Bibliographic record
Abstract
We examine whether or not conditional conservatism is observed at the aggregate level. Using both the Basu (1997) model and the Ball and Shivakumar (2006) models, we find some evidence consistent with conditional accounting conservatism at the aggregate level. Our results show that the interactive slope coefficient measuring the difference in sensitivity for aggregate accounting earnings is approximately three times as sensitive to negative returns as it is positive returns. Our results also demonstrate that the inclusion of macroeconomic indicators and discount rate variables to the conditional conservatism models improves model specification significantly. For example, when equal-weighted return is used as a gain or loss proxy the inclusion of macroeconomic indicators and discount rate proxies into the regression model increases the adjusted R 2 from 5% to 53%. Based on empirical evidence from this study we recommend that researchers who study the relation between aggregate accounting earnings and the fundamental characteristics of accounting information use both macroeconomic indicators and discount rate variables as explanatory variables in regression models.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".