Goodwill accounting and asymmetric timeliness of earnings
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate how fair value reporting and increased managerial discretion under the new goodwill accounting affect the asymmetric timeliness of earnings;, i.e. accounting conservatism. Design/methodology/approach Various empirical models are applied to a sample of 11,034 firms. To capture a cross‐sectional variation in asymmetric timeliness of earnings, Kahn and Watts' C_Score is adopted. Findings It is found that financial reporting for firms with purchased goodwill has become more conservative after the enactment of the new standard. However, once an increase in conservatism that is not attributable to new goodwill accounting is controlled for, it is found that accounting earnings for firms with purchased goodwill become less conservative. Research limitations/implications The results should be interpreted with caution, because the effect of concurrent events other than the adoption of SFAS 142 on reported earnings is not perfectly controlled. Practical implications The results of this paper support Watts' assertion that new goodwill accounting impairs accounting earnings' ability to reflect the economic earnings in a timely manner, but these results should be interpreted with caution, as the main objective of goodwill accounting is not to improve accounting conservatism. Originality/value This paper makes a timely contribution to the debate of fair value accounting by focusing on the impact of SFAS 142 on the asymmetric timeliness of earnings. By employing all available firms with purchased goodwill balances rather than relying on firms that report impairment losses, our research design better captures the impact of SFAS 142 on financial reporting.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".