Investing in Projected Russell 2000 Stock Additions: A Viable Investment Strategy or a Loser s Game?

Transcription

1 May 2011 Investing in Projected Russell 2000 Stock Additions: Abstract Existing studies on the annual rebalancing of the Russell indexes utilize the actual additions and deletions of these indexes to draw conclusions about the price/return dynamics of related portfolios. We approach this issue from the perspective of running an investment strategy which goes long in the projected additions to the Russell 2000 index. We investigate several dimensions, such as the type of filters applied to construct the investment portfolio, the time of its formation, and the success rate of projection/forecasting the right stock additions. Our results reveal that the quality of index projections as well as transaction costs largely determine the strategy s profitability. Vitaly Serbin Director, ITG, Inc. Milan Borkovec Managing Director, ITG, Inc. Xuepeng Sun Assistant Vice President, ITG, Inc info@itg.com The authors would like to thank Ian Domowitz, John Carillo, Charles Behette and Konstantin Tyurin for their support, comments, and suggestions. Any opinions expressed herein reflect the judgment of the individual authors at this date and are subject to change, and do not necessarily represent the opinions or views of Investment Technology Group, Inc. The information contained herein has been taken from trade and statistical services and other sources we deem reliable, but we do not represent that such information is accurate or complete, and it should not be relied upon as such. The analyses discussed herein are derived from aggregated ITG client data and are not meant to guarantee future performance or results. This report is for informational purposes. This report does not provide any form of advice (investment, tax, or legal). No part of this report may be reproduced or retransmitted in any manner without permission. The Frank Russell Company is not affiliated with ITG. Russell TM is a trademark of the Frank Russell Company. The Russell 1000, Russell 2000, and Russell 3000 indices are trademarks of the Frank Russell Company. All trademarks, service marks, and trade names not owned by ITG are owned by their respective owners Investment Technology Group, Inc. All rights reserved. Not to be reproduced or redistributed without permission

2 I. Introduction Every year at the end of June, the Russell Investment group reconstitutes its family of indexes. The associated investment opportunities and challenges are evident from the dual goals of indexers and speculators during the Russell reconstitution period: indexers need to keep their tracking errors under control, while speculators are looking to take advantage of the shifting demand and supply for the index additions and deletions. Several studies have focused on the return behavior of portfolios of Russell stock additions and deletions (for instance, Madhavan (2003) and Onaev and Zdorovtstov (2007)). The consensus in these papers is that the excess demand for Russell stock additions implies positive returns on the spread portfolio which is long in the index additions and short in the index deletions. The reported positive return is realized in the period prior to the effective rebalancing day. However, after the effective day the return on this spread portfolio turns negative. It is well documented that these return dynamics result in significant losses to long-term index investors (Chen et al., 2005, Cai and Houge, 2008). The contribution of our paper to this area of research is as follows. First, we review and extend the existing evidence on return dynamics of Russell 2000 stock additions before the effective rebalancing date to a more recent time period. To the best of our knowledge, there are no published studies on the annual rebalancing of the Russell indexes that use data after Second, we vary the portfolio formation date and the stock universe and analyze different scenarios. Specifically, we investigate the profitability of a simple investment strategy that invests in stocks that are anticipated to enter the Russell 2000 index on the effective day. Determining the optimal initiation date for the portfolio is one of the building blocks of this strategy. To identify the other important input of the strategy stock selection we build the investment strategy using the simulated projections rather than the actual stock additions. This approach avoids the look-ahead bias, allowing us to draw valuable lessons for practical use, and quantifies the impact of the quality of Russell index projections on the performance of the investment strategy. In the following section we describe the annual Russell reconstitution process in more detail and give additional reasons for this study. Section 3 explains our methodology, Section 4 presents the results, and Section 5 concludes. II. Motivation and details It has long been understood that the relative transparency of the rules guiding Russell indexes rebalancing leads to profitability of speculative investment strategies.1 One of the simplest and most obvious strategies is to buy projected Russell stock additions prior to the effective date and sell them to the indexers on that day. Similarly, one can sell short projected Russell stock deletions and buy them back from indexers on the effective date, although short selling is harder to accomplish, especially in illiquid stocks. Onaev and Zdorovtstov (2007) report an average 11.64% return on the portfolio of Russell stock additions between 2000 and For a complete list of rules for inclusion into Russell indexes please see the document Equity Indexes Construction and Methodology that can be downloaded from the Russell Investment group website documents/methodology.pdf. Investment Technology Group, Inc. (ITG) 2

