Liquidity, Growth and Profitability of Non-financial Public Listed Malaysia: A Malaysian Evidence
Bibliographic record
Abstract
This study examines the relationship between liquidity, growth and profitability of non-financial firms listed on the Bursa Malaysia. Specifically, this study examines the relationship between liquidity and growth on profitability for 50 non-financial public listed firms in Malaysia. Using panel data technique on 250 observations across a five-year period, this study shows that liquidity has a strong positive relationship with profitability in terms of return on asset of the firms. However, liquidity in terms of quick ratio has no impact on profitability. This study also shows that firm growth in terms of sales growth has a negative relationship with profitability. However, this study shows that liquidity and growth in general do not influence profitability in terms of return on equity, although the result shows that sustainable growth rate has a positive relationship on profitability. This study highlights the importance of these measures in measuring performance. The findings in this study provide guidelines to the firms on the measures that best to be used in evaluating performance so that appropriate strategies can be adopted to increase performance.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".