Bibliographic record
Abstract
ABSTRACT In this paper we examine the time-series and cross-sectional volatility in analyst forecasts. We derive a bound on the degree of variation in forecasts, analogous to the variance bound literature in finance, and document the frequency and circumstances surrounding violations of this bound. We find that the time-series of individual forecasts are excessively volatile approximately 17 percent of the time, affecting up to 50 percent of the aggregate market value of stocks. We also find that the market-wide frequency of excessively volatile forecasts in a year is positively correlated with aggregate stock market volatility and market sentiment, and is negatively correlated with future aggregate stock returns. We find that the cross-section of analyst forecasts are excessively volatile approximately 8 percent of the time, and observe that excessively volatile forecasts are more common for larger firms. As a precursor to identifying the underlying causes and consequences of excessively volatile forecasts, we describe the time period characteristics, analyst characteristics, and firm characteristics that are associated with these events.
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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 | 0.006 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".