3 (the returns are not risk-adjusted) during the period starting 21 days and ending 1 day prior to the effective date. They also report an average 6.77% return on the spread portfolio between stock additions and deletions over the same period. In comparison, Madhavan (2001) reports an average return of 5.12% on the portfolio of Russell stock additions and 6.83% return on the spread portfolio of additions and deletions during the months between March and June in 1996 to The above results seem to suggest a strong potential for easy and almost risk-free investment profits. However, as always, the devil lies in the details, and the conclusions of those papers must be taken with a grain of salt. First, the above-mentioned papers have not focused on the uncertainty in the Russell index changes, and thus used actual Russell index additions and deletions to derive the reported average returns. In practice, the actual list of stocks going into or out of the rebalanced Russell indexes is unknown until the date on which the preliminary version of the revised index is released by the Russell Investment group. As a consequence, it is likely that part of the reported returns cannot be captured due to the inability to perfectly forecast all index changes. The second practical limitation of these Russell rebalancing investment strategies are transaction costs that are especially high for less liquid stocks. Consequently, buying or selling Russell stocks that are expected to be added or deleted from the Russell 2000 index can be expensive. In this paper, we take the practical approach to analyzing the predictability of the Russell index changes and study returns of the portfolios of predicted index additions that are net of transaction costs. Not surprisingly, the simple investment approach of buying the projected Russell 2000 stock additions and holding them until the effective day loses its attractiveness once trading costs and the uncertainty about the composition of the rebalanced index are factored in. Figure 1 below depicts the averaged cumulative return on the equal-weighted portfolio of projected Russell 2000 additions from different formation dates to the effective day over the period of 2005 to For simplicity, we omit the details of the return computation and stock selection here and discuss them in the next section. Figure 1 contains three average cumulative returns: the blue bars illustrate the average cumulative portfolio returns of actual stock additions and the green and red bars depict the average cumulative portfolio returns of projected stock additions before and after transaction costs were subtracted, respectively. The horizontal axis represents the portfolio formation time: it varies from 8 weeks prior to the ranking date (roughly, the beginning of April, which we call formation week #1) to 3 weeks prior to the ranking date (roughly, the second week of May). Several observations are in order. First, portfolios of actual index stock additions exhibit a monotonic decline in return as they are formed later in time. For instance, portfolios formed at the beginning of April yield over 12% average cumulative return until the effective date, while portfolios formed closer to mid-may yield only approximately 4%. This is intuitive, as knowing the new index composition earlier allows for more return opportunities due to stocks fundamentals and to the excess demand from indexers. The situation changes dramatically once we consider portfolios of projected index additions which are depicted by the green series. The returns do not depend much on the portfolio formation time and are actually slightly rising as portfolio gets formed later in time (from approximately 2.5% for portfolios formed 7-8 weeks prior to effective date to approximately 3% for portfolios formed 3-4 weeks prior to that date). A possible explanation is the interaction of two opposing effects. As the holding period increases we can capture additional excess return; however, with the earlier formation date the quality of index projections also goes down. Investment Technology Group, Inc. (ITG) 3

