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Fundamentals and exchange rate forecastability with simple machine learning methods

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  • Amat, Christophe
  • Michalski, Tomasz
  • Stoltz, Gilles

Abstract

Using methods from machine learning we show that fundamentals from simple exchange rate models (PPP or UIRP) or Taylor-rule based models lead to improved exchange rate forecasts for major currencies over the floating period era 1973–2014 at a 1-month forecast horizon which beat the no-change forecast. Fundamentals thus contain useful information and exchange rates are forecastable even for short horizons. Such conclusions cannot be obtained when using rolling or recursive OLS regressions as used in the literature. The methods we use – sequential ridge regression and the exponentially weighted average strategy, both with discount factors – do not estimate an underlying model but combine the fundamentals to directly output forecasts.

Suggested Citation

  • Amat, Christophe & Michalski, Tomasz & Stoltz, Gilles, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 1-24.
  • Handle: RePEc:eee:jimfin:v:88:y:2018:i:c:p:1-24
    DOI: 10.1016/j.jimonfin.2018.06.003
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    Cited by:

    1. Yuchen Zhang & Shigeyuki Hamori, 2020. "The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(3), pages 1-16, March.
    2. Colombo, Emilio & Pelagatti, Matteo, 2020. "Statistical learning and exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1260-1289.
    3. Jeremy Fouliard & Michael Howell & Hélène Rey, 2020. "Answering the Queen: Machine Learning and Financial Crises," NBER Working Papers 28302, National Bureau of Economic Research, Inc.
    4. Amat, Christophe & Michalski, Tomasz & Stoltz, Gilles, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 1-24.
    5. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, Open Access Journal, vol. 13(5), pages 1-29, March.
    6. Malo Huard & Rémy Garnier & Gilles Stoltz, 2020. "Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method," Working Papers hal-02794320, HAL.

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    More about this item

    Keywords

    Exchange rates; Forecasting; Machine learning; Purchasing power parity; Uncovered interest rate parity; Taylor-rule exchange rate models;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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