Domesticating the Factor Zoo with Economic Theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <italic>expected</italic> 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 <italic>expected</italic> 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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it