Associated effects of index composition changes: an ...

Managerial Finance Associated effects of index composition changes: an evidence from the S&P CNX Nifty 50 index Abdul Rahman Prabina Rajib

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Article information: To cite this document: Abdul Rahman Prabina Rajib , (2014),"Associated effects of index composition changes: an evidence from the S&P CNX Nifty 50 index", Managerial Finance, Vol. 40 Iss 4 pp. 376 - 394 Permanent link to this document: http://dx.doi.org/10.1108/MF-01-2013-0010 Downloaded on: 06 February 2015, At: 20:49 (PT) References: this document contains references to 37 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 116 times since 2014*

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Associated effects of index composition changes: an evidence from the S&P CNX Nifty 50 index

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376 Received 20 January 2013 Revised 18 July 2013 17 October 2013 Accepted 21 November 2013

Abdul Rahman and Prabina Rajib Vinod Gupta School of Management, Indian Institute of Technology Kharagpur, Kharagpur, India Abstract Purpose – The purpose of this paper is to test the long-term effects of price and volume with the help of Downward Sloping Demand Curve (DSDC) hypothesis, and also the short-term price and volume effects with the help of Price Pressure Hypothesis (PPH) for the index revisions on the S&P CNX Nifty 50 index. Design/methodology/approach – In order to report the long-term and short-term effects, the current study reviews two testable hypotheses, namely, DSDC hypothesis and PPH. The study has used the event study approach by including GARCH (1, 1) conditional variance in the market model. Findings – The results report that, the added stocks experienced a significant increase in price and volume on the effective date; whereas the deleted stocks experienced significant volume levels and insignificant price levels on the effective date. Accordingly, the study finds support in favor of PPH. Research limitations/implications – The study could not find evidence to support the most studied DSDC hypothesis. Practical implications – Index reorganization presumably affects the fund managers, domestic as well as international investors. As a result, studying the effect of index changes is a subject of attention to academicians and investors alike. Originality/value – The study contributes to the body of knowledge on index inclusion and exclusion effects by providing Indian evidence on long-term and short-term price and volume effects, and also documenting contrary results to the previous Indian and global research works. Keywords Abnormal return, Abnormal volume, DSDC, PPH Paper type Research paper

Managerial Finance Vol. 40 No. 4, 2014 pp. 376-394 r Emerald Group Publishing Limited 0307-4358 DOI 10.1108/MF-01-2013-0010

1. Introduction Changes in the composition of the index are an exclusive opportunity to examine the price and volume pattern for companies getting included to/excluded from the index. Inclusion (exclusion) of a stock to a benchmark index not only affects the stock price and volume of shares traded, but also reveals many other kinds of information about the companies. At a broad level, the effect of index change on companies is known as “index effect.” Many studies have been undertaken, and a number of hypotheses have been tested by researchers in relation to index effect. These hypotheses are Downward Sloping Demand Curve (DSCD) hypothesis, Price Pressure Hypothesis (PPH), Liquidity Cost Hypothesis (LCH), Information Content Hypothesis (ICH) and Market Segmentation/Investor Recognition Hypothesis (IRH). DSDC hypothesis assumes that, different stocks are not close substitutes in the knowledge of investors. When a specific stock experiences increasing (decreasing) demand shocks, then the price and volume tend to move upward (downward) to a new equilibrium. Applying this to index effect, a permanent increase (decrease) in price and volume can be expected following an index revision. PPH posits that, the increase (decrease) in price and volume should be associated with increased buying (selling) of a stock in the short run. Hence, the price and volume will increase for included stocks

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and will decrease for excluded stocks. However, the DSDC hypothesis differs from the PPH based on the duration of inclusion/exclusion effect on price and volume. The DSDC hypothesis posits that, the change in price is permanent whereas the PPH posits that the change in price is temporary, and an immediate reversal will follow. Both the hypotheses assume that the information effects probably have no role to play. LCH states that, stocks included to the index become economical for investors to trade since there is increase in liquidity, and decrease in transactions costs. Inclusion leads to more frequent trading and reduction in trading costs of a stock, while the exclusion causes the reverse. ICH says that index inclusion or exclusion reveals new information which is beneficial to the investors, which in turn affects the stock prices permanently. IRH posits that new competent investors are drawn towards the firm by market-attracted information leading to a permanent stock price appreciation. When the inclusion of stock takes place, there is an opportunity for a new competent investor group which leads to a positive price effect permanently. The reverse is invalid for exclusions since investors are still acquainted with these stocks. This hypothesis does not hold any presumption regarding trading volume changes. The emerging markets are still strong in their economic growth, and over the past two decades their share in the global economy has increased enormously. This achievement is due to the growing consumption between the emerging economies, besides the exports to the developed world. Despite this, the FTSE emerging market index fell 10 percent at the end of April 2012 questioning the ability of emerging market economies in maintaining the economic growth. There are few studies on inclusions/exclusions in the emerging markets like India. The above-mentioned hypotheses have been extensively tested in an international context while the literature on the impact of index change on companies in India is lacking. Index funds provide broad and low-cost exposure to the fast moving emerging markets, and also these funds change in line with the developments in the underlying markets when indices rebalance. Also India has a huge amount of investment riding on indices in the form of index funds, which mimics the composition of the index in terms of investment weights. Thus, whenever a change in the composition of the index takes place, the index funds rebalance their portfolio holdings. Hence, the impact of index change on the price and volume of added (deleted) stocks, as well as changes in the investors’ profile of these companies would be important considerations to these index funds. The Indian market is unlike the global markets, and it is something that is seldom understood. It is the market influenced by many internal and external factors, and besides that, the preferences of Indian consumers (specifically investors) are unpredictable. Moreover, Indian investors are significantly more different in their behavior than their global peer groups. The Indian stock market has seen considerable development in recent years. It has the second highest listed companies after the USA. Moreover, India’s position in terms of market capitalization is in relation to the international markets. As per the Global Stock Markets Fact Book (2012), India ranked ninth among G-20 countries in terms of market capitalization, and ranks third in attracting foreign investments as reported by the World Investment Report (2013), and Foreign Institutional Investors (FIIs) have immense faith in Indian financial markets, and consequently, FII inflows have increased by 31 percent in 2013. The inclusion and exclusion process of an index follows a very standardized pattern of analyzing the performance of the incumbent and the new entrant, and moreover, it sends out a signal regarding the financial health of the included (excluded) firms or sectors. Thus, the included and excluded stocks are bound to experience shock

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in its price and volume traded volatility. The fund managers are often thought to be responsible for these effects. Considering the intense growth in the number of funds benchmarked to different international stock indices, it is inherent to examine such effects in the Indian scenario. Moreover, index reorganization presumably affects the fund managers, domestic as well as international investors. As a result, studying the effect of index changes is a subject of attention to academicians and investors alike. In light of these discussions, the objective of this study is to test the long-term effect of abnormal returns and volume with the help of DSDC hypothesis, and to test the short-term effect of abnormal returns and volume with the help of PPH for index changes on the S&P CNX Nifty 50 index. The paper is organized in the following manner: Section 2 briefly explains about the index review policy in detail followed by the detailed discussion on previous literature and lists down the testable hypotheses in Section 3, then, Section 4 explains the data and methodology. Finally, Section 5 deals with the discussion of the observed results and concludes this paper.

