Index composition changes and the cost of incumbency

Journal of Banking & Finance 34 (2010) 2500–2509

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Index composition changes and the cost of incumbency André F. Gygax a, Isaac Otchere b,* a b

Department of Finance, University of Melbourne, Australia Sprott School of Business, Carleton University, Canada

a r t i c l e

i n f o

Article history: Received 19 March 2009 Accepted 10 April 2010 Available online 14 May 2010 JEL classification: G14 G20 O31 Keywords: S&P 500 index incumbent effects Portfolio rebalancing effects Industry momentum

a b s t r a c t This paper provides evidence of information effects and portfolio rebalancing effects that occur when stocks are added to or excluded from the S&P 500 index and finds that incumbents in the index realize negative excess returns when S&P revises the composition of the index. We also find that for incumbents that are in the same industry as the added firm, the price-pressure effects are mitigated by positive industry-level information and momentum effects. For index exclusions, the magnitude of the loss sustained by incumbents from the same industry as the excluded firm is larger than that realized by the non-industry incumbents, as the negative information and momentum effects reinforce the price-pressure effects. Our results suggest that changes in the composition of the index are not information-free events; however, the portfolio rebalancing effects dominate the industry information effects. Ó 2010 Elsevier B.V. All rights reserved.

1. Introduction This paper investigates whether and how index composition changes affect the share price of incumbents existing in the S&P 500 index. Extant literature documents positive (negative) abnormal returns for firms that are included in (excluded from) the S&P 500 index.1 The index effects are attributed to the temporary and permanent price-pressure hypotheses or the downward-sloping demand curve explanation (e.g., Harris and Gurel, 1986; Shleifer, 1986; Pruitt and Wei, 1989; Lynch and Mendenhall, 1997; Wurgler and Zhuravskaya, 2002) and information effects (Jain, 1987; Dhillon and Johnson, 1991). These studies examine the index effects on firms that are added to or excluded from the index. In this study, we examine the effects of index composition changes on index incumbents in the same industry as the added or excluded firm, as well as in other industries. The incumbents in the index present a natural laboratory to test and disentangle the price-pressure and information-effect hypotheses. First, analyzing the index effects on incumbents in the index allows us to test the information-effect hypotheses. The S&P 500 index consists of an elite group of high-quality stocks that are of * Corresponding author. Tel.: +1 613 520 2600x2731; fax: +1 613 520 4427. E-mail addresses: [email protected] (A.F. Gygax), [email protected]. ca (I. Otchere). 1 Other researchers also document index effects for other indexes. See, for example, Kaul et al. (2000) for the TSX index in Canada, and Liu (2009) for the Nikkei index in Japan. 0378-4266/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2010.04.007

interest to large investors. By their choice of candidates for inclusion or exclusion from the index, Standard & Poor’s (S&P), an agency that specializes in rating companies, could be certifying the financial health of the firms and possibly the industry as a whole, as its policy is to have the index be as representative of the economy as possible.2 As a result, industries that have been performing well will tend to have a disproportionately high rate of inclusion in the S&P 500 index. Consequently, we could observe an increase in the number of firms from those industries that are added to the index, and vice versa. The addition of a firm to the index could reinforce the appeal of the industry incumbents that are in the index; as a result, they also could experience a positive reaction, as investors demand more of these high-quality stocks. While this argument suggests that non-index industry counterparts could react to the index revision, in this paper, we focus on industry counterparts in the index for liquidity reasons, because there is evidence that suggests that information is more rapidly incorporated into the most liquid stocks such as index stocks, and rather more slowly in less liquid non-index stocks (Barberis et al., 2005). Also, a recent study by Cai (2007) finds that non-index industry counterparts of firms added to the S&P 500 index realize positive returns, evidence he attributes to the existence of industry information effects. Second, focusing on incumbents in the index allows us to test the portfolio rebalancing or price pressure hypothesis. We argue 2 David Blitzer reiterates this view in his report presented at the 2006 CRSP Forum held at the University of Chicago (Blitzer, 2006).

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that changes in the composition of the index can create an incentive for index funds linked to the S&P 500 index to trade not only the shares of firms added to or excluded from the index, but also the shares of incumbents in the index, as fund managers will have to rebalance their portfolios in accordance with the new weights of the stocks in the index.3 The change in weight that accompanies index additions or exclusions, and the attendant rebalancing of portfolios that are tied to the S&P 500 index could induce selling (buying) of the shares of incumbents in the index if the index revision leads to a decrease (increase) in the weights of the incumbents. This externality motivates us to examine index effects on incumbents in the index. To disentangle the price-pressure effect from the information effect, we develop a model of the index revision process using the index incumbents and the added/excluded firms. We then test the implications of our model by performing cross-sectional analyses on our sample of industry and non-industry incumbents in the index. We also examine the extent to which the industry effects are influenced by industry momentum as well as whether the magnitude of the portfolio-rebalancing effects has changed over time by examining index effects in two sub-periods, namely, 1978–1989 and 1990–2006. The sub-periods are distinguished by the announcement regime that existed at that time. The first sub-period is characterized by a simultaneous announcement and implementation policy whereby changes to the index were announced and implemented on the same day. A five-day advanced notification regime during which the names of the firms to be excluded from and added to the index are pre-announced came into effect in late 1989 and has been in place since then. The introduction of the new announcement policy regime has drastically changed the procedure by which index composition changes are made. We examine how this regime change has affected the magnitude of the price-pressure effect resulting from index composition changes. We summarize our results as follows. First, we find that index composition changes bode ill for incumbents, as they experience negative excess returns when S&P revises the composition of the index. Second, consistent with the information-effect hypothesis, for index incumbents that are in the same industry as the added firm, the negative price-pressure effects are mitigated by positive industry information (momentum) effects; as a result, the magnitude of the total index effects is smaller than that experienced by non-industry incumbents. Similarly, for exclusions, the magnitude of the loss sustained by the incumbent industry peers is larger than that experienced by non-industry incumbents, as the negative industry effects reinforce the price-pressure effects. Third, the information effects are more pronounced in the case of index exclusions than inclusions, thus suggesting that investors react more strongly to bad news than to good news. Furthermore, consistent with the portfolio-rebalancing hypothesis, we find that abnormal returns realized by the incumbents are strongly positively related to changes in the weights of the incumbents and thus portfolio rebalancing activity of index funds. The documented index effects exist even after controlling for firm size, growth, and the trading interval. The implication of our results is that index composition changes are not information-free events. Our paper contributes to the literature on index composition changes and investor behavior in a number of ways. First, it pro3 The extent of the price pressure will depend on the modus operandi of the index funds, that is whether they are pure replicators of the index or whether they are optimizers and use a stratified approach to replicate the index with less than 500 firms. While carrying out this study, we interviewed a couple of large index fund managers and also studied several index fund product brochures. Most of the available information indicates that many index managers replicate the index. Therefore, in this study, we operate on the assumption that most of the index funds are pure replicators.

