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Predicting Foreign Exchange EUR/USD direction using machine learning

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  • Kevin Cedric Guyard
  • Michel Deriaz

Abstract

The Foreign Exchange market is a significant market for speculators, characterized by substantial transaction volumes and high volatility. Accurately predicting the directional movement of currency pairs is essential for formulating a sound financial investment strategy. This paper conducts a comparative analysis of various machine learning models for predicting the daily directional movement of the EUR/USD currency pair in the Foreign Exchange market. The analysis includes both decorrelated and non-decorrelated feature sets using Principal Component Analysis. Additionally, this study explores meta-estimators, which involve stacking multiple estimators as input for another estimator, aiming to achieve improved predictive performance. Ultimately, our approach yielded a prediction accuracy of 58.52% for one-day ahead forecasts, coupled with an annual return of 32.48% for the year 2022.

Suggested Citation

  • Kevin Cedric Guyard & Michel Deriaz, 2024. "Predicting Foreign Exchange EUR/USD direction using machine learning," Papers 2409.04471, arXiv.org, revised Oct 2024.
    • Handle: RePEc:arx:papers:2409.04471
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    References listed on IDEAS

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    1. Deniz Can Yıldırım & Ismail Hakkı Toroslu & Ugo Fiore, 2021. "Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-36, December.
    2. Xiao Zhong & David Enke, 2019. "Predicting the daily return direction of the stock market using hybrid machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-20, December.
    3. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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