Institutional Ownership, Investor Recognition and Stock Performance around Index Rebalancing: Evidence from Indian Market

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Institutional Ownership- The corporate disclosures cover ownership patterns showing overall institutional ownerships and non-promoter ownerships. This section computes institutional

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ownership proxies considering non-promoter ownership patterns. As stock indexing due to rebalancing event affects non-promoter ownership which is non-strategic in nature, the proxies are developed as below to highlight institutional activism during re-balancing. The ownership percentage is the ratio of shareholding by institutional investors to Total share outstanding. It is calculated as

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For each firm we compute the standard deviation of the distribution of quarterly cash flows over a 3- year moving window centered on each year included in the panel data.

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(i)Institutional Ownership (IO) - The Institutional ownership percentage is calculated based on Total Institutional Equity ownership compared to Total number of Equity outstanding shares. Shares held by institutions as non-promoters. 𝑂𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝𝐼𝑂 =

Total Institutional Investors Equity ownership Total number of Equity outstanding shares

(ii) Mutual Fund Ownership (MF) – Percentage of shares held by mutual funds in nonpromoter capacity with respect to total outstanding shares 𝑂𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝𝑀𝐹 =

Total Equity ownership held by Mutual Funds Total number of Equity outstanding shares

(iii) Bank & Financial Institutions Ownership (Bank)–Percentage of Shares held by

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banks, financial institutions, and insurance cos. as non-promoters with respect to total outstanding shares. 𝑂𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝𝐵𝑎𝑛𝑘𝑠 =

Total Equity ownership held by Banks Total number of Equity outstanding shares

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(iv) Foreign Institutional Ownership (FII)–Percentage of Shares held by foreign institutional investors as non-promoters with respect to total outstanding shares

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Total Equity ownership held by FIIs Total number of Equity outstanding shares

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𝑂𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝𝐹𝐼𝐼 =

Shadow Cost- Prior studies by Chen et al. (2004), and Chan et al. (2013) use shadow cost as a proxy measure to determine investor recognition. Accordingly, if investors are informed of the

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subset of all stocks, and hold only the stocks they recognize, those investors will be inadequately diversified and demand a premium. This premium is computed by shadow cost, for the nonsystematic risk carried by investors. The breadth of ownership increases as stock gains

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recognition, leading to a decrease in shadow cost. Following Chen et al. (2004), the following is the

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computation of shadow cost:

𝑆ℎ𝑎𝑑𝑜𝑤 𝐶𝑜𝑠𝑡 =

𝑆𝑡𝑑𝑑𝑒𝑣(𝑚, 𝑖) 𝐹𝑖𝑟𝑚𝑆𝑖𝑧𝑒 𝑋 𝑇𝑜𝑡𝑎𝑙 𝑀𝐶𝑎𝑝 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑆ℎ𝑎𝑟𝑒ℎ𝑜𝑙𝑑𝑒𝑟𝑠



Residual Standard Deviation (Stddev (m,i))- variable computed as the difference between yearly returns standard deviation of market and stocks. Firm Size is Market capitalization of the firm 11

directly pulled in from CMIE. Total MCAP is Nifty500 market capitalization, and Number of Shareholders is the total number of shareholder of the firm.

3.3 Model Specifications

Index Rebalancing Event and Institutional Ownership We follow Chan et al. (2013), and Gompers and Metric (2001) model specification in examining the effect of Institutional ownership on stock abnormal return after Index re-balancing

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events. The model is represented as follows: 𝐴𝑅𝑖,𝑡 = 𝛼 + 𝛽1 𝛥𝐼𝑂𝑖,𝑡 + 𝛽2 𝑀𝑘𝑡𝑅𝑒𝑡𝑖,𝑡 + 𝛽3 𝐿𝑜𝑔(𝐴𝑔𝑒)𝑖,𝑡 + 𝛽4 𝐿𝑜𝑔(𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒)𝑖,𝑡 + 𝛽5 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 + 𝛽6 𝐷𝑢𝑚_𝑉𝑎𝑙𝑢𝑒𝑖,𝑡 + 𝜀𝑖,𝑡

(1)

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where, dependent variable is AR is abnormal return. We estimate the above regression across all stock categories by using following variables, IOis non-promoter institutional ownership.

