How Has Regulation FD Affected the Operations of Financial Analysts?*
Bibliographic record
Abstract
Abstract In this paper, we analyze how financial analysts generate information, make decisions about firm coverage, and try to maintain their forecasting accuracy after the passage of Regulation Fair Disclosure (“Reg FD”). Using the model developed by Barron, Kim, Lim, and Stevens 1998, we find that analysts are investing more effort in idiosyncratic information discovery. In order to do this, individual analysts appear to be reducing coverage for well‐followed firms while increasing coverage of firms that were less followed prior to Reg FD. Analysts who had preferential links with firms that they covered, such as analysts from large brokerage houses, tend to have greater forecast accuracy in the pre‐FD period. However, these analysts are unable to sustain their forecasting superiority in the post‐FD period, which suggests that there has been a leveling of the information playing field among analysts. Overall, our results reflect a trend toward greater reliance on idiosyncratic information discovery on part of the financial analysts.
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How this classification was reachedexpand
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".