Ratio analysis comparability between Chinese and Japanese firms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it