Bußmann, N. and Giudici, P. and Tanda, A. and Yu, Ellen Pei-yi (2025) Explainable Machine Learning to predict the cost of capital. Frontiers in Artificial Intelligence 8 , ISSN 2624-8212.
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Abstract
This study investigates through explainable AI the impact of financial and non-financial factors on a firm's ex-ante cost of capital, which is the reflection of investors' perception of a firm's riskiness. We apply the XGBoost algorithm and the Shapley value approach to a sample of more than 1,400 listed companies worldwide. Results indicate the most relevant financial and non-financial indicators are: firm size, ROE, firm portfolio risk, and regulatory quality. These factors influence firm's ex-ante cost of equity by changing its investors' risk perception. We also suggest the relevance of ESG factors in predicting the cost of capital.
Metadata
| Item Type: | Article |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School |
| Research Centres and Institutes: | Accounting and Finance Research Centre |
| Depositing User: | Ellen Yu |
| Date Deposited: | 03 Jun 2025 10:40 |
| Last Modified: | 21 Sep 2025 05:01 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/55604 |
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