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Nearest-Neighbour Predictions in Foreign Exchange Markets

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Computational Intelligence in Economics and Finance

Part of the book series: Advanced Information Processing ((AIP))

Abstract

The purpose of this paper is to contribute to the debate on the relevance of non-linear predictors of high-frequency data in foreign exchange markets. To that end, we apply nearest-neighbour (NN) predictors, inspired by the literature on forecasting in non-linear dynamical systems, to exchange-rate series. The forecasting performance of univariate and multivariate versions of such NN predictors is first evaluated from the statistical point of view, using a battery of statistical tests. Secondly, we assess if NN predictors are capable of producing valuable economic signals in foreign exchange markets. The results show the potential usefulness of NN predictors not only as a helpful tool when forecast daily exchange data but also as a technical trading rules.

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Author information

Authors and Affiliations

  1. Universidad de Las Palmas de Gran Canaria, Islas Canarias, Spain

    Fernando Fernández-Rodríguez & Julián Andrada-Félix

  2. Universidad Complutense and Fedea, Madrid, Spain

    Simón Sosvilla-Rivero

Authors
  1. Fernando Fernández-Rodríguez
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  2. Simón Sosvilla-Rivero
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  3. Julián Andrada-Félix
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Editors and Affiliations

  1. AI-ECON Research Center Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  2. Department of Electrical and Computer Science, Duke University, P.O. 90291, 27708-0291, Durham, NC, USA

    Paul P. Wang

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Fernández-Rodríguez, F., Sosvilla-Rivero, S., Andrada-Félix, J. (2004). Nearest-Neighbour Predictions in Foreign Exchange Markets. In: Chen, SH., Wang, P.P. (eds) Computational Intelligence in Economics and Finance. Advanced Information Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06373-6_14

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  • DOI: https://doi.org/10.1007/978-3-662-06373-6_14

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