Relating Company Size and Financial Performance in Agricultural Firms Listed in the Nairobi Securities Exchange in Kenya
Bibliographic record
Abstract
<p>Company/firm size is among the many variables that is significant in assessing the profitability of a company. Therefore, this paper seeks to evaluate the effect of company size on the financial performance of listed agricultural companies in Kenya. The theory of economies of scale that links benefits arising from company size, cost management and production volumes was utilized. Secondary data was extracted from the annual reports comprising of financial statement from the period 2003 to 2013 and analyzed using a pooled OLS model. Company size was measured using the total assets (Log of assets) while financial performance was measured by return on assets (ROA), return on equity (ROE) and earnings per share (EPS). The regression results present the goodness of fit for the regression between log total asset and ROA, ROE and EPS as 0.112, 0.113 and 0.074 respectively. The overall model of ROA, ROE and EPS was significant with F statistic of 9.334, 11.096 and 5.901 respectively. The relationship between log total asset and financial performance measures was positive and significant for ROA (b1= 0.033, p value, 0.003) and ROE (b1= 0.049, p value, 0.001) and. EPS (b1= 3.866, p value, 0.018). These results indicate that company size as measured by total assets affects financial performance of agricultural companies listed in NSE positively and significantly. Company size had positive and statistical significance on all the three indicators of the financial performance disclosing that large companies were found to have a competitive advantage over small firms.</p>
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".