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Intraday Foreign Exchange Rate Forecasting Using Sparse Grids

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Sparse Grids and Applications

Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 88))

Abstract

We present a machine learning approach using the sparse grid combination technique for the forecasting of intraday foreign exchange (fx) rates. The aim is to learn the impact of trading rules used by technical analysts just from the empirical behaviour of the market. To this end, the problem of analyzing a time series of transaction tick data is transformed by delay embedding into a D-dimensional regression problem using derived measurements from several different exchange rates. Then, a grid-based approach is used to discretize the resulting high-dimensional feature space. To cope with the curse of dimensionality we employ sparse grids in the form of the combination technique. Here, the problem is discretized and solved for a collection of conventional grids. The sparse grid solution is then obtained by linear combination of the solutions on these grids. We give the results of this approach to fx forecasting using real historical exchange data of the Euro, the US dollar, the Japanese Yen, the Swiss Franc and the British Pound from 2001 to 2005.

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Notes

  1. 1.

    Observe that a change of 1. 0 − 4 in our target attribute is roughly the size of a pip (the smallest unit of the quoted price) for eur/usd.

  2. 2.

    Different back ticks might result in a better performance, but we restricted our experiments to equal back ticks for reasons of simplicity.

  3. 3.

    Note that a different order might result in a different performance.

  4. 4.

    Furthermore only relatively few trades take place with fma which makes this a strategy with a higher variance in the performance.

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Acknowledgements

We thank Bastian Bohn and Alexander Hullmann for their assistance with the numerical experiments.

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Authors and Affiliations

  1. Institut für Numerische Simulation, Universität Bonn, Wegelerstr. 6, D-53115, Bonn, Germany

    Jochen Garcke & Michael Griebel

  2. Fraunhofer SCAI, Schloss Birlinghoven, D-53754, Sankt Augustin, Germany

    Jochen Garcke

  3. Institut für Mathematik, Johann Wolfgang Goethe-Universität, D-60054, Frankfurt am Main, Germany

    Thomas Gerstner

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  1. Jochen Garcke
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Correspondence to Jochen Garcke .

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Editors and Affiliations

  1. , Institut für Numerische Simulation, Universität Bonn, Wegelerstr. 6, Bonn, 53115, Germany

    Jochen Garcke

  2. Inst. Numerische Simulation, Universität Bonn, Wegelerstr. 6, Bonn, 53115, Germany

    Michael Griebel

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Garcke, J., Gerstner, T., Griebel, M. (2012). Intraday Foreign Exchange Rate Forecasting Using Sparse Grids. In: Garcke, J., Griebel, M. (eds) Sparse Grids and Applications. Lecture Notes in Computational Science and Engineering, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31703-3_4

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