Impact of Adopting International Financial Reporting Standards: Empirical Evidence from Turkey
Bibliographic record
Abstract
In this study, the impact of adopting International Financial Reporting Standards (IFRSs) on listed companies in Turkey was examined. We observed the financial statements that were prepared in accordance with IFRS and local GAAP and researched the standards which included more relevant information. We worked on the financial statements of the companies in the Istanbul Stock Exchange (ISE) that operated in the manufacturing industry. In our findings, we determined that the financial statements prepared in accordance with local GAAP and IFRS were statistically different. Significant differences were identified in inventories, fixed asset, long term liability and stockholders’ equity accounts in the financial statements. In addition, current ratios, receivables turnover ratios, asset turnover ratios, total liabilities/tangible assets, fixed assets turnovers, equity turnover rates, short term liabilities/total debts and short term liabilities/total assets ratios based on IFRS financial statements were statistically and significantly distinguished from the stated ratios of local GAAP financial statements. We were unable to observe statistically significant differences in book value/market value ratio analysis depending on the market value under local GAAP and IFRS. However, in subsector analysis, we identified that some subsector groups have been affected from the transition to IFRS.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".