The Disappearing Index Effect: Greenwood & Sammon (2025)

What this is. The paper’s core results, the model (demand-curve price impact decomposition), and the empirical method behind the decline of the S&P 500 index effect: enough to know what changed and why, without reading all 42 pages. To replicate or extend, read the original at https://doi.org/10.1111/jofi.13410.

TL;DR

The abnormal return earned by a stock added to (or removed from) the S&P 500 peaked at roughly 7-16% in the 1990s and has since fallen to essentially zero in the 2010s, despite index-tracking assets growing from near zero to approximately 7% of market capitalization. Greenwood and Sammon (2025) show this is not explained by changing firm composition. The primary drivers are (i) the growing share of “migrations” from the S&P MidCap 400, where simultaneous forced selling by MidCap trackers offsets forced buying by S&P 500 trackers, and (ii) a factor-of-20 decline in the demand-curve multiplier M (the price impact per unit demand shock), reflecting that active managers and non-S12 institutions now step in to absorb the index demand shock. The paper interprets this as the market adapting to a predictable, repeated trading opportunity, consistent with Lo’s (2004) adaptive markets hypothesis.

Early studies by Shleifer (1986) and Harris and Gurel (1986) documented the original index inclusion effect. Bennett, Stulz, and Wang (2020) first noted its decline between 1997 and 2017; this paper extends their analysis to the full 1980-2020 period and covers both additions and deletions. Preston and Soe (2021) also document the decline, and Vijh and Wang (2022) document smaller returns for MidCap-to-S&P migrations. McLean and Pontiff (2016) provide the analogy that anomalies decay after academic publication. Chinco and Sammon (2024) estimate the passive-ownership share is larger than conventionally measured, which implies even larger mechanical demand shocks than previously assumed.

Core results

Magnitudes and significance as reported; \*\*\*/\*\*/\* = 1%/5%/10%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	S&P 500 addition CAR fell from 7.4% in the 1990s to statistically indistinguishable from zero in the 2010s	Table I, p. 667; Figure 2, p. 667	1980s: 3.4%, 1990s: 7.4%***, 2000s: 5.2%***, 2010s: 0.8% (insignificant). Decline 2000s to 2010s: -4.3pp*** (SE 0.93)
R2	S&P 500 deletion CAR (removal effect) likewise collapsed to zero	Table I, p. 667; Figure 2, p. 667	1980s: -4.6%**, 1990s: -16.1%***, 2000s: -12.4%***, 2010s: -0.6% (insignificant). Decline 2000s to 2010s: +11.8pp*** (SE 2.56)
R3	Changing firm composition (size, arbitrage risk, turnover, analyst coverage) explains only a small part of the decline	Table II, pp. 671-672	With controls, decade fixed effects fall from 7.4% to 7.4% (1990s) and from 0.8% to 1.8% (2010s); residual 2010s vs 1990s gap remains -5.6pp*** (p=0.000)
R4	Index migrations from MidCap explain a large portion of the decline in addition returns	Table III, p. 676; Figure 6, p. 677	Direct additions: 10.2% (1990s), 8.8% (2000s), 5.4% (2010s). Migrations: 6.7% (1990s), 2.7% (2000s), -1.8% (2010s). Migration-nonmigration gap widens from 3.6pp to 7.2pp
R5	The demand-curve multiplier M declined by a factor of roughly 20 for additions from the 1990s to 2010s	Table V, p. 683	Additions M: 6.75 (1995-99), 3.58 (2000-09), 0.37 (2010-20); implied elasticity -0.15 to -2.72. Deletions M: 10.76 (1995-99), 4.52 (2000-09), 0.70 (2010-20); implied elasticity -0.09 to -1.44
R6	Decline in M is robust to composition controls and to extended windows that capture front-running	Table VI, pp. 684-685	With controls (col 2): M falls from 6.6 to 0.7 (additions). Extended window Ann-20 to Eff+1 (col 3): M falls from 9.1 to 1.3. Factor-of-7 decline even under the most generous front-running window
R7	Index effect decline extends to other index families (Russell 1000/2000, S&P MidCap, SmallCap, Nasdaq 100)	Table VIII, p. 693	Pooled additions: 4.2pp decline from 2000s to 2010s (5% significant). Pooled deletions: 10.3pp decline (1% significant). Individual index results weaker statistically

Overall (paper’s conclusion). The index effect grew from the 1980s through the 1990s as passive investing expanded, deepening the arbitrage opportunity. The market then adapted: active managers, institutional investors, and coordinated trading desks now provide liquidity around index events, eliminating the abnormal return on average despite continued growth in index fund assets. The primary mechanisms are migrations from the S&P MidCap (which offset demand shocks) and the rise of sophisticated liquidity provision (which lowers the multiplier M by a factor of ~20). Increased predictability of index changes plays only a minor role.

