Impact of IFRS on the Financial Statements of Select IT Companies in India
Bibliographic record
Abstract
Globalization of economies and shift in financial environment from the traditional bank based one to a market based one necessitated a uniform financial reporting language across countries to facilitate comparisons. This resulted in the establishment of International Accounting Standard Board (IASB) which issued International Financial Reporting Standards (IFRS), a global standard for company financial statements. More than 120 countries, including European Union, Australia, Canada have already adopted IFRS. India was expected to converge with IFRS from April 2016 for listed and unlisted companies with a net worth of more than ` 500 crores. However, few Indian companies listed internationally are voluntarily reporting IFRS. The present study aimed to understand the effect of this voluntary reporting of IFRS on key financial ratios of four selected IT sector companies. The study compared 12 major financial ratios under IFRS and Indian Generally Accepted Accounting Principles (IGAAP) as reported in their financial statements for a period of 5years from 2009-10 to 2013-14. For the purpose of the study, financial ratios representing four key dimensions of companies namely liquidity, leverage, profitability, and efficiency were considered. To understand the statistical significance of the difference between the ratios, Wilcoxon signed rank test, a non parametric test was used. Of the 12 ratios analyzed, 10 were found to be statistically significant. Further, the study explained the financial statement items which cause the difference in the ratios of these companies. The results indicated current liability and shareholder's equity to be significant at the 10% level, thus explaining the difference in financial statement items under 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".