Bibliographic record
Abstract
Lou and Sadka, in a study that was published in 2011, examine the effect of stock liquidity characteristics on stock performance during the 2008–2009 crisis. Their conclusion is that liquidity risk, and not the liquidity level, explains stock performance during the crisis. Lou and Sadka measure liquidity via Amihud’s illiquidity measure. I construct a new measure of illiquidity, based on transaction-by-transaction price changes and conduct a similar analysis to that in Lou and Sadka. My findings show that, controlling for liquidity risk, the level of liquidity has incremental explanatory power for stock performance during the crisis. My analysis suggests that the level of liquidity and liquidity risk are both important facets of stock liquidity and that there might be an interaction or overlap between the two.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.009 | 0.034 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.013 |
| 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".