Bibliographic record
Abstract
This dissertation contains three essays on corporate finance. In Essay One, we provide empirical evidence on the adverse effects of supplier firms’ environmental risk exposures on their relationships with principal customers. We document that supplier firms with high environmental risk are less likely to have principal customers. Moreover, from the principal customers’ perspective, a higher level of environmental risk lowers a supplier firm’s probability of being selected relative to its industry peers by its potential customer. Conditional on an ongoing relationship with principal customers, supplier firms with high environmental risk have lower sales to principal customers and shorter relationship durations. These results are more pronounced when customers’ environmental risk is lower. Collectively, our findings suggest that improving the trading relationship with principal customers is an important channel through which firms can benefit economically from being environmentally responsible. Essay Two investigates the capital structure implications of corporate environmental liabilities, which are captured using the amount of firms’ toxic production-related waste. We document that firms with higher environmental liabilities maintain lower financial leverage ratios, suggesting that environmental liabilities work as a substitute for financial liabilities. The substitution effect is more pronounced for larger firms, firms covered by more analysts, firms that have higher sales to principal customers, and firms with greater community concerns. Further analysis shows that less environmentally responsible firms have a lower fraction of bank debt in total debt, all else equal, consistent with the notion that banks are more environmentally sensitive than other lenders. Overall, our findings imply that being environmentally responsible can enhance firms’ debt capacity and improve the availability of bank credit. Essay Three provides evidence that options trading affects firms’ financing decisions. We find that firms with exchange-listed options are more likely to issue equity as opposed to debt. They issue equity more frequently but in smaller amounts and maintain low leverage ratios. The effect of options trading on financing decisions is more pronounced for firms with larger options trading volume, higher information asymmetry, and greater short-sale constraints. Further analysis shows that optioned firms attract more short-term institutional investors, have greater analyst coverage, and experience higher abnormal returns at seasoned equity offerings announcements. These findings are consistent with the notion that options trading reduces firms’ information asymmetry, which makes equity financing more favourable.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".