Does Illiquidity Matter? An Errors-in-Variables Perspective
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it