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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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