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vides evidence on the winners and losers of index composition changes from a different perspective than what has previously been documented. Prior studies including Shleifer (1986), Harris and Gurel (1986), Pruitt and Wei (1989), Beneish and Whaley (1996), Lynch and Mendenhall (1997) and Wurgler and Zhuravskaya (2002) examine the abnormal returns realized by firms added to or excluded from the S&P index. We extend this literature by documenting evidence of two forms of incumbency effects, namely, portfolio-rebalancing and industry effects. We thus shed new light on the index-effect phenomenon and demonstrate that the effects are more pervasive than has been documented in the extant literature. Second, by examining index effects on incumbents in the index, we are able to disentangle price-pressure effects from information effects and show that most of the effects experienced by the incumbents result from portfolio rebalancing activities. Third, our study adds to the literature that documents evidence of industry effects for corporate events such as earnings restatements (Gleason et al., 2008), bank failures (Aharony and Swary, 1983), merger proposals (Eckbo, 1983), corporate security offerings (Szewczyck, 1992), bankruptcy announcements (Lang and Stulz, 1992), going private transactions (Slovin et al., 1991), share repurchases (Hertzel, 1991), privatizations (Eckel et al., 1997 and Otchere, 2009), and dividends (Firth, 1996). The rest of the paper is structured as follows. In Section 2, we develop our model of the index composition process and formalize our testable hypotheses. Section 3 discusses the data and methodology. The results are presented in Section 4. Section 5 provides the concluding comments. 2. Testable hypotheses 2.1. Portfolio rebalancing effects In this paper, our primary objective is to examine the price effects of S&P 500 index composition changes on incumbents that are in the index. We posit that index funds’ portfolio-rebalancing activity that accompanies index composition changes will affect the price of incumbents in the index, as when a firm is added to the index, index fund managers will have to rebalance their portfolios in accordance with the new weights of the stocks in the index. They will have to sell the shares of incumbents whose weights have reduced, and vice versa. The financial press provides anecdotal evidence of portfolio-rebalancing effects that occur when the composition of an index changes. An article published in the Australian Financial Review entitled ‘‘Investors clear a few lines for Telstra” cites one analyst who argues that in anticipation of the increase in the weight of Telstra Corp (the largest telecommunication carrier in Australia) in the S&P/ASX index following the firm’s subsequent privatization that increased its float, institutional investors including Merrill Lynch and ABN AMRO sold a substantial number of the shares of Cable and Wireless Optus (CWO) – Telstra’s industry counterpart in the index – to raise funds to buy Telstra’s shares. Consequently, CWO’s share price fell in anticipation of the increase in Telstra’s weight in the index (Australian Financial Review September 2, 1999). This substitution effect motivates us to analyze the portfolio rebalancing effects on both industry and non-industry incumbents in the S&P 500 index. 2.2. Index composition changes and information effects The extant literature shows that investors use announcements made by, or events relating to, one firm to make inferences about other firms that operate in the same industry because those firms may be affected by common industry factors. Prior studies that document evidence of industry information-transfer effects in-

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clude Aharony and Swary (1983) for bank failures, Eckbo (1983) for merger proposals, Lang and Stulz (1992) for bankruptcy announcements, Hertzel (1991) for share repurchases, and Eckel et al. (1997) for privatizations, among others. Drawing on these studies, we conjecture that index additions (exclusions) could convey favorable (unfavorable) information about the added firm’s economic prospects as well as those of the industry counterparts. An important criterion that S&P uses to select candidates for inclusion in the index is the economic importance of the sector in which the stock is classified. The Index Committee tries to keep the weight of each sector in the index in line with the sector’s weight in the universe of stocks in the S&P stock guide database. As an industry experiences significant growth and becomes more dominant in the economy, its importance will be reflected in the index.4 According to Robert Boss of Standard & Poor’s, ‘‘S&P has a preference for adding new companies to an index from a sector that will correct an underweight condition. For example, with the growing importance of the technology sector in the US economy [in 2000], the weight of the technology sector in the pool of universe increased. Accordingly, S&P increased the number of technology companies in the S&P 500 index from 18 to 78. As a percentage of the market capitalization of the index, this adjustment was an increase from 8% to 35%. At the same time, the percentage of technology stocks in the universe of stocks increased from 13% to 40%” (S&P, 2000). Similarly, exclusions could reflect bleak industry prospects that can cause a sector to be out of favor with investors. Changes in the composition of the S&P 500 index could therefore reflect changing industry prospects and could thus become a vehicle for correcting overweight or underweight sectors of the economy. The addition of a firm to the index could reinforce the investment appeal of the industry incumbents in the index, and vice versa. The foregoing discussion leads us to make two conjectures. First, index additions (exclusions) generate spillover effects for incumbents from the same industry as the firm being added to the index. Second, because of the portfolio-rebalancing activities of index funds, we would observe significant index effects on incumbent index stocks. We formalize these two hypotheses in the next section. 2.3. Model and hypotheses development Assume an economy has two types of agents, namely, index traders and non-index (or industry) traders. The index traders track specific benchmark indexes and rebalance their portfolios in response to index composition changes made by the index administrators. The industry traders are investors who actively trade stocks in a specific industry based on fundamental shocks as well as pricing inconsistencies among stocks within an industry. In this economy, shocks to prices emanate from four sources, namely, market-wide factors, which impact all stocks, index factors, which affect stocks in an index, industry factors that influence assets in a particular industry, and idiosyncratic factors that are specific to each firm. The index effects affect the firms proportion4 The Standard and Poor’s Index Committee employs five main criteria when examining an index membership candidate, namely, market capitalization, industry or sector representation, public float, liquidity and fundamental analysis carried out with the aim to ensure that such firms have sound financial stability. Potential candidates that satisfy the inclusion criteria are initially included in a replacement pool created by S&P. S&P maintains that stocks entering the index are selected in order to achieve a fair representation of the economic sectors in the index. If, for some reason, a particular industry is no longer economically important, the number of firms in this industry would be reduced within the index. If a firm is removed from the index, it is replaced by a firm from the replacement pool, which may or may not come from the same sector.

ally based on their weights in the index. Shocks to index firms can be stated as

ei;t ¼ cM fM;t þ cI fI;t þ cN fN;t þ ð1  c2M  c2I  c2N Þ0:50 fi;t ;

