Evaluating the financial performance of listed REIT firms in South Africa: A 7-step DuPont model technique
Bibliographic record
Abstract
This research paper presents empirical information about the use of the 7-step DuPont model in the context of publicly traded Real Estate Investment Trust (REIT) firms operating in South Africa. The DuPont model is a crucial paradigm for performance analysis, however, scholars have given its numerous parts varying ratings. As a result, it is now very necessary to examine it. The Generalized Least Square models (fixed effect and random effect) are used to evaluate the financial performance in a DuPont model context. The empirical study uses panel data from six REIT companies that are publicly traded on the Johannesburg Stock Exchange and spans the years 2005 through 2021. The findings of the study suggest a strong positive relationship between return on sales and return on equity. Additionally, a correlation was found, indicating a negative association between various operational items and return on equity. However, a notable positive correlation is shown between total asset turnover and return on equity. Contrary to the aforementioned findings, the fixed charge ratio, tax return, equity multiplier, as well as sales and management expenses, all reported an insignificant association with return on equity. The study avows that the seven-step DuPont model is the most appropriate model for assessing performance as it better explains the performance of listed REIT companies in South Africa. It may be inferred that the seven-step DuPont model provides a more comprehensive explanation of parameters associated with firms.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".