Corporate Green Bond Issuances: An International Evidence
Bibliographic record
Abstract
Using an international sample of corporate Green bond issuances over the recent period, this paper highlights the potential consequences of the issuance of a Green bond on the issuer’s financial performance. Starting with a first sample of 2079 Green bond issuances of 190 unique issuers from 2009 to 2018, we investigate only corporate green bond issuances. Our final sample contains 475 green bonds issued by 145 unique firms. We find that the market reacts negatively to the announcement of green bond issuances. In particular, results show that the stock market reacts on the day of the green bond announcement date and the day after, and that the cumulative abnormal return is between −0.5% and −0.2%, depending on the asset pricing model (CAPM, the 3-factor Fama and French models, and the 4-factor Carhart models). This effect is mainly noticeable at the first Green Bond issuance and in developed markets. Our results provide evidence that the investors react in the same manner for Green bonds as for conventional or convertible bonds. This evidence suggests that green debt offerings convey unfavorable information about the issuing firms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".