The impact of credit default swaps on corporate capital structure \nand investment policies
Bibliographic record
Abstract
Credit default swaps (CDSs) are credit derivatives whose primary purposes include hedging and the trading of credit risks. Unlike other derivatives, such as options or futures, CDSs materially alter lender-borrower relations and thus have real economic effects on the companies referenced by the CDSs. In this thesis, I explore the impact of CDS trading on the cost of capital, corporate capital structure, and corporate social responsibility. \n \nFirst, I use the universe of U.S. public firms to examine the impact of CDS trading on a firm’s cost of capital during the period 2001 – 2018. My results robustly show that the inception of CDSs causes a significant reduction in a firm’s weighted average cost of capital (WACC). Further analyses reveal that highly levered firms tend to reduce their debt weight, while firms with low leverage increase their usage of debt. Moreover, CDS referenced firms adjust their debt types by using more arm-length debts, while they simultaneously decrease the usage of revolving credits and term loans from banks. The alteration in capital financing choices may be ascribed to the improved information environment and reflects the fact that CDS trading increases debt renegotiation costs but simultaneously also reduces capital supply-side frictions. \n \nAfter confirming that CDS can impact firms’ financing decisions, I further investigate whether CDS trading can affect a company’s investment in corporate social and environmental activities. A longitudinal sample spanning from 2002 to 2017 across 11 countries and regions was constructed to evaluate the impact. I find that the inception of CDS trading causes a significant reduction in the metric of environmental emission reduction. In addition, the initiation of CDS trading weakly but negatively influences other aspects of CDS firms’ social and environmental performance. Further analysis reveals that investments in emission reduction activities have no relationship to shareholder value creation, whereas engaging in CSR activities related to, e.g., employee, community, or eco-product innovation, etc., increases shareholder wealth. Collectively, my findings reveal one of the downsides of CDSs arising from CDS-protected lenders who become less accommodating over post-CDS periods.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".