CEO partisan bias and management earnings forecast bias
Bibliographic record
Abstract
Abstract Research concludes that managers’ political orientation influences their decision-making and offers the political connections and risk tolerance hypotheses as explanations. We investigate partisan bias as an additional way political orientation may influence managers’ decisions. Partisan bias results in individuals whose partisan orientation aligns with that of the US president expressing more optimistic economic expectations. We examine whether partisan bias is present in managers’ annual earnings forecasts. We find that firms with CEOs whose partisanship aligns with that of the US president issue more optimistically biased annual earnings forecasts than firms with other CEOs. Higher-ability CEOs, however, are less susceptible to partisan bias. Additionally, we find that overestimating customer demand contributes to the forecast over-optimism of partisan-aligned CEOs and results in greater firm overinvestment. Furthermore, investors fail to discount the news in forecasts of partisan-aligned CEOs, and their firms’ post-forecast abnormal returns are lower.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".