Toronto M5S 3E6 AN EMPIRICAL STUDY OF RISK PREMIUMS ON CANADIAN
Bibliographic record
Abstract
This paper estimates the cost of equity capital for a sample of Canadian telcos using annual data from 1966-1995. The novelty of the research is that it analyses results for a regulated sector, similar to US studies, but from a different institutional environment, using different techniques. The different institutional factors are the differential taxation of investment income in Canada and portfolio restrictions designed to keep equity capital in Canada. Together these factors serve to lower the cost of equity capital relative to the yield on long term Canada bonds. As a result the utility risk premium is lower in Canada than the US. The different estimating techniques involve a components of growth application of the Gordon model and a market to book model that allows the cost of equity to be inferred from market price behaviour. The contribution to the literature is a further extension of the use of the Gordon model for estimating the cost of equity capital and a deeper insight into the role played by institutional factors. Two significant new results are the superiority of estimating a risk premium over similarly taxed instruments, such as preferred shares in Canada, and the structural change in Canadian utility risk premiums that occurred after the dramatic 1983 increase in real interest rates. See Gordon [1962] for the clearest exposition of his model
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".