ð1Þ

where fM represents market-wide factors, fI represents the industryspecific factors, fN represents index effects, and fi are idiosyncratic factors.5 Suppose an index has three firms with market capitalization as follows: A, $20 m; B, $40 m; and C, $60 m. The weights of the stocks are: 1/6, 1/3 and 1/2, respectively. Suppose further that there is an index revision and stock D, with a market value of $30 m (and from the same industry as C) replaces A. The weights of the incumbents B and C in the index must decline. Index investors who mimic the benchmark portfolio will have to dispose of some of their investments in B and C (if they are to minimize tracking error); the reverse also holds.6 The preferences of index fund investors to hold the index have to be accommodated by those non-index traders who find an increased supply of A, B, and C and an increased demand of D thrust upon them. Accordingly, the price of these stocks will have to change as a result of demand and supply elasticities and cross substitutes. Specifically, the price of A, B, and C will drop because of the fall in weights and the attendant portfolio rebalancing by index funds, and that of D will rise. Therefore, we propose hypothesis one as follows: H1: Following the addition of stock D to the index, (a) DPD,t > 0; (b) DPA,t < 0; (c) DPB,t < 0; (d) DPC,t < 0, where DP is the change in price. Why will D’s price go up when S&P adds it to the index? We conjecture two possibilities. First, S&P (which is similar to an analyst) has information, and it conveys that information by its choice of firms added to or excluded from the index (information hypothesis). The second possible explanation is related to the preferred habitat hypothesis of Barberis et al. (2005), which posits that investors are in a preferred habitat based on their investment philosophy. A subset of these investors, consisting of investors who insist on mimicking the index, will demand an added stock from the non-index investors and will similarly pass off a deleted stock to them. These actions will cause the price of the added (excluded) firm to increase (decrease) due to the pricepressure effect. The addition of D (with a higher weight than the replaced stock A) will cause the weights of the existing stocks in the index to reduce, which in turn will induce changes in the portfolios that track the benchmark index. Now suppose that there is information in S&P’s selection of D, and suppose also that C is an incumbent industry peer of D. Then, based on the foregoing discussion, C should not fall by as much because of the positive information contained in the announcement. In fact, it may even increase if the industry effect is large enough. Therefore, for C (the incumbent industry peer), DPC can be negative, zero, or positive, depending on whether the portfoliorebalancing effect outweighs, offsets, or is overwhelmed by the information effect. We should also find that following the addition of stock D to the index, stock A (the replaced stock) falls the most because it is being excluded from the index, and stock B falls more than C (the industry peer) because the price-pressure effect on stock C is mitigated by positive-industry effects. Therefore, H2: Suppose stock D is added to the index, the net effect of the addition will be DPA < DPB < DPC.

5

The model is similar to that employed by Ambrose et al. (2007). If the added firm, D, has a market value of, say, $10 m, then the weights of the incumbents will increase. However, as shown in Table 1 Panel B and Panel C, the market values of added firms are usually larger than the market values of the replaced firms; as a result, the weights of the incumbents will drop following index additions. 6

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A.F. Gygax, I. Otchere / Journal of Banking & Finance 34 (2010) 2500–2509 Table 1 Frequency distribution and descriptive statistics of S&P 500 index inclusions and exclusions. Year

Inclusion

Exclusion

Year

Inclusion

Exclusion

Year

Inclusion

Exclusion

Panel A: S&P 500 index inclusions and exclusions 1978 13 13 1979 15 15 1980 16 16 1981 21 21 1982 29 30 1983 21 20 1984 30 30 1985 28 28 1986 29 29 1987 27 27

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

27 30 13 10 13 16 19 32 26 36

27 30 13 13 10 17 18 34 27 34

1998 1999 2000 2001 2002 2003 2004 2005 2006 Total

46 44 58 30 24 9 20 20 31 733

48 42 58 30 23 9 20 20 31 733

Inclusion event statistics N Panel B: Descriptive statistics of the addition sample Assets 445 Sales 444 3-Year average Sales 441 CAPEX 406 Sales/CAPEX 379 EPS 444 Market Cap 452

Peers statistics

Mean

Median

N

Mean

Median

9832.6 3430.8 2966.6 254.5 32.0 2.23 5,365,876

2219.7 1711.2 1449.5 90.0 16.6 1.82 3,850,139

19,889 19,884 19,982 19,842 16,568 17,825 20,460

36,709.7 8421.6 7792.3 458.9 39.0 2.38 13,994,189

4981.9 3,659.0 3426.6 149.5 20.1 2.07 5,192,227

Exclusion event statistics N Panel C: Descriptive statistics of the deletion sample Assets 115 Sales 115 3-Year average sales 184 CAPEX 113 Sales/CAPEX 111 EPS 108 Market Cap 187

Peers statistics

Mean

Median

N

Mean

Median

8399.3 5253.7 4622.9 220.5 44.9 2.43 2,582,169

2002.9 1943.0 1,788.5 72.0 28.9 0.03 936,545

20,474 20,467 20,659 19,235 18,391 20,420 21,111

24,683.7 7824.9 7223.5 459.0 33.0 2.25 9,966,701

3986.9 3660.6 3395.3 161.7 20.9 2.04 3,803,339

Double-peers statisticsa

Non-peers statistics N

Mean

Panel D: Descriptive statistics of non-peers and double-peers samples Assets 183,351 22,451.1 Sales 183,351 8267.1 3-Year average sales 183,716 7610.3 CAPEX 182,848 564.6 Sales/CAPEX 182,512 30.5 EPS 183,336 2.24 Market Cap 183,734 10,821,883

Median

N

Mean

Median

4829.12 4037.4 3736.2 204.7 18.0 2.08 4,230,665

8354 8350 8556 7421 6949 8330 8630

28,158.2 6623.0 6013.6 365.5 35.2 2.75 8,282,421

3647.1 3092.0 2888.8 120.0 20.7 2.43 2,735,121

This table presents the frequency distribution and descriptive statistics of the ‘event’ sample and the incumbents over the period 1978–2006. Panel A shows the yearly distribution of the number of inclusions and exclusions in the S&P 500 index, Panel B presents the descriptive statistics of the addition sample, Panel C presents similar statistics for the deletion sample, and Panel D presents the statistics for the non-peers and double-peers samples. a A double peer exists when the GICS code of the added and the deleted firm are the same. We perform an additional robustness test on the information hypothesis using this subsample.

