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Artificial Intelligence-Assisted Machine Learning Methods For Forecasting Green Bond Index: A Comparative Analysis

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  • Yunus Emre Gür
  • Ahmet İhsan Şimşek
  • Emre Bulut

Abstract

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.

Suggested Citation

  • Yunus Emre Gür & Ahmet İhsan Şimşek & Emre Bulut, 2025. "Artificial Intelligence-Assisted Machine Learning Methods For Forecasting Green Bond Index: A Comparative Analysis," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 9(4), pages 628-655.
    • Handle: RePEc:ahs:journl:v:9:y:2025:i:4:p:628-655
    DOI: https://doi.org/10.30784/epfad.1495757
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    References listed on IDEAS

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    4. Tu, Chuc Anh & Rasoulinezhad, Ehsan & Sarker, Tapan, 2020. "Investigating solutions for the development of a green bond market: Evidence from analytic hierarchy process," Finance Research Letters, Elsevier, vol. 34(C).
    5. Zhenyi Wang & Wen Dong & Kun Yang, 2022. "Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China," IJERPH, MDPI, vol. 19(16), pages 1-26, August.
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    More about this item

    Keywords

    Green Bonds; Machine Learning; Rational Quadratic Gaussian Process Regression; SHAP Analysis; Nonlinear Relationships;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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