4 Figure 1: Cumulative return on the portfolio of R2000 adds (avg ) return, % actual adds projected adds - no costs projected add - net of costs portfolio formation week Accounting for transaction costs results in a further decline in the returns on the portfolios of projected Russell 2000 stock additions. After accounting for costs, the strategy of buying the projected Russell additions yields average returns that range between 1.3% and 2.5% over the period of 2 to 3 months prior to the effective date. Again, the trend of rising returns for portfolios formed closer to effective date is visible (from 1.3% for the portfolios that were formed in early April to 2.5% for portfolios with formation date in mid May). The above discussion indicates that the excess returns on the portfolios of Russell 2000 stock additions documented in the literature do not easily translate into a profitable investment strategy. The quality of the projections as well as accounting for transaction costs is important as both can push the realized net returns of the investment strategy into a negative territory. In the next sections we turn to the specifics of constructing an investment strategy that can still be profitable. In particular, we explore the portfolios that contain only subsets of projected Russell stock additions and vary the date on which these portfolios are formed. In addition, we study the level of forecasting precision necessary to run a profitable investment strategy based on projected Russell 2000 stock additions. III. Methodology and data The study covers the period between 2005 and We use Bloomberg end of day date to derive price, market capitalization, volume and volatility data. In order to avoid complications inherent in shorting stocks, we only consider investment strategies that are based on our predictions of the Russell 2000 stock additions. It is a common practice in the industry to forecast stock additions and to many other sell-side companies, ITG forecasts projected weight changes for stocks added to and excluded from existing Russell indexes on a weekly basis starting from mid- April each year. The illustrative results presented in section 2 were based on these projections. Investment Technology Group, Inc. (ITG) 4

5 In what follows, we simulate the list of projected Russell 2000 additions and use it as a supplement to ITG s projections. Using the Monte-Carlo simulation approach that is described below allows us to abstract from a specific index projection methodology, and makes the research results replicable by others. The dates on which the simulation is done coincide with the dates on which the actual ITG s projections for a particular year had been issued. In order to do comparison across years we operate in terms of weeks rather than calendar dates. For instance, regardless of the specific year, portfolios formed at the beginning of April (e.g or ) are considered to be formed in the week #1. Overall, we generate 6 weekly lists of simulated Russell 2000 stock additions each year. The first one is done in early April (week #1) and the last one is created in mid-may (week #6). 2 The simulation is implemented as follows: 1. We compare the Russell 2000 index composition prior to rebalancing and post-rebalancing in order to come up with the list of actual index additions for a given year. Stocks downgraded from Russell 1000 to Russel are excluded. Suppose the length of this list is N. 2. We assume a certain success rate (call it p) of predicting index additions. We define the base success rate p as p = [# of correctly predicted additions] / [total number of actual additions]. We vary p from 0.2 to 0.4 with the increment of 0.05 (i.e. we consider five values of base success rate in total). For each base success rate, we produce an adjusted success rate that depends on the simulation week. For week #1 (early April) we set the adjusted success rate equal to the base rate. After the first week, we increment the base rate by 0.04 each week, i.e. P week t = p + (# of week -1) Consequently, in week #6 (the last simulation week which corresponds to mid May), the adjusted success rate will be 15% higher than the one used for the simulation in week #1. This adjustment reflects the increasing precision of index projections as the time gets close to the ranking date. These parameters are loosely calibrated using past ITG s projections We go down the list of actual Russell 2000 additions. For each stock i from the list (i = 1 to N) we draw an uniform random number from 0 to 1, and check whether it is smaller than P week t. If the answer is yes then stock i is included into the simulated list of additions; if the answer is no then a random stock, which is matched with stock i with respect to sector, market cap and 12-month trailing momentum, is included into the simulated list of stock additions. The matching is accomplished by placing stock i into one of the 250 groups formed from independent sorts (by 10 sectors, 5 market cap groups and 5 momentum groups), and then drawing a random name from that group. 4 Note that the random stock that is drawn via this characteristicsmatching procedure cannot be in the list of actual index additions. Finally, we make 2 For instance, simulation dates for 2009 are: , , , , , One of the reasons why we set the base success rates so low is that stocks below $1 are not considered as candidates for the Russell 2000 additions in ITG s projections. This is because Russell Investment group does not include penny stocks (stock price below $1) into its Russell indexes. However, many stocks that are priced below the $1 threshold as of the projection date can still enter the index due to a late, positive price run-up. 4 The sector information is from GICS. The market cap groups were formed using market cap percentiles of NYSE-listed stocks obtained from Ken French s website data_library. html. Group 1 contains stocks with market capitalization below the 10th percentile of the NYSE-listed stock universe, group 2 contains stocks in the 10-20th percentile range, and so on. Technically, 10 market groups are possible, however, only the bottom five are of practical relevance, since we are dealing with a small cap Russell 2000 index. Momentum groups are formed by applying percentiles to a 12-month cumulative return across all common US stocks. Investment Technology Group, Inc. (ITG) 5

