Climate Disasters and Analysts’ Earnings Forecasts: Evidence from the United States
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
We examine the relationship between climate disasters and analysts’ earnings forecasts in the United States. We find that climate disasters are associated with deteriorated analyst forecast properties proxied by forecast errors and forecast dispersion. We reason that the volatility of return on assets and of cash flows, and lower financial statement comparability, are three potential channels through which climate disasters influence analyst forecast properties. We also find that this relationship is more pronounced for firms in climate-vulnerable industries. Results from the market reaction tests further support our main findings by showing that the stock market responds less strongly to positive earnings surprises during periods of high climate disasters. Our results are robust to a battery of sensitivity tests, including a two-stage least squares approach and a difference-in-differences specification. Overall, the results shed light on the association between climate disasters and analysts’ earnings forecasts, which has significant implications for academics, investors, and standard setters.
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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 | 0.004 | 0.001 |
| 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 it