Mutual Forbearance and Competition Among Security Analysts
Bibliographic record
Abstract
Research in industrial organization and strategic management has shown that rivals competing with each other in multiple markets are more willing to show each other mutual forbearance, i.e., compete less aggressively, within their spheres of influence, i.e., the markets in which each firm dominates. Sell-side equity analysts typically cover multiple stocks in common with their rivals. We examine the impact of this “multipoint contact” for mutual forbearance on two key dimensions of competition among security analysts: forecast accuracy and information leadership (issuing earnings forecasts or stock recommendations that influence rival analysts). We find that multipoint contact is associated with analysts exerting greater information leadership on stocks within their own spheres of influence. We also find greater forbearance related to information leadership under Regulation Fair Disclosure (Reg FD). In contrast, multipoint contact was not associated with greater forecast accuracy on stocks within analysts’ spheres of influence, either before or under Reg FD. Our analysis is among the first to consider mechanisms of competition among securities analysts and also adds to the literature on Reg FD by demonstrating that the increased workload imposed on analysts after Reg FD fostered mutual forbearance as a response. This paper was accepted by Mary Barth, accounting.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".