Inclusions

99 20 9 0 20 0 0 20 1 0 20 2 0 20 3 0 20 4 0 20 5 0 20 6 0 20 7 0 20 8 0 20 9 1 20 0 11

Exclusions

No. of Companies

8 7 6 5 4 3 2 1 0

19 9 1 8

Figure 1. (a) Inclusion and exclusion to and from the S&P CNX Nitfy 50 index from 1998-2011; (b) trading days between AD and ED

No. of Companies

2. About the S&P CNX Nifty 50 index The S&P CNX Nifty 50 index (Nifty hereafter) is based on the stocks listed and traded on the National Stock Exchange (NSE) and maintained by the India Index Services & Products Limited (IISL). IISL is a joint venture between NSE and Credit Rating and Information of Services of India Limited (CRISIL), and IISL has a marketing and licensing agreement with Standard & Poor’s. Nifty started with a base value of 1,000 on November 3, 1995. The constituents of the Nifty are selected on the basis of pre-determined criteria directed to the companies to be included in the indices as well as in consideration of their ability to represent relevant sectors. The Nifty tracks the behavior of a portfolio of 50 companies which are reputed, largest and the most liquid of the approximately 935 companies listed on the NSE. It captures approximately 60 percent of its equity market capitalization, and covers 22 sectors of the Indian economy. During 1995, till August 2011 (last announcement date taken in this study) 66 companies have been included (excluded) to and from the index. The maximum number of inclusions/exclusions to the Nifty happened in the years 1998 and 2007 as shown in Figure 1(a). Further, the percentage of market capitalization held by Nifty and S&P CNX 500 from 1998 to 2011 is given in Table I. The Nifty index on an average has a market capitalization of 26 percent. So, even though there are 450 extra companies in the S&P CNX 500 index, these companies add about 16 percent of the total market capitalization.

20 18 16 14 12 10 8 6 4 2 0

Inclusions Exclusions

1-5

6-10 11-15 16-20 21-25 26-30 31-35 Days of Difference

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Year

S&P CNX Nifty 50

S&P CNX 500

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

24 27 24 27 27 28 27 26 26 26 27 28 27 27

45 42 45 42 41 40 42 42 42 42 42 39 41 41

Source: Indian Securities Market – Review (A NSE Publication)

3. Literature review In most stock markets, stocks included (excluded) from a popular index shows significant positive (negative) abnormal returns, and the trading volume of stocks is positively affected by the event. The research works with respect to DSDC and PPH that explain the effects of changes in the index associated with the price and volume are explained below. 3.1 DSDC hypothesis A body of literature examining the effect of DSDC hypothesis for inclusions (exclusions) to and from various indices is explained below. Shliefer (1986), Lynch and Mendenhall (1997) examined the impact of price and volume in relation to the changes in S&P 500. They documented positive abnormal returns of 2.79 and 3.81 percent around the announcement date. Kaul et al. (2000) studied TSE 300 index, and found significant abnormal returns of 2.34 percent, and remarkably high trading volume. Wurgler and Zhuravskaya (2002) tested the predictions of price responses of stocks included in the S&P 500 index, and found a flat demand curve for the stocks with close substitutes. Denis et al. (2003) examined investors’ earnings expectations for the firms added to the S&P 500 index. They documented that index inclusion is not an information free event. Biktimirov (2004) examined the effect of demand on stock prices of TIPs 35 and TIPs 100, and found that, a permanent decline in price accompanied by significant abnormal volume was due to decrease in demand. Further, Chakrabarti et al. (2005) found that the stocks included to MSCI index experienced a significantly positive abnormal return of 3.4 percent on the day following the announcement, and a decrease in the abnormal return after ten days following the effective date. Mazouz and Saadouni (2007) observed that the abnormal returns which are ordinary least square (OLS)-based indicated temporary price effect; whereas the ARCH adjusted abnormal returns experienced permanent price effect for FTSE 100 index revisions. Liu (2011) reported a significant price hike for the inclusions to the Nikkei 225 index, which is likely to be permanent despite temporary price reversals.

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Table I. Market capitalization (in %) held by the S&P CNX Nifty 50 and S&P CNX 500 indexes

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3.2. PPH Some of the previous studies examining PPH for inclusions (exclusions) to and from various indices are here as under. Harris and Gurel (1986) observed an increase in abnormal return of 3.13 percent from inclusions to the S&P 500 index, and this increase has almost reversed after two weeks. Chung and Kryzanowski (1998) observed temporary movement of stock prices from their equilibrium values, and the reversal of abnormal returns around the announcement window in subsequent periods. Duque and Madeira (2004) and Peterson (2004) reported temporary positive effects for inclusions, and negative effects for exclusions of Lisbon’s PSI 20 index and S&P 500 index. Biktimirov (2004) evidenced temporary significant changes in prices, trading volume, and institutional ownership for included and excluded stocks of the Russell 2000 index. Further, Kumar (2007) reported a significant increase (decrease) in stock prices on the announcement and effective dates for the Nifty index revisions, and that these prices were reverted after a week’s time. However, the study has not found any abnormal volumes on both the event dates. Gowri Shankar and Miller (2006) experienced temporary price and volume effects in the post-announcement period, and also increase (decrease) in the institutional ownership for the auditions (deletions) to and from the S&P Small Cap index. Bildik and Gulay (2008) reported temporary positive (negative) price and volume effects for inclusions (exclusions) to and from the ISE index. Moreover, the volume volatility was also significantly affected. Hrazdil (2009) evidenced temporary price and volume effects around the effective date for the changes associated with the S&P 500 index inclusions. Schmidt et al. (2011) found significant positive (negative) price and volume effects around the announcement date for the S&P/ASX 200 index revisions. Selvam et al. (2012) reported a short-term negative effect on the intrinsic value of relevant stocks for both inclusions and exclusions to and from the Nifty index and on both the announcement and effective dates, and also that excluded stocks experienced greater volatility. The changes in the index composition have been studied by many researchers from all over the world, and the permanent or temporary effect of these index revisions on the price and volume of the company’s stock has been attributed to the increased (decreased) demand from the index funds. Moreover, index change has effect on various aspects like institutional ownership, disclosure risk, cost of capital, etc. Therefore, very few works have been undertaken in this aspect in India. Kumar (2007) has examined the price and volume effect for inclusions and exclusions to the Nifty index during 1996-2003. Therefore, many significant changes might have happened in the Indian capital market in terms of listing of new companies, FII investment, increase in number of index funds, etc. Similarly, Selvam et al. (2012) has studied the price effect of additions and deletions to the Nifty index, but they have not reported the volume effect. Moreover, during 2003-2011, the number of index funds tracking Nifty index has grown from eight to 28. However, the ETF market has seen very limited growth in general, and the number of ETFs tracking Nifty has seen only incremental increase, i.e. two to four. Table II below shows the number of index funds and ETFs tracking Nifty index. Furthermore, the research studies undertaken by Kumar (2007) and Selvam et al. (2012) contradict with the results of the current study as observed in Section 3. Therefore, the current study fills this gap by studying associated effects of changes in the index composition. The findings of this research work will be of use to Indian equity investors in general, and included/excluded companies in particular. If DSDC holds true, price/volume

