Is the Demand for Corporate Insurance a Habit? Evidence from Directors' and Officers' Insurance
Bibliographic record
Abstract
Of the many fundamental questions left unanswered in finance, one relates to corporate risk management practices. It is still relatively unclear what are the reasons that motivate risk neutral corporations to manage their idiosyncratic risk. Our contention in this paper is that corporate insurance purchases are driven by habit rather than an optimal approach to corporate risk management. Because public access to corporate insurance purchases and risk management strategies is limited at best, we examine a particular aspect of the corporate demand for insurance for which public information is available: Directors' and Officers' (D&O) insurance. Information regarding D&O insurance purchases has been publicly available in Canada since 1993. Our results suggest that the decision to insure as well as the amount of coverage purchased (policy limit and deductible) are more driven by the previous year's decision than any other. We find that a corporation's fundamental financial and governance measures do not appear to have any impact on the decision to purchase insurance nor on the amount of insurance to purchase. Our results suggest that corporations may not choose optimally their risk management decisions; rather they may rely more on a force of habit than on a clear and concise strategy to manage corporate risk. As a result, and in contrast to Core (1997, 2000) and Chalmers et al. (2002), we find no evidence of managerial opportunism in regards to D&O insurance coverage. Une des grandes questions fondamentales qui demeurent en finance est pourquoi des firmes présumément neutres au risque achètent de l'assurance et gèrent leur risque. Notre hypothèse est que l'achat d'assurance est plus lié aux habitudes prises par les corporations qu'à une décision étudiée de gérer les risques. Étant donné la quasi-impossibilité d'obtenir des données publiques sur l'achat d'assurance des corporations et leur gestion de risques, nous examinons un aspect particulier de cette demande pour laquelle l'information existe dans le public, soit l'assurance de la responsabilité civile des administrateurs et des dirigeants. Cette information est disponible dans le public au Canada depuis 1993 seulement. Nos résultats suggèrent que la décision de s'assurer et le niveau de couverture semblent être déterminés uniquement par la décision de la firme à l'année précédente. Ainsi aucun facteur fondamental (santé financière ou gouvernance) de l'entreprise ne semble pouvoir expliquer la décision d'une entreprise de s'assurer ni la limite son type de couverture. Nous concluons que les corporations ne gèrent pas leur risque de manière optimale puisqu'elles semblent baser leur décision d'assurance davantage sur leurs habitudes que sur une stratégie claire et concise de gestion des risques d'entreprise. Par conséquent, et contrairement aux résultats obtenus par Core (1997, 2000) et Chalmers et al. (2002), nous ne trouvons aucune raison d'affirmer que l'achat d'assurance est lié à l'opportunisme des dirigeants.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".