An Analytical and Empirical Measure of the Degree of Conditional Conservatism
Bibliographic record
Abstract
The Vuolteenaho (2002) return decomposition is linear because it assumes that the market's return expectations are obtained solely from accounting information. By restricting accounting recognition rules to specific (and primarily) negative future cash flow shocks, conservative accounting drives a wedge between the market's return expectations that are based upon all positive and negative cash flow shocks and return expectations that are based solely on accounting numbers. This insight allow us to derive analytically a nonlinear relation between revisions to returns and earnings news for conservative firms, of which the Basu relation is a special case. This nonlinear relation is shown to be mathematically equivalent to two linear relations conditioned on the firm's degree of conservatism. From these relations, we derive a model-based measure of the degree of conservatism at the firm-year level which is a function of the determinants of conditional conservatism. To account for the endogeneity of the firm's degree of conservatism and mitigate potential sample selection bias (Dietrich, Muller and Reidl 2007), the model is implemented empirically using a switching regression approach in which the switch point, namely, the degree of conservatism, is both unobservable and endogenously determined. Consistent estimates of the parameters of the switching regression, including the endogenous determinants of conservatism posited by Watts, are obtained by simultaneous maximum likelihood estimation. The results indicate that the degree of conservatism is a positive function of contractual information asymmetry and litigation risk but a negative function of taxes.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".