6 sure that there are no duplicates in the simulated additions list (i.e. the same random stock cannot be included into the simulated list more than once). Once the simulated list of Russell 2000 index additions is constructed, we form an equallyweighted portfolio containing the names in the list. While one can choose any other weighting (for instance weighting by projected money flow, money flow days, or projected tracking error), we believe that the equally-weighted scheme is preferable since the weights do not rely on any additional estimated quantity, and therefore are more robust to erroneous forecasts. After the portfolio is constructed, it is held from the formation date until the effective date. Individual stock returns are computed from closing prices obtained from Bloomberg and adjusted for corporate actions. We also subtract the return on the Russell 2000 index to mimic the common practice of hedging Russell bets. The portfolio constructed at the formation date is a buy-and-hold portfolio, i.e. we do not rebalance it due to newer and possibly better index projections. The cumulative adjusted returns of this portfolio between formation date and one of the three dates mentioned above will be the subject of our further analysis and can be considered as the excess return relative to the Russell 2000 index. We present both raw and net returns of the investment strategy portfolio. The net returns are obtained by subtracting the estimated total transaction costs associated with entering the long positions. We use ITG s Agency Cost Estimator (ACE) cost estimates for a 1-day VWAP strategy and assume that a $10mln portfolio has been invested into the strategy. This cost estimate is an aggressive choice, since we assume that the trading should be completed in one day. 5 ACE cost estimates are also used for subdividing the portfolio of simulated projected stock additions into liquidity groups. For each name on the portfolio formation date, we determine the cost estimate of buying 10,000 shares and then use the 20th, 40th, 60th and 80th percentiles of the distribution of these cost estimates as thresholds for the liquidity group subdivision. The most liquid stocks are assumed to have the lowest estimated costs (0-20% of sample distribution), while the most illiquid ones have the highest estimated costs (80-100% of sample distribution). In section 4, we will analyze the realized excess returns of various portfolios of projected index additions. Specifically, we will compare the performance of the portfolio including all simulated projected stocks additions with the portfolios that exclude the stocks that belong to the most illiquid sub-groups. IV. Results In what follows, we present our results that are based on the Monte-Carlo simulation methodology discussed in the previous section. Figure 2 presents the net return of the investment strategy between the portfolio formation date and the effective date. The chart has two dimensions: the horizontal axis shows the index of the formation week (from the earliest, #1 5 As a robustness check, we also compute the cost with a 10% Volume Participation rate. The corresponding results predictably fall between 1-day VWAP and no-cost scenarios, and they are omitted here for the sake of brevity. Investment Technology Group, Inc. (ITG) 6

