Is There Safety in Numbers? The Effects of Forecast Accuracy and Forecast Boldness on Financial Analysts' Credibility with Investors*
Bibliographic record
Abstract
This paper reports the results of an experiment that examines how analyst forecast accuracy (i.e., how close an analyst's forecast is to realized earnings) and forecast boldness (i.e. how far the analyst's forecast is from the consensus forecast) affect the analyst's perceived credibility and investors' willingness to rely on and purchase the analyst's future reports. We hypothesize and find that forecast boldness magnifies the effect of forecast accuracy on these variables. That is, analysts who provide accurate, bold forecasts experience more positive consequences than those who provide accurate, non-bold forecasts, and analysts who provide inaccurate, bold forecasts experience more negative consequences than those who provide inaccurate, non-bold forecasts. We also find that these effects are not symmetric - the negative consequences of being bold and inaccurate exceed positive consequences of being bold and accurate. Our results are not sensitive to the level of the analyst's prior reputation.
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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 | 0.011 | 0.223 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".