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The control variables are Market returns (𝑀𝑘𝑡𝑅𝑒𝑡), natural log of firm age (Log(Age)), natural log of firm size (Log(Firmsize)), stock return volatility (Volatility) and dummy variable if stock is

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undervalued (Dum_Value).In another extended regression model, we individually incorporated institutional ownership of mutual funds, FIIs and banks in the base model (eq. 1).

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𝐴𝑅𝑖,𝑡 = 𝛼 + 𝛽1 𝐼𝑂𝑀𝐹,𝑖,𝑡 + 𝛽2 𝛥𝐼𝑂𝐵𝐴𝑁𝐾,𝑖,𝑡 + 𝛽3 𝐼𝑂𝐹𝐼𝐼,𝑖,𝑡 + 𝛽4 𝑀𝑘𝑡𝑅𝑒𝑡𝑖,𝑡 + 𝛽5 𝐿𝑜𝑔(𝐴𝑔𝑒)𝑖,𝑡 + 𝛽6 𝐿𝑜𝑔(𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒)𝑖,𝑡 + 𝛽7 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 + 𝛽7 𝐷𝑢𝑚_𝑉𝑎𝑙𝑢𝑒𝑖,𝑡 + 𝜀𝑖,𝑡

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(2)

In this model, variables IOMF, IOBank and IOFII are non-promoter institutional ownership for

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Mutual funds, Banks and Foreign Institutional investors respectively.

Index rebalancing event and Investor Recognition The effect of Investor recognition on a stock return during post-event and pre-event period

is computed using the following models. For each of the index re-balancing period i.e. post and preevent, the following regression model is applied (Chen et al.; 2004).To examine the relationship, we estimate the following two models-

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𝐴𝑅𝑖 = 𝛼 + 𝛽1 𝛥𝑆ℎ𝑎𝑑𝑜𝑤𝐶𝑜𝑠𝑡𝑖 + 𝛽2 𝑀𝑘𝑡𝑅𝑒𝑡𝑖 + 𝛽3 𝐿𝑜𝑔(𝐴𝑔𝑒)𝑖 + 𝛽4 𝐿𝑜𝑔(𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒)𝑖 + 𝛽5 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖 + 𝛽5 𝐷𝑢𝑚_𝑉𝑎𝑙𝑢𝑒𝑖 + 𝜀𝑖,𝑡 (3) 𝐴𝑅𝑖 = 𝛼 + 𝛽1 𝛥𝑆ℎ𝑎𝑑𝑜𝑤𝐶𝑜𝑠𝑡𝑖 + 𝛽2 𝛥𝐼𝑂𝑖 + 𝛽3 𝑀𝑘𝑡𝑅𝑒𝑡𝑖 + 𝛽4 𝐿𝑜𝑔(𝐴𝑔𝑒)𝑖 + 𝛽5 𝐿𝑜𝑔(𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒)𝑖 + 𝛽6 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖 + 𝛽7 𝐷𝑢𝑚_𝑉𝑎𝑙𝑢𝑒𝑖 + 𝜀𝑖,𝑡 (4) Where, dependent variable is abnormal stock return (Chen et al.; 2004). The change in shadow cost and change in institutional ownership is computed as follows- 𝛥𝑆ℎ𝑎𝑑𝑜𝑤𝐶𝑜𝑠𝑡𝑖 = 𝑆ℎ𝑎𝑑𝑜𝑤𝐶𝑜𝑠𝑡𝑖,𝑝𝑜𝑠𝑡 − 𝑆ℎ𝑎𝑑𝑜𝑤𝐶𝑜𝑠𝑡𝑖,𝑝𝑟𝑒 and, 𝛥𝐼𝑂𝑖 = 𝐼𝑂𝑖,𝑝𝑜𝑠𝑡 − 𝐼𝑂𝑖,𝑝𝑟𝑒

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(5) In above Eq. (3), ShadowCosti,post and ShadowCosti,pre is Shadow cost for a firm a quarter before and after index rebalancing event respectively. Variable ΔShadowCost is change in Shadow cost between post and pre-index rebalancing event which is constructed following Chen et al. (2004).

