The Allocational Effects of the Precision of Accounting Estimates
Bibliographic record
Abstract
ABSTRACT This paper studies the allocational effects associated with the precision of accounting estimates when the precision of estimates is a choice variable for firms. One part of the paper considers the effects of the observability of precision choices. We show that, generally, making precision choices private increases firms' equilibrium precision choices and also, as a by‐product, their equilibrium investment choices. We further show that, when firms' precision choices are private, there may be a “disclosure trap,” in which, unless investors conjecture the owner has chosen an estimate with the highest possible precision, the owner will respond to investors' conjecture by choosing an estimate whose precision is higher than investors' conjecture. In a multifirm version of the model with endogenous investment, we show that the equilibrium investment by the firm increases in the precision of the firm's own estimate and decreases in the precisions of other firms' estimates. Finally, we show that, in a setting where the firm's initial owner sells his stake in the firm over the course of two periods, with disclosures of estimates of the firm's value occurring prior to each sale of shares, if the precisions of the estimates are public, the equilibrium precisions of the estimates increase over time when the owner sells a sufficiently large fraction of the firm in the first period, and otherwise the equilibrium precisions of estimates remain constant over time.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Analytical model of the allocational effects of accounting estimate precision; accounting theory.
This theoretical paper studies accounting-estimate precision and firm allocation, not research itself.
Accounting/finance theory of estimate precision for capital allocation; not scientific research evaluation.
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 | 0.023 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".