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Year

No. of index funds

No. of ETFs

2003 2004 2005 2006 2007 2008 2009 2010 2011

08 12 12 23 23 20 20 22 28

2 2 2 2 2 2 2 3 4

Source: Indian Securities Market – Review (A NSE Publication)

of included stocks experience a long-term upward movement. Hence, it makes sense for the investors to buy shares of included companies as soon as the announcement of the inclusion happens, without waiting to invest after the effective date of inclusion. It even makes better sense to sell shares soon after the announcement as not only the price falls after the exclusion, volume trading also goes down, thus affecting the liquidity. Similarly, if PPH holds true, in case of inclusion, retail investors with long-term investment horizon should not rush to invest in a company as because it is now part of an index. Similarly, retail investors should not also rush to sell the excluded shares as the price decline is temporary, and is likely to reverse in the short term. However, an investor with a short-term horizon should sell (or short sell) the shares immediately after the announcement as price falls after exclusion. 4. Data and methodology The list of included and excluded stocks as well as the effective date (henceforth ED) of change is available on the NSE web site. However, the announcement date (henceforth AD) for index change has been collated from the past archives of IISL press release. The sample period starts from October 1998 and ends on August 2011. During this period 56 companies have been included to as well as 56 companies have been excluded from the index. Of these 56 companies three pairs of companies had to be eliminated due to the non-availability of ADs. In addition to these, 13 companies from the inclusion list and six from the exclusion list are not part of this study due to insufficient data. This leaves with 40 inclusions and 46 exclusions. From the 47 exclusions, another seven companies are removed from our study as these companies were part of M&A activities. Hence in the final sample, there are 40 inclusions and 40 exclusions. The company specific daily price-volume data, and the Nifty index data have been taken from the NSE’s web site’s (www.nseindia.com) archive. The adjusted price of included and excluded stocks has been taken to calculate the abnormal return. Event study methodology has been used in this research work. To study the price and volume effect, event windows around the AD and ED are identified. AD is the date when the index revision committee announces that a company will be included (excluded) from the index. The index revision committee also announces ED, the date from which the new company will be actually included to the index (or the old company removed from the index). There is no thumb rule for selecting the number of days for an event window to test various hypotheses. But, different researchers have used different time periods to test the DSDC hypothesis and the PPH. Based upon the previous research works, we have

S&P CNX Nifty 50 index

381 Table II. Number of Index funds and ETFs tracking S&P CNX Nifty 50 index

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382

used 60 days after the ED to test the DSDC hypothesis, and ten days after the ED to test the PPH. The null hypothesis is that, the daily mean cumulative abnormal returns (hence forth MCARs) should be equal to zero, and the daily mean average volume ratios (hence forth MAVRs) should be equal to 1 during the event period for all the testable hypotheses. 4.1 Abnormal return calculation using market model parameters and conditional heteroskedasticity The index change effect is analyzed by studying the abnormal returns around the AD and the ED. The daily abnormal returns are calculated as the stock’s excess return on day “t” over the index return. For calculating the daily return, the adjusted prices are taken. The daily return Rt is calculated in the following manner:   pt ð1Þ Rt ¼ In pt1 where Pt is the stock/index adjusted closing price at time “t” and Pt1 is the stock/index adjusted closing price at time t1. The returns are calculated by estimating a regression using OLS method: Ri;t ¼ ai; j þ bi; j Rm;t þ ei;t

ð2Þ

The parameters of the OLS estimates ai, j and bi,j in Equation (2) are based on the assumption that the error term is homoskedastic with a mean zero and a constant variance. The standard GARCH (1, 1) model is employed to deal with the ARCH effect in the residuals of the model, since the ARCH effect is shown to affect the efficiency of estimators jointly with the magnitude and the statistical significance of the abnormal returns associated with a given event (Mazouz and Saadouni, 2007). Moreover, Bera et al. (1988) documented the ARCH adjusted market model as more efficient, and Diebold et al. (1988) reported that the residuals obtained using the single factor model display strong ARCH properties. In this model, the return on stock is dependent on the market as well as the estimated GARCH (1, 1) variance. This particular model assumes the event period as a subset of the estimation period. Therefore, the parameters that examine the index revisions are already fitted to the condition. The GARCH (1, 1) model does not have predicting character, and loses a major part of its significance already by extending the estimation period to the event window. The GARCH (1, 1) model has the following form: Ri;t ¼ ai; j þ bi; j Rm;t þ li; j s2i;t þ ei;t

ð3Þ

Under the GARCH (1, 1) specification as explained by Bollerslev (1987), the conditional variance of the error term in Equation (3) s 2i,t is modeled as follows: s2i;t ¼ ji;j þ di;j1 e2i t1 þ yi;j1 s2i;t1

ð4Þ

where the indicator j is the estimated period, i.e. 252 days including the event period; ji,j the permanent conditional variance component; di,j1 the ARCH term, and can be interpreted as information about the previous periods’ volatility; yi, j1 is the GARCH

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term, which is the previous periods’ forecasted variance. The abnormal returns are calculated by substituting the parameters given in Equation (3):   ARi;t ¼ Ri;t  ai; j þ bi; j Rmt þ li; j s2i;t ð5Þ The daily average abnormal returns, and the MCARs, which are specified in Equation (5) above measures the price effect. The standard t-statistic is applied to test the OLS abnormal return estimates, but applying the same method for GARCH-based abnormal returns to test the significance different from zero may not be reliable. Hence, GARCH-based statistic of Savickas’s (2003) was adopted by the current study which was also adopted by Mazouz and Saadouni (2007), to test whether the cumulative abnormal returns are significantly different from zero. The GARCH-based statistic can be explained as follows: N P Si;s N

i1 GARCH  test ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2ffi  P N N P Si;s 1 Si;s N ðN 1Þ N i1

