The Commitment to <scp>Income‐Decreasing</scp> Accounting Choices as a Credible Signal to Reducing Information Asymmetry: The Case of Asset Revaluations*
Bibliographic record
Abstract
ABSTRACT Bagnoli and Watts (2005) proposed that a manager could reduce information asymmetry by choosing an income‐decreasing accounting choice that signals the firm's relatively good future prospects. A limitation in testing this theory is that most income‐decreasing accounting choices over time reverse such that aggregated earnings would be the same, regardless of the choice. One income‐decreasing accounting choice that never reverses is the choice of upward asset revaluation, where the resulting gains are recognized through other comprehensive income and reduce future earnings by increasing future depreciation expense. In the United Kingdom, prior to FRS15, firms had the option to upwardly revalue on a one‐time basis. FRS15, and subsequently International Financial Reporting Standards, however, require those firms that upwardly revalue precommit to revalue on a consistent basis. This precommitment sacrifices future reporting discretion, which, according to the aforementioned study, serves as a costly signal of a firm's relatively good future prospects that reduces information asymmetry. The choice not to upwardly revalue, therefore, serves as a signal of a firm's relatively poor future prospects and also reduces information asymmetry, but this choice does not require precommitment such that the reduction in information asymmetry would be less than the choice to precommit to upward revaluations. Using a propensity‐score matched‐pair design on a sample of United Kingdom firms to test our predictions during the period requiring precommitment, we find lower forecast dispersion, lower return volatility, and a lower cost of capital for firms that precommit to upward asset revaluations, relative to those firms that choose not to upwardly revalue their operating assets. Keywords: upward asset revaluations, income‐decreasing accounting choice, information asymmetry, precommitment
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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 | 0.026 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".