridge University Press, Cambridge

    Google Scholar 

  40. Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art, Int. Jour. of Forecasting 14:35-62

    Article  Google Scholar 

  41. Zirilli JS (1966) Financial Prediction Using Neural Networks. International Thomson Computer Press, London

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bielecki, A., Hajto, P., Schaefer, R. (2008). Hybrid Neural Systems in Exchange Rate Prediction. In: Brabazon, A., O’Neill, M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77477-8_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-77477-8_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77476-1

  • Online ISBN: 978-3-540-77477-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics