FORECASTING THE EURO EXCHANGE RATE USING DEEP LEARNING ALGORITHMS AND MACHINE LEARNING ALGORITHMS

Yunus Emre Gür

Makale detayları

FORECASTING THE EURO EXCHANGE RATE USING DEEP LEARNING ALGORITHMS AND MACHINE LEARNING ALGORITHMS

Yazarlar:

Yunus Emre Gür

Yayıncı:

Istanbul Ticaret University

Ana Başlıklar:

Ekonometrik ve İstatistiksel Yöntemler | Finansal Ekonometri

Dergi:

İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi Vol.23 Issue 49, pp.1435-1456

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001321339

pISSN:

1303-5495

Anahtar kelimeler:

Özet:

 Given that time series forecasts are of great importance in the financial world, the main objective of this study is to forecast Euro prices and examine the contribution of these forecasts to financial decision-making processes. Since the Euro is an important component of international trade and investment, accurate price forecasts are of strategic importance for many financial institutions and investors. In this study, we compare the performance of deep learning algorithms and classical machine learning methods for forecasting Euro prices: support vector machines SVM, Extreme Gradient Boosting XGBoost, long short-term memory LSTM, and gated recurrent units GRU. These methods represent different algorithms that are widely used in financial forecasting and give successful results. The dataset used in the study was divided into two parts: 80 training and 20 testing, and it is also indicated how each algorithm behaved during the training process and which parameters were chosen. The results are presented by comparing the performance of these algorithms, and it is found that the GRU algorithm provides better accuracy than the others. Therefore, the GRU algorithm was chosen to forecast Euro prices for the next 12 months, and the forecasting process was carried out. The results of this study are expected to provide an important perspective to financial decision-makers by comprehensively comparing the performance of deep learning and traditional approaches in Euro price forecasting. It also includes potential research avenues for future work and suggestions for the development of new methods in this area.

 Given that time series forecasts are of great importance in the financial world, the main objective of this study is to forecast Euro prices and examine the contribution of these forecasts to financial decision-making processes. Since the Euro is an important component of international trade and investment, accurate price forecasts are of strategic importance for many financial institutions and investors. In this study, we compare the performance of deep learning algorithms and classical machine learning methods for forecasting Euro prices: support vector machines SVM, Extreme Gradient Boosting XGBoost, long short-term memory LSTM, and gated recurrent units GRU. These methods represent different algorithms that are widely used in financial forecasting and give successful results. The dataset used in the study was divided into two parts: 80 training and 20 testing, and it is also indicated how each algorithm behaved during the training process and which parameters were chosen. The results are presented by comparing the performance of these algorithms, and it is found that the GRU algorithm provides better accuracy than the others. Therefore, the GRU algorithm was chosen to forecast Euro prices for the next 12 months, and the forecasting process was carried out. The results of this study are expected to provide an important perspective to financial decision-makers by comprehensively comparing the performance of deep learning and traditional approaches in Euro price forecasting. It also includes potential research avenues for future work and suggestions for the development of new methods in this area.

 Given that time series forecasts are of great importance in the financial world, the main objective of this study is to forecast Euro prices and examine the contribution of these forecasts to financial decision-making processes. Since the Euro is an important component of international trade and investment, accurate price forecasts are of strategic importance for many financial institutions and investors. In this study, we compare the performance of deep learning algorithms and classical machine learning methods for forecasting Euro prices: support vector machines SVM, Extreme Gradient Boosting XGBoost, long short-term memory LSTM, and gated recurrent units GRU. These methods represent different algorithms that are widely used in financial forecasting and give successful results. The dataset used in the study was divided into two parts: 80 training and 20 testing, and it is also indicated how each algorithm behaved during the training process and which parameters were chosen. The results are presented by comparing the performance of these algorithms, and it is found that the GRU algorithm provides better accuracy than the others. Therefore, the GRU algorithm was chosen to forecast Euro prices for the next 12 months, and the forecasting process was carried out. The results of this study are expected to provide an important perspective to financial decision-makers by comprehensively comparing the performance of deep learning and traditional approaches in Euro price forecasting. It also includes potential research avenues for future work and suggestions for the development of new methods in this area.

