Domesticating the Factor Zoo with Economic Theory
Bibliographic record
Abstract
Confusion around the so-called factor zoo is largely due to a failure to distinguish between “attribution” factors and “priced” factors emanating from an asset pricing model. Attribution factors have a zero expected mean, do not emanate from asset pricing models, are high in number, can be short term, and should not drive investment policy. Priced factors should have nonzero expected premiums, emanate from an asset pricing model, be low in number, be long term, and influence investment policy. Empirical attempts to tame the factor zoo that distinguish between useful, useless, and redundant factors are helpful but could benefit from an overarching theory. The popularity asset pricing model (PAPM), an equilibrium model in which priced factors primarily emanate from the collective tastes of investors, provides a framework for identifying and understanding priced factors, leading to a domesticated factor farm.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".