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Forecasting Foreign Exchange Rates With Artificial Neural Networks: A Review

Author

Listed:
  • WEI HUANG

    (Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, People's Republic of China;
    School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1, Asahidai, Ishikawa 923-1292, Japan)

  • K. K. LAI

    (Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China)

  • Y. NAKAMORI

    (School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1, Asahidai, Ishikawa 923-1292, Japan)

  • SHOUYANG WANG

    (Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, People's Republic of China)

Abstract

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. In this paper, we attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, preparing data, and network architecture. There is no consensus about the factors. In different cases, various decisions have their own effectiveness. We also describe the integration of ANNs with other methods and report the comparison between performances of ANNs and those of other forecasting methods, and finding mixed results. Finally, the future research directions in this area are discussed.

Suggested Citation

  • Wei Huang & K. K. Lai & Y. Nakamori & Shouyang Wang, 2004. "Forecasting Foreign Exchange Rates With Artificial Neural Networks: A Review," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 3(01), pages 145-165.
    • Handle: RePEc:wsi:ijitdm:v:03:y:2004:i:01:n:s0219622004000969
    DOI: 10.1142/S0219622004000969
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    Citations

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    Cited by:

    1. Haider A. Khan & Shahryar Ghorbani & Elham Shabani & Shahab S. Band, 2024. "Enhancement of Neural Networks Model’s Predictions of Currencies Exchange Rates by Phase Space Reconstruction and Harris Hawks’ Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 835-860, February.
    2. Sevcan Uzun & Ahmet Sensoy & Duc Khuong Nguyen, 2023. "Jump forecasting in foreign exchange markets: A high‐frequency analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 578-624, April.
    3. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    4. Prabhas Kumar Rath, 2023. "Nexus Between Indian Financial Markets and Macro-economic Shocks: A VAR Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(1), pages 131-164, March.
    5. Deniz Ersan & Chifumi Nishioka & Ansgar Scherp, 2020. "Comparison of machine learning methods for financial time series forecasting at the examples of over 10 years of daily and hourly data of DAX 30 and S&P 500," Journal of Computational Social Science, Springer, vol. 3(1), pages 103-133, April.
    6. Lin, Yao-San & Li, Der-Chiang, 2010. "The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems," European Journal of Operational Research, Elsevier, vol. 207(1), pages 121-130, November.
    7. Anqiang Huang & Kin Keung Lai & Han Qiao & Shouyang Wang & Zhenji Zhang, 2018. "Does Interval Knowledge Sharpen Forecasting Models? Evidence from China’s Typical Ports," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 467-483, March.
    8. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2012. "Directional forecasting in financial time series using support vector machines: The USD/Euro exchange rate," DUTH Research Papers in Economics 5-2012, Democritus University of Thrace, Department of Economics.
    9. Alisa Bilal Zoric, 2016. "Predicting customer churn in banking industry using neural networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 14(2), pages 116-124.
    10. Yi Xiao & Shouyang Wang & Ming Xiao & Jin Xiao & Yi Hu, 2017. "The Analysis for the Cargo Volume with Hybrid Discrete Wavelet Modeling," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 851-863, May.
    11. Marcos Vizcaíno-González & Juan Pineiro-Chousa & Jorge Sáinz-González, 2017. "Selecting explanatory factors of voting decisions by means of fsQCA and ANN," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 2049-2061, September.
    12. Mohamed Ibrahim Nor & Tajul Ariffin Masron & Tariq Tawfeeq Yousif Alabdullah, 2020. "Macroeconomic Fundamentals and the Exchange Rate Volatility: Empirical Evidence From Somalia," SAGE Open, , vol. 10(1), pages 21582440198, January.
    13. Nyoni, Thabani, 2018. "Modeling and Forecasting Naira / USD Exchange Rate In Nigeria: a Box - Jenkins ARIMA approach," MPRA Paper 88622, University Library of Munich, Germany, revised 19 Aug 2018.
    14. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.

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