Hello and welcome back to another paid post!
Today we are looking at the famous demand-shock anomaly in equities. When S&P announces an addition to the 500, every fund tracking the index has to buy the stock by the effective date, and for decades that forced buying pushed the price up beforehand and let it fall back after. The question here is whether that still happens, and what to make of a run-up that has recently returned while the fall-back has not.
Let’s dive right in.
Introduction
The mechanism behind the index effect is price pressure. When S&P announces an addition, every fund tracking the index has to buy the stock, and has to finish buying by the effective date. That demand is large, mechanical, and indifferent to price. If the market cannot supply the shares at the prevailing price, the price rises, and once the buying stops it drifts back toward whatever fundamentals support.
Harris and Gurel documented this in 1986, finding a jump of roughly three percent around announcement that unwound within a fortnight. Shleifer published a companion result the same year and drew the sharper conclusion, that demand curves for stocks slope downward, which contradicts the textbook assumption that a stock’s price is pinned by its cash flows and that any quantity can be absorbed at that price.
The reversal is the half of the claim that does the work. A permanent repricing would mean inclusion changed something real about the company. A temporary one means index funds moved the price by leaning on it, and the price recovered when they stopped leaning. Without the reversal there is no evidence that demand did anything.
Greenwood and Sammon revisited the question and titled their paper The Disappearing Index Effect. Their explanation is migrations. A stock joining the S&P 500 today usually leaves the S&P MidCap 400 the same day, so funds tracking the 400 are forced sellers against the 500’s forced buyers, and the two flows partly cancel.
Data and Methodology
The sample is every addition to the S&P 500 recorded in the CRSP membership file between January 1995 and December 2024, which comes to 778 events. Everything is keyed on permno rather than ticker, so there is no ticker-mapping problem and no survivorship bias from companies that later delisted.
Abnormal returns are four-factor: market, size, value and momentum, with loadings estimated per event over days 300 through 121 before the effective date. The estimation window closes six months ahead of the event so that none of the pre-event drift leaks into the betas and gets subtracted as risk. I report the version that excludes the estimated alpha, since an alpha fitted over a window that may itself contain drift would quietly remove the thing I am trying to measure. The with-alpha figures are in the notebook and tell the same story at larger magnitudes.
CRSP records effective dates rather than announcement dates, and that distinction shapes everything downstream. S&P guarantees at least three business days of notice and announces quarterly changes roughly five trading days ahead, so the announcement lands somewhere in the days just before the effective date and moves around from event to event. I use days 10 through 1 before the effective date as the run-up window, days 30 through 11 before it as a background window that should contain no announcement at all, and the twenty trading days after it for the reversal.
Of the 778 additions, 642 have enough prior trading history to estimate betas. The 136 that do not are disproportionately recent listings, which matters and which I return to.
Results
The late 1990s run-up over the final ten trading days is 9.56% across 138 additions at t=12.84. The number worth sitting with is not that one. It is that 93.5% of those additions had a positive run-up. This was not a tendency you needed a regression to see. It was close to a rule.
The 2000s deliver 5.47% across 246 additions at t=8.53, with 76.0% of events positive. The decay has started and the effect is still unmistakable.
The 2010s deliver 0.57% across 188 additions, and a coin flip. The median run-up is 0.30% and 52.7% of events are positive.
Whether 0.57% is statistically distinguishable from zero depends on how you count. Treating each addition as an independent observation gives p=0.142. Averaging within effective dates first, which is the honest correction given that additions cluster on quarterly rebalance days, gives p=0.027. I would not build anything on either. An effect where barely half the events point the right way is not something you can put on.
From 2020 through December 2024, across the 70 additions with estimable betas, the run-up is 3.39% at t=3.39. That is the number this post exists to explain.
Table 1: Four-factor abnormal returns by era, excluding the estimated alpha. Share positive refers to the run-up window.
The decay through 2019 is replication. It lines up with Greenwood and Sammon closely enough that I take the pipeline as clean, which matters mostly because of what the last row does.