Improving Financial Reports by Revealing the Accuracy of Prior Estimates*
Bibliographic record
Abstract
Abstract Several researchers (e.g., Lundholm 1999; Ryan 1997; Petroni, Ryan, and Wahlen 2000) have proposed a reporting mechanism to enhance the reliability of estimates and other forward‐looking information in financial reports. Their proposals require companies to report reconciliations of prior‐year estimates to actual realizations as supplemental information in their financial reports. Such disclosures would enable investors to distinguish between accurate and opportunistic reporting behavior, and, arguably, should create incentives for companies to estimate accurately in the first place. Our study provides evidence on these proposals. Specifically, we conduct two experiments within the context of an important intangible asset requiring estimation ‐ software development costs. Our results show that the proposed reporting mechanism is effective in communicating information about the accuracy of financial estimates. We find, however, that not all disclosures are equally useful. The most effective disclosures explicitly describe the implications of misestimation (if any) on both the balance sheet and on earnings, thereby reducing the computational complexity associated with less explicit disclosures. Furthermore, our results show that when the disclosures explicitly describe the implications of misestimation, investors reward accurate estimators but do not explicitly punish those who are inaccurate. We conclude that information about previous estimate accuracy is useful to investors and that regulators should consider the type of disclosure, because not all disclosures may be equally effective in creating management incentives for accurate estimation. Moreover, the competitive advantage conferred on firms that provide accurate estimates arguably should create incentives for all companies to estimate accurately in the future.
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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 | 0.045 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".