S&P 500 index inclusion announcements: does the S&P committee tell us something new?
Bibliographic record
Abstract
Purpose The purpose of this paper is to directly examine the information hypothesis of S&P 500 index inclusion announcements by investigating the degree to which information beyond Standard & Poor's eight stated criteria enters the inclusion decision. Design/methodology/approach Isolating a sample of S&P 500 additions and their eligible candidates during 1987‐2004, this paper employs logistic analysis that identifies factors ex post beyond the stated criteria that help distinguish the type of information that influences the final selection decision and that is arguably priced at the inclusion announcements. Findings The evidence indicates that, when choosing among new S&P 500 candidates, the S&P's committee relies primarily on publicly available information related to enterprise risk and historical performance. Material, private insight into future value‐relevant information plays at most a small part in the selection. Research limitations/implications The results suggest that index additions convey limited new information about added firms. Studies analysing index additions should start with the presumption that index inclusion announcements are information‐free events, and focus on the consequences of index inclusions such as liquidity, awareness or arbitrage risk, in their relation to index premia. Originality/value The results indicate that the previous evidence supporting the information hypothesis using the S&P 500 inclusions is not compelling.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".