Key components of working capital management: Investment performance in Malaysia
Bibliographic record
Abstract
This study attempts to examine the role of working capital management components on four commons which are distinctive dimensions of business investment performance in Malaysia. The analysis covers 431 listed companies for the period 2000-2017 post the Asian financial crisis. The four performance indicators are return on assets (to proxy book return on overall business assets), return on equity (to proxy book return on shareholders' fund), Tobin's Q (to proxy firm valuation) and stock performance (to proxy real shareholder wealth). Our results indicate that working capital components of receivables collection period, inventory conversion period, payables deferral period, overall cash conversion cycle, current ratio, quick ratio, and cash ratio have generally exhibited important relationships with investment performance before and after the 2007-2008 subprime crisis. We would like to highlight the very robust negative effect of receivables collection period and cash conversion cycle. In addition, it is worth noting the distinctive roles of cash conversion cycle components and working capital liquidity ratios. While overly high liquidity position is usually viewed as inefficiency and detrimental for profitability, our panel data analysis consistently show that a high liquid position is favourable if the impact of cash conversion cycle is well considered. Hence, it is crucial for managers to prioritize the importance of working capital requirements to enhance the value of investors.
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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".