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Exchange Rate Forecasting with Advanced Machine Learning Methods

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  • Jonathan Felix Pfahler

    (Department of Statistics, University of Augsburg, Universitaetstr. 16, D-86159 Augsburg, Germany)

Abstract

Historically, exchange rate forecasting models have exhibited poor out-of-sample performances and were inferior to the random walk model. Monthly panel data from 1973 to 2014 for ten currency pairs of OECD countries are used to make out-of sample forecasts with artificial neural networks and XGBoost models. Most approaches show significant and substantial predictive power in directional forecasts. Moreover, the evidence suggests that information regarding prediction timing is a key component in the forecasting performance.

Suggested Citation

  • Jonathan Felix Pfahler, 2021. "Exchange Rate Forecasting with Advanced Machine Learning Methods," JRFM, MDPI, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2021:i:1:p:2-:d:707566
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    References listed on IDEAS

    as
    1. Mark, Nelson C, 1995. "Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability," American Economic Review, American Economic Association, vol. 85(1), pages 201-218, March.
    2. Francis Vitek, 2005. "The Exchange Rate Forecasting Puzzle," International Finance 0509005, University Library of Munich, Germany.
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    Cited by:

    1. Kazım Berk Küçüklerli & Veysel Ulusoy, 2024. "Sentiment-Driven Exchange Rate Forecasting: Integrating Twitter Analysis with Economic Indicators," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(3), pages 1-4.
    2. Simiso MSOMI & Harold NGALAWA, 2023. "The Movement of Exchange Rate and Expected Income: Case of South Africa," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 7(2), pages 65-89.

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