Geographic Peer Effects in Management Earnings Forecasts*
Bibliographic record
Abstract
ABSTRACT Because of clear economic links among industry peers, prior work has focused on documenting industry peer effects in various settings. Yet, while links also exist among firms in the same geographic area, few studies document geographic peer effects. We fill this gap by examining whether there are geographic peer effects in management earnings forecasts. We find that the likelihood that a firm voluntarily provides an earnings forecast is sensitive to the extent to which other firms in the same geographic area provide earnings forecasts. This geographic peer effect in forecasting is stronger for firms with greater exposure to local institutional investors, and when firms do forecast, liquidity improves more when a larger fraction of their geographic peers forecast. Furthermore, we use instrumental variable techniques to help alleviate the concern that these geographic peer effects are driven by omitted local economic factors that can lead firms to make similar disclosure decisions. Overall, our findings suggest that geographic peer effects in disclosure choices arise in part due to firms responding to capital market incentives created by local investors. Our study, therefore, contributes to the literature by documenting a unique dimension of forecasting decisions.
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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 | 0.003 | 0.041 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".