The Conservatism Principle and the Asymmetric Timeliness of Earnings: An Event‐Based Approach*
Bibliographic record
Abstract
Abstract We test the asymmetric timeliness hypothesis by using information in extreme events as a measure of good/bad news. Our focus on extreme events is motivated by two arguments. First, the accounting concept of materiality in conjunction with litigation risk influences managers and auditors to make more conservative choices with respect to material events. Second, focusing on extreme shocks minimizes the probability that accounting slack may obscure the effect of asymmetric timeliness (Beaver and Ryan 2005). We identify individual events using short‐window extreme returns, since long‐window returns would aggregate the effect of multiple events and thus limit our ability to detect the asymmetry. Taken together, these features of our research design provide a more powerful test of asymmetric timeliness. Consistent with prior studies, we document that the correlation between bad news and concurrent earnings is significantly higher than that between good news and concurrent earnings. Our analysis of extreme events also enables us to document higher correlation of good news with earnings two or more quarters ahead. This is in contrast to prior studies that were unable to document asymmetry in the relation between returns and subsequent earnings in the opposite direction to that between returns and concurrent earnings. Our paper contributes to the growing literature on conservatism by modifying the Basu methodology to enhance the power of the test of asymmetric timeliness.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".