Unveiling the quantitative impact of capital structure on firm value: A study of manufacturers of food, produce companies in South Africa
Bibliographic record
Abstract
This study examines the impact of capital structure on firm value within the food manufacturing sector of South Africa, addressing a critical gap in the literature on emerging markets. Using a balanced panel dataset of eight listed firms from 2007 to 2018, the research utilizes panel regression models—Common Effect (CEM), Fixed Effect (FEM), and Random Effect (REM)—with the Hausman test indicating REM as the optimal choice. Key findings demonstrate that profitability (RA), debt-to-equity ratio (DE), and firm size (FS) significantly enhance stock prices at a 1% significance level. In contrast, liquidity (CR) negatively affects stock prices (10% significance), while asset growth (AG) shows no significant impact. These results challenge traditional capital structure theories, emphasizing that South African firms strategically use debt for tax advantages despite market volatility, a stark contrast to developed economies where liquidity is typically prioritized. The study highlights the contextual significance of macroeconomic factors, such as energy shortages and regulatory policies (e.g., Black Economic Empowerment), in influencing financing decisions. By bridging the gap between classical theories and emerging market dynamics, this research provides actionable insights for policymakers to encourage sustainable capital structures, for investors to reconsider the role of liquidity in volatile environments, and for the government to develop better policies to support businesses. This research is novel; it is among the first to investigate the link between firm value and capital structure specifically for food manufacturing companies in South Africa over 12 years. It is distinctive because it frames capital structure choices within the unique industrial and economic environment of South Africa, contributing a framework for optimizing firm value in similar emerging markets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".