The effect of demand on stock prices: new evidence from S&P 500 weight adjustments
Bibliographic record
Abstract
Purpose Many papers have argued that there are long‐run downward‐sloping demand curves (LRDDC) for stocks. The purpose of this paper is to analyze this hypothesis using a new, unique, and ostensibly information‐free event: the re‐weighting of the Standard & Poor (S&P) 500 index from market based to free‐float based, which involves a significant shift in supply that, under the LRDDC, should result in significant and permanent price movements. Design/methodology/approach Event study methodology is used to examine abnormal returns and trading activity around the free‐float weight implementation dates for S&P 500 firms with various investable weight factors. Findings As a result of S&P 500 index re‐weighting, affected stocks experience statistically significant excess returns of −1.54 percent during the event week. This return is reversed during the following 30 days as trading volume returns to normal levels. These results are contrary to previous studies that analyze ostensibly informational events and/or different exchanges. Research limitations/implications Results of this study indicate that arbitrage appears to be effective in eliminating a long‐term mispricing, which challenges the validity of the LRDDC hypothesis. Originality/value This study contributes to the body of literature on the S&P 500 index firms by providing supporting evidence for the price‐pressure hypothesis.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".