Does Illiquidity Matter? An Errors-in-Variables Perspective
Bibliographic record
Abstract
Illiquidity is well known in the literature to be an important risk factor to consider in financial models of return. However, there is not much consensus on which measure should be used as a proxy for illiquidity. Our contributions mainly focus on the Pástor-Stambaugh measure in the context of the Fama-French three factor and more recently on the new five-factor model. In this survey article, we discuss our contributions on the subject in an errors-in-variables perspective. In particular, we propose new robust instruments that are developed and applied in different stages of our research. Robustness tests of these new instruments are performed in this research. Overall, our new instruments coupled with the GMM estimator show that either in a cross sectional, panel data, or recursive/rolling regression compared to the Kalman filter framework, that the most significant factor seems to be the market factor. This might be seen as in line with Cochrane’s concern about a “zoo of factors”.
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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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".