The Implications of Exclusion of Conservatism from Conceptual Framework for Financial Reporting
Bibliographic record
Abstract
Financial Accounting Standards Board (FASB) excluded conservatism from conceptual framework in 2010 to achieve accounting neutrality which is absence of bias from financial estimates. Conservatism has been a widely accepted practice rather a well-established theory, therefore, its exclusion raised tension among accounting researchers and practitioners. Watts (2003) predicted that attempt FASB to ban conservatism is likely to fail and produce unintended consequences. Implications of exclusion of conservatism are not examined yet. We analyze whether this ban reduces accounting conservatism and more importantly improves accounting neutrality. We contribute to accounting literature by showing implications of exclusion of conservatism and testing predictions of Watts (2003). We used modified Basu (1997) model to examine change in conservatism. We measured neutrality using “absence of bias” notion of its theoretical definition. Using parsimonious statistical techniques, we find that conservatism has declined after its exclusion from conceptual framework. Undesirably, accounting neutrality has also declined. Further analysis suggests that decline in neutrality is driven by a fraction of non-compliers. We attribute FASB’s failure in achieving neutrality to such reasons as managerial misinterpretation of exclusion of conservatism, lack of incentives to achieve neutrality, lack of understanding about neutrality, and lack of inclination towards neutrality.
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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 | 0.072 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| 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".