Does the Way Variables Are Calculated Change the Conclusions to Be Drawn? A Study Applied to the Ratio ROI (Return on Investment)
Bibliographic record
Abstract
This research aims to analyse the financial performance of companies using one of the most used profitability indicators, the return on investment (ROI), which measures the company’s performance in terms of the profit generated over time. To this end, several different methods are used to calculate the ROI indicator, considering the different calculation methods used by different authors over the years. The use of different ROI calculation formulas has been identified in the literature, leading to different conclusions. Based on a sample of 2805 Portuguese companies, it examines how the different indicators react to the different variables analysed, using nine different econometric models. Through this study, it is possible to verify that the different variables that depend on the return on investment have different results, namely that the variables “age” and “size” have a negative effect on the return on investment. On the other hand, “financial leverage” and “ROA” have a positive impact on the contribution to the return on investment. We also found that the different variables behave similarly for virtually all types of ROI calculation, although not completely harmonious, especially in terms of impact. The results are empirically vital, as they alert researchers and companies to the need for standardised formulas for calculating variables such as ROI so that results are not distorted. Using one to the detriment of the other impacts the results obtained and the analyses to be carried out. How empirical research will continue to use the ROI metric will always depend on its users’ discretion and free will.
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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 | 0.104 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".