Theory / model

The paper has no formal theoretical model but uses a simple structural equation as the organizing framework. Price impact is modeled as a constant-elasticity demand curve hit by a demand shock (equation 1, p. 658):

$$\text{Price Impact}_{it} = M \times D_{it} \tag{1}$$

where $\text{Price Impact}_{it}$ is the percentage change in price, $D_{it}$ is the percentage of market capitalization bought upon index addition (or sold upon deletion), and $M$ is minus one over the demand elasticity. Given the rise in indexation, a naive application of this model predicts that price impact should have grown since the 1980s, because the demand shock $D$ has been growing. The paper’s puzzle is that average price impact (CAR) has instead declined, implying $M$ must have fallen substantially.

Taking means of equation (1) by decade and separating migrations (which face offsetting demand from MidCap trackers) from direct additions gives the decomposition (equation 4, p. 681):

$$\overline{\text{CAR}} = M \times \bar{D} = M \times \left( w \cdot \bar{D}_{\text{Migrations}} + (1 - w) \cdot \bar{D}_{\text{NonMigrations}} \right) \tag{4}$$

where $w$ is the fraction of additions or deletions that are migrations and $\bar{D}_{\text{Migrations}}$ and $\bar{D}_{\text{NonMigrations}}$ are their respective average net demand shocks. This allows the paper to back out $M$ as the ratio of average CAR to the average weighted demand shock, separately by decade beginning in 1995 (when MidCap data start).

Identification. The paper is descriptive: it documents time-series variation in event-study returns and uses a structural decomposition to separate the demand-shock channel (migrations) from the multiplier channel (liquidity). There is no causal identification design. The variation exploited is the historical widening of passive ownership and the exogenous timing of S&P 500 inclusion decisions.

Method

The estimator for individual-event abnormal returns is the market-adjusted cumulative abnormal return (CAR), defined as (equation 2, p. 665):

$$\text{CAR}_{it} = R_{it} - R_{S\&P\,500, t} \tag{2}$$

where for announcement returns $R$ is the cumulative return from the day before to the day after the announcement, and for effective-date returns $R$ is the cumulative return from the day before implementation to the day after. The total return (announcement + effective window) spans the last trading day before announcement to the first trading day after the effective date; the average window is 4.8 days for additions and 5.8 days for deletions (p. 665).

To test whether composition shifts explain the trend, the authors run a cross-sectional regression on the pooled event-level sample (equation 3, p. 670):

$$\text{CAR}_{it} = b_1 \text{Turn}_{i,t-1} + b_2 \text{Size}_{i,t-1} + b_3 \text{WZ}_{i,t-1} + b_4 \text{Cover}_{i,t-1} + \sum_{k=1}^{4} \gamma_k \mathbf{1}_{\text{era}=k} + e_{it} \tag{3}$$

where $\text{Turn}_{i,t-1}$ is abnormal turnover in the month before the index change (volume/shares outstanding minus market average), $\text{Size}_{i,t-1}$ is market capitalization relative to total S&P 500 capitalization at announcement, $\text{WZ}_{i,t-1}$ is the Wurgler and Zhuravskaya (2002) arbitrage-risk measure (CAPM residual variance over the prior year), $\text{Cover}_{i,t-1}$ is analyst coverage from IBES, and $\mathbf{1}_{\text{era}=k}$ are decade fixed effects. All characteristics are demeaned. Standard errors use White (1980) heteroskedasticity-consistent variance estimators (Table II, p. 672).

For the multiplier estimation, the regression interacts the average net demand shock $D_{\text{era}=k}$ with decade indicators (equation 5, p. 686):

$$\text{CAR}_{it} = b_1 \text{Turn}_{i,t-1} + b_2 \text{Size}_{i,t-1} + b_3 \text{WZ}_{i,t-1} + b_4 \text{Cover}_{i,t-1} + \sum_{k=1}^{3} \gamma_k \mathbf{1}_{\text{era}=k} \times D_{\text{era}=k} + e_{it} \tag{5}$$

The $\gamma_k$ coefficients estimate the multiplier $M$ for each decade after controlling for firm characteristics (Table VI, pp. 684-685). The paper builds on the event-study and panel-regression primitives.