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The control variables are Market returns (𝑀𝑘𝑡𝑅𝑒𝑡), natural log of firm age (Log(Age)), natural log of firm size (Log(Firmsize)), stock return volatility (Volatility) and dummy variable if stock is

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undervalued (Dum_Value).

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4. Results and Findings

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4.1 Summary Statistics and Correlations

Table 2 reports descriptive statistics data for all the variables considered in this study for events between 2002 to 2016, with quarterly financial data set. There are 20,629 firm-quarter

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observations in our sample. The mean market capitalization of Nifty 500 is INR 27689.35 (USD 425 Billion), while the average market capitalization of stocks in the sample of rebalancing is INR 238.40

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(USD 3.6 Billion), which is approximately 9% of market capitalization value. We missed a few observations for some variables as information data was unavailable in the Prowess database. The average age of the firms in the sample is more than 39 years. Sample firms have a wide shareholding pattern. The average (median) number of shareholders is more than 261,000 (95,940). The mean and median value of share traded in a quarter is more than 11million and 9 million respectively.



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Table 3 presents descriptive data for stock ownership structure; Panel A shows the stock additions event and Panel B shows the stock deletions events summary for institutional ownership on a quarterly basis. The stock addition events are classified into three categories- Emergent, Heavy Weights and Upgrade. The Panel-A shows a total of 17,148 firm-quarter observations in additions category, where 8,574 observations in Emergent; 5,734 observations in Heavy Weight and 1,693 observations in Upgrade category. The mean Institutional ownership for all additions category is 26.50 percent with maximum ownership in Upgrades 32.14 percent and minimum in Heavy Weight category as 19.6 percent. Across all the additions stock categories, the percentage ownership of institutional investors is highest and mutual funds ownership is lowest. Panel B of Table 3 reports descriptive

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ownership statistics for stocks that are Loser and Demote from the Nifty index. The stock events are classified into two groups- Loser and Demote. There are total 7,645 firm-quarter observations, where, 7,154 observations for Loser and 491 for Demotecategorystocks. Similar to Panel A, the ownership for demoted stocks is highest for institutional investors is highest, and bank ownership is lowest. However, for Loser, the Mutual fund ownership is highest and lowest for banks. Overall, Table 3 indicates that

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for the considered sample of stocks in our sample period, in general, institutional ownership is highest irrespective of index addition or deletion events. Panel-C in Table 3 shows the descriptive of our

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interest variables and other control variables. The mean value of firm age in the sample is 39.34years. The average market capitalization of the benchmark market index NIFTY 500 is around INR 23,552

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billion. The mean value of the abnormal stock return is -0.001 percent with a minimum and maximum of -2.18 percent and 2.21 percent, respectively.

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Table 4 shows the correlation among various categories of institutional ownership. Panel A

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shows a correlation among various institutional ownerships in stock addition categories. The Emergent category stocks show the highest correlation of (82 percent) between all Institutional

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Ownership (IO) and foreign institutional investors (IOFII). However, the correlation between bank (IOBank) and mutual fund ownership (IOMF) is negative for Emergent at -14 percent, Heavyweights show -6 percent correlations. Panel B shows the correlation between various institutional ownerships in stock deletion categories. The deletions category shows the highest correlation between Institutional Ownership (IO) and foreign institutional investors (IOFII). The obvious reason for the high correlation between overall institutional ownership (IO) and foreign institutional investors (IOFII) is high ownership of FIIs in indexed stock. Table 3 clearly shows that FIIs ownership is

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highest among all the institutional investors. The mutual fund (IOFII) and bank (IOBank) display a negative 8 percent for Loser category and 9 percent for Demote category. In general, the negative correlation between Banks and FII implies an interesting direction that foreign investors and banks may be trading in opposite directions.



4.2 Regression Results Calendar time abnormal return

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4.2.1

We employ the calendar time approach to measure the abnormal returns associated with 𝐴 𝑜𝑟 𝐷 index additions and deletions (Chan et al.; 2013). The 𝑅𝑝𝑡 is a portfolio’s return for month t, with

A and D in the superscript indicating stocks added to index and deleted from index, respectively; R ft

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is the risk-free interest rate; (Rmt - Rft) is the market excess return; SMBt is the difference in the returns of portfolios of small and large-cap stocks; HMLt is the difference between the returns of portfolios of high and low book-to-market ratio stocks; and MOMt is the highest monthly portfolio

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return minus the lowest monthly portfolio return over the previous 12-month period excluding event quarter. We expect the value of the intercept (α), which measures the quarterly abnormal return, is

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zero under a null hypothesis of no abnormal performance.