ð6Þ

i1

s P A Ri;t S

t1 ffiffiffiffiffiffiffiffiffiffiffi Si;s ¼ s s ^ P hi;t t1

ð7Þ

S

where N ¼ 40, S ¼ window length. The GARCH-test follows the student’s t-distribution with N1 degrees of freedom. This test statistic informs whether the average abnormal return observed over a window of length s is significant. One sample Wilcoxon’s signed-rank tests are also performed to test the robustness of the results. 4.2 Abnormal volume Abnormal trading volume is measured by the volume ratio. The volume ratio measures the stock volume relative to the market volume to detect the abnormal trading volume during the event period (Harris and Gurel, 1986). It is a standardized measure of period t trading volume in security i, adjusted for market variation. An estimation period is taken to calculate the ratio of daily trading volume to average daily trading volume over the 14 weeks, i.e. 70 trading days prior to the AD between the periods AD92 to AD22. The ratio is calculated as follows: V Rit ¼

Vit Vm  Vmt Vi

ð8Þ

where Vi is the average trading volume of stock during the period from AD92 to AD22; Vm is the average trading volume of the market during the period from AD92 to AD22; Vit represents the trading volume of the stock i on day t in the event period; and Vmt represents the market trading volume on day t in the event period.

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Now the cross-sectional means (AVRt) are computed by taking the average of VRit values of all the stocks for the t-th day. The mean value is 1 if there is no change in volume during the event period relative to the prior 14 weeks: 1X AVRt ¼ ð9Þ VRit N

384

The mean of AVRt is used to test whether the average volume ratio is significantly different from 1 in an event window of length s:

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t2 P

MAVRs ¼

AVRt

t1

s

ð10Þ

To test the statistical significance of MAVRs, two-tailed t-tests and also one sample Wilcoxon’s singed-rank tests are performed. 4.3 The event and the event windows The current study analyzes the Nifty index inclusions and exclusions. The two important event dates are the AD and the ED for inclusion and exclusion. Similar to the developed markets, the number of days between AD and ED varies from one to 32 trading days. The mean trading days between AD and ED are 22.5, and that of the median is 25. The trading days between AD and ED are shown in Figure 1(b). The MCARs and the MAVRs in the current study are reported over five different event windows. First, the pre-announcement event window runs from 20 days before the AD through two days prior to the announcement. This window is estimated to study the anticipated effects of price and volume. Second, the AD window begins on AD2 and ends on AD þ 1. Generally, the announcement is made after the market is closed. Hence, AD þ 1 have been taken as the AD. This window predicts the price and volume effect around the day of the announcement. Third, the post-announcement event window runs from AD þ 2 through the day prior to the ED. This window predicts the trading by some index funds between AD and ED1, causing positive (negative) abnormal returns for inclusions (exclusions) over this interval. Fourth, ED window is estimated with the intention of obtaining an idea about the price and volume effect on the ED. Fifth, the short-term post-change window runs from the day after ED to ten days after the ED (ED þ 10). This window assists us to find out the temporary price effects. To predict the permanent price and volume effect, a long-term postchange window will be estimated which runs from ED þ 11 to 60 days after the ED. 5. Empirical results 5.1 Price effect of inclusions and exclusions Table III presents the daily MCARs for the stocks included and excluded to and from the respective Nifty index around the AD and ED of inclusion and exclusion. Daily MCARs for stocks included to the Nifty index in the pre-announcement period, i.e. 20 days prior to the AD are significant at the 5 percent level. The anticipation effect cannot be noticed during the pre-announcement period. Similarly, there is no anticipation effect in case of exclusions, because the MCARs for stocks excluded from the Nifty index in the pre-announcement period are positive and insignificant. Moreover, the MCARs are steady throughout the pre-announcement period for exclusions. These results can be evidenced from Figure 2(a).

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The daily MCARs around the AD for inclusions are 1 percent and insignificant, and that of exclusion are 2 percent and significant at the 1 percent level. It can be experienced from Figure 2(b) that, the MCARs have a negative impact on the day prior to the announcement and as well as on the AD. But for exclusions the negative price impact started on the day prior to the AD, i.e. AD1, and continued till the day after the announcement. The AD window results depart from the previous studies of Harris and Gurel (1986), Lynch and Mendenhall (1997), Chakrabarti et al. (2005), Kumar (2007) and Petajisto (2011) wherein the abnormal returns around the AD were significantly positive (negative) for both inclusions and exclusions. Over the next 24 day period, i.e. AD þ 2 through the pre-change day, stocks included to the index reported to have negative and significant daily MCARs whereas, the stocks excluded from the index evidenced positive and insignificant daily MCARs. Negative significant MCARs for inclusion is contrary to what should have happened, i.e. soon after the inclusion announcement, index fund managers as well as other investors should start buying the stocks of these companies resulting in positive significant MCARs. However, the finding of this paper is a significant departure compared to other research findings. Kumar (2007) reported insignificant positive (negative) MAARs, and Selvam et al. (2012) reported negative MCARs for both inclusions and exclusions. For exclusions, insignificant MCARs, though positive could be discounted as a non-event. However, compared to research findings where exclusions are associated with significant negative MCARs during this period also remains as an anomaly. The movements of MCARs during the post-announcement period are shown in Figure 2(c). The results on the ED for inclusions shows a positive significant MCAR of 0.7 percent, and an insignificant negative MCAR for exclusions. This positive and negative price impact of inclusions and exclusions reversed after five and seven days of ED as can be evidenced from the short-term post-change date window which runs from ED þ 1 to ED þ 10. The daily MCARs during this period were negatively significant for inclusions at the 10 percent level, and positively insignificant for exclusions. These results are in contrast to the findings reported by Kumar (2007) where the prices got reversed in nine days after the ED for inclusions. Also the current results contradict with the previous studies of Harris and Gurel (1986), Jain (1987), Lynch and Mendenhall (1997), and Petajisto (2011) where the prices reverted in a different time frame for inclusions and exclusions. The short-term price reversal can be evidenced from Figure 2(d). Further, significant negative and positive daily MCARs for inclusions and exclusions were noticed during the long-term post-change date window which runs Inclusions Interval

MCARs

t-statistic

MCARs

AD21 to AD2 AD1 to AD þ 1 AD þ 2 to ED1 ED ED þ 1 to ED þ 10 ED þ 11 to ED þ 60

0.08 0.01 0.07 0.007 0.02 0.03

2.50** 0.36 2.81* 1.93*** 1.88*** 0.71

0.007 0.02 0.03 0.0019 0.003 0.04

Notes: *,**,***Significant at the 0.01, 0.05 and 0.10 levels, respectively

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385

Exclusions t-statistic 0.20 4.34* 1.19 0.03 0.14 1.49

Table III. Mean cumulative abnormal returns (MCARs) for inclusions and exclusions of S&P CNX Nifty 50 index

