Accounting Standards and International Portfolio Holdings
Bibliographic record
Abstract
ABSTRACT Do differences in countries' accounting standards affect global investment decisions? We explore this question by examining how accounting distance, the difference in the accounting standards used in the investor's and the investee's countries, affects the asset allocation decisions of global mutual funds. We find that investors tend to underweight investees with greater accounting distance. Using the mandatory adoption of International Financial Reporting Standards (IFRS) as an event that changed the accounting standards of various country-pairs, we examine how two sources of changes in accounting distance—(1) changes due to IFRS adoption of the investee, and (2) changes due to IFRS adoption in the investor's country—affect global portfolio allocation decisions. We find that the tendency to underinvest in investees with greater accounting distance significantly weakens when accounting distance is reduced, either from an investee's IFRS adoption or from IFRS adoption in the investor's country. The latter finding holds despite the fact that IFRS adoption in the investor's country had no impact on the accounting standards under which the investee firms present their financial information; the only change is in the investor's familiarity with these standards. This suggests that differences in accounting standards affect investor demand by imposing greater information-processing costs on those less familiar with the reporting standards.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".