The index effects on incumbent firms following the exclusion of a firm from the index will be somewhat similar to what has been predicted for the additions sample. For example, if the replaced stock A is in the same industry as incumbent C, and assuming that the exclusion conveys information about the industry, then we expect the incumbent industry counterpart C to experience a larger drop in price than the non-industry incumbent B, as the negative industry effects will reinforce the portfolio-rebalancing effects. Therefore, H3: Following the exclusion of stock A from the index, the net effect of the exclusion on incumbent firms will be DPA < DPC < DPB.

exclusions, together with the announcement and effective dates from S&P. Over the 29-year period, there were 733 additions to and exclusions from the index (inclusions are usually induced by exclusions).7 The average number of inclusions per year is 26. However, as Panel A of Table 1 shows, S&P 500 inclusions were more frequent in the late 1990s and early 2000s, with the maximum yearly inclusion of 58 occurring in 2000 and the lowest number of firms (9) added to the index in 1991. Because our primary objective in this study is to examine the effects of index composition changes on index peers, we create corresponding industry peer and non-peer samples for additions and exclusions. At the time of the index revision, all industry counterparts existing in the index are considered candidates for our same industry incumbent sample, and the other index firms are designated as non-industry incumbents.

3. Sample selection, data and methodology We focus on the impact of S&P 500 index additions and exclusions on incumbents existing in the index over the period January 1978 to December 2006. We obtain our sample of additions and

7 Note that the number of inclusions does not always match the number of exclusions, as in some years firms removed from the index at the end of the year are not replaced until the beginning of the following year.

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Stock returns data come from the Center of Research in Securities Prices (CRSP) database, and accounting data come from Compustat. To obtain our sample, we apply filters to eliminate events that could confound the results, such as mergers and acquisitions, spin-offs, leverage buyouts, and changes in the classes of shares.8 We also exclude cases of index changes where the permanent numbers (CRSP PERMNO) of the added firm and the excluded firm are the same. We eliminate incumbents of firms that were added to and/or excluded from the index on the same day because including multiple firms from the same industry or different industries that were added to or excluded from the index on the same day would contaminate the results or possibly create offsetting effects. Also, to include them in the study, we require the industry peers and non-industry incumbents to have stock price data at least 200 days before the announcement of the index composition change and 10 days after the effective date of the change in the CRSP database. Our final sample comprises 452 additions and 187 deletion observations, of which 268 additions and 130 deletions occurred after October 1989 when S&P adopted the 5-day advance notification regime. The same industry sample consists of 20,460 observations for additions and 21,111 for deletions. Our non-industry peer sample has 183,734 firm observations. We present descriptive statistics for the sample in Panels B, C and D of Table 1. We observe that firms added to the index during the study period are larger and more profitable than the replaced firms, but are smaller and less profitable than the incumbents in the index. Consistent with Chen et al. (2004), we calculate abnormal returns for the sample firms using S&P value-weighted index returns as benchmark returns. The expected returns are based on a and b obtained from regressions estimated using observations from day 200 to day 21. We calculate abnormal returns for the sample firms for different event windows over the 11-day (5 to +5) period surrounding the announcement and effective dates.

4. Results 4.1. Index inclusion and exclusion effects on incumbents We examine our initial hypothesis by analyzing the market reaction of the sample of incumbents to index composition changes. Our hypotheses are tested using 452 index-addition and 187 index-exclusion observations. The preliminary results for the incumbents are presented in Table 2. Panel A shows the announcement effects of index additions and exclusions on incumbents, while Panel B reports the effective-day returns for the sample. In general, we document small but significantly negative abnormal returns for incumbents following the announcement of the inclusion of firms in the index. Incumbents that are in the same industry as the added firm realized abnormal returns of 0.01% (t-statistic = 1.77) during the 11-days (CAR-5, 5) surrounding the announcement date. However, the exclusion results are much stronger. Incumbents from the same industry as the excluded firm consistently realized significantly negative abnormal returns. The non-industry incumbents also realized abnormal returns of 0.13% during the 11 days surrounding the announcement date.9 On the effective date of the index addition, both the industry and non-industry index incumbents experienced significant index effects. The 5-day and 11-day cumulative abnormal returns of 0.19% and 0.30% realized by incumbents that are in the same

8

Chen et al. (2004) use similar criteria to select their sample. We also examine the abnormal returns realized by the firms that were added to and excluded from the index. Our results are similar to those reported in earlier studies and thus are not reported here. However, they are available from the authors upon request. 9

Table 2 Incumbents excess returns. Event day

Addition industry peers (N = 14,103)

Deletion industry peers (N = 14,155)

Panel A: Announcement-date excess returns AR (0) 0.01% 0.06% (0.80) (3.07)** CAR 0.01% 0.07% (1, +1) (0.40) (3.51)*** CAR 0.05% 0.20% (2, +2) (0.03) (6.95)*** CAR 0.01% 0.57% (5, +5) (11.77)*** (1.77)** Addition industry peers (N = 20,459) Panel B: Effective-date excess returns AR (0) 0.05% (3.24)*** CAR 0.03% (1, +1) (5.09)*** CAR 0.19% (2, +2) (6.80)*** CAR 0.30% (5, +5) (6.01)***

Deletion industry peers (N = 21,111)

Non-industry peers (N = 124,031) 0.01% (3.16)*** 0.03% (2.84)** 0.04% (3.78)*** 0.13% (6.36)*** Non-industry peers (N = 183,734)

0.10% (7.71)*** 0.13%

0.04% (6.54)*** 0.01%

(5.85)*** 0.10%

(3.55)*** 0.02%

(2.94)*** 0.33%

(3.39)*** 0.02%

(7.54)***

(2.53)**

This table presents the announcement period and effective period excess returns realized by incumbents in the S&P 500 index following the addition to and exclusion from the index. The abnormal returns are calculated using the CRSP valueweighted returns as benchmark returns. Cumulative abnormal returns are the sum of the mean abnormal returns over the event windows centered on the inclusion and exclusion announcement and effective dates. Panel A shows the announcement-period abnormal returns for the industry incumbents and non-industry incumbents following a change in the index, while Panel B presents similar results for the sample for the effective date of the change to the index. N is the number of observations. The number of observations in the announcement period is smaller because S&P adopted the advanced notification regime in October 1989. * The mean returns are significantly different from zero at 10%. ** The mean returns are significantly different from zero at 5%. *** The mean returns are significantly different from zero at 1%.

