Distress Risk Puzzle and Analyst Forecast Optimism
Bibliographic record
Abstract
A general consensus in the literature is that financial analysts make optimistic forecasts. That is, they tend to underreact to negative but overreact to positive information. In this study, we invoke this idea to provide an explanation for the distress risk puzzle, the phenomenon that high distress risk firms deliver anomalously low subsequent returns. We find that analysts underestimate the implication of the poor performance of higher distress risk firms, and thus make EPS and sales forecasts that are generally more optimistic than those for the lower distress risk firms. Because market respond to the analyst forecasts, investors initially overvalue the high distress risk firms; later on, when those firms report less than expected performance, analysts revise their forecasts downwards that in turn cause the high distress risk firms to earn low future returns composing of both immediate-forecast-revision responses and post-forecast-revision price drifts. We further document that (quarter) earnings announcements convey a substantial amount of information that roughly drives more than 60% of the analyst forecast revisions and 30% of the revision-related market responses.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".