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Using Recurrent Neural Networks To Forecasting of Forex

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  • V. V. Kondratenko
  • Yu. A Kuperin

Abstract

This paper reports empirical evidence that a neural networks model is applicable to the statistically reliable prediction of foreign exchange rates. Time series data and technical indicators such as moving average, are fed to neural nets to capture the underlying "rules" of the movement in currency exchange rates. The trained recurrent neural networks forecast the exchange rates between American Dollar and four other major currencies, Japanese Yen, Swiss Frank, British Pound and EURO. Various statistical estimates of forecast quality have been carried out. Obtained results show, that neural networks are able to give forecast with coefficient of multiple determination not worse then 0.65. Linear and nonlinear statistical data preprocessing, such as Kolmogorov-Smirnov test and Hurst exponents for each currency were calculated and analyzed.

Suggested Citation

  • V. V. Kondratenko & Yu. A Kuperin, 2003. "Using Recurrent Neural Networks To Forecasting of Forex," Papers cond-mat/0304469, arXiv.org.
    • Handle: RePEc:arx:papers:cond-mat/0304469
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    References listed on IDEAS

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    1. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    2. Filippo Castiglione, 2001. "Forecasting Price Increments Using An Artificial Neural Network," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 45-56.
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    Cited by:

    1. Ondrej Bednar, 2021. "The Causal Impact of the Rapid Czech Interest Rate Hike on the Czech Exchange Rate Assessed by the Bayesian Structural Time Series Model," International Journal of Economic Sciences, European Research Center, vol. 10(2), pages 1-17, December.

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