The Impact of Nondisclosure of Geographic Segment Earnings on Earnings Predictability
Bibliographic record
Abstract
We address whether nondisclosure of geographic segment earnings after implementation of Statement of Financial Accounting Standards No. 131 (SFAS 131) has an impact on the earnings predictability of multinational companies. An understanding of how nondisclosure of accounting information affects the predictability of a firm's earnings will be of importance to financial statement users, managers, auditors, and standard setters. The quality of geographic disclosures is especially important as foreign operations represent a growing portion of many U.S. multinational companies and these operations can vary considerably on risk and return characteristics. Prior research has focused almost exclusively on issues involving line of business segment reporting after implementation of SFAS 131. However, SFAS 131 has noticeably affected geographic earnings disclosures. Firms that define their operating segments on any basis other than geographic area are no longer required to disclose geographic earnings. We find that nondisclosure of geographic earnings has no effect on analysts' forecast accuracy or dispersion. We conclude that the Financial Accounting Standards Board's decision to no longer require disclosure of geographic earnings for secondary segments has not hampered users' ability to predict earnings of U.S. multinational companies.
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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 | 0.005 | 0.068 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".