 Zaman serisi tahminlerinin finans dünyasında büyük önem taşıdığı düşünüldüğünde, bu çalışmanın temel amacı Euro fiyatlarının tahmin edilmesi ve bu tahminlerin finansal karar alma süreçlerine katkısının incelenmesidir. Euro, uluslararası ticaret ve yatırımın önemli bir bileşeni olduğu için doğru fiyat tahminleri, birçok finansal kurum ve yatırımcı için stratejik öneme sahiptir. Bu çalışmada, Euro fiyatlarını tahmin etmek için derin öğrenme algoritmaları ve klasik makine öğrenmesi yöntemlerinin performansları karşılaştırılmıştır: Destek Vektör Makineleri SVM, Aşırı Gradyan Artırma XGBoost, Uzun Kısa Vadeli Hafıza LSTM ve Geçitli Tekrarlayan Birim GRU. Bu yöntemler, finansal tahminlerde yaygın olarak kullanılan ve başarılı sonuçlar veren farklı algoritmaları temsil etmektedir. Çalışmada kullanılan veri seti, 80 eğitim ve 20 test olmak üzere ikiye bölünmüş ve her bir algoritmanın eğitim süreci boyunca nasıl davrandığını ve hangi parametrelerin seçildiği de belirtilmiştir. Sonuçlar, bu algoritmaların performansları karşılaştırılarak sunulmuş ve GRU algoritmasının diğerlerine göre daha iyi bir doğruluk sağladığı görülmüştür. Bu nedenle, gelecek 12 aylık Euro fiyatlarını tahmin etmek için GRU algoritması seçilmiş ve tahmin işlemi gerçekleştirilmiştir. Bu çalışmanın sonuçlarının, Euro fiyat tahmininde derin öğrenme ve geleneksel yaklaşımların performanslarını kapsamlı bir şekilde karşılaştırarak finansal karar alıcılara önemli bir bakış açısı sunacağı düşünülmektedir. Ayrıca, gelecek çalışmalar için potansiyel araştırma yollarını ve bu alanda yeni yöntemlerin geliştirilmesine yönelik önerileri de içermektedir.

Makale detayları

FORECASTING THE EURO EXCHANGE RATE USING DEEP LEARNING ALGORITHMS AND MACHINE LEARNING ALGORITHMS

Yazarlar:

Yunus Emre Gür

Yayıncı:

Istanbul Ticaret University

Ana Başlıklar:

Ekonometrik ve İstatistiksel Yöntemler | Finansal Ekonometri

Dergi:

İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi Vol.23 Issue 49, pp.1435-1456

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001321339

pISSN:

1303-5495

Time Series Forecasting

Deep Learning

Machine Learning

Euro Exchange Rate Forecast

Gated Recurrent UnitZaman Serisi Tahmini

Derin Öğrenme

Makine Öğrenimi

Euro Döviz Kuru Tahmini

Geçitli Tekrarlayan Birim

Sayı

Makale detayları

Yazarlar: Yunus Emre Gür

Yayıncı: Istanbul Ticaret University

Ana Başlıklar: Ekonometrik ve İstatistiksel Yöntemler | Finansal Ekonometri

Dergi: İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi Vol.23 Issue 49, pp.1435-1456

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001321339

pISSN: 1303-5495

Sayı

Yazarlar:

Yunus Emre Gür

Yayıncı:

Istanbul Ticaret University

Ana Başlıklar:

Ekonometrik ve İstatistiksel Yöntemler | Finansal Ekonometri

Dergi:

İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi Vol.23 Issue 49, pp.1435-1456

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001321339

pISSN:

1303-5495