7 to the latest, #6), while the vertical axis illustrates the cumulative returns on the portfolio formed in that week and held until the effective date. Different colors depict different levels of success rate used to simulate the list of Russell 2000 additions. As mentioned previously, we vary the base success rate form 20% to 40%. For each year in the period between 2005 and 2010 and each success rate, we draw 10 random samples. We then average the portfolio returns resulting from each draw. The returns presented in Figure 2, Panel A-C, are averaged across years. Figure 2: Portfolio return until effective date Return,% Panel A: no liquidity filters portfolio formation week Success Rate 20% 25% 30% 35% 40% Panel A depicts the average returns on the portfolios when stocks have not been filtered out by liquidity. The average number of stocks in these portfolios is 218. There are two main lessons that we learn from the chart in Panel A. First, for lower base success rates (20-30%) the average cumulative returns of our Russell strategies trend up as the portfolios are formed later in time. However, as the base success rate gets to 35% and higher, this upward trend disappears, and we do not benefit materially from the improved predictions of the Russell stock additions used to form the strategy portfolio. Second, even the lowest base success rate (20%) results in a profitable investment strategy in the period between 2005 and Portfolios formed before mid April earn % return in excess of the Russell 2000 index, while portfolios formed after mid-april yield between 1.5% and 2.5% excess return. Improving the precision in forecasting Russell 2000 index additions yields even higher average net excess returns up to 4% for a 40% base success rate when the portfolios are formed at the end of April. Next, we explore whether imposing liquidity constraints leads to improved performance of the strategy for the same level of base success rate. Investment Technology Group, Inc. (ITG) 7

8 Return,% Panel B: top 20% by estimated market impact are excluded Success Rate portfolio formation week 20% 25% 30% 35% 40% Dropping the 20% of the stocks that are most expensive to trade, reduces the average number of securities left in the portfolio to 155. The reduced universe of stocks changes the picture in a positive way. The cumulative portfolio returns go up across all base success rates relative to the case when we keep all stocks. For instance, for the 30% base success rate all portfolios exhibit average net returns above 3% regardless of the week in which they were formed. As a consequence, one can capture the same returns with less forecasting skill. To illustrate this, we note that the line corresponding to the 20% base success rate in Panel B roughly matches the line for the 30% base success rate in Panel A with no filters. Finally, it is interesting to note that the upward trend in the cumulative portfolio returns across portfolio formation time continues for all but the 40% base success rate scenarios. Pushing the same idea even further, we filter the top 40% of the stocks by estimated market impact cost. As a result of the stricter liquidity filter, the average number of names in our portfolios goes down to 98. Return,% 7.00 Panel C: top 40% by estimated market impact are excluded portfolio formation week Success Rate 20% 25% 30% 35% 40% Investment Technology Group, Inc. (ITG) 8

9 The results in Panel C are qualitatively very similar to the ones displayed in Panel B. However, the improvement in return which results from applying a more aggressive liquidity filter is not as impressive as before. A possible reason could be that the stocks that should contribute the most to the positive performance of our strategy are less liquid. 6 Hence, there is a tradeoff between capturing the highest possible return and facing high implementation shortfall costs. Developing this line of thought even more, it is important to look not only at the stocks included into the investment strategy portfolio, but also at the stocks which are omitted from this portfolio. For a given base success rate, our simulation analysis relies on random draws, i.e. all stocks from the actual index addition list are equally likely to be picked for inclusion into the strategy portfolio. One possibility is to alter the simulation algorithm in order to allow some groups of stocks to be drawn more often than the others. However, building the simulation experiment around any particular heuristic is subjective. In what follows, we take a different approach in order to gain additional insight on the characteristics of stocks that are typically missed in projections but that can be important contributors to the investment strategy returns. We take the historical ITG projections and compare this set of projected Russell 2000 stock additions with the actual stock additions. The stocks in the actual Russell 2000 additions list but omitted from the ITG projections are analyzed separately. In the discussion that follows we label these stocks Missed additions. While the methodology used to produce the projections is ITG-specific, the ensuing evidence makes us believe that other providers of Russell index rebalancing projections might be facing similar issues and therefore tend to omit similar stocks. Table 1 Market Cap group ITG s predictions Actual additions Missed additions 1 (smallest) 71% 71% 85% 2 22% 21% 8% 3 4% 4% 4% 4 2% 2% 1% 5 (largest) 1% 1% 1% Table 1 presents the breakdown of ITG s stock projections and actual stock additions by market capitalization group across We also provide this breakdown for the list of missed additions. As can be easily seen from the table, most of the stocks in both ITG s projection list and the actual list belong to the smallest market capitalization group (71% of all names in both lists). Furthermore, it is evident that ITG s predictions have the propensity to miss out the smallest stocks: the weight of these stocks in the portfolio of missed additions is 85%. While most of the actual and projected stock additions fall in this group, 85% is significantly higher than 71%. This suggests a significantly higher likelihood of missing out on the smallest market capitalization stocks. 6 We confirmed this indirectly by having repeated the simulation exercise for market-cap weighted portfolios. The average raw returns of the market-cap weighted portfolios turned out to be lower due to the smaller weights on the most illiquid stocks. However because of the lower transaction costs, the resulting net returns for the market-cap weighted portfolios were at par with those observed for the equally-weighted portfolio. 7 The definition of the market capitalization groups is discussed in Section 3. Investment Technology Group, Inc. (ITG) 9

