Political incentives and analyst bias: Evidence from China
Bibliographic record
Abstract
Abstract This study extends extant research on the determinants of financial analyst bias by examining the role that political incentives play. Using a series of scheduled provincial political events in China, we document that analysts are significantly more likely to issue favorable recommendations or revise their recommendations upward during political event periods, and the effect of political events on optimism is larger for analysts employed by brokerage firms affiliated with politicians. Cross‐sectional evidence suggests that the impact of political events on analyst optimism is concentrated in those provinces where capital market development is a more important performance indicator for politicians or where the incumbent politicians face a pending promotion. Stock return analyses reveal that favorable recommendations issued during political event periods are significantly less profitable in the long run and are less credible according to investor perceptions. Reinforcing our main evidence, we also find that financial analysts are more likely to issue optimistic earnings forecasts during political event periods. Collectively, our results imply that political incentives distort analyst opinions and political‐economic factors affect the corporate information environment in China.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".