Bibliographic record
Abstract
ABSTRACT We examine the impact of Seasonal Affective Disorder (SAD) on financial analysts. We hypothesize and find that analysts are more pessimistic, less precise, and more asymmetric in their boldness in the fall, as indicated by their forecasts of quarterly earnings. The effects are apparent in all forecast horizons analyzed and robust across multiple specifications. Importantly, pessimism in fall forecast revisions shows analyst-specific persistence, providing a strong indication that the effect is a result of SAD rather than other coincident factors. We also find evidence of a reversal in pessimism in the spring. Additional analyses show that analyst forecasts exhibit less seasonality than equity returns, and that the presence of analyst forecasts in the fall is associated with attenuation in the seasonal pattern in stock returns. Overall, the evidence suggests that SAD affects both financial analysts and equity investors, but the effect on the latter is stronger. JEL Classifications: G11; G12; G14; G41; M41. Data Availability: Data are available from public sources cited in the text.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".