industry as the added firm are strongly significant at <1% (t-statistic = 6.8 and 6.01, respectively). The non-industry peers also experienced significant abnormal returns of 0.04% (t-statistic = 6.54) on the effective date. Similar to the announcement-day effects, the effective-day abnormal returns realized by industry peers of firms excluded from the index are larger than those of the addition event. The results presented in Table 2 provide preliminary evidence of incumbency effects resulting from index composition changes. 4.2. Overlapping event periods and total index effects The results presented in Table 2 provide some evidence of index incumbency effects. However, these results are possibly biased because of measurement issues relating to overlapping announcement and effective event periods. During our study period, Standard and Poor’s implemented two announcement policy regimes, namely, a simultaneous announcement and implementation regime [from 1978:01 to 1989:10] and an advanced notification regime [from 1989:11 to 2006:12]. As Table 1 Panel A shows, about 40% of the index additions occurred during the pre-October 1989 period when the announcement and the effective dates were the same. Consequently, the cumulative abnormal

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returns presented separately for the announcement and effective dates are fraught with measurement errors.10 Since October 1989, S&P has maintained an interval of one week between the announcement and the effective dates. Occasionally, it uses a shorter interval or longer interval than five days. For our sample, the mean interval is five days, and the mode is four days. The interval for our sample is higher than that of Beneish and Whaley (1996), who observe a mean interval of 4.15 days for a small sample of 33 firms that were added to the S&P index. Though not shown here, for about 39% of the additions that occurred during the advance-notification period, the trading interval is 63 days. This invariably ensures that the announcement period overlaps with the effective period. Consequently, the cumulative abnormal returns estimated separately for the announcement and effective periods could be biased. Even for cases where the interval is P5 days, there is evidence that some index funds start buying shares of affected firms before the effective date of implementation in order to minimize tracking errors. As a result, focusing on the effective date abnormal returns could underestimate the price-pressure effects resulting from portfolio rebalancing activities. Therefore, in this section, we use a single event window to estimate the total index effects (AR(ADED)), and we present the results in Table 3.11 Consistent with H1A and H1B, for the addition event, the focal firms’ abnormal returns of 6.65% are strongly significant (t-statistic = 22.69), with about 85% of the sample firms realizing positive abnormal returns. The reaction is even stronger for exclusions. Shareholders of the replaced firms lost 11.79% of their wealth (t-statistic = 15.28), with 75% of the firms realizing negative abnormal returns.12 The incumbents that are in the same industry as the replaced firms also realized abnormal returns of 0.32% (t-statistic = 9.90). Consistent with our hypothesis H1C and H1D, both the industry incumbents (peer group C in our model) and the non-industry incumbents (peer group B) realized significantly negative abnormal returns of 0.06% and 0.32% (t-statistics of 3.38 and 8.06), respectively. Interestingly, and consistent with the industry information hypothesis (H2), the magnitude of the negative index effects experienced by the incumbents that are in the same industry as the added firm is smaller than that realized by the industry peers of the deleted firms, suggesting that positive industry effects associated with additions mitigate the price pressure effects. While the abnormal returns realized by the incumbents of the added firms are not different from those realized by the non-industry incumbents, we find this to be consistent with the information hypothesis. For exclusions, the magnitude of the abnormal returns realized by same industry incumbents is greater than that realized by the non-industry incumbents, suggesting that exclusion events convey negative information about the prospects of the industry counterparts in the index. This negative information effect reinforces the price-pressure effects. These results should be interpreted with caution because given the economically trivial nature of the incumbent effects, the statistically significant abnormal returns are primarily due to the large sample size, which in turn causes the standard errors to become very small. Two features of our results are worth highlighting. First, though the excess returns realized by the incumbents are small, the magnitude of industry information effects is nonetheless similar to what has been documented in the information transfer literature for corporate events such as earnings restatements (Gleason 10 This problem affects prior index composition studies that examine both announcement- and effective-day returns. We thank Mark Huson for providing insightful comments on this problem. 11 We re-estimate the index effects for the simultaneous announcement and implementation regimes in the next section. 12 We present these results here because the approach used to estimate the index effects for the firms added to or excluded from the index, and the attendant results are new.

Table 3 Excess returns from the announcement date to the effective date. Subsample (number of observations)

Mean CAR (%)

Patell Z

% Positive

Generalized sign Z

Additions (N = 267) Deletions (N = 127) Addition industry peers (N = 14,103) Deletion industry peers (N = 14,155) Non-industry peers (N = 124,031)

6.65 11.79 0.06

22.69*** 15.28*** 3.38***

84.6 25.2 48.0

11.87*** 4.97*** 0.86

0.32

9.90***

45.7

6.28***

0.06

8.06***

48.3

0.61

Excess returns (index effects) realized by sample firms from the announcement date to the effective date are presented in this table. The data cover the period 1989:11–2006:12. The abnormal returns are calculated using the CRSP valueweighted returns as benchmark returns. Cumulative abnormal returns (CAR) are the sum of the mean abnormal returns from the announcement date to the effective date. N is the number of observations. * The mean excess returns are significantly different from zero at 10%. ** The mean excess returns are significantly different from zero at 5%. *** The mean excess returns are significantly different from zero at 1%.

et al, 2008), bank failures (Aharony and Swary, 1983), bankruptcy announcements (Lang and Stulz, 1992), going-private transactions (Slovin et al., 1991), share repurchases (Hertzel, 1991), privatization (Eckel et al., 1997; Otchere, 2009), and dividends (Firth, 1996). Second, consistent with the information hypothesis, we find that for exclusions, the loss sustained by the same industry incumbents is larger than that experienced by the non-industry index incumbents. These findings are consistent with our conjecture that negative industry-information effects reinforce the portfolio-rebalancing effects associated with the exclusion event, albeit the addition sample industry peers abnormal returns are not greater than those of the non-industry incumbents. The results thus far show that both information and portfolio-rebalancing effects account for the index effects, especially for deletion events. 4.3. Disentangling the information and industry effects from portfolio rebalancing effects The index effects that we document above appear to emanate from information and price-pressure or portfolio-rebalancing effects. In this section, we disentangle information effects from portfolio-rebalancing effects using a model that we implicitly define as: þ

þ

þ

!

DPit ¼ f Dxit ; Dheit ; DhIit ;

ð2Þ

where DP is the change in price, Dx is the change in the weights of the firms in the index, Dheit is the change in idiosyncratic information and DhIit is the change in the industry information set. We explicitly state the model as:

ARi ¼ ai þ bi þ ci InfoDi þ ki IndMomentumi þ ni Inter v ali þ Xi Controlsi þ ei ;

ð3Þ

where AR is the abnormal returns of the incumbent firms and x is the weight of the firm in the index and is estimated as the market value of the firm divided by the total index value. The change in weight is estimated as the difference between the weight of the stock on the day before the index revision announcement and the day prior to the effective date of the change; IndMomentum is the industry momentum variable; it takes on a value of 1 if the industry has increased (decreased for deletion) its weight based on the number of firms in the index over the previous quarter and 0 otherwise.13 InfoD is the information dummy variable that 13 We thank an anonymous referee for suggesting the inclusion of the industry momentum variable in the regression.

