Analyzing the effect of using international accounting standards on the development of emerging capital markets
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze the consequences of using international accounting standards (IAS/IFRS) for the development of capital markets located in developing countries (emerging capital markets). Design/methodology/approach The authors conduct an empirical study using a sample of 38 developing countries with capital markets, starting by comparing the means of the different measures studied before and after the use of IAS/IFRS. A multivariate statistical analysis is conducted based on the estimation of a model of panel data with fixed effects. Findings The results show that the development of the emerging capital markets is positively and significantly associated with the use of international accounting standards. Practical implications The paper's findings are of interest to several different parties, primarily the national accounting standardization body, the IASB, many international organizations and international investors. Originality/value The paper describes an empirical study, conducted on a group of developing countries, which provides a better understanding of the potential consequences of the use of IASB standards. The paper is also a meaningful contribution to the international accounting literature, as it examines an interesting subject that has not yet been investigated.
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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 | 0.006 | 0.029 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".