Separating Winners from Losers Among Value and Growth Stocks in Canada Another Step in the Value Investing Process
Bibliographic record
Abstract
The paper investigates two questions (a) whether there is value premium in a sample of Canadian non-interlisted stocks for the period May 1, 1985 – April 30, 2009, and (b) whether an additional step to screening for possibly undervalued stocks can be employed to separate the good stocks from the bad ones, as not all low P/E stocks are worth investing in. The paper extends this analysis to both value and growth stocks. We document a consistently strong value premium over the May 1, 1985 – April 30, 2009 sample period, which persists in both bull and bear markets, as well as in recessions and recoveries. We show that the value premium is not driven by a few outliers, but it is pervasive. Our results are consistent with, but, in general, stronger than, those of other Canadian and US studies. We were able to construct a composite score indicator (SCORE), combining various fundamental and market metrics, which enabled us to predict future stock returns and separate the winners from the losers among value and growth stocks. A strategy which would involve shorting the high SCORE value stocks and buying the low SCORE value stocks would have beaten the low P/E portfolio by about 30% over the May 1, 1985 – April 30, 2009 period. On the other hand, shorting the high SCORE growth stocks and buying the low SCORE growth stocks would have beaten the high P/E portfolio by about 40% over the same period. We also find that the return of a portfolio strategy that buys (sells) stocks that rank low (high) in the composite score indicator has significant explanatory power in an asset pricing model framework. Results remain robust out of sample.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".