The Impact of Liquidity on the Capital Structure: Evidence from Malaysia
Bibliographic record
Abstract
<p class="Content">For many years, liquidity of a company’s asset and its effect on the optimal debt level has been a controversial issue among scholars in finance studies. Prior studies have demonstrated that in some countries, asset liquidity increased debt level while in other countries liquid companies were less leveraged and more regularly financed by their own capital. This study investigates the effect of liquidity on the capital structure among the 300 listed companies in the Main market of Bursa Malaysia from 2005 to 2013 fiscal years. Pooled OLS is applied to investigate the impact of liquidity ratios on different Debt ratios. Liquidity of a company, which is the independent variable of this study, is measured by two common ratios which are: quick ratio and current ratio. Additionally, the Debt/Equity and Debt/Asset ratios represent the capital structures based on the short-term, long-term and total debt. The results show that all the measures of liquidity have significant impacts on all the proxies of leverage. According to the results, Quick ratio has a positive effect on leverage; although, Current ratio is negatively related to leverage. Moreover, short-term debt is more influenced by liquidity compared to long-term debt.</p>
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".