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takes on a value of 1 if the incumbent is in the same industry as the added firm and 0 otherwise; Interval is the number of trading days between the announcement and effective dates; Controls is a matrix of firm size (market capitalization) and growth (three-year pre-event sales growth); b, c, k, n, and X are parameters to be estimated; b captures the portfolio-rebalancing effects; c captures information effects; k captures industry momentum effects, while n captures the impact of the length of the interval between the announcement and effective dates on abnormal returns. We expect the change in weight to be positively related to abnormal returns because if the weight of the firm in the index drops following the index revision, index funds will have to sell some of the shares of the firm; hence, if the portfolio-rebalancing hypothesis accounts for the abnormal returns, then b > 0. If index additions (deletions) have an information effect on incumbents, then c > 0 (c < 0). Similarly, if the momentum effect accounts for the returns, then k > 0. We present the regression results in Table 4. The coefficients of most interest to us are b, c and k which measure the portfoliorebalancing effects, information effects, and momentum effects, respectively. We observe that the coefficient of Dxi is positive and strongly significant in all the regressions. The results presented in columns 2 and 4 show that the information effect coefficient (for deletions) and the portfolio-rebalancing effect coefficients are strongly significant at 1%, although the information dummy for additions is positive but not significant. Similarly, the results presented in columns 3 and 5 show that the portfolio-rebalancing effects for both the industry incumbents and non-industry incumbents are positive and strongly significant, signifying that a greater change in weight leads to greater abnormal returns realized by the index incumbents. The negative abnormal returns realized by the incumbents are induced by a reduction in the weights of the incumbents in the index.14 This evidence reflects the pricepressure effect resulting from the portfolio-rebalancing activities of index funds. The coefficient of the information dummy for index exclusions is also significant, but the magnitude is small. However, the momentum dummy is significantly positive (negative) for additions (deletions), suggesting that for additions, most of the information comes from industry momentum. Consistent with Li et al. (2008), we find that the momentum effect influences the returns of industry incumbents of both the added and deleted firms; however it affects the incumbents of the deleted firms (losers) more than incumbents of the added firms (winners). The differential momentum effect of momentum on return is similar to that documented by Asem (2009) for dividend payers and non-dividend payers. The interval variable is also positively related to the abnormal returns, suggesting that a longer interval between the announcement and effective dates leads to greater abnormal returns. Also, consistent with the event-study results, the regression results (both idiosyncratic information and industry momentum effects) for the exclusion events are stronger in terms of their economic and statistical significance. The exclusion event conveys information about the incumbents and the industry, as the incumbents experience negative returns beyond what can be attributed to portfolio-rebalancing effects. For the non-industry incumbents in the index, the variable of most interest to us, Dxi, is positive and strongly significant. As a result, part of the incumbents’ AR(ADED) is attributed to portfolio rebalancing effects, and a portion, albeit small, is attributed to information effects.15 These index

14 The descriptive statistics presented in Table 1 show that the added firms are larger than the excluded firms; therefore, additions bring about a fall in the weights of the incumbents in the index. 15 This finding is in contrast to that of Rakowski and Wang (2009), who find that information effects dominate price-pressure effects resulting from the flow of mutual funds.

Table 4 Decomposition of index effects Dependent variable

Addition-event excess returns

Deletion-event excess returns

Model

(1)

(2)

(1)

(2)

Intercept

0.006 (2.43)** 121.98 (50.58)*** 0.001 (1.23)

0.006 (2.46)**

0.009 (3.75)*** 130.77 (52.48)*** 0.01 (8.32)***

0.009 (3.75)***

Dx InfoD

0.002 (5.17)*** 0.0004 (4.79)*** 0.0003 (1.51) 0.000 (0.15)

0.001 (1.30) 95.61 (19.66)*** 130.60 (47.03)*** 0.002 (5.13)*** 0.0004 (4.74)*** 0.0003 (1.55) 0.000 (0.08)

0.03 84,734

0.03 84,734

DxSameInd DxNonInd Industry momentum Interval Size Growth Adjusted R2 N

0.01 (9.14)*** 0.0001 (1.29) 0.001 (4.16)*** 0.000 (1.34)

0.01 (8.32)*** 132.83 (20.66)*** 130.41 (48.25)*** 0.01 (9.14)*** 0.0001 (1.29) 0.001 (4.16)*** 0.000 (1.34)

0.03 85,193

0.03 85,193

Panel A presents the regression results for the addition event where the dependent variable represents the abnormal returns realized by the incumbents in the index. For the results presented in panel B, the dependent variable is the exclusion event excess returns. Specifically, we estimate the following regression: ARðADEDÞ ¼ ai þ bi Dxi þ ci InfoD þ ki Momentumi þ ni Interv ali þ Xi Controlsi þ ei ;

where AR(ADED) represents the abnormal returns from the announcement to the effective period; Dx is the change in the weight of a firm; InfoD is the information dummy variable that takes on a value of 1 if the added (excluded) firm is in the same industry as the peer and 0 otherwise. Momentum is a dummy variable that takes on a value of 1 if the industry has increased (decreased for deletion) its weight based on the number of index constituents of that industry over the previous quarter and 0 otherwise. The rest are as defined. The t-statistics are based on White heteroskedasticity-consistent standard errors. * Significance level of 10%. ** Significance level of 5%. *** Significance level of 1%.

effects remain significant even after controlling for the trading interval, size, and growth. 4.4. Further test of the portfolio rebalancing effects Index fund managers may trade the shares of firms affected by the index revision before the effective date; however, doing so could generate significant tracking errors. To minimize tracking errors, index fund managers rebalance their portfolios as close to the effective dates as possible.16 Commenting on S&P’s announcement in March 2006 that it would be including Google in the S&P 500 index, Kathleen Pender of the San Francisco Chronicle wrote: Index funds are likely to buy most of their Google stake on or around March 31, the day it replaces Burlington Resources in the index . . . The goal of most index funds is to track their benchmark as closely as possible. Google will be added to the S&P 500 index at its closing price next Friday . . . Ken Uematsu, an index-fund manager with T. Rowe Price, says he will probably buy his Google stake late Friday to minimize the risk of paying too much and falling behind the index (San Francisco Chronicle, March 25, 2006).

16 Kappou et al. (2010) find that index funds, concerned more with tracking error than profits appear to cluster their rebalancing activities close to the effective date.

