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Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques

Citations

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Cited by:

  1. Biswas, Rita & Li, Xiao & Piccotti, Louis R., 2023. "Do macroeconomic variables drive exchange rates independently?," Finance Research Letters, Elsevier, vol. 52(C).
  2. Mohammad Abdullah & Mohammad Ashraful Ferdous Chowdhury & Ajim Uddin & Syed Moudud‐Ul‐Huq, 2023. "Forecasting nonperforming loans using machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1664-1689, November.
  3. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
  4. Fernandez, Raul & Palma Guizar, Brenda & Rho, Caterina, 2021. "A sentiment-based risk indicator for the Mexican financial sector," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(3).
  5. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.
  6. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.
  7. 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.
  8. Alexakis, Christos & Gogas, Periklis & Petrella, Giovanni & Polemis, Michael & Salvadè, Federica, 2025. "Investigating the investment readiness of European SMEs: A machine learning approach," International Review of Financial Analysis, Elsevier, vol. 105(C).
  9. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas & Konstantinos Diamantaras, 2015. "Market sentiment and exchange rate directional forecasting," Algorithmic Finance, IOS Press, vol. 4(1-2), pages 69-79.
  10. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
  11. Lu, Yao & Zhao, Zhihui & Tian, Yuan & Zhan, Minghua, 2024. "How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
  12. He Jiang, 2023. "Forecasting global solar radiation using a robust regularization approach with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1989-2010, December.
  13. Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2019. "Exchange rate returns and volatility: the role of time-varying rare disaster risks," The European Journal of Finance, Taylor & Francis Journals, vol. 25(2), pages 190-203, January.
  14. Alexandridis, Antonios K. & Panopoulou, Ekaterini & Souropanis, Ioannis, 2024. "Forecasting exchange rate volatility: An amalgamation approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 97(C).
  15. Rangan Gupta & Vasilios Plakandaras, 2019. "Efficiency in BRICS Currency Markets Using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Journal of Economics and Behavioral Studies, AMH International, vol. 11(1), pages 152-165.
  16. Plakandaras, Vasilios & Gupta, Rangan & Wohar, Mark E., 2017. "The depreciation of the pound post-Brexit: Could it have been predicted?," Finance Research Letters, Elsevier, vol. 21(C), pages 206-213.
  17. Bangzhu Zhu & Shunxin Ye & Ping Wang & Julien Chevallier & Yi‐Ming Wei, 2022. "Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 100-117, January.
  18. Bolivar, Osmar, 2024. "GDP nowcasting: A machine learning and remote sensing data-based approach for Bolivia," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(3).
  19. Theophilos Papadimitriou & Periklis Gogas & Athanasios Fotios Athanasiou, 2020. "Forecasting S&P 500 spikes: an SVM approach," Digital Finance, Springer, vol. 2(3), pages 241-258, December.
  20. Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
  21. Paolo Fornaro & Henri Luomaranta, 2020. "Nowcasting Finnish real economic activity: a machine learning approach," Empirical Economics, Springer, vol. 58(1), pages 55-71, January.
  22. Tasadduq Imam, 2021. "Model selection for one‐day‐ahead AUD/USD, AUD/EUR forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1808-1824, April.
  23. Darvas, Zsolt & Schepp, Zoltán, 2025. "Forecasting the daily exchange rate of the UK pound sterling against the US dollar," Finance Research Letters, Elsevier, vol. 71(C).
  24. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
  25. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.
  26. Juan Carlos Escanciano & Ricardo Parra, 2024. "Extending the Scope of Inference About Predictive Ability to Machine Learning Methods," Papers 2402.12838, arXiv.org, revised May 2025.
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