Does religiosity enhance the quality of management earnings forecasts?
Bibliographic record
Abstract
Abstract This study investigates whether firms located in areas with higher levels of religiosity disclose higher‐quality management earnings forecasts than do other firms. Using a US sample of 4,655 firm‐year observations over the period 2001 to 2014, we find that firms headquartered in counties with higher proportions of religious adherents issue earnings forecasts that are less optimistically biased and that the effect of religiosity is concentrated in firms with weak monitoring mechanisms. We also find that religiosity mitigates pessimistic bias in management earnings forecasts, but only for those issued by firms operating in low litigation industries. This result suggests that when the litigation risk is high, both ethicality and risk aversion are at work and their competing effects likely offset each other. Additionally, we document that forecasts issued by firms in more religious areas trigger stronger stock price reactions than those issued by other firms and that the effect is limited to forecasts containing optimistic bias. Overall, our results show that religiosity enhances the quality of management earnings forecasts, but the effect varies based on different conditions.
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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 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".