0.02

0

0

–0.005 –0.01 MCARs_INC

–0.015

MCARs_EXC

MCARs_EXC

–0.06

–0.02

–0.08

–0.025

0.01

0

0.005

–0.04 MCARs_EXC

–0.06

0

+1

ED

+8

+7

ED

+5

+6

ED

+4

ED

ED

+2

+3

ED

+1

0 –0.005

ED

MCARs_INC

ED

–0.02

MCARs

0.02 AD+2 AD+3 AD+4 AD+5 AD+6 AD+7 AD+8 AD+9 AD+10 AD+11 AD+12 AD+13 AD+14 AD+15 AD+16 AD+17 AD+18 AD+19 AD+20 AD+21 AD+22 AD+23 AD+24 ED–1

MCARs

–0.01 –0.015

MCARs_INC MCARs_EXC

–0.08

–0.02

0.045 0.035 MCARs_INC

0.025 0.015 0.005

–0.005 –0.015

Figure 2. Mean cumulative abnormal returns (MCARs) of S&P CNX Nifty 50 index

MCARs_EXC

ED+11 ED+13 ED+15 ED+17 ED+19 ED+21 ED+23 ED+25 ED+27 ED+29 ED+31 ED+33 ED+35 ED+37 ED+39 ED+41 ED+43 ED+45 ED+47 ED+49 ED+51 ED+53 ED+55 ED+57 ED+59

MCARs

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386

+9

–0.04

AD+1

ED

MCARs_INC

AD

ED

–0.02

MCARs

AD–1

AD AD–21 AD–20 – AD 19 – AD 18 – AD 17 AD–16 – AD 15 AD–14 – AD 13 – AD 12 – AD 11 – AD 10 AD–9 AD–8 AD–7 – AD 6 – AD 5 – AD 4 – AD 3 –2

MCARs

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–0.025

Notes: (a) The pre-announcement period for inclusions and exclusions; (b) the announcement period for inclusions and exclusions; (c) the post-announcement period for inclusions and exclusions; (d) the short-term post-change period for inclusions and exclusions; and (e) the long-term post-change period for inclusions and exclusions

from ED þ 11 to ED þ 60. The prices during this period were moving in a haphazard manner for both inclusions as well as exclusions. This can be evidenced from Figure 2(e). The Wilcoxon signed-rank test shows significant MCARs at 5 percent for all the periods except for the period surrounding the AD for both the inclusions and exclusions. A short-term price reversal has been evidenced for both the inclusions and exclusions after the ED, and hence allows the current study to support the PPH, but not the DSDC hypothesis. The short-term price reversals for inclusions and exclusions to and from the index has been found in the previous studies of Duque and Madeira (2004) and Peterson (2004). 5.2 Volume effects of inclusions and exclusions The MAVRs should be different than one, indicating that trading activity of the included (excluded) stock moved up (down) abnormally due to change in the index composition.

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Table IV documents the volume patterns around AD and ED for inclusions as well as exclusions. The results report that, the MAVRs for inclusions and exclusions during all the periods presented, evidenced significant trading activity of more than one. The MAVRs during the pre-announcement period (AD21 to AD2) are more than one and is significant at the 1 percent level. This shows the abnormal trading activity and an anticipation effect for both inclusions and exclusions. The anticipation effect can be evidenced from Figure 3(a). This shows that the market is able to pre-empt about the inclusions, and as well as for the exclusions. The results are similar to that of Lynch and Mendenhall (1997) where they documented that, the abnormal volume raised unremarkable up until one day before the announcement. There was an increase of MAVRs by 20 percent above normal for inclusions, and by 19 percent above normal for exclusions around the AD. This can be evidenced from Figure 3(b). Furthermore, the MAVRs during the post-announcement period (AD þ 2 to ED1) are above normal and significant at 1 and 5 percent levels for inclusions and exclusions. Even though, the trading activity is above normal for inclusions and exclusions, there was a decrease of 13 percent for exclusions compared to MAVRs of AD window. Similar to pre-announcement trading activity (AD21 to AD2), the pre-trade pattern occurring before the ED as can be evidenced from Figure 3(c) is that, there may be competition between index fund managers and other index funds in trying to get enhanced returns by taking some tracking error risks as reported by Yun and Kim (2010). The results on the ED documented an increase of volume for both inclusions and exclusions by 59 and 29 percent above normal levels. The volume levels decreased significantly during the short-term post-change period which runs for ten days from ED þ 1. This can be experienced from Figure 3(d). The MAVRs during the long-term post-change period which runs for 60 days from ED þ 11 increased significantly at the 1 percent level for both inclusions and exclusions. Furthermore, the Wilcoxon signedrank test document results for MAVRs in par with the results of MCARs. To summarize, these findings show that, the volume ratio rose slightly at the time of announcements, for inclusions as well as exclusions. Then it decreased and increased gradually during the post-announcement period for inclusions and exclusions, peaks around the actual change day, and then falls during the post-change period. Moreover, it can be noticed that, the trading activity associated with inclusions as well as exclusions are happening on the event dates only. To explore whether there is a permanent volume effect, the MAVRs for 60 days after the ED is analyzed. The study reports that the volume ratio has no clear cut trend. Inclusions Interval AD21 to AD2 AD1 to AD þ 1 AD þ 2 to ED1 ED ED þ 1 to ED þ 10 ED þ 11 to ED þ 60

MAVRs

t-statistic

MAVRs

1.09 1.20 1.19 1.59 1.24 1.26

4.13* 1.61 5.22* 1.69** 4.08* 7.86*

1.14 1.19 1.06 1.29 1.16 1.28

Notes: *,**,***Significant at the 0.01, 0.05 and 0.10 levels, respectively

S&P CNX Nifty 50 index

387

Exclusions t-statistic 3.84* 1.51 1.9** 1.15*** 2.62* 5.01*

Table IV. Mean average volume ratios (MAVRs) for inclusions and exclusions of S&P CNX Nifty 50 index

