Does It Pay to Issue Green? An Institutional Comparison of Mainland China and Hong Kong’s Stock Markets Toward Green Bonds
Bibliographic record
Abstract
The stock market is an indicator of investor sentiment when it comes to new information or innovative firm-level products. Green bonds are both innovative and unique in terms of their higher information disclosures and understanding the impact of sustainable finance on investor outlook for a company's stock. Using the comparative case of Mainland China and Hong Kong's stock market, we examine whether green bond announcements from 2016 to 2019 can create significant investor reactions. By employing the event study methodology, we confirm that both markets react in a positive way toward green bond announcements. This reinforces the reputational and financial benefits of green bonds. We find that issuers that are non-banks, environmentally friendly firms as well as those issuing non-general bonds, create a more positive reaction, whereas ownership aspects do not matter as much for investors. However, even among those issuers listed in both markets, certain institutional dynamics like strategic framing and source credibility tend to reinforce a firm's institutional legitimacy and are seen as being more prominent for investor reaction. The policy implications of our study show that the stock market reaction among two connected economies, where previously varying institutional contexts have resulted in regional differences, are now equally supportive of sustainable financial markets like the green bond. As seen with the positive stock market sentiment, governments and listed issuers can now better align their policies and internal strategies, allowing the low-carbon transition to be a financially attractive opportunity for all investors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".