IDEAS home Printed from https://ideas.repec.org/a/ahs/journl/v9y2025i4p628-655.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://dergipark.org.tr/tr/download/article-file/3981433
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.30784/epfad.1495757?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Joseph Zhi Bin Ling & Albert K. Tsui & Zhaoyong Zhang, 2021. "Trading Macro-Cycles of Foreign Exchange Markets Using Hybrid Models," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    2. 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).
    3. Reboredo, Juan C., 2018. "Green bond and financial markets: Co-movement, diversification and price spillover effects," Energy Economics, Elsevier, vol. 74(C), pages 38-50.
    4. Nan Jing & Qi Liu & Hefei Wang, 2021. "Stock price prediction based on stock price synchronicity and deep learning," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 1-21, June.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Riaz Tabassum & Selama Aslam Izah & Nor Normaziah Mohd & Hassan Ahmad Fahmi Sheikh, 2024. "Meaningful Review of Existing Trends, Expansion, and Future Directions of Green Bond Research: A Bibliometric Approach," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 34(1), pages 1-36, March.
    2. Naeem, Muhammad Abubakr & Nguyen, Thi Thu Ha & Nepal, Rabindra & Ngo, Quang-Thanh & Taghizadeh–Hesary, Farhad, 2021. "Asymmetric relationship between green bonds and commodities: Evidence from extreme quantile approach," Finance Research Letters, Elsevier, vol. 43(C).
    3. Chaofeng Tang & Kentaka Aruga & Yi Hu, 2023. "The Dynamic Correlation and Volatility Spillover among Green Bonds, Clean Energy Stock, and Fossil Fuel Market," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    4. Man, Yuanyuan & Zhang, Sunpei & Liu, Jianing, 2023. "Dynamic connectedness, asymmetric risk spillovers, and hedging performance of China's green bonds," Finance Research Letters, Elsevier, vol. 56(C).
    5. Md. Bokhtiar Hasan & Md. Mamunur Rashid & Tapan Sarker & Muhammad Shafiullah, 2024. "Exploring the determinants of green bond market development in Bangladesh," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 14(1), pages 213-233, March.
    6. Rehman, Mobeen Ur & Raheem, Ibrahim D. & Zeitun, Rami & Vo, Xuan Vinh & Ahmad, Nasir, 2023. "Do oil shocks affect the green bond market?," Energy Economics, Elsevier, vol. 117(C).
    7. Kocaarslan, Baris, 2023. "Funding liquidity risk and the volatility of U.S. municipal green bonds during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 58(PD).
    8. Akhtaruzzaman, Md & Banerjee, Ameet Kumar & Ghardallou, Wafa & Umar, Zaghum, 2022. "Is greenness an optimal hedge for sectoral stock indices?," Economic Modelling, Elsevier, vol. 117(C).
    9. Liu, Min, 2022. "The driving forces of green bond market volatility and the response of the market to the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 288-309.
    10. Kocaarslan, Baris & Soytas, Ugur, 2023. "The role of major markets in predicting the U.S. municipal green bond market performance: New evidence from machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    11. Kocaarslan, Baris & Mushtaq, Rizwan, 2024. "The impact of liquidity conditions on the time-varying link between U.S. municipal green bonds and major risky markets during the COVID-19 crisis: A machine learning approach," Energy Policy, Elsevier, vol. 184(C).
    12. Bhutta, Umair Saeed & Tariq, Adeel & Farrukh, Muhammad & Raza, Ali & Iqbal, Muhammad Khalid, 2022. "Green bonds for sustainable development: Review of literature on development and impact of green bonds," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    13. Miklesh Yadav & Nandita Mishra & Shruti Ashok, 2023. "Dynamic connectedness of green bond with financial markets of European countries under OECD economies," Economic Change and Restructuring, Springer, vol. 56(1), pages 609-631, February.
    14. Yousaf, Imran & Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2024. "Dynamic spillovers and connectedness between crude oil and green bond markets," Resources Policy, Elsevier, vol. 89(C).
    15. Migliavacca, Milena & Goodell, John W. & Paltrinieri, Andrea, 2023. "A bibliometric review of portfolio diversification literature," International Review of Financial Analysis, Elsevier, vol. 90(C).
    16. Wang, Moran & Li, Xuerong & Wang, Shouyang, 2021. "Discovering research trends and opportunities of green finance and energy policy: A data-driven scientometric analysis," Energy Policy, Elsevier, vol. 154(C).
    17. Skare, Marinko & Gavurova, Beata & Sinkovic, Dean, 2023. "Regional aspects of financial development and renewable energy: A cross-sectional study in 214 countries," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1142-1157.
    18. Taghizadeh-Hesary, Farhad & Rasoulinezhad, Ehsan & Shahbaz, Muhammad & Vinh Vo, Xuan, 2021. "How energy transition and power consumption are related in Asian economies with different income levels?," Energy, Elsevier, vol. 237(C).
    19. Wang, Kai-Hua & Zhao, Yan-Xin & Jiang, Cui-Feng & Li, Zheng-Zheng, 2022. "Does green finance inspire sustainable development? Evidence from a global perspective," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 412-426.
    20. Zirek, Duygu & Unsal, Omer, 2023. "Green bonds: Do investors benefit from third-party certification?," Global Finance Journal, Elsevier, vol. 58(C).

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ahs:journl:v:9:y:2025:i:4:p:628-655. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ersan Ersoy (email available below). General contact details of provider: https://epfjournal.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.