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Given index fund managers’ desire to minimize tracking errors, we argue that price pressure effects resulting from portfolio-rebalancing activities will be more prevalent around the effective date of the change. To test this conjecture, we re-estimate Eq. (3) using the effective-day abnormal returns for the whole study period (from 1978 to 2006) as the dependent variable. We expect the abnormal returns on the effective date to be dominated by portfolio-rebalancing effects; as a result, we expect b to be strongly significant. The results of the regressions are presented in Table 5. Consistent with our conjecture, we find that the coefficient for the industry incumbents dummy variable is strongly significant at <1% and that the information variable coefficient for the effective period abnormal returns regression is not significant. The results suggest that for index additions, the abnormal returns documented for the peers on the effective date are due to price-pressure effects resulting from portfolio-rebalancing activities. We document similar results for the exclusion event. The effective-day abnormal returns realized by the peers following index exclusions are also attributed to portfolio-rebalancing effects. The coefficient of Dxi for the same industry incumbents and non-industry incumbents is positive and strongly significant at <1%. The coefficient suggests that a one basis-point drop in the weight of a stock in the index leads to a 1.39% drop in price of non-industry incumbents. The industry (information) momentum variable is only significant in the deletion regression. The coefficient of interval is positive and statistically significant, suggesting that a shorter interval between the announcement date and the effective date leads to lower abnormal returns on the effective date. This result may reflect the fact that fund managers start Table 5 Regression analysis of the incumbents’ effective-day excess returns. Dependent variable

Addition event excess returns

Deletion event excess returns

Model

(1)

(2)

(1)

(2)

Intercept

0.0003 (2.01)** 101.06 (52.35)*** 139.44 (130.78)*** 0.00002 (0.08) 0.0001 (0.34)

0.001 (1.33) 101.11 (52.40)*** 139.33 (130.70)*** 0.0003 (1.28) 0.00001 (0.09) 0.0001 (1.95)* 0.0001 (1.85)* 0.000 (0.91)

0.001 (4.52)*** 129.39 (50.36)*** 140.17 (132.42)*** 0.001 (4.47)*** 0.004 (8.49)***

0.001 (1.18) 129.01 (50.19)*** 139.05 (132.19)*** 0.001 (4.48)*** 0.004 (8.85)*** 0.0001 (4.47)*** 0.0001 (1.05) 0.000 (0.27)

0.1735 94,521

0.1738 94,343

0.1739 95,825

0.1759 94,274

DxSameInd DxNonInd InfoD Industry momentum Interval Size Growth Adjusted R2 N

Panel A presents the regression results for the sample where the dependent variable is the incumbents’ effective-period excess. For the results shown in Panel B, the dependent variable is the exclusion-event excess returns. The data cover the period 1978–2006. We estimate the following regression: ARðEDÞ ¼ ai þ bi Dxi þ ci InfoD þ ki Momentumi þ ni Interv ali þ Xi Controlsi þ ei ;

where AR(ED) represents the abnormal returns of the incumbent firms on the effective day; Dx is the change in weight; InfoD is the information dummy variable that takes on a value of 1 if the incumbent is in the same industry as the added firm and 0 otherwise; Momentum is a dummy variable that takes on a value of 1 if the industry has increased (decreased for deletion) its weight based on the number of index constituents of that industry over the previous quarter and 0 otherwise; Interval is the number of trading days between the announcement date and the effective date. The t-statistics are based on White heteroskedasticity-consistent standard errors. * The coefficients are significantly different from zero at 10%. ** The coefficients are significantly different from zero at 5%. *** The coefficients are significantly different from zero at 1%.

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rebalancing their portfolios before the effective date. The goodness-of-fit statistic for the regressions is about 17%. In general, the results presented in Table 5 provide additional evidence that strongly supports the portfolio-rebalancing hypothesis. 4.5. Robustness tests 4.5.1. Benchmark index effects In this section, we perform a number of tests to assess the robustness of our results. The first test relates to the benchmark used to estimate the index effects. The abnormal returns reported in this study are based on CRSP value-weighted index returns, as our focus is on changes in weights, which in turn are based on market capitalization. However, there is evidence that abnormal returns are a function of the benchmark index used to compute returns. To ensure that our results are robust to the benchmark returns used in the study, we also estimate index effects based on CRSP equally-weighted index returns. In unreported results, we find that the use of alternative benchmark returns does not alter our findings. 4.5.2. Time horizon sub-period robustness test Our study period straddles two sub-periods: 1978–1989 and 1990–2006. The sub-periods are distinguished by the announcement regime that existed at that time. The first sub-period is characterized by a simultaneous announcement and implementation regime in which changes to the index were announced and implemented on the same day. After 1989, S&P introduced a new announcement policy regime that drastically changed the procedure by which index composition changes are made. A five-day advanced-notification regime in which the names of the firms to be excluded from and added to the index are pre-announced came into effect in late 1989 and has been in place since then. In this section, we examine whether the portfolio-rebalancing effects that we document above are due to the announcement policy regime by separately estimating the index incumbency effects for the two non-overlapping sub-periods, namely, 1978–1989 and 1990– 2006, using the announcement period excess returns. The results of the analysis for the sub-periods are presented in Table 6. Consistent with expectations, we find strong support for the portfolio rebalancing hypothesis during the simultaneous announcement and implementation regime. The significance and magnitude of the portfolio-rebalancing effect following index revision during the simultaneous announcement and implementation period are greater than those of the advance notification regime period that is currently in place. With no lead time for the index funds to gradually rebalance their portfolios, the price-pressure effects were much stronger during the former period than during the advance-notification regime. The results presented in columns 4 and 5 show that the portfolio-rebalancing effects are not significant and that the magnitude of the coefficient has decreased significantly.17 In addition, models (3) and (4) have very low explanatory power, whereas models (1) and (2) have adjusted-R2 values of 27% and 28%, respectively. The foregoing analysis suggests that our model specification in Table 5 captures the portfolio-rebalancing effects when these are expected to occur, that is, at the effective addition/ deletion date or shortly before that date. 4.5.3. Information effect robustness test The results presented above show that changes in the index composition convey industry information that mitigates or reinforces the price-pressure effects. In Eq. (3), we capture the information effect using an indicator variable that takes on a value of 1 if 17 This finding is consistent with the evidence in studies that show a perceived reduction of index effects over time because of the increased number of investors (including hedge funds) who try to take advantage of it (Ding et al., 2009).

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the added firm and the replaced firm are in the same industry as the incumbents.

