Ratio analysis comparability between Chinese and Japanese firms
Bibliographic record
Abstract
Purpose Firms in different countries operate in different business environments and prepare financial statements following, by necessity, their own countries' accounting standards. Benchmarks for assessing financial ratios of firms in different countries are likely to be different. In conducting financial ratio analyses, each country's unique cultural, business, financial, and regulatory characteristics have to be taken into consideration, for these external factors may exert significant effects on measurements of financial data. This study aims to investigate challenges in comparing financial ratios between Japanese firms and Chinese firms. Design/methodology/approach This study compares ten major financial ratios of 75 Chinese firms with financial ratios of 75 matched sample Japanese firms to determine if a common benchmark for each of the financial ratios can be applied to firms in both countries. Findings The results show significant differences in liquidity, solvency, and activity ratios between firms from these two countries. Further examination of differences in accounting standards, economic, and institutional environments between these two countries suggests that these external factors have significant effects on financial ratios and may have contributed to the observed differences. Originality/value This study is among the first to investigate the comparability of ratios between Japanese firms and Chinese firms to uncover potential challenges and warn investors of such challenges.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".