1.8

1.6

1.6

1.4

1.4

1.2 MAVRs

1 0.8 0.6

0.2

0.2

MAVRs_EXC

AD–21 AD–20 AD–19 AD–18 AD–17 AD–16 AD–15 AD–14 AD–13 AD–12 AD–11 AD–10 AD–9 AD–8 AD–7 AD–6 AD–5 AD–4 AD–3 AD–2

0

AD–1

AD

AD+1

1.8 1.6

1 0.8 MAVRs_INC

0.6 0.4

MAVRs_EXC

ED+9

ED+10

ED+6

0

ED+5

0.2 ED+4

MAVRs_EXC

ED+8

MAVRs_INC

1.2

ED+7

MAVRs

1.4

AD+2 AD+3 AD+4 AD+5 AD+6 AD+7 AD+8 AD+9 AD+10 AD+11 AD+12 AD+13 AD+14 AD+15 AD+16 AD+17 AD+18 AD+19 AD+20 AD+21 AD+22 AD+23 AD+24 ED–1

MAVRs

3.5 3 2.5 MAVRs

2 1.5 1

MAVRs_INC

0.5 MAVRs_EXC

0 ED+11 ED+13 ED+15 ED+17 ED+19 ED+21 ED+23 ED+25 ED+27 ED+29 ED+31 ED+33 ED+35 ED+37 ED+39 ED+41 ED+43 ED+45 ED+47 ED+49 ED+51 ED+53 ED+55 ED+57 ED+59

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0

2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

MAVRs_EXC

0.4

MAVRs_INC

0.4

MAVRs_INC

0.6

ED+3

388

1 0.8

ED+2

MAVRs

1.2

ED+1

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Figure 3. Mean average volume ratios (MCARs) of S&P CNX Nifty 50 index

Notes: (a) The pre-announcement period for inclusions and exclusions; (b) the announcement period for inclusions and exclusions; (c) the post-announcement period for inclusions and exclusions; (d) the short-term post-change period for inclusions and exclusions; and (e) the long-term post-change period for inclusions and exclusions

This can be evidenced from Figure 3(e). Therefore, the trading volume patterns, unlike the price effects, find support with regard to the PPH. The volume results are in contrast to the results of Kumar (2007) wherein no abnormal volumes were observed throughout the event period for both inclusions and exclusions. Further, similar results for trading volume patterns have been found in previous studies of Harris and Gurel (1986), Gowri Shankar and Miller (2006), Chuang et al. (2009) and Qiu and Pinfold (2007). The price and volume results show that the Nifty index is more volatile in nature, since it is characterized by wide price fluctuations and heavy volume trading. The standard deviation of the aforesaid index is at an average of 25.33 percent since

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2001 (as per the CRISIL Fund Insights, 2013). Furthermore, the expected effect has been observed on the ED instead of the AD specifically for inclusions, which probably shows that, the index fund managers and the investors are concentrating more on the ED rather than the AD, and these two factors might be the reason for the significant parting of the findings from the previous research studies.

S&P CNX Nifty 50 index

5.3 Auxiliary tests for DSDC and PPH To examine the presence of permanent effect and temporary effect in the price and volume, the study intends to test further the DSDC hypothesis and the PPH with the help of univariate and multivariate regression analysis. Shliefer (1986) has estimated a cross-sectional regression of AD abnormal return (ADARi) on AD abnormal volume (ADVOLi) and usual volume (USVOLi,t) for inclusions. According to him, a significant positive slope of ADVOLi and a significant negative slope of USVOLi,t being consistent with the DSDC hypothesis. A similar test has been undertaken in this paper to test the permanent price and volume effects by including ADARi as the dependent variable, and announcement day volume (ADVOLi) and usual volume (USVOLi,t) which is the one month, i.e. 21 day’s average of the daily volume during the pre-announcement period as the independent variables. The estimated model is as follows:

389

ADARi ¼ ai þ bADVOLi þ yUSVOLi;t þ ei

ð11Þ

According to Equation (11), a significant positive slope of ADVOLi and a significant negative slope of USVOLi,t for inclusions, and a vice-versa for exclusions are consistent with the DSDC hypothesis. Furthermore, the study intends to test the prediction of PPH, that the event day price increase/decrease for each stock in the test sample is completely reversed over the subsequent days. For testing the predictions of the PPH and DSDC hypothesis, Kaul et al. (2000) has estimated a cross-sectional regression, by regressing post-announcement week cumulative abnormal returns for 15 weeks on the announcement week abnormal returns for each firm. According to them, the PPH can be consistently predicted with a slope of 1 in the regression; whereas the DSDC hypothesis can be predicted with a slope of zero which indicates permanent price effect. A similar type of test has been carried out by Biktimirov (2004) and Gowri Shankar and Miller (2006) by regressing post-announcement day CAR for 60 days on the AD abnormal returns for each firm. Following the methodological concept of Biktimirov (2004) and Gowri Shankar and Miller (2006), the study intends to predict the PPH by regressing the post-effective day CAR (EDCAR1T,j) on the ED abnormal returns (EDAR0,j) for each firm “ j” as given in Equation (12): EDCAR1T;j ¼ a þ lEDAR0;j þ etT;j

ð12Þ

A negative slope for inclusions and a positive slope for exclusions in this regression indicate a temporary price effect, by full reversal of event day returns in the postchange period (ED) as estimated by the PPH. Kaul et al. (2000) in their model computed the weekly CARs, starting with the post-event week and advancing repeatedly for 15 weeks following the event. These weekly CARs are then regressed on the announcement week returns. They rejected the

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hypothesis of the regression in one test that the slope is 1, and accepted the hypothesis that the slope is zero in another test. Similarly, Biktimirov (2004) reported similar results for 60 days following the event. Both the studies found support for DSDC hypothesis. However, Gowri Shankar and Miller (2006) reported a slope of 1 for their regression for 60 days following the event. Their study supported the PPH. The current study adopts a different time period of estimating windows for this model compared to the one estimated in Section 4.3. The post-event interval starts with the post-ED, and extends in five-day increments to 20 days after the ED. A significant negative slope for inclusions, and a significant positive slope for exclusions are consistent with the PPH. 5.4 Results of DSDC hypothesis The study estimates a cross-sectional regression of ADARi on ADVOLi and USVOLi,t similar to that of Shliefer (1986). The results of Equation (11) are presented in Table V. The results presented in Table V shows that, the slope of ADVOLi is negatively insignificant, and that of USVOLi,t is positively insignificant for both the inclusions and exclusions. Even though ADVOLi is negative and USVOLi,t is positive for exclusions, they are insignificant. The MCAR on the day of announcement is very low, i.e. 1 percent for inclusions and 2 percent for exclusions even though one of them is significant (refer Table III in Section 5). Similarly, the MAVR was 1.20 for inclusions; whereas it was 1.19 for exclusions (refer Table IV in Section 5). This demonstrates that, either the index funds might be buying slowly, or the other investors might be withdrawing from the market due to trading by index funds. The results documented in our study in relation to Equation 11 are contrary to the results evidenced by the earlier studies of Shliefer (1986); where his study found significant positive slope for ADVOLi, and significant negative slope for USVOLi,t. Therefore, from the results demonstrated above, we do not find any evidence in support of the DSDC hypothesis. 5.5 Results of PPH The current study estimated a cross-sectional regression by regressing post-effective day CAR (EDCAR1T,j) on the ED abnormal returns (EDAR0,j) for firm “ j.” The results of Equation (12) are presented in Table VI. Variable

