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

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

Authors
Swetha Karamadi1, Gopalakrishnan Chinnasamy2, *, S. Vinoth3
1Research Scholar, Faculty of Management, CMS Business School, Jain Deemed-to-be University, Bengaluru, 560001, India
2Professor, Faculty of Management, CMS Business School, Jain Deemed-to-be University, Bengaluru, 560001, India
3Professor, Faculty of Management, CMS Business School, Jain Deemed-to-be University, Bengaluru, 560001, India
*Corresponding author. Email: dr.gopalakrishnan_c@cms.ac.in
Corresponding Author
Gopalakrishnan Chinnasamy
Available Online 6 November 2025.
DOI
10.2991/978-94-6463-896-7_2How to use a DOI?
Keywords
Green Bonds; Machine Learning; Investor Preferences; Behavioural Finance; Sustainable Investments
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.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 3rd International Conference on Artificial Intelligence in Economics, Finance and Management (ICAIEFM 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
6 November 2025
ISBN
978-94-6463-896-7
ISSN
2352-5428
DOI
10.2991/978-94-6463-896-7_2How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Swetha Karamadi
AU  - Gopalakrishnan Chinnasamy
AU  - S. Vinoth
PY  - 2025
DA  - 2025/11/06
TI  - Predicting Investor Preferences and Behavioural Drivers in Green Bond Investments using Machine Learning Techniques
BT  - Proceedings of the 3rd International Conference on Artificial Intelligence in Economics, Finance and Management (ICAIEFM 2025)
PB  - Atlantis Press
SP  - 7
EP  - 22
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-896-7_2
DO  - 10.2991/978-94-6463-896-7_2
ID  - Karamadi2025
ER  -