Institutional dual holdings and expected crash risk: Evidence from mergers between lenders and equity holders
Bibliographic record
Abstract
Abstract Exploiting mergers between lenders and shareholders of the same firm as an exogenous shock to shareholder–creditor conflicts, we examine the causal effect of these conflicts on firms' ex ante expected stock price crash risk evident in the options implied volatility smirk. The decrease in conflicts of interest between lenders and shareholders induces dual holders to encourage the disclosure of more information to alleviate costly information asymmetry with other investors and better execute their oversight role in constraining managers' bad news suppression. Consistent with expectations, we find that a firm's ex ante expected crash risk declines after a shareholder–creditor merger. We also report strong, robust evidence that the negative impact of mergers on firms' expected crash risk increases when institutional investors or lenders have a greater stake in the treatment firms or when shareholder–creditor conflicts are apt to be exacerbated. Additionally, we document that firms issue management earnings forecasts (especially bad news forecasts) more frequently after these mergers. Finally, we find that expected crash risk decreases more after mergers in firms suffering worse information asymmetry and with weak monitoring mechanisms. Our evidence suggests that option market participants value the dual holder's role in deterring managers' bad news hoarding.
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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 | 0.023 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".