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
This paper tests the existence of excessive comovement among firms in the S&P 500. Using a fuzzy regression discontinuity approach I show that membership in the S&P 500 leads to significant positive excess comovement in the long term. I evaluate a traditional, liquidity based explanation and a friction based explanation, and find no evidence that liquidity is driving excess comovement in the sample. I show that the previous lack of evidence for excess comovement shown in Chen, Singal, Whitelaw (2016) is due to heterogeneous effects on firms who are newly included versus those that are established members. One potential explanation is that immediately after inclusion, investors take time to rebalance and fully integrate the new stock into the group, reducing observed increases in comovement in the short term. These results constitute new evidence of frictions when exposed to large classes of noise traders with correlated demands, such as those populating the S&P 500.
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.
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) | 0.000 | 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 | 0.000 |
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