Table 6 Robustness check: Announcement regime change results Announcement and effective date excess returns 1978–1989

Announcement date excess returns 1990– 2006

Model

Additions (1)

Deletions (2)

Additions (3)

Deletions (4)

Constant DxSameInd

0.0004 (0.33) 368.08 (57.02)*** 223.66 (110.76)*** 0.0003 (0.80) 0.0002 (0.85)

0.001 (0.90) 337.74 (37.68)*** 231.09 (116.37)*** 0.0001 (0.18) 0.001 (1.90)*

0.00001 (0.18) 0.00001 (4.70)***

0.0003 (0.35) 0.00001 (3.46)***

0.0001 (0.07) 1.09 (0.53) 0.078431 (0.07) 0.0001 (0.30) 0.00004 (0.24) 0.00003 (1.04) 0.0001 (0.88) 0.000 (0.31)

0.003 (3.27) 3.13 (1.13) 1.76 (1.55) 0.001 (4.21)*** 0.002 (3.88)*** 0.00002 (0.67) 0.0002 (2.97)** 0.000 (1.57)

0.0001 94,274

0.0016 94,272

Dependent variable

DxNonInd InfoD Industry momentum Interval Size Growth Adjusted R2 N

0.28 40,418

0.27 40,530

Panel A presents the regression result for the (1) addition and (2) exclusion event, where the dependent variable is the announcement/effective day abnormal returns realized by the incumbents in the index. The data cover the period 1978–1989. Panel B presents the regression result for the (3) addition and (4) exclusion event, where the dependent variable is the announcement-day abnormal returns realized by the incumbents in the index. The data cover the period 1990–2006. We estimate the following regression: ARðADÞ ¼ ai þ bi Dxi þ ci InfoD þ ki Momentumi þ ni Interv ali þ Xi Controlsi þ ei ;

where AR represents the abnormal returns of the incumbent firms; Dx is the change in the weight of incumbent firm; InfoD is the information dummy variable that takes on a value of 1 if the incumbent firm is in the same industry as the added firm and 0 otherwise; Momentum is a dummy variable that takes on a value of 1 if the industry has increased (decreased for deletion) its weight based on the number of index constituents of that industry over the previous quarter and 0 otherwise. Controls are as defined; interval is the number of days between the announcement date and the effective date. The t-statistics are based on White heteroskedasticity-consistent standard errors. * The coefficients are significantly different from zero at 10%. ** The coefficients are significantly different from zero at 5%. *** The coefficients are significantly different from zero at 1%.

the added firm and the incumbents are from the same industry and 0 otherwise. An alternative test of the information hypothesis is to examine index effects on incumbents that are in the same industry as the added and replaced firms. If index additions and exclusions convey industry information, then in cases where the added firm, the replaced firm, and the incumbents are from the same industry, changes in the index composition will not convey any industry information. To test this proposition, we re-estimate Eq. (3) using info2 as the information dummy. This variable takes on a value of 1 if the added firm, the replaced firm, and the incumbents are from the same industry and 0 otherwise. The results of the regression (with t-statistics in parentheses) are ARðADEDÞ ¼ 0:01 þ 14:44Dxi Nonind ð2:76Þ ð4:31Þ þ 11:31Dxi SameInd þ 0:003INFO2 ðfor additionsÞ ð6:70Þ

ð0:88Þ

ARðADEDÞ ¼ 0:01 þ 13:31Dxi Nonind ð2:74Þ ð5:50Þ þ 14:50Dxi SameInd  0:004INFO2 ðfor exclusionsÞ ð2:79Þ

ð1:24Þ

Consistent with the predictions of the information hypothesis, the information dummy variable is not significant in cases where

5. Summary and conclusion With over a trillion dollars of wealth in index funds tied to the S&P 500 index, firms added to or excluded from the index are subject to enormous buying pressure. Prior studies show that such firms earn significantly positive and negative abnormal returns, respectively. In this study, we examine the effects of revisions of the S&P 500 index membership on the share price of incumbents in the index and find that the changes bode ill for incumbents, as they earn significantly negative abnormal returns, primarily because of portfolio-rebalancing activities. Interestingly, for incumbents in the same industry as the added firms, the negative portfolio-rebalancing effects associated with index additions are mitigated by positive industry momentum effects. Similarly, for index exclusions, the magnitude of the loss sustained by the same industry incumbents is larger than that experienced by the nonindustry incumbents, as the negative industry effects reinforce the price-pressure effect. A noteworthy feature of our results is that the information effect associated with exclusions is greater than that of additions. This finding is consistent with the assertion that investors react more strongly to bad news than they do to good news. The incumbents’ abnormal returns realized at the time when the change becomes effective are positively related to changes in the weights of the incumbents, thus suggesting that portfoliorebalancing activity accounts for the effective-day abnormal returns. The documented index effects exist even after controlling for idiosyncratic factors such as firm size and growth. Collectively, our results are consistent with the conjecture that idiosyncratic information effects, industry momentum effects and price-pressure effects account for the documented index effects. Our results therefore suggest that index composition changes are not information-free events, although most of the abnormal returns realized by the incumbents are attributed to portfolio-rebalancing effects. Acknowledgements We thank Ike Mathur (the editor), an anonymous referee, and David Blitzer (Managing Director and Chairman of the S&P 500 Index Composition Committee), Ben Amoako-Adu, Mike Hemler, Peter Swan, Vijay Jog, and Brian Smith for helpful comments. We are indebted to Mark Huson for many insightful comments and suggestions that improved the paper substantially. Some of this work was done while André Gygax was visiting the Graduate School of Finance, Vienna University of Economics and Business. We gratefully acknowledge financial support from the Department of Finance, University of Melbourne. All remaining errors are ours. References Aharony, J., Swary, I., 1983. Contagion effects of bank failures: Evidence from capital markets. Journal of Business 56, 305–322. Ambrose, B.W., Lee, D.W., Peek, J., 2007. Comovement after joining an index: Spillovers of non-fundamental effects. Real Estate Economics 35, 57–90. Asem, E., 2009. Dividend and price momentum. Journal of Banking and Finance 33, 486–494. Barberis, N., Shleifer, A., Wurgler, J., 2005. Comovement. Journal of Financial Economics 75, 283–317. Beneish, M.D., Whaley, R.E., 1996. An anatomy of the S&P game: The effects of changing the rules. Journal of Finance 51, 1909–1930. Blitzer, D.M., 2006. The S&P 500, CRSP 2006 Forum. Standard and Poor’s report. Cai, J., 2007. What’s in the news? Information content of S&P 500 additions. Financial Management 36, 113–124. Chen, H., Noronha, G., Singal, V., 2004. The price response to S&P 500 index additions and deletions: Evidence of asymmetry and a new explanation. Journal of Finance 59, 1901–1930.

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