(6)

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𝐴 𝑜𝑟 𝐷 𝑅𝑝𝑡 = 𝛼 + 𝛽𝑚 (𝑅𝑚𝑡 − 𝑅𝑓𝑡 ) + 𝛽𝑠 𝑆𝑀𝐵𝑡 + 𝛽ℎ 𝐻𝑀𝐿𝑡 + 𝛽𝑘 𝑀𝑂𝑀𝑡 + 𝜀𝑡

Table 5 reports results for excess returns of the portfolios against the Fama–French three

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factors and Carhart’s momentum factor. The regression results show portfolio of added stocks has a significant and positive coefficient at 3.4% and significant at 1% confidence level. However, for

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deleted stocks, the intercept value is negative and statistically insignificant. The sub-category analysis reveals that stock categories like Emergent, Heavy Weight and Losers shows positive coefficients significant at 1% confidence level, but insignificant for Upgrade and Demote categories. The results are partially consistent with US market literature of Chan et al. (2013), supporting positive returns for additions but fails to support the asymmetric price response of deleted stocks. The coefficient of SMB is higher for deleted stocks at 1.035 at 1% confidence level compared to added stocks at 0.828 at 1% confidence level, which shows that deleted stocks are much smaller than added stocks. The coefficient of HML factor is positive but insignificant except Upgrade. 15

Interestingly, the coefficient of MOM is consistently positive and significant suggesting that return of added and deleted stocks is influenced by the market momentum.



4.2.2

Institutional Ownership and Stock Performance

In this section, we examine hypotheses (H1 and H2) by analyzing the dataset for two subsample periods: three-year pre-index rebalancing sub-sample and three years’ post index rebalancing

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sub-sample.6 Table 6 reports the estimation results for the effect of change in institutional ownership on stock performance (Eq. 1). Results for pre and post events periods are presented in Panel-A and Panel-B respectively. Our primary interest variable is the quarterly change in institutional ownership (ΔIO). In Panel A, the coefficient for change in institutional ownership (ΔIO) for additions and deletions category stocks is positive and significant 0.029 and 0.011 respectively. The results indicate

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that institutional investors are agnostic to index rebalancing during pre-event period. Additionally, the coefficient for ΔIO for additions sub-categories shows that stocks under these three categories

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outperform the benchmark. In the pre-index rebalancing period, the coefficient for valuation dummy (dum_value) is positive and significant (partially) for additions including Emergent and negative

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(though insignificant) for deletion category. This implies that overvalued shocks that are included (deleted) to an index continue to generate positive (negative) returns. Overall, Panel A results show that institutional investors value all the stocks, in general, equally and invest without discretion during

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the pre-event period. This may be attributed to stocks characteristic; as most of the stocks considered for index rebalancing events are stable and large stocks.

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Panel B shows the coefficient for change in institutional ownership is positive7 for the additions (all sub-categories) but negative for the deletions. The results indicate an interesting difference in the

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institutional investors' perception towards the deleted/demoted stocks in the post-event period. We believe that post-event institutional investors reduce ownership in the stocks that are demoted to Nifty Next 50 or removed from the Nifty index. The reduction of ownership in the deletion category stock results in a decline in the stock returns. The coefficient of valuation (dum_value) for additions and 6

For the index rebalancing events that occurred in year 2016, our post-event analysis is limited to two years till year 2018. 7 The coefficient of change in institutional investor ownership for Upgrade category stocks is positive but insignificant.

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deletions is negative for the post-event subperiod suggesting that overvalued stocks do not generate positive returns for the investors. The direction of the coefficient of stock valuation dummy shows that stock overvaluation is valued more by the investors in pre-event period compare to post-event period.

