Liquidity Analysis Using Cash Flow Ratios as Compared to Traditional Ratios in the Pharmaceutical Sector in Jordan
Bibliographic record
Abstract
The purpose of this study is to examine the liquidity position of the Jordanian pharmaceutical sector using the traditional ratios as compared to the more recently developed cash flow ratios.The research involved the comparison between traditional ratios and cash flow ratios of the big seven companies of the pharmaceutical industry in Jordan over six years period (2007–2012). The companies were all from the same sector, and the data was obtained from the annual reports of these companies.The findings of the study revealed the following:- There are differences between the traditional ratios which relied heavily on the values derived from balance sheet, and cash flow ratios which relied heavily on values derived from statement of cash flows.- A conclusion on the liquidity of the company based only on traditional ratios could lead to incorrect decisions.- Analysis based on traditional ratios should be compared with cash flow ratios before reaching any conclusion regarding financial liquidity position.- The study showed that there were examples of companies that had good traditional ratios. While their cash flow ratios were weak. In contrast, there were also companies that had poor traditional ratios, but the cash flow ratios showed a better liquidity position. The cash flow ratios provide more information than traditional ratios in measuring the liquidity position of the company. As a result of testing the study hypotheses, and applying SPSS, the significant differences between the cash flow ratios and traditional ratios are determined to measure the liquidity of the Jordanian pharmaceutical Companies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".