International GAAP Differences: The Impact on Foreign Analysts
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the relation between differences in accounting standards across countries and foreign analyst following and forecast accuracy. We develop two measures of differences in generally accepted accounting principles (GAAP) for 1,176 country-pairs. We then examine the impact of these measures of accounting differences on foreign analysts. In so doing, we utilize a unique database that identifies the location of financial analysts around the world, creating a sample that covers 6,888 foreign analysts making a total of 43,968 forecasts for 6,169 firms from 49 countries during 1998–2004. We find that the extent to which GAAP differs between two countries is negatively related to both foreign analyst following and forecast accuracy. Our results suggest that GAAP differences are associated with economic costs for financial analysts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it