Evaluating the Financial Performance of Colombian Companies: A Data Envelopment Analysis Without Explicit Inputs and Technique for Order Preference by Similarity to the Ideal Solution Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The evaluation and ranking of companies in any sector are generally based on a single measure of financial success, so the results obtained vary according to the classification criteria used. This study applies a multi-criteria approach to develop a classification of the largest companies in Colombia based on their financial results for the period 2022–2023. An analysis of 100 companies was conducted, utilizing four critical criteria: operating income, net profit, total assets, and equity. The evaluation followed a two-stage process. In the first stage, the weights or importance of each selected criterion were objectively established using data envelopment analysis without explicit inputs (DEA-WEIs). This approach reveals that operating income (35.23%) and total assets (28.57%) are the most influential criteria, while net profit is the least influential (13.51%). In the second stage, companies are ranked using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), with the results highlighting Refinería de Cartagena, Empresas Públicas de Medellín, and Terpel S.A. as the top-performing companies. The classification shows clear differentiation, forming two statistically distinct groups validated through discriminant analysis, achieving a 100% correct classification rate. These findings provide actionable insights for benchmarking and improving financial performance in the corporate sector.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it