10 Table 2 presents a similar analysis, but instead of grouping by market capitalization we group by the 12-month momentum. Table 2 Momentum group ITG s predictions Actual additions Missed additions % of total % of total % of total 1 (negative moment) 5% 7% 13% 2 12% 14% 18% 3 14% 15% 18% 4 22% 21% 20% 5 (positive moment) 47% 43% 32% While the portfolio of predicted additions on average contains only 5% of stocks from the bottom 12-month momentum group, the list of missed additions contains 13% of stocks from this category. In other words, the stock from the actual additions list and the lowest momentum category is 2.5 times more likely to end up in the list of omitted names than in the list of predicted names. This result happens at the expense of the stocks with positive momentum: stocks from the highest momentum group constitute almost 50% of ITG s predicted additions, while they constitute only 32% of the list of missed additions. A look at the corresponding percentages for the actual stock additions reveals that 43% of the actual additions come from the group of extreme winners which is not so different from the 47% in ITG s list. This is in line with the intuition that generally the stocks get included into Russell 2000 index because of their strong recent return performance. 8 The main lesson from the evidence presented in Tables 1 and 2 is that a certain subset of Russell 2000 index additions is intrinsically very difficult to predict. The stocks in this subset tend to belong to the lowest market capitalization group and be among the worst performers according to their returns in the last 12-month period. 9 These stocks happen to be included into the final Russell 2000 index primarily thanks to their quick turnaround in the period between the portfolio formation date and the ranking date. Figure 3 below presents an illustrative example of such stock. Imergent Inc. (symbol IIG) saw its market capitalization shrink substantially in the period between September 2008 and April In 2009, the first ITG forecast of Russell 2000 index additions was made on April 6 (depicted by the green arrow in the chart on page 11). As of the beginning of April 2009, IIG s market capitalization was fluctuating around $50mln, well below the $77.6mln lower cutoff that made stocks eligible for the inclusion 8 As mentioned in Section 3, we do not look at the downgrades from Russell 1000 to Russel The following statistics can be used as additional evidence in support of this conclusion. The percentages of stocks which are both in the smallest size group and in the bottom momentum group are: in the portfolio of predicted additions 3.5%, in the portfolio of actual additions 5.1%, in the portfolio of omitted additions 10.6% (i.e. three times more likely than in the portfolio of ITG s predictions). Investment Technology Group, Inc. (ITG) 10

