The Effect of Cash Holdings on Financial Performance: Evidence from Middle Eastern and North African Countries
Bibliographic record
Abstract
This work aimed to examine the effect of corporate cash holdings on financial performance. The data covered 536 non-financial firms for the 2006–2020 period from 11 MENA region countries. This study used fixed- and random-effects testing models. To the best of the authors’ knowledge, this is the first study that aimed to study the effect of corporate cash holdings on financial performance in MENA countries in two aspects: linear and non-linear relationships. By using the return on assets, return on equity, earnings before interest, and the tax margin as the indicators of financial performance, we developed two groups of models investigating the linear and non-linear relationships between cash holdings and profitability measures. The models included several control variables, namely leverage, firm size, sales growth rate, tangibility, dividend pay-out ratio, and gross domestic product (GDP) growth rate. The results of this study revealed that both the linear and non-linear models produced significant results for the return on assets and the return on equity, but for the earnings before interest and tax margins, the linear model was insignificant. The non-linear models indicated an optimal level of cash holdings. In this context, the policymakers must actively evaluate these policies, such as working capital management and its effect on financial performance. In addition, the policymakers must consider macroeconomic conditions when designing corporate cash-holding policies.
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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 | 0.004 |
| 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 | 0.000 |
| 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".