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Currency Forecasting using Multiple Kernel Learning with Financially Motivated Features

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  • Tristan Fletcher
  • Zakria Hussain
  • John Shawe-Taylor

Abstract

Multiple Kernel Learning (MKL) is used to replicate the signal combination process that trading rules embody when they aggregate multiple sources of financial information when predicting an asset's price movements. A set of financially motivated kernels is constructed for the EURUSD currency pair and is used to predict the direction of price movement for the currency over multiple time horizons. MKL is shown to outperform each of the kernels individually in terms of predictive accuracy. Furthermore, the kernel weightings selected by MKL highlights which of the financial features represented by the kernels are the most informative for predictive tasks.

Suggested Citation

  • Tristan Fletcher & Zakria Hussain & John Shawe-Taylor, 2010. "Currency Forecasting using Multiple Kernel Learning with Financially Motivated Features," Papers 1011.6097, arXiv.org.
    • Handle: RePEc:arx:papers:1011.6097
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    References listed on IDEAS

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    1. Fernando Perez-cruz & Julio Afonso-rodriguez & Javier Giner, 2003. "Estimating GARCH models using support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 163-172.
    2. Christian Ullrich, 2009. "Forecasting and Hedging in the Foreign Exchange Markets," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-00495-7, December.
    3. Stephan K. Chalup & Andreas Mitschele, 2008. "Kernel Methods in Finance," International Handbooks on Information Systems, in: Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), Handbook on Information Technology in Finance, chapter 27, pages 655-687, Springer.
    4. Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-364, Oct.-Dec..
    5. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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