Is illiquidity priced in an international factor pricing model? A dynamic panel data application with robust <scp>IV</scp>
Bibliographic record
Abstract
Abstract In the setting of a dynamic panel data framework, we investigate the international five‐factor Fama–French (2017) model augmented with traditional illiquidity factors (Amihud, Journal of Financial Markets, 2002, 5, 31–56; Amihud, Critical Finance Review, 2019, 8, 203–221; Pástor and Stambaugh, Journal of Political Economy, 2003, 111, 642–685; Pástor and Stambaugh, Critical Finance Review, 2019, 8, 277–299) to determine if any of these factors are priced. Since illiquidity measures are endogenous, we propose an algorithm that generates robust instruments which are combined with a GMM estimator to cope with both the endogeneity issues surrounding illiquidity and other eventual specification errors. In this dynamic framework, we generally find that the most significant factors correspond to market and size but illiquidity may matter depending on the level of the beta. We find that illiquidity has more impact on returns in expansion than in recession. However, the bid‐ask spread seems to behave differently from the other illiquidity measures.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".