Relative Levels of CEO Inside Debt and the Impact of Hedging on Shareholder Value
Bibliographic record
Abstract
This study examines the question of whether poorly diversified CEOs with high levels of inside debt engage the firm in costly hedging activity to reduce personal risk exposure at the expense of shareholder wealth. This study utilizes multifactor asset pricing regressions on the returns from self-financing portfolios of hedging firms that are long firms with high levels of CEO inside debt and short those with low levels. When these returns are value-weighted there is no evidence of significant abnormal returns, suggesting in aggregate hedging activity is not carried out at shareholder expense. Using equally-weighted returns that emphasize the typical smaller firms, however, result in significant negative abnormal returns, suggesting these firms lack the managerial sophistication and economies of scale to hedge efficiently, but still engage in costly hedging activity to mitigate CEOs’ personal risks at the expense of shareholders. In all models, high CEO debt firms are less risky than their counterparts, mostly in terms of market, size, and profitability risks as evidenced with significant factor loadings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".