11 into the new Russell 2000 index. As a consequence, IIG did not make it onto the list of ITG forecasted additions to Russell However, the stock s price went up significantly between the portfolio formation date and the ranking date (depicted with an orange color) and resulted in a market capitalization of IIG that was sufficient to make the stock a constituent of the new Russell 2000 index. Figure 3: Example of the hard-to-forecast stock included into Russell 2000 index Market Cap, ' Imergent, Inc. (IIG) Market Cap Prior to 2009 Russell Reconsitution red line -- actual R2000 cutoff in 2009; green line -- projection date; orange line-- ranking date 1/2/2008 2/2/2008 3/2/2008 4/2/2008 5/2/2008 6/2/2008 7/2/2008 8/2/2008 9/2/ /2/ /2/ /2/2008 1/2/2009 2/2/2009 3/2/2009 4/2/2009 5/2/2009 6/2/2009 7/2/2009 8/2/2009 V. Conclusions Our research paper addresses several shortcomings in the existing literature on the annual rebalancing of the Russell indexes. First, we update the time period used in the research, and extend it up to Second, we look at the Russell rebalancing from the investment point of view, using only predictions of actual stock additions to the index in constructing a simple investment strategy. The strategy consists of buying predicted Russell 2000 stock additions and holding them until the Russell effective date. Third, we investigate the level of success rates of predicting the index additions, which is a key practical consideration in order to keep the strategy profitable. We look at several implementation details related to this investment strategy and investigate their contribution to its profitability. We vary the portfolio formation date (from early April to mid-may) and check whether portfolios formed earlier or later are more profitable. We investigate whether filtering out low liquidity stocks contributes positively to the strategy s performance. Finally, we verify whether certain subsets of stocks are more likely than others to be omitted from the list of projected index additions. Our results and conclusions are as follows: Our Monte-Carlo simulations reveal that, for any base success rate between 20 and 40%, we can run a consistently profitable investment Investment Technology Group, Inc. (ITG) 11

12 strategy of $10mln. Imposing a simple liquidity filter (dropping 20% of the projected additions that are the most expensive to trade) allows running a profitable investment strategy that captures the same returns with less forecasting skills. For instance, the investment strategy with 30% base success rate yields the same returns as the one with 40% and no filters. However, further restricting the universe in terms of liquidity is counterproductive, as the corresponding portfolio returns remain the same or can even deteriorate. There is a slight upward trend in the profitability of this investment strategy as the investment portfolio is formed later in time. Forming the portfolio in late April or early May, in combination with a moderate liquidity filter, provides an approximate 2.5% return in excess of the Russell 2000 index with only 20% success rate in predicting the index additions. However, forming this portfolio in the first half of April would result in a 1.5-2% return only. We demonstrate that the projected (non-simulated) list of index additions would very likely omit securities of the companies with very small market capitalization and with a weak recent return performance. Inclusion of these companies into the final rebalanced Russell 2000 index is largely based on the turnaround in their performance and the rapid price run-up between portfolio formation and ranking dates. It is likely, however, that the failure to correctly predict the index inclusion of the lowest market capitalization stocks does not matter for the overall performance of most index funds, as they are mostly concerned with maintaining the low tracking error. References Cai J., and Houge T., Long-Term Impact of Russell 2000 Index Rebalancing, 2008, Financial Analysts Journal, Vol. 64-4, pp Chen, H., Singal, V. and Noronha, G., Index Changes and Losses to Index Fund Investors, 2005, Financial Analysts Journal, Vol. 62-4, pp ITG Inc. ITG ACE Agency Cost Estimator, 2007, technical document, available at: events/papers/ace_white_paper_ pdf. Madhavan, A., The Russell Reconstitution Effect, 2003, Financial Analysts Journal, Vol. 59-4, pp Onaev Z., and Zdorovtstov V., Russell Reconstitution Effect Revisited. 2007, working paper, available at SSRN: Russell Investments, Russell U.S. Equity Indexes Construction and Methodology, 2011, available at: com/indexes/documents/methodology.pdf. Investment Technology Group, Inc. (ITG) 12