Coefficients

t-statistic

R2

ADARi ¼ ai þ bADVOLi þ yUSVOLi;t þ ei

ð11Þ

where ADARi is the announcement date abnormal return; ADVOLi the announcement date trading volume; and USVOLi,t, the usual volume which is the average trading volume for one month, i.e. 21 days prior to the announcement date Inclusions to the CNX Nifty 50 index C 0.001 0.28 ADVOLi USVOLi,t 0.64 Exclusions to the CNX Nifty 50 index Table V. C 0.01 Results of auxiliary test 0.66 for the Downward Sloping ADVOLi 0.37 Demand Curve hypothesis USVOLi,t

0.75 1.44

0.07

1.22 0.94

0.07

Windows

C

EDAR0,j

t-statistic

S&P CNX Nifty 50 index

R2

EDCAR1T;j ¼ a þ lEDAR0;j þ etT;j

ð12Þ

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where EDCAR1-T,j is the post-effective date cumulative abnormal return for each firm; and EDAR0,j the effective date abnormal return for each firm Inclusions to the CNX Nifty 50 index ED þ 1 to ED þ 5 0.003 ED þ 6 to ED þ 10 0.02 ED þ 11 to ED þ 15 0.0005 ED þ 16 to ED þ 20 0.008 ED þ 21 to ED þ 25 0.01 ED þ 26 to ED þ 30 0.01 Exclusions to the CNX Nifty 50 index ED þ 1 to ED þ 5 0.001 ED þ 6 to ED þ 10 0.01 ED þ 11 to ED þ 15 0.0002 ED þ 16 to ED þ 20 0.005 ED þ 21 to ED þ 25 0.005 ED þ 26 to ED þ 30 0.002

391 0.411 0.11 0.1 0.25 0.18 0.39

1.88*** 1.93*** 0.22 0.64 0.6 1.12

0.02 0.002 0.001 0.01 0.01 0.03

0.80 0.19 0.21 0.40 0.20 0.76

1.97*** 1.82*** 0.43 0.88 0.36 1.15

0.12 0.003 0.005 0.02 0.004 0.04

Note: ***Significant at 0.10 levels

A significant negative slope for inclusions, and a significant positive slope for exclusions are consistent with the PPH. The results reported in the table above demonstrates that, the prices for inclusions reversed after five days of the ED which falls in the second interval of ED þ 6 to ED þ 10. Hence, the slope of the first interval is negative and significant. Similarly, the slope estimates for the next interval for inclusions experience the same. Further, the slope estimates for the third and fourth interval are positive, and the remaining intervals are negative. This shows that the prices are being reversed in a short span for inclusions. The slope estimates for exclusions significantly increased from 0.8 in the first interval to 0.19 in the second interval. The remaining three intervals experienced positive slopes, but there was a decline in the slope of the fifth interval, i.e. ED þ 21 to ED þ 25. This shows that for exclusions also the prices reversed after seven days of the ED. Therefore, the current study is unable to reject the hypothesis that, the slope is negative for inclusions and positive for exclusions for the price reversal during the post-change period that extends to 30 days beyond the ED. Therefore, the results confirm that there is a reversal of ED returns in the postchange period, and paves the way to support the PPH. The investors should invest less in the companies getting included to the benchmarked index, as the prices will get reversed within a short span of five days. Similarly, in case of exclusions, the investors should not drop the excluded company’s stock from their portfolio, as the decreased price will reach its normal position within a short period of seven days. 5.6 Summary and conclusion The current study tests the DSDC hypothesis and the PPH by analyzing changes in stock price and trading volumes of companies included (excluded) to and from the Nifty index during 1998-2011. The study observed short-term price and volume effect, hence providing support to the PPH. The evidence demonstrated here serves as a stage of progress for in depth analysis of the index change effect in the emerging markets like India.

Table VI. Results of auxiliary test for the price pressure hypothesis

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The price and volume effect have been noticed less on the AD, and more on the ED for inclusions and as well as for exclusions. This shows that, probably the index fund managers are concentrating less on the AD compared to the actual change day. The results show that, there is no evidence of permanent price effect, but there were shortterm price reversals. The study also found that the trading volumes tend to move significantly before the AD to 60 days beyond the ED in a haphazard manner, even though they are above normal for inclusions and as well as for exclusions. This could be due to the fact that, stocks normally take a few weeks to adjust after inclusion in or exclusion from the indices. The price results reported by the current study diverge from the previous research works of Harris and Gurel (1986), Lynch and Mendenhall (1997), Peterson (2004), Kumar (2007), Qiu and Pinfold (2007), and Selvam et al. (2012); however, the volume results are similar to that of Lynch and Mendenhall (1997), Gowri Shankar and Miller (2006), Chuang et al. (2009) and in contrast to Kumar (2007). The study also estimated additional tests to predict the long-term and short-term price and volume effect, and found meaningful evidence in support of short-term price pressures leading to the PPH. The prices for inclusions and exclusions reversed after five days of the actual change date. This study contributes to the body of literature on the index effect by providing Indian evidence. Improvements can be made to this study by factoring industry effect into the model to identify, whether inclusion/exclusions based on industry specific issues have a bearing on price and trading volume. Further, there must be some inherent difference in the behavior of the equity investors which might be influencing the significant departure of the DSDC hypothesis. References Bera, A., Bubnys, E. and Park, H. (1988), “Conditional heteroscedasticity in the market model and efficient estimates of betas”, The Financial Review, Vol. 23 No. 2, pp. 201-214. Biktimirov, E.N. (2004), “The effect of demand on stock prices: evidence from index fund rebalancing”, The Financial Review, Vol. 39 No. 3, pp. 455-472. Bildik, R. and Gulay, G. (2008), “The effects of changes in index composition on stock prices and volume: evidence from the Istanbul stock exchange”, International Review of Financial Analysis, Vol. 17 No. 1, pp. 178-197. Bollerslev, T. (1987), “A conditional heteroskedastic time series model for speculative prices and rates of return”, The Review of Economics and Statistics, Vol. 69 No. 3, pp. 542-547. Chakrabarti, R., Huang, W., Jayaraman, N. and Lee, J. (2005), “Price and volume effects of changes in MSCI indices – nature and causes”, Journal of Banking and Finance, Vol. 29 No. 5, pp. 1237-1126. Chuang, H.L., Liao, T.L. and Yu, M.T. (2009), “Price pressure around exchange listings”, paper presented at The Financial Management Association International Conference, Reno, NV, October 21-24, available at: http:/nthur.lib.nthu.edu.tw/dspace/handle/987654321/45156 (accessed July 12, 2012). Chung, R. and Kryzanowski, L. (1998), “Are the market effects associated with revisions to the TSE 300 index robust?”, Multinational Finance Journal, Vol. 2 No. 1, pp. 1-37. Crisil Fund Insights (2013), “Investment thoughts”, May, pp. 1-4, available at: http://crisil.com/pdf/ capitalmarket/CRISIL-fund-insights-may13.pdf (accessed 15 September 2013). Denis, K., McConnell, J., Outchinnikov, A. and Yu, Y. (2003), “S&P 500 index inclusions and earnings expectations”, The Journal of Finance, Vol. 58 No. 5, pp. 1821-1840. Diebold, F., Im, J. and Lee, C. (1988), “Conditional heteroscedasticity in the market. Board of the governors of the Federal Reserve System”, Finance and Economics Discussion Series No. 42.

