Bibliographic record
Abstract
Criticizes previous research on price/earnings ratios (PER) for neglecting their historical links with interest rates and analyses the causal links between interest ratres and the PERs of the Toronto Stock Exchange 300 Index (TSE300) and of seven major Canadian industries 1965‐1997. Explains the methodology and identifies three “distinct PER regimes”: 1965‐1974 (average PER 17.17), 1975‐1982 (average PER 8.92) and 1983‐1997 (average PER 17.2 with a higher standard deviation). Looks at the economic conditions for each period and suggests that current PERs “may not be too high”. Finds a negative correlation between PERs and treasury bill rates, differing between industries; and that the bill rate explains 95 per cent of PER variation for the TSE300, although it is not significant for the gold and silver industry. Adds that divided payout ratios and lower investor risk aversion are positively related to PERs, but that growth rate has a more variable influence. Summarizes the findings and their implications for PER forecasters.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".