The Higher Moments of Future Earnings
Bibliographic record
Abstract
ABSTRACT We evaluate whether reported accounting numbers are informative about earnings uncertainty and whether earnings uncertainty is priced. We use quantile regressions to forecast the standard deviation, skewness, and kurtosis of future earnings. These three moments are important measures of earnings uncertainty because they reflect the size of the average deviation from expected earnings and the amount of extreme upside potential, extreme downside risk, or both. We develop a novel approach for evaluating the reliability of our forecasts and we show that they are reliable. We also document that: (1) equity prices are increasing (decreasing) in the standard deviation and skewness (kurtosis) of lead return on equity and (2) credit spreads are increasing (decreasing) in the standard deviation and kurtosis (skewness) of lead return on assets. Our results indicate that historical financial statements are informative about earnings uncertainty and that earnings uncertainty is priced. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: C21; C53; G17; M41.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".