Forecasting value-weighted real returns of TSE portfolios using dividend yields
Bibliographic record
Abstract
We assess the ability of dividend yields denoted by DYt, to forecast value-weighted real returns, denoted by Rt ,T of Toronto Stock Exchange (TSE) portfolios for following return horizons, T: monthly, quarterly, and one to four year. Fama and French [4] applied similar methods to the New York Stock Exchange and found the forecast power increases as the return horizon increases. We find that the Fama and French methods generalize to TSE portfolios, however, it does not apply to all portfolios. We also determine that the Fama and French approach may not lie on solid statistical ground, in that the residual variance is not time invariant. With these drawbacks in mind we consider using the methods of Dynamic Linear Models as discussed in West and Harrison [13], which allow the model parameters to be time varying. We conclude that for the majority of the portfolios, the two methods agree, however, the regression DLM approach does slightly better in comparison with the methods of Fama and French in terms of standarized forecast errors.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".