Asset Securitizations and Stock Price Crash Risk: Evidence from Nonfinancial Firms
Bibliographic record
Abstract
SYNOPSIS This study examines the relation between asset-backed securitizations and future stock price crash risk in nonfinancial firms. We argue that the gain-on-sale accounting treatment for off-balance-sheet securitizations facilitates managers’ withholding of bad earnings news, leading to higher crash risk. Using a propensity score-matched sample of U.S. nonfinancial firms, we find that firms engaging in off-balance-sheet securitizations are associated with higher crash risk, especially for firms with gain on sales from securitizations. In 2010, the Financial Accounting Standards Board implemented SFAS 166/167 to tighten the criteria for securitization transactions to receive off-balance-sheet treatment. However, our difference-in-differences analysis shows no significant effect of SFAS 166/167 on reducing securitizing firms’ crash risk. Further analyses reveal that firms engaging in off-balance-sheet securitization before SFAS 166/167 conduct more real activity-based earnings management after SFAS 166/167. This evidence suggests that firms could continue to hide bad news through alternative channels as substitutes. Data availability: Data are available from the sources described in the paper.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".