Does Religiosity Enhance the Quality of Management Earnings Forecasts
Bibliographic record
Abstract
This study investigates whether firms located in areas with higher levels of religiosity disclose higher-quality management earnings forecasts than other firms. Using a U.S. sample of 5,826 firm–year observations over the period 1997 to 2014, we find that firms located in more religious areas disclose less optimistically biased and more accurate earnings forecasts. We find no evidence of more conservatively biased forecasts for those firms. In addition, we document that earnings forecasts from firms in more religious areas trigger stronger stock price reactions than the forecasts from other firms. Overall, our results provide evidence that higher levels of religiosity contribute to more credible, trustworthy management earnings forecasts that are valued by investors. The positive association between religiosity and the quality of management forecasts suggests that between ethicality and risk aversion, two key traits of religious individuals, ethicality seems to play a major role in shaping managers’ disclosure behavior. To the best of our knowledge, this is the first study to establish a link between the degree of religiosity and the quality of management earnings forecasts, an important channel of corporate voluntary disclosure.
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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 | 0.000 |
| 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".