Empirical specifications

All specifications are event-study regressions with market-adjusted returns. Key design choices:

• Sample: 736 S&P 500 additions and 731 deletions from Siblis Research, 1980-2020, matched to CRSP; pre-1990 announcement dates from Barberis, Shleifer, and Wurgler (2005). Observations with ACPERM within 100 days (acquisitions) and spinoffs excluded. Consistent sample for characteristic regressions requires Thompson S12 matching.
• Return window (baseline): Last trading day before announcement to first trading day after effective date (total window, roughly 5 days average). Sensitivity: extended to Ann-20 (20 trading days pre-announcement) to Eff+1 to capture front-running (Table VI cols 3-4, 7-8).
• Event-study benchmark: S&P 500 index return over the same window (market-adjusted). The market return is used rather than risk-model betas to avoid contamination from the index change itself (p. 665).
• Composition controls (Table II): CAR regressed on demeaned $\text{Turn}$, $\text{Size}$, $\text{WZ}$, $\text{Cover}$, and decade dummies; N=610 additions, 237 deletions; robust standard errors (White 1980). R-squared of 0.26-0.35 for additions.
• Migration analysis (Tables III, V, VI): MidCap changes from Siblis Research from 1995; MidCap 400 trackers identified by name/return correlation (at least 99.5% correlation to the MidCap index). Net demand shock D measured as Thompson S12 net buying by index trackers (change in split-adjusted shares held quarter before to quarter after, divided by shares outstanding).
• Multiplier estimation (Tables V-VI): M backed out as $\overline{\text{CAR}} / \bar{D}$ by decade (Table V), then via the interacted regression (equation 5) with characteristic controls (Table VI). Demand elasticity implied as $\varepsilon = -1/M$.
• Institutional ownership (Table VII): Thompson 13F ownership changes quarter before to quarter after compared to tracker net buying; active/passive distinction from Appel, Gromley, and Keim (2016).
• Other indices (Table VIII): Russell 1000/2000 from FTSE Russell 1990-2020; S&P MidCap/SmallCap from Siblis 1995-2020; Nasdaq 100 from Siblis 1995-2020. Returns market-adjusted; events use 10 days before effective date to one day after (no announcement dates for MidCap/SmallCap/Nasdaq); clustered by year.

Datasets used

Dataset	Role in paper	Wiki page
CRSP daily stock returns and shares outstanding	Cumulative abnormal returns; split-adjustment factors; market-cap computation	WRDS / CRSP (licensed)
Thompson S12 mutual fund holdings (quarterly)	Identifying S&P 500 and MidCap 400 tracking funds; measuring net buying/selling around index changes	WRDS (licensed)
Thompson 13F institutional holdings (quarterly)	Changes in total institutional ownership around index changes (Table VII)	WRDS (licensed)
Siblis Research S&P 500 addition/deletion history	Announcement and effective dates for S&P 500 changes, 1980-2020; also MidCap 400 and SmallCap changes	No page yet
Barberis, Shleifer, and Wurgler (2005)	Pre-1990 S&P 500 addition announcement dates not in Siblis	No page yet
IBES analyst coverage	Analyst coverage count for each firm at earnings announcement before the index change (control variable Cover)	WRDS (licensed)
WRDS Intraday Indicators (TAQ-based)	Value-weighted average effective bid-ask spread (percent effective spread) from TAQ data 1993-2022, using Holden-Jacobsen (2014) method	WRDS (licensed)
Virtu Financial implementation shortfall	Implementation shortfall for midcap stocks 2009-2021	No page yet
FTSE Russell index membership	Russell 1000 and Russell 2000 additions/deletions, 1990-2020	No page yet

Sample: S&P 500 changes 1980-2020; MidCap/SmallCap/Nasdaq/Russell changes from 1990 or 1995 depending on data source. Returns at daily frequency; holdings at quarterly frequency.

When to read the full paper

Use the original if you are: replicating the demand-curve decomposition or extending the multiplier analysis to post-2020 data; studying the mechanics of passive investing and price impact around index rebalancing events; testing whether similar patterns hold for factor-index (e.g., ESG or smart-beta) additions/deletions; or evaluating the market efficiency implications of mechanical, predictable institutional demand shocks. Table I (p. 666-667) gives the full year-by-year CAR history; Tables V-VI (pp. 683-685) give the migration-adjusted M estimates; Table VIII (p. 693) gives the other-index results.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(2), April 2025. This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. The paper is paywalled (Wiley VOR licence); only extracts are reproduced here.

Greenwood, Robin, and Marco Sammon. “The Disappearing Index Effect.” The Journal of Finance 80, no. 2 (April 2025): 657–698. DOI: 10.1111/jofi.13410. © 2024 the American Finance Association. All rights reserved. Extract-only reproduction under fair-use/commentary.