The changes to the S&P 500 index provide a unique laboratory for assessing the degree to which institutional versus individual investors capitalize on available arbitrage opportunities. We provide new evidence on the S&P 500 game using intraday data and examining the role of institutional versus individual investors in both open hours and after-hours trading. Using a sample of 135 changes to the S&P 500 index, we find the highest returns from the S&P game are obtained by investors who enter the game at the beginning of the after-hours session of the announcement date. Profits from arbitrage remain even after accounting for the bid-ask spread. Introduction

The "S&P game," a term coined by Beneish and Whaley (1996, 1997, 2002), represents the arbitrage that occurs in response to addition of stocks in the S&P 500 index.1 It is conducted by both index fund managers and other traders. When changes in an index are announced, index fund managers rebalance their portfolio by buying the added firms. Traditionally, the index fund managers attempted to time the market by jamming the close-buying the stock during the last hour of trading on the day prior to the effective date. More recently, traders attempt to game the index fund managers by getting in the game earlier (on the announcement date) and selling higher to index fund managers on the effective date (Luskin, 2000). Thus, the added stocks may experience price effects at the time of the announcement and around the effective date.

The S&P game serves as a unique laboratory to assess arbitrage gains for the following reasons. First, the event (since October 1989) is anticipated fully, as the announcement occurs in advance of the effective date when the index officially is changed. Therefore, it allows some time for arbitrageurs to capitalize on the potential discrepancy in prices, which easily could be done by individual investors. Since 1989, Standard & Poor's has announced the change in the index shortly after the market closes. The effective date commonly is set for five days from the time of the announcement, but has ranged from one day to 20 days. Risk arbitrageurs anticipate that index funds will need to rebalance their portfolios accordingly and take positions in advance of the index funds. While there is strong evidence of gains by risk arbitrageurs, there is limited information about the flow of trading activity in response to the change in the index.

Second, because these events have occurred over time, they allow us to test whether time-varying conditions have altered the price adjustment process. Given that arbitrage has been documented in the past, the arbitrage returns in recent years should be reduced as a result of the increased participation by individual investors. Third, the increasing use of after-hours trading over time may have caused a shift in the proportion of the arbitrage returns that occur in that market versus during normal trading hours.

In particular, our paper differs from previous research in that we assess the impact of the index changes from a microstructure perspective to address the following questions:

* What is the timing of the price adjustment? How long does it take for prices to adjust? Because the announcement occurs after hours, what is the degree of price adjustment in the first after- hours market session versus the subsequent normal day market? Has the proportion of total arbitrage returns that occurs in the after- hours market increased recently in response to the increased trading activity of the night market? If so, this is the first evidence that investors should transact after the close of the day session of the announcement of the change in the S&P Index.

* Are arbitrage profits still possible even after considering the actual spread between bid and ask prices? If so, retail investors should operationalize these trading strategies to take advantage of index funds' participation.

* What is the proportion of total arbitrage activity that is attributed to institutional versus individual investors? Has this proportion changed over time? If so, retail investors successfully are taking advantage of information technology increasingly available to them in order to generate trading profits.

Based on a sample of additions to the S&P 500 index from 1999 to 2002, we are able to address the microstructure-related questions surrounding the arbitrage process. We find that the largest arbitrage profits are earned by investors who enter the game at the beginning of the after-hours session of the announcement date. The arbitrage is profitable even after accounting for the bid-ask spread.

Theoretical and Empirical Implications Timing of Price Adjustment

Previous studies find that semi-strong market efficiency is violated due to index arbitrage. Index funds would like to reduce their purchase price by rebalancing before the effective date, such as the night session following the announcement date. Participating too early in the game (such as during the night session on the announcement date), however, could result in a larger tracking error, which is especially a concern if the strategy results in a larger downside error relative to the index and when there is a long time delay between the announcement and effective dates. Nonetheless, if the returns from the game are available in the pre- opening session or at the open on the day prior to the effective date, index funds would be able to avoid tracking error. If index funds are attempting to rebalance before the effective date, the price adjustment is expected to occur more rapidly.

We also test whether any evidence of market inefficiency is isolated in the afterhours markets or also persists during normal trading hours. According to Barclay and Hendershott (2003), the after-hours market has become more popular over time. Therefore, the participants in the after-hours market may accrue a larger proportion of the arbitrage return than in the past. We hypothesize that most of the arbitrage will occur in the first session that traders can use the public information, which is the night session of the announcement date.

Evidence on Changes to Indices

Jain (1987) and Dhillon and Johnson (1991) document positive and significant changes in the stock prices of firms that are added to the S&P 500 index. Lynch and Mendenhall (1997) assess 71 changes in the S&P 500 index over the period from October 1989 to 1995. During this period, the additions to, or deletions from, the index are announced by S&P before they become effective, which may allow some time for risk arbitrageurs to accumulate shares of the added stock before the index funds respond. The authors document positive and significant returns following the announcements. Beneish and Whaley (1995) and Beneish and Whaley (1997) document adjustments in trading volume of firms that are added to a stock index.

Beneish and Whaley (1996) find that arbitragers earn significant abnormal returns prior to the effective date, though these returns subsequently are reversed, indicating that index funds are being front run by gamers. Beneish and Whaley (2002) examine the role of after hours trading and find that significant returns still can be made and are increasing over time, despite their conjecture that such abnormal returns would be driven away. Beneish and Whaley (2002) also examine trading firms deleted from the S&P 500, and find that this new game in town also generates significant abnormal returns. Their papers, however, do not investigate the role of individual versus institutional investors in generating gaming returns, nor do they assess whether significant abnormal returns are possible after accounting for transactions costs.

Data and Methodology

Data

The list of firms added to the S&P 500 Index from January 1999 to December 2002 is obtained from the Standard & Poor's (hereafter, S&P). During this four-year period, there are a total of 151 firms that were added to the S&P 500 index.2 Daily returns are obtained from the CRSP to calculate the abnormal and cumulative abnormal returns. To analyze the trading and price movements during various intraday periods, the trade-by-trade price and quotes data are utilized. These trades and quotes data are obtained from the NYSE TAQ database.