Statistical techniques vs. machine learning models: a comparative analysis for exchange rate forecasting in fragile five countries
| dc.contributor.author | Bakır, Muhammed Raşid | |
| dc.contributor.author | Bakırtaş, İbrahim | |
| dc.contributor.author | Ölmez, Emre | |
| dc.date.accessioned | 2023-10-06T07:39:33Z | |
| dc.date.available | 2023-10-06T07:39:33Z | |
| dc.date.issued | 2023 | |
| dc.department | İktisadi ve İdari Bilimler Fakültesi | |
| dc.description.abstract | In 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 Naïve 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. | |
| dc.identifier.doi | 10.24818/18423264/57.3.23.18 | |
| dc.identifier.endpage | 312 | en_US |
| dc.identifier.issn | 0424-267X | |
| dc.identifier.issue | 3 | en_US |
| dc.identifier.scopusquality | Q3 | |
| dc.identifier.startpage | 295 | en_US |
| dc.identifier.uri | https:/dx.doi.org10.24818/18423264/57.3.23.18 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/11082 | |
| dc.identifier.volume | 57 | en_US |
| dc.identifier.wos | WOS:001078305500018 | |
| dc.identifier.wosquality | Q3 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Bucharest University of Economic Studies | |
| dc.relation.ispartof | Economic Computation and Economic Cybernetics Studies and Research | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Emerging Market | |
| dc.subject | Exchange Rate | |
| dc.subject | Forecasting Methods | |
| dc.subject | Machine Learning | |
| dc.subject | Monetary Policies | |
| dc.title | Statistical techniques vs. machine learning models: a comparative analysis for exchange rate forecasting in fragile five countries | |
| dc.type | Article |
