Emotions and Managerial Judgment: Evidence from Sunshine Exposure
Bibliographic record
Abstract
ABSTRACT We examine the role and economic consequences of emotions in shaping the judgment of corporate executives. Analyzing a large sample of U.S. public firms, we find that sunshine-induced good mood leads managers to make upwardly biased earnings forecasts. Importantly, our evidence implies that managers become less susceptible to the sunshine priming effect in unambiguous settings, when their forecasts are subject to stricter external monitoring, and when they have stronger incentives to issue accurate forecasts. Additional tests show that equity market participants discern less informative signals from forecasts influenced by sunshine and that managers prone to the sunshine priming effect impose costs on their firms in the form of higher information risk and equity financing costs. Reflecting that labor markets also play a disciplinary role, we find that mood-prone managers suffer adverse career outcomes. We provide the first large-scale analysis on the nuanced ways in which emotions affect top executives. JEL Classifications: G02; G30; M40; M41.
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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 | 0.013 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".