The Impact of Liquidity and Leverage on the Financial Performance of the Johannesburg Stock Exchange-Listed Consumer Goods Firms
Bibliographic record
Abstract
Understanding the role of liquidity and leverage is crucial in assessing financial performance, particularly in the consumer goods sector. This study examined the impact of liquidity and leverage on financial performance, using a sample of 13 consumer goods firms listed on the Johannesburg Stock Exchange (JSE) from 2014 to 2024. Despite the present literature on this association, few traceable studies have investigated this phenomenon, and there is a dearth of literature in this sector. The dependent variable for this study was financial performance, and the return on assets (ROA) was employed as a proxy for financial performance. The independent variables employed for this study were liquidity (LIQ), leverage (LEV), and the quadratic term of leverage (LEV2). However, Net profit margin (NPM), inventory turnover (INVT), average collection period (ACP), firm size (FS), and its quadratic term (FS2) were the control variables. The researcher performed the Durbin–Wu–Hausman test, the Breusch–Pagan LM test, redundant fixed effect testing, the Hausman test, and the panel heteroskedasticity LR test before employing the suitable model. After employing the panel least squares (PLS), the fixed effects (FE) model was considered appropriate and efficient for this study. Applying the model, the researcher found a statistically significant and positive impact of LIQ, LEV2, NPM, and FS2 on ROA. Furthermore, a statistically significant and negative impact of ACP on ROA. However, the impact of LEV and FS was negative and statistically insignificant on ROA. Furthermore, the impact of INVT on ROA was statistically positive and insignificant. To improve the financial performance of the firms efficiently, this study recommends that financial managers of consumer goods firms should pay special attention to maintaining and monitoring a healthy liquidity ratio and implement sound working capital management. Furthermore, integrate the strategic liquidity planning into their financial decision-making. The findings highlight that while a moderate level of leverage might not increase financial performance, a strategic increase in debt to a certain optimal level can improve the financial performance of a firm.
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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 | 0.002 | 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".