Is the Decline in the Value Relevance of Accounting Driven by Increased Conservatism
Bibliographic record
Abstract
This paper examines the association between conservatism and the value relevance of accounting information over the 1975 through 2004 period. We measure conservatism using approaches developed in Penman and Zhang, The Accounting Review 77:237–264, (2002) and Beaver and Ryan, Journal of Accounting Research 38:127–148, (2000) and value relevance using (1) adjusted R2 from regressions of price on earnings and book values, (2) adjusted R2 from regressions of returns on earnings and changes in earnings, and (3) returns earned by perfect foresight of earnings and book values. We find no evidence that firms with increasing conservatism exhibit greater declines in value relevance. Rather, we observe most significant declines in value relevance for firms where conservatism has not increased. When we adjust financial statements for the effects of conservatism, we find that the value relevance of adjusted numbers is generally lower and trends in value relevance unaffected. Based on these results, it is implausible that increasing conservatism drives the decline in value relevance.
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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 | 0.004 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".