Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative Analysis
The main objective of this study is to contribute to the literature by forecasting green bond index with different machine learning models supported by artificial intelligence. The data from 1 June 2021 to 29 April 2024, collected from many sources, was separated into training and test sets, and sta...
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Ekonomi ve Finansal Araştırmalar Derneği
2024-12-01
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| Series: | Ekonomi, Politika & Finans Araştırmaları Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/3981433 |
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| author | Ahmed İhsan Şimşek Yunus Emre Gür Emre Bulut |
| author_facet | Ahmed İhsan Şimşek Yunus Emre Gür Emre Bulut |
| author_sort | Ahmed İhsan Şimşek |
| collection | DOAJ |
| description | The main objective of this study is to contribute to the literature by forecasting green bond index with different machine learning models supported by artificial intelligence. The data from 1 June 2021 to 29 April 2024, collected from many sources, was separated into training and test sets, and standard preparation was conducted for each. The model's dependent variable is the Global S&P Green Bond Index, which monitors the performance of green bonds in global financial markets and serves as a comprehensive benchmark for the study. To evaluate and compare the performance of the trained machine learning models (Random Forest, Linear Regression, Rational Quadratic Gaussian Process Regression (GPR), XGBoost, MLP, and Linear SVM), RMSE, MSE, MAE, MAPE, and R² were used as evaluation metrics and the best performing model was Rational Quadratic GPR. The concluding segment of the SHAP analysis reveals the primary factors influencing the model's forecasts. It is evident that the model assigns considerable importance to macroeconomic indicators, including the DXY (US Dollar Index), XAU (Gold Spot Price), and MSCI (Morgan Stanley Capital International). This work is expected to enhance the literature, as studies directly comparable to this research are limited in this field. |
| format | Article |
| id | doaj-art-5d0a4a0f99744b0b9b96d913448d08bb |
| institution | Kabale University |
| issn | 2587-151X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ekonomi ve Finansal Araştırmalar Derneği |
| record_format | Article |
| series | Ekonomi, Politika & Finans Araştırmaları Dergisi |
| spelling | doaj-art-5d0a4a0f99744b0b9b96d913448d08bb2025-01-03T21:45:08ZengEkonomi ve Finansal Araştırmalar DerneğiEkonomi, Politika & Finans Araştırmaları Dergisi2587-151X2024-12-019462865510.30784/epfad.1495757957Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative AnalysisAhmed İhsan Şimşek0https://orcid.org/0000-0002-2900-3032Yunus Emre Gür1https://orcid.org/0000-0001-6530-0598Emre Bulut2https://orcid.org/0000-0002-2884-1405FIRAT ÜNİVERSİTESİFIRAT ÜNİVERSİTESİFIRAT ÜNİVERSİTESİThe main objective of this study is to contribute to the literature by forecasting green bond index with different machine learning models supported by artificial intelligence. The data from 1 June 2021 to 29 April 2024, collected from many sources, was separated into training and test sets, and standard preparation was conducted for each. The model's dependent variable is the Global S&P Green Bond Index, which monitors the performance of green bonds in global financial markets and serves as a comprehensive benchmark for the study. To evaluate and compare the performance of the trained machine learning models (Random Forest, Linear Regression, Rational Quadratic Gaussian Process Regression (GPR), XGBoost, MLP, and Linear SVM), RMSE, MSE, MAE, MAPE, and R² were used as evaluation metrics and the best performing model was Rational Quadratic GPR. The concluding segment of the SHAP analysis reveals the primary factors influencing the model's forecasts. It is evident that the model assigns considerable importance to macroeconomic indicators, including the DXY (US Dollar Index), XAU (Gold Spot Price), and MSCI (Morgan Stanley Capital International). This work is expected to enhance the literature, as studies directly comparable to this research are limited in this field.https://dergipark.org.tr/tr/download/article-file/3981433green bondsmachine learningrational quadratic gaussian process regressionshap analysisnonlinear relationshipsyeşil tahvillermakine öğrenmesirasyonel kuadratik gauss süreci regresyonushap analizidoğrusal olmayan i̇lişkiler |
| spellingShingle | Ahmed İhsan Şimşek Yunus Emre Gür Emre Bulut Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative Analysis Ekonomi, Politika & Finans Araştırmaları Dergisi green bonds machine learning rational quadratic gaussian process regression shap analysis nonlinear relationships yeşil tahviller makine öğrenmesi rasyonel kuadratik gauss süreci regresyonu shap analizi doğrusal olmayan i̇lişkiler |
| title | Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative Analysis |
| title_full | Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative Analysis |
| title_fullStr | Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative Analysis |
| title_full_unstemmed | Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative Analysis |
| title_short | Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative Analysis |
| title_sort | artificial intelligence assisted machine learning methods for forecasting green bond index a comparative analysis |
| topic | green bonds machine learning rational quadratic gaussian process regression shap analysis nonlinear relationships yeşil tahviller makine öğrenmesi rasyonel kuadratik gauss süreci regresyonu shap analizi doğrusal olmayan i̇lişkiler |
| url | https://dergipark.org.tr/tr/download/article-file/3981433 |
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