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Deep networks for predicting direction of change in foreign exchange rates

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  • Svitlana Galeshchuk
  • Sumitra Mukherjee

Abstract

Trillions of dollars are traded daily on the foreign exchange (forex) market, making it the largest financial market in the world. Accurate forecasting of forex rates is a necessary element in any effective hedging or speculation strategy in the forex market. Time series models and shallow neural networks provide acceptable point estimates for future rates but are poor at predicting the direction of change and, hence, are not very useful for supporting profitable trading strategies. Machine learning classifiers trained on input features crafted based on domain knowledge produce marginally better results. The recent success of deep networks is partially attributable to their ability to learn abstract features from raw data. This motivates us to investigate the ability of deep convolution neural networks to predict the direction of change in forex rates. Exchange rates for the currency pairs EUR/USD, GBP/USD and JPY/USD are used in experiments. Results demonstrate that trained deep networks achieve satisfactory out‐of‐sample prediction accuracy.

Suggested Citation

  • Svitlana Galeshchuk & Sumitra Mukherjee, 2017. "Deep networks for predicting direction of change in foreign exchange rates," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(4), pages 100-110, October.
  • Handle: RePEc:wly:isacfm:v:24:y:2017:i:4:p:100-110
    DOI: 10.1002/isaf.1404
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    References listed on IDEAS

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    1. Engel, Charles, 2014. "Exchange Rates and Interest Parity," Handbook of International Economics, in: Gopinath, G. & Helpman, . & Rogoff, K. (ed.), Handbook of International Economics, edition 1, volume 4, chapter 0, pages 453-522, Elsevier.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Jacob A. Frenkel, 1983. "Exchange Rates and International Macroeconomics," NBER Books, National Bureau of Economic Research, Inc, number fren83-1.
    4. Nag, Ashok K & Mitra, Amit, 2002. "Forecasting Daily Foreign Exchange Rates Using Genetically Optimized Neural Networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 501-511, November.
    5. Christopher J. Neely & Lucio Sarno, 2002. "How well do monetary fundamentals forecast exchange rates?," Review, Federal Reserve Bank of St. Louis, vol. 84(Sep), pages 51-74.
    6. Lukas Menkhoff & Mark P. Taylor, 2007. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Journal of Economic Literature, American Economic Association, vol. 45(4), pages 936-972, December.
    7. Dornbusch, Rudiger, 1976. "Exchange rate expectations and monetary policy," Journal of International Economics, Elsevier, vol. 6(3), pages 231-244, August.
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    Cited by:

    1. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. "“An application of deep learning for exchange rate forecasting”," AQR Working Papers 202201, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2022.
    2. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    3. Firouzi, Shahrokh & Wang, Xiangning, 2019. "A comparative study of exchange rates and order flow based on wavelet transform coherence and cross wavelet transform," Economic Modelling, Elsevier, vol. 82(C), pages 42-56.
    4. Adebayo Felix Adekoya & Isaac Kofi Nti & Benjamin Asubam Weyori, 2021. "Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi," FinTech, MDPI, vol. 1(1), pages 1-19, December.

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