Financial Situation Unique Indicator for Electric Sector Firms
Bibliographic record
Abstract
This paper develops a unique indicator to identify the financial situation of firms in the electric sector in Brazil. The National Electric Energy Agency (ANEEL) regulates this sector through five dimensions: indebtedness, efficiency, investment, profitability, and pay-out ratio. Each of these dimensions contains one or two indicators. Based on these indicators, we develop a unique indicator that shows companies' financial situation. To create a unique indicator, we follow the idea of Altman’s solvency indicator. But, we use a logit regression. Our dependent variable is Global Performance of Continuity which indicates the financial situation of the firm. Our independent variables are based on the five dimensions of the ANEEL indicators for financial situation. We collect data from 2011 to 2018. This research follows three main steps: (1) Collection of the data from the ANEEL database; (2) Creation of variables based on ANEEL’s five dimensions of performance; and (3) Econometric proceedings with variables according to ANEEL’s data and indicators of each dimension. First, we estimate one regression with all variables created based on ANEEL’s five dimensions. Then, we make improvements to find a more suitable model with different combinations of variables. We chose the best model by analysing the Akaike information criterion (AIC). Our results show that the unique indicator we create to evaluate firm performance is based on Debt, Efficiency, Investment (CapexA) and the Pay-out Ratio.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".