GREEN BOND INDEX PRICE FORECASTING: COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS

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dc.contributor.authorİŞGÜZAR, SEDA
dc.contributor.authorFendoğlu, Eda
dc.contributor.authorSimsek, Ahmed Ihsan
dc.contributor.authortürkoğlu, muammer
dc.date.accessioned2025-10-24T18:03:26Z
dc.date.available2025-10-24T18:03:26Z
dc.date.issued2024
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThe aim of this study is to compare the performance of different models using machine learning algorithms to predict the price of the green bond index in Japan. In the study, 693-day dataset collected between 06.05.2021-02.05.2024 was used. Nikkei225, USD/JPY and crude oil prices were determined as input data. 80% of the data was reserved for training and 20% for testing. RF, MLP, GBR, XGBoost, LSTM, SVR, Catboost and Linear Regression methods were used as prediction models. Performance evaluations were made on metrics such as MSE, RMSE, MAE, MAPE and R2. The GBR model showed the best performance in the training set, while XGBoost and RF models produced more successful predictions in the test set. The contribution of this study to the literature is to demonstrate the usability of artificial intelligence-based prediction models in sustainable finance and green bond markets. The results obtained serve as a guide for investors and analysts and offer practical solutions to increase interest in green projects.
dc.identifier.doi10.14780/muiibd.1481251
dc.identifier.endpage589
dc.identifier.issn2149-1844
dc.identifier.issn2587-2672
dc.identifier.issue3
dc.identifier.startpage568
dc.identifier.trdizinid1295941
dc.identifier.urihttps://doi.org/10.14780/muiibd.1481251
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1295941
dc.identifier.urihttps://hdl.handle.net/20.500.12899/2224
dc.identifier.volume46
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofMarmara Üniversitesi İktisadi ve İdari Bilimler Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzTR-Dizin_20251023
dc.subjectMachine Learning
dc.subjectFinancial Forecasting
dc.subjectTime series analysis
dc.subjectGreen bonds
dc.subjectDecision Support.
dc.titleGREEN BOND INDEX PRICE FORECASTING: COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS
dc.typeArticle

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