Global Financial Crisis, Working Capital Management, and Firm Performance: Evidence From an Islamic Market Index
Bibliographic record
Abstract
This research investigates the impact of working capital management (WCM) on the profitability and market performance of firms that constitute an Islamic market index (Karachi Meezan Index [KMI-30]) in Pakistan during 2002–2013. The data have been divided into three parts, that is, preglobal (2002–2007), during (2007–2008), and postglobal financial crisis period (2008–2013), to examine the proposed relationship in different macroeconomic settings. Net trade cycle (NTC) and its components are used to measure the WCM efficiency, while NTC square is used to proxy the impact of excessive holdings of working capital on corporate performance. The econometric models are calculated in a generalized method of moments (GMMs)-based regression environment to ensure the robustness of empirical outcome. The results reveal that, as opposed to conventional businesses, KMI-30 firms are more ethical in their short-term financial management. Besides, such firms adopted a conservative WCM policy during the global financial crisis of 2007–2008. Furthermore, we confirm the presence of a concave relationship between working capital levels and firm performance as NTC is positively, whereas NTC square is negatively, related to firm performance. This article makes a significant contribution to the extant literature as it evaluates the impact of WCM on the profitability and market performance of Islamic market indexed firms under varying macroeconomic conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".