STATISTICAL TECHNIQUES VS. MACHINE LEARNING MODELS: A COMPARATIVE ANALYSIS FOR EXCHANGE RATE FORECASTING IN FRAGILE FIVE COUNTRIES

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dc.authoridBAKIR, Muhammed Rasid/0000-0001-8261-5487
dc.authorwosidBAKIR, Muhammed Rasid/JEF-2242-2023
dc.contributor.authorBakir, Muhammed Rasid
dc.contributor.authorBakirtas, Ibrahim
dc.contributor.authorOlmez, Emre
dc.date.accessioned2024-03-09T18:48:44Z
dc.date.available2024-03-09T18:48:44Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractIn 2013, the Federal Reserve (Fed) announced the end of its expansionary monetary policy, which had a significant impact on certain countries. These countries, colloquially referred to as the fragile five, were heavily dependent on financial capital flows, which led to deviations from inflation targets due to the exchange rate pass-through effect. Consequently, monetary authorities and other financial actors need accurate exchange rate forecasts to mitigate these deviations and improve the effectiveness of monetary policy. This study aims to forecast the exchange rates of the fragile five countries using both traditional statistical methods and machine learning techniques. The traditional statistical methods used in this study include Naive Drift, Theta, Holt's Exponential Smoothing and ARIMA models, while the machine learning methods include RNN, LSTM, GRU and CNN architectures. The results show that machine learning methods outperform traditional statistical methods in terms of prediction accuracy for all countries. While statistical methods show a directional accuracy rate between 47% and 60%, RNN, one of the machine learning models, shows an accuracy rate between 80% and 90%. Overall, these results suggest that machine learning methods can provide more accurate exchange rate forecasts for the fragile five countries than traditional statistical methods. These findings may be valuable for monetary authorities and financial actors seeking to improve the effectiveness of monetary policy in these countries.en_US
dc.identifier.doi10.24818/18423264/57.3.23.18
dc.identifier.endpage312en_US
dc.identifier.issn0424-267X
dc.identifier.issn1842-3264
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85172240456en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage295en_US
dc.identifier.urihttps://doi.org/10.24818/18423264/57.3.23.18
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1447
dc.identifier.volume57en_US
dc.identifier.wosWOS:001078305500018en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEditura Aseen_US
dc.relation.ispartofEconomic Computation and Economic Cybernetics Studies and Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine Learning; Forecasting Methods; Emerging Market; Monetary Policies; Exchange Rateen_US
dc.titleSTATISTICAL TECHNIQUES VS. MACHINE LEARNING MODELS: A COMPARATIVE ANALYSIS FOR EXCHANGE RATE FORECASTING IN FRAGILE FIVE COUNTRIESen_US
dc.typeArticleen_US

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