Bibliographic record
Abstract
Abstract Analysts in a bank's research department cover firms that have no relationship with the bank as well as companies in which the bank has a strategic interest. Officially, banks must establish Chinese Walls around their research departments to allow the analysts to work independently and to avoid the flow of insider information. We examine analyst behavior under long-term bank-firm relationships using ownership data and analysts' earnings per share forecasts for German companies from 1994 to 2001. We find evidence that is consistent with analysts reconciling their employers' interests with their own career concerns. They seem to use their information advantage strategically by releasing favorable and thereby more precise reports when the market underestimates earnings. In order not to jeopardize the bank-client relationship, they suppress negative information when the market is too optimistic. Combining situations where the market over- and underestimates earnings, we can replicate the unconditional positive bias in analyst forecasts found in the previous literature. Despite the bias in affiliated analysts' forecasts, they nonetheless selectively communicate valuable information to investors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.004 |
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".