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Predicting Investor Preferences and Behavioural Drivers in Green Bond Investments using Machine Learning Techniques

In: Proceedings of the 3rd International Conference on Artificial Intelligence in Economics, Finance and Management (ICAIEFM 2025)

Author

Listed:
  • Swetha Karamadi

    (CMS Business School, Jain Deemed-to-be University, Research Scholar, Faculty of Management)

  • Gopalakrishnan Chinnasamy

    (CMS Business School, Jain Deemed-to-be University, Professor, Faculty of Management)

  • S. Vinoth

    (CMS Business School, Jain Deemed-to-be University, Professor, Faculty of Management)

Abstract

This paper discusses the determinants of investor preferences and behavioural drivers in green bond investments using techniques of machine learning. From the 280 respondents, analysis of data has helped in identifying the major demo-graphic, behavioral, and sentiment-related factors influencing green bond adoption. The leading types of advanced predictive models used in unveiling the in-sights of investor behaviour and decision-making include decision trees, random forests, and gradient boosting classifiers. Findings show that age, financial literacy, environmental consciousness, risk tolerance, and sentiment scores shape in-vestment preferences significantly. Although some demographic variables are of limited statistical significance in this study, it shows the importance of targeted financial literacy programs, positive market narratives, and segmentation strategies for increasing adoption in green bonds. The results can also help policymakers and financial institutions by offering some actionable insights regarding how to increase the penetration of sustainable finance.

Suggested Citation

  • Swetha Karamadi & Gopalakrishnan Chinnasamy & S. Vinoth, 2025. "Predicting Investor Preferences and Behavioural Drivers in Green Bond Investments using Machine Learning Techniques," Advances in Economics, Business and Management Research, in: Bejoy Joseph & Devi Sekhar R (ed.), Proceedings of the 3rd International Conference on Artificial Intelligence in Economics, Finance and Management (ICAIEFM 2025), pages 7-22, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-896-7_2
    DOI: 10.2991/978-94-6463-896-7_2
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