Principles-Based Accounting Standards, Earnings Management and Price Efficiency
Bibliographic record
Abstract
The issue of principles-based accounting standards has been attracting growing interest since the emergence of the International Financial Reporting Standards (IFRS) as a global phenomenon, and the United States consideration of IFRS adoption. This paper studies the effect of a move towards principles-based accounting standards on price efficiency in the equity market. I assume a move towards principles-based standards requires the firm’s manager to use more of his private, though more subjective, information for financial reporting. I model the manager’s reporting decision as a trade-off between increased compensation through earnings management and a cost associated with earnings management (such as litigation, SEC enforcement, and manipulation effort). I find that the effect of a move towards principles-based accounting standards on price efficiency is non-monotonic. When standards are highly rules-based, reducing the use of rules-based standards increases price efficiency. However, at some point, this relation reverses. The optimal mix of rules and principles reflects a trade-off between two types of effects on price efficiency: predictive ability and comparability. In addition, expected earnings management is non-monotonic in the use of rules-based standards. Finally, I find that rules intensity and managerial compensation incentives act as complements, such that higher managerial compensation incentives require more rules-based standards for price efficiency to be maximized.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 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".