Financial Distress Premium or Discount? Some New Evidence
Bibliographic record
Abstract
This study investigates the contradiction in the finding of a positive distress risk premium in Vassalou and Xing’s study and the finding of a negative distress risk premium, i.e., a distress risk discount, in several other studies. Using the default likelihood measure calculated following Vassalou and Xing’s procedure for 1965–2023, we show that excluding outliers and including the time period beyond the end of Vassalou and Xing’s sample period in 1999 makes a difference in the results. Overall, using portfolio sorting and Fama-MacBeth regressions, this study supports the existence of a distress risk discount. This study also documents that the financial distress risk is negatively reflected in security prices even after accounting for size and book-to-market risk factors. Furthermore, it demonstrates that the negative distress risk premium is strong and persistent across economic expansions, recessions, and the COVID-19 pandemic.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".