Risk-adjusted performance of the utilities industry in the United States and Canada
Bibliographic record
Abstract
This paper examines the risk-adjusted performance of the utilities industry in both the United States and Canada from 1970 to 2001 using five measures of risk-adjusted performance. Risk-adjusted performance is analyzed using the Sharpe ratio, the Jensen Alpha, the M2 , the Fama-French three-factor model, and a conditional CAPM model adjusted on market movements. We analyze the effect of deregulation on the industry and test whether the geographic location or the SIC classification are indicative of superior performance. We also analyze the volatility of the market, subsectors, and firms within the utilities industry to determine the volatility patterns of stock returns in the industry. The utilities industry in both the US and Canada have outperformed their respective markets on a risk-adjusted basis for the full period. Deregulation had a positive effect on the performance of US utilities, however, deregulation did not affect the performance of Canadian utilities. Geographic location does not provide an indication of superior performance within the US utilities industry, however, according to the SIC classification, gas companies clearly outperform other subsectors in the industry whereas water companies have the lowest performance. In the Canadian market, eastern companies have the highest performance, followed by western companies, then the central companies. According to the SIC classification, the Canadian electricity subsector shows signs of higher performance relative to the gas subsector and the other subsectors of the industry. Volatility analysis shows no trend in the volatility of the US and Canadian markets. In both countries, subsector volatility is higher than the market volatility, however, firm-level volatility is higher than the market and the subsector volatility. Analysis shows that volatility has increased dramatically since 1998 for both markets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".