Empirical Estimation of the Cost of Equity: An Application to Selected Brazilian Utilities Companies
Bibliographic record
Abstract
We provide an extensive set of alternative models for the estimation of the real cost of equity in a sample of utilities firms in Brazil with monthly data from March 2006 to June 2011. The traditional CAPM is rejected, together with the Fama-French factors, due to a poor fit. Additional factors improve the fit of the models and the estimated betas and real cost of equity increase relative to the traditional CAPM and Fama-French models. Accounting for conditional heteroskedasticity shows that autocorrelation of variances is more important than news effects. The inclusion of higher order terms shows that the third order term is mostly significant and positive indicating preference for skewness in this sample period. Our estimates of betas and the implied predicted real cost of equity show that, across the best models, betas are significantly below unity in the range 0.26-0.73. The predicted real cost of equity, across the best models, for Brazil in this sector and sample period is in the range of 8.7% to 13.2% per year.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".