Benefiting from Insurance : An empirical analysis of directors’ and officers’ liability insurance on Canadian corporations’ capital structure
Bibliographic record
Abstract
In this thesis, we examine the role of Directors’ and Officers’ Liability Insurance, as a proxy\nof comprehensive corporate insurance, in strategic risk management within corporations\nand its impact on various aspects of a firm’s capital structure. Our empirical study is\ngrounded in a dataset containing Canadian corporations listed on the S&P/TSX Index,\nexamining insurance and financial data to understand how alternations in insurance\npremium levels affect a firm’s capital structure, valuation, and risk profile.\nOur findings indicate that insurance reduces the asset volatility, lowers cost of debt and\npositively influence its leverage. Furthermore, enhanced insurance appears to beneficially\naffect the enterprise value, likely due to increased tax benefits from increased leverage\nand an improved risk profile.\nThe application of the Leland model in this research allows us for the validation of our\nfindings by examining the connection between insurance and asset volatility, as determined\nthrough the Merton model. Our analysis implies that insurance will reduce the asset\nvolatility thus enhancing its risk profile. Leland’s theory further supports our findings,\nasserting that a reduction in asset volatility will yield similar changes in its capital\nstructure.\nOverall, our findings suggest that insurance is advantageous for companies, leading to\nreduced volatility and cost of debt, and positively impacting the firm’s leverage and\nenterprise value.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".