Asset Securitizations and Stock Price Crash Risk: Evidence from Nonfinancial Firms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it