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Neural Network Pruning Applied to Real Exchange Rate Analysis

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  • Kaashoek, Johan F
  • van Dijk, Herman K

Abstract

Neural networks are fitted to real exchange rates of several industrialized countries. The size and topology of the networks is found through the use of multiple correlation coefficients, principal component analysis of residuals and graphical analysis of network output per hidden layer cell and input layer cell. These pruned neural networks are good approximations to varying non-linear trends in real exchange rates. Non-linear dynamic analysis shows that the long-term equilibrium values of several European currencies correspond to the actual values within the European Monetary System. Based on its long-term equilibrium value, the Euro appears to be undervalued vis-a-vis the US dollar at the introduction of the Euro on 1 January 1999. Copyright © 2002 by John Wiley & Sons, Ltd.

Suggested Citation

  • Kaashoek, Johan F & van Dijk, Herman K, 2002. "Neural Network Pruning Applied to Real Exchange Rate Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(8), pages 559-577, December.
    • Handle: RePEc:jof:jforec:v:21:y:2002:i:8:p:559-77
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    Cited by:

    1. H.K. van Dijk, 2004. "Twentieth Century Shocks, Trends and Cycles in Industrialized Nations," De Economist, Springer, vol. 152(2), pages 211-232, June.
    2. Armin Shmilovici & Yoav Kahiri & Irad Ben-Gal & Shmuel Hauser, 2009. "Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 33(2), pages 131-154, March.
    3. Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
    4. Hong-Yu Lin & Kuentai Chen, 2015. "The Trend of Average Unit Price in Taipei City," Research in World Economy, Research in World Economy, Sciedu Press, vol. 6(1), pages 133-142, March.
    5. Angela He & Alan Wan, 2009. "Predicting daily highs and lows of exchange rates: a cointegration analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1191-1204.
    6. Indranil Ghosh & Tamal Datta Chaudhuri, 2017. "Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates," Studies in Microeconomics, , vol. 5(2), pages 105-131, December.

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