Does Quality/Junk (QMJ) continue to “Rescue” the size effect in the stock market?
Bibliographic record
Abstract
The size premium has been challenged along many fronts, however, according to the “Size Matters if You Control Your Junk” these challenges are dismantled when controlling for the quality, or the inverse “junk”. And recently, COVID has hit the market, according to “Stock Return and the COVID-19 pandemic: Evidence from Canada and the US”, there is a wide-ranging impact. At the same time, the effect of the COVID pandemic has hit the US market and showed giant changes. In order to find out whether the QMJ factor is still useful during the new period. This article focuses on the impact during the COVID on size premiums. The research methodology was inspired by “A Five-Factor Asset Pricing Model”, “The Capital Asset Pricing Model: Theory and Evidence”, “Momentum” and “The Cost of Mass Gatherings During a Pandemic”. Note that, according to “Market Efficiency, Long-Term Returns, and Behavioral Finance”, the QMJ factor may enhance the prominence of size premium. This is the focus of this article. this article provides a more accurate forecasting model in the new era to help predict the size effect that will occur in the market.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".