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Duque, J. and Madeira, G. (2004), “Effects associated with index composition changes: evidence from the euronext lisbon stock exchange”, Working Paper No. 5, Instituto Superior de Economia e Gestao, Lisbon, available at: www.repository.utl.pt/bitstream/10400.5/2258/1/ N52004.pdf (accessed October 15, 2012). Global Stock Markets Fact Book (2012), “Standard & poor’s”, April, available at: http:// us.spindices.com/documents/additional-material/2012-sp-global-stock-markets-factbook.pdf (accessed 15 October 2013). Gowri Shankar, S. and Miller, J.M. (2006), “Market reaction to changes in the S&P SmallCap 600 index”, The Financial Review, Vol. 41 No. 3, pp. 339-360. Harris, L. and Gurel, E. (1986), “Price and volume effects with changes in the S&P 500 list: new evidence for the existence of price pressures”, The Journal of Finance, Vol. 41 No. 4, pp. 815-829. Hrazdil, K. (2009), “The price, liquidity and information asymmetry changes associated with new S&P 500 inclusions”, Managerial Finance, Vol 35 No. 7, pp. 579-605. Jain, P.C. (1987), “The effect on stock price of inclusion in or exclusion from the S&P 500”, Financial Analysts Journal, Vol 43 No. 1, pp. 58-65. Kaul, A., Mehrotra, V. and Morck, R. (2000), “Demand curves for stocks do slope down: new evidence from an index weights adjustment”, The Journal of Finance, Vol. 55 No. 2, pp. 893-912. Kumar, S.S.S. (2007), “Price and volume effects of S&P CNX nifty index reorganizations”, Metamorphosis – A Journal of Management Research, Vol. 6 No. 1, pp. 9-32. Liu, S. (2011), “The price effects of index inclusions: a new explanation”, Journal of Economics and Business, Vol. 63 No. 2, pp. 152-165. Lynch, A.W. and Mendenhall, R.R. (1997), “New evidence on stock price effects associated with changes in the S&P 500 index”, The Journal of Business, Vol. 70 No. 3, pp. 351-383. Mazouz, K. and Saadouni, B. (2007), “New evidence on the price and liquidity effects of the FTSE 100 index revisions”, International Review of Financial Analysis, Vol. 16 No. 3, pp. 223-241. Petajisto, A. (2011), “The index premium and its hidden cost for index funds”, Journal of Empirical Finance, Vol. 18 No. 2, pp. 271-288. Peterson, A.C. (2004), “Price effects associated with changes in the standard & poor’s 500 index composition: the removal and replacement of seven non-US companies”, Economics Discussion Papers No. 0404, University of Otago, Dunedin, ISSN 0111-1760, available at: http://otago. ourarchive.ac.nz/bitstream/handle/10523/1074/DP0404.pdf (accessed September 22, 2012). Qiu, M. and Pinfold, J. (2007), “Price and trading volume reactions to index constitution changes: the Australian evidence”, Managerial Finance, Vol. 34 No. 1, pp. 53-69. Savickas, R. (2003), “Event-induced volatility and tests for abnormal performance”, The Journal of Financial Research, Vol. XXVI No. 2, pp. 165-178. Schmidt, C., Lucy, Z. and Chris, T. (2011), “Index effects: further evidence for the S&P/ASX 200”, paper presented at 24th Australasian Finance and Banking Conference, Sydney, October 27, available at: http://dx.doi.org/10.2139/ssrn.1914170 (accessed October 25, 2012). Selvam, M., Indhumathi, G. and Lydia, J. (2012), “Impact on stock price by the inclusion to and exclusion from CNX Nifty index”, Global Business Review, Vol. 13 No. 1, pp. 39-50. Shliefer, A. (1986), “Do demand curves for stocks slope down?”, The Journal of Finance, Vol. 41 No. 3, pp. 579-590. World Investment Report (2013), “World investment report”, Published by United Nations, available at: http://unctad.org/en/publicationslibrary/wir2013_en.pdf (accessed 10 September 2013).

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Wurgler, J. and Zhuravskaya, E. (2002), “Does arbitrage flatten demand curves for stocks?”, The Journal of Business, Vol. 75 No. 4, pp. 583-608. Yun, J. and Kim, T.S. (2010), “The effect of changes in index constitution: evidence from the Korean stock market”, International Review of Financial Analysis, Vol. 19 No. 4, pp. 258-269.

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Web reference available at: http://www.nseindia.com/content/us/us11sl.htm?&lang ¼ en us&output ¼ json (accessed October 15, 2012). Further reading Biktimirov, E.N., Cowan, A.R. and Jordan, B.D. (2004), “Do demand curves for small stocks slope down?”, The Journal of Financial Research, Vol. XXVII No. 2, pp. 161-178. Bloomstrand, J. and Safstrand, T. (2010), “The index effect OMXS 30 vs STOXX 50”, master thesis, Department of Finance, Stockholm School of Economics, Stockholm, available at: http://arc.hhs.se/download.aspx?MediumId ¼ 1044 (accessed October 28, 2012). Chen, H., Norohna, G. and Singal, V. (2004), “The price response to S&P 500 index inclusions and exclusions: evidence of asymmetry and a new explanation”, The Journal of Finance, Vol. 59 No. 4, pp. 1901-1929. Dhillon, U. and Johnson, H. (1991), “Changes in the S&P 500 list”, Journal of Business, Vol. 64 No. 1, pp. 75-85. Kang, J., Liu, M.H. and Ni, S.X. (2002), “Contrarian and momentum strategies in the China stock market: 1993-2000”, Pacific Basin Finance Journal, Vol. 10 No. 3, pp. 243-265. Corresponding author Abdul Rahman can be contacted at: [email protected]

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