Overall, Table 6

results on change in ownership around index-rebalancing events are

consistent with Pruitt and Wei (1989) findings that additions category stocks get more attention from the institutional investors after addition to index. However, this distinction is not very clear in the preindex rebalancing period. We believe that institutional investors in the Indian context, give more

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weight to index rebalancing events that may result in a change in ownership structure in post index rebalancing events. In general, our results partially support the hypotheses H1 and H2 confirming a positive relationship between change in institutional ownership and stock return for index additions and negative for stock deletions respectively.

Investor Recognition and Stock performance

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4.2.3

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In this sub-section, we present test results for the“investor recognition hypothesis”. Table-7

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presents descriptive statistics of shadow cost across all the event sub-categories. The average number of shareholders and Firm size is highest for Heavy Weights followed by Upgrade and Emergent

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category. In the deletions category; the Loser sub-category has the lowest number of average shareholders compare to the Demote category. Across the events sub-categories, the average number of shareholders is highest in Demote, followed by HeavyWeight, Losers, Emergent, and Upgrade.

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Further, the average firm size is highest for the addition category showing the increasing trend of

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market capitalization compared to the deletion category.



Table 8 presents descriptive statistics for shadow cost for three years pre and post-index

rebalancing events. Panel-A shows descriptive statistics for pre-index rebalancing events, where institutional ownership is highest (28.31%) for stock category upgrade and lowest for Demote (20.42%) category stocks. However, in the post-event period (Panel-B), institutional ownership is highest for Upgrade and lowest for Demote. Comparison of Panel A and B shows that the shadow cost is higher for stock additions and lower for deletions. Panel C presents mean difference t-test 17

results based on Panel-A and B. The t-test result shows that the number of shareholders increases for stock additions category after index rebalancing. However, shadow cost and stock abnormal return decrease for additions in the post-event period. During the post-event period, institutional ownership (IO), and the number of shareholders decrease while shadow-cost and abnormal return increase for deletions. Overall, table-8 shows that added stock categories of Emergent, Heavyweight and Upgrades increase the investor base and decline in shadow cost, while for demote category shows an increase in investor base while loser category shows declining investor base.



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Further, we analyze the variation in shadow cost three years pre and post-index rebalancing events. Table-9 shows shadow-cost during post–event and pre-event across stock categories. Panel A shows the mean and median descriptive of Shadow Cost in each quarter. Panel-B post-pre average shadow cost t-statistics using paired t-test. In general, t-test results affirm table-8 findings that

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shadow cost (both Mean and Median) after index-rebalancing declines for additions stocks while it increases for deletions.

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In general, our results for the long-term changes in the shadow cost for additions support H3 and are consistent with existing studies. For example, Chen et al. (2004), Baran and King (2012) find that the shadow cost is reduced for stocks added to the S&P 500 index, Elliott, Ness and Walker (2006) find

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that the shadow cost of added stocks is reduced based on a sample of S&P 500 constituent stocks. However, we observe that an increase in shadow cost for deletions indicating that there is a reduction in investor recognition. Our results are partially distinct from the findings in the developed markets

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and fail to support H4. Chen et al. (2004) argue that investors cannot be made unaware of stocks that are deleted from the index, as they continue to hold stocks in a portfolio for diversification benefits.

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We attribute contradiction in our result with Chen et al. (2004) to investors' behavior of “attention loss” for the deleted category (Seasholes and Wu; 2007, Barber and Odean; 2008). The results in Indian market revealing an increase in shadow cost can be explained by investors selling deleted stocks specifically in Loser category. Though we have not empirically examined the components of shadow cost to explain changes shadow cost, however, we believe that investors treat index rebalancing as an information event carrying bad news for deleted stocks.

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4.2.4

Investor Recognition and Institutional Ownership

We have also conducted additional analysis to validate both the propositions related to institutional ownership and investor recognition on stock performance (Chan et al. 2013), focusing the index rebalancing event. Table 10 present the results for regression Eqn. (4) discussed in the methodology section. We run two models- aggregate institutional ownership and individual, institutional ownership for both stock additions and stock deletions categories. In table 10, column (2) and (4) presents results for aggregate ownership while column (3) and (5) show results for individual ownership. The coefficient of change in shadow cost (ΔShadowCost) is negative for

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added stocks and aggregate change in institutional ownership is positive (ΔIO) which means that a decrease in shadow cost and increase in institutional ownership explain the abnormal return of stocks that experience index rebalancing events. Comparison of results in column (2) and (5) indicate that at an aggregate level, institutional investors increase ownership in stocks that are added to an index and reduce for deleted or demoted stocks. Further, results in column (3) show that in general, holdings of

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mutual funds, FIIs are negatively sensitive to index rebalancing events, whereas the banks are positively sensitive to index rebalancing events and they change ownership around index rebalancing

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event. We also examine this relationship separately for addition and deletion events.

