DIVIDEND YIELD STRATEGIES : DOGS OF THE DOW AND HOUNDS OF THE BAY
Bibliographic record
Abstract
Over the years 'Dogs of The Dow' strategy has become an increasingly popular and intensely argued subject for both practitioners and academicians.This thesis examines the multifarious aspects of the 'Dogs of The Dow' (DoD) strategy and highlights both the euphemism of the believers and reservations of the skeptics.Further on, we empirically test the DoD strategy over a 16 year period from 1990 to 2005.A parallel study, Hounds of The Bay (HOB) is also carried out for the Canadian markets, over the same time period, to test if such a dividend yielding strategy has merits outside of the US market.Overall based on our research and empirical tests, we believe the effect of such a dividend yielding strategy has diminished in the recent years in both US and Canadian markets, while in the Canadian context it may be more subtle.However, this is no indication or a reason to believe that it may not work in the future or more importantly in other countries.Of critical importance was the support of our professor, Dr. Peter .C. Klein for making us see light even when the chips were down, his want for perfection and intellectual insights into the practical aspects of this thesis were always a help in keeping our cylinders burning and making us put in our best.Thanks from the core.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".