Credit Rating Agency and Equity Analysts’ Adjustments to <scp>GAAP</scp> Earnings
Bibliographic record
Abstract
Abstract Moody's analysts and sell‐side equity analysts adjust GAAP earnings as part of their research. We show that adjusted earnings definitions of Moody's analysts are significantly lower than those of equity analysts when companies exhibit higher downside risk, as measured by volatility in idiosyncratic stock returns, volatility in negative market returns, poor earnings, and loss status. Relative to the adjusted earnings definitions of equity analysts, adjusted earnings definitions of Moody's analysts better predict future bankruptcies, yet they fare significantly worse in predicting future earnings and operating cash flows. These findings persist after controlling for optimism incentives of analysts, reporting incentives of companies, credit rating levels, and industry and year effects. Our findings suggest that credit rating agencies cater to their clients’ demand for a more conservative interpretation of company‐reported performance than what is offered by equity analysts.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".