Social Control Agents’ Effectiveness in Capital Markets: The Role of Analysts’ Political Ideology
Bibliographic record
Abstract
Scholars have highlighted that social control agents (SCAs) can cause significant economic penalties when targeting a firm in capital markets. However, the effectiveness of such actions by SCAs is not always uniform. In this study, we focus on the micro-foundations of this heterogenous effect by studying the role of a key intermediary in capital markets, namely financial analysts. Our paper explores the effect of SCA actions on analysts’ recommendations, highlighting that analysts’ theoretical ideology plays an important role in effectuating SCA actions in capital markets. Using analysts’ recommendations for U.S. defense firms between 1998 and 2017 and a novel hand-collected database of analysts’ political ideology, we find that liberal-leaning analysts are more likely to issue sell recommendations for target firms, particularly when the SCAs’ criticism is directed to actions which are distant from the core business of the focal firms.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".