Bibliographic record
Abstract
The index effect, or the excess returns of a stock added to a leading index, is one of the most researched pricing anomalies in finance. Is the index effect shrinking? To answer this question, we study the index effect for headline indices of five of the biggest equity markets in the world: U.S. (S&P 500), Canada (S&P/TSX 60), Japan (Nikkei 225), U.K. (FTSE 100) and the Germany (DAX 30). We find that excess returns for index additions have diminished over the past five years. The median excess return of S&P 500 additions was 3.8% for the past five years, compared to 6.0% for the five years prior. The declining pattern is also observed in Nikkei 225, S&P/TSX 60 and DAX 30, but not the FTSE 100. The diminishing index effects may be attributed to several factors: First, the index effect has fallen victim to its own popularity. As more arbitrageurs have come in to the market, arbitrage profits have reduced. Second, changes in market structure and trading patterns of index funds have dented the index effect. The index effect may never vanish completely. But its days as a profitable trading strategy may be numbered. Alternative indexrelated profit opportunities involve trading index changes in the options market or trading index share changes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".