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Predicting direction shifts on Canadian–US exchange rates with artificial neural networks

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  • Jefferson T. Davis
  • Athanasios Episcopos
  • Sannaka Wettimuny

Abstract

The paper presents a variety of neural network models applied to Canadian–US exchange rate data. Networks such as backpropagation, modular, radial basis functions, linear vector quantization, fuzzy ARTMAP, and genetic reinforcement learning are examined. The purpose is to compare the performance of these networks for predicting direction (sign change) shifts in daily returns. For this classification problem, the neural nets proved superior to the naïve model, and most of the neural nets were slightly superior to the logistic model. Using multiple previous days' returns as inputs to train and test the backpropagation and logistic models resulted in no increased classification accuracy. The models were not able to detect a systematic affect of previous days' returns up to fifteen days prior to the prediction day that would increase model performance. Copyright © 2001 John Wiley & Sons, Ltd.

Suggested Citation

  • Jefferson T. Davis & Athanasios Episcopos & Sannaka Wettimuny, 2001. "Predicting direction shifts on Canadian–US exchange rates with artificial neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 83-96, June.
    • Handle: RePEc:wly:isacfm:v:10:y:2001:i:2:p:83-96
    DOI: 10.1002/isaf.200
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    References listed on IDEAS

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    1. Hean‐Lee Poh & Jingtao Yao & Teo Jašic, 1998. "Neural networks for the analysis and forecasting of advertising and promotion impact," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(4), pages 253-268, December.
    2. Theodossiou, Panayiotis, 1994. "The Stochastic Properties of Major Canadian Exchange Rates," The Financial Review, Eastern Finance Association, vol. 29(2), pages 193-221, May.
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    Cited by:

    1. Tasadduq Imam & Kevin Tickle & Abdullahi Ahmed & William Guo, 2012. "Linear Relationship Between The Aud/Usd Exchange Rate And The Respective Stock Market Indices: A Computational Finance Perspective," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 19-42, January.
    2. Daniel E. O'Leary, 2009. "Downloads and citations in Intelligent Systems in Accounting, Finance and Management," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 21-31, January.
    3. Xiaojie Xu & Yun Zhang, 2022. "Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 169-181, July.
    4. Alejandro Parot & Kevin Michell & Werner D. Kristjanpoller, 2019. "Using Artificial Neural Networks to forecast Exchange Rate, including VAR‐VECM residual analysis and prediction linear combination," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(1), pages 3-15, January.

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