Investing in green bonds and its impact on stock prices and profitability for banks A study of his analysis of a sample of Canadian banks, Royal Bank, as a model For the period (2005-2022)
Bibliographic record
Abstract
Investing in clean energy projects requires non-traditional tools, including green bonds, as they provide a set of advantages that qualify the projects to obtain financing. Therefore, this study aims to demonstrate the impact of Canadian banks introducing green bonds in their list of investments and the extent of their impact on the prices and returns of their shares. In the financial market , The study relied on the most important test for normal distribution, which is the Kolmogorov-Smirnov test, and a time series for a period of (18) years, from (2005 - 2022), as the bank did not deal in green bonds for the period (2005 - 2013), that is, for a period of (9) years. Green bonds were dealt with for the period (2014-2022), The results of the study showed that there was a clear increase in the stock price and its profitability after investing in green bonds over the previous period of this investment. In order to test the significance of this increase, the statistical program (Statgraphics V.18) was used.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".