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The column (4) shows that the coefficient for shadow cost is positive and IO is negative,

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suggesting that an increase in shadow cost due to deletion from the major index, results as a reduction in institutional investors holding. Further, column (5) result reveals that the coefficient of ownership for mutual funds and FIIs is positive (negative) for stocks that are added (deleted) to the

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Nifty index. However, the coefficient for bank ownership is positive for additions and deletions. This indicates that mutual funds and FIIs perceive index deleted stocks as adverse stock and reduce the

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ownership after the re-balancing event. However, ownership of banking institutions do not change that may be due to the passive portfolio management style normally practiced by the banks. We also find that shadow cost reduces (increases) for additions (deletions) supporting investor recognition hypothesis. Other control variables are in general non-significant except valuation dummy, which is 1 for cheap stock and 0 otherwise (Dum_value). The coefficient of Dum_value is negative for additions and positive for deletions. Earlier studies do not find any specific direction valuation dummy.

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Our results and additional analysis find support for investor recognition hypothesis and information content of index rebalancing events for the institutional investors.

5. Summary and Conclusion

The existing studies on the effect of institutional ownership pattern and investor recognition on stock index performance around index rebalancing, but the focus of these studies been developed markets. However, emerging markets in general and India, in particular, is not empirically explored. We investigate the effect of the change in institutional ownership and investor’s recognition of index

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stock performances around index rebalancing events in the Indian market. The dataset covers events from 2002-2016 for two major indices- Nifty 50 and Nifty Next 50. Both indices are rebalanced simultaneously as Nifty Next 50, where stocks are added, deleted and transited in between Indices; the sample set is divided into five categories of Emergent, Heavy Weight, Upgrade, Demote and Loser for more in-depth insight into rebalancing events. The calendar time abnormal return following

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three years shows partial consistency with developed markets. The stocks directly added to Nifty 50 (Heavy Weight) and Nifty Nifty Next 50 (Emergent) shows positive returns. The deletions in loser

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and Nifty Next 50 i.e. Upgrade and Demotes.

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category shows positive returns, where as insignificant returns of stocks moving between Nifty 50

We observe an increase in the institutional investors’ ownership for stock addition to an index, while stock deletions from an index demonstrate a decline in institutional ownership post index

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rebalancing events. Moreover, the Foreign institutional investors (FIIs) and Mutual funds (MFs) in the Indian market possess superior information to compare to the Banks. The superiority of

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information may be due to the information processing capabilities of FIIs and MFs. Therefore, active trading strategies practices by FIIs and MFs may have a dominant effect compared to passive investment strategies by Banks. We also find support for investor recognition hypothesis where

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stock additions (deletions) results as decrease (increase) in shadow cost post index rebalancing event. Our results are slightly different from developed markets (Chen et al.; 2004, Chan et al.; 2013), where authors argue that investors cannot be made unaware of stocks and they continue to hold the stocks for diversification benefit. However, in the case of the Indian market, the evidence is contrary. We find that stocks deleted stocks show an increase in shadow cost after index rebalancing. We believe (though not empirically examined) that this difference in our results may be due to

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“attention-loss” phenomena among Indian investors for deletion category stocks (Seasholes and Wu;

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2007, Barber and Odean; 2008).

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Cai, J., &Houge, CFA, T. (2008). Long-term impact of Russell 2000 index rebalancing. Financial Analysts Journal, 64(4), 76-91. https://doi.org/ 10.246/faj.v64.n4.7 Chan, K., Kot, H. W., & Tang, G. Y. (2013). A comprehensive long-term analysis of S&P 500 index additions and deletions. Journal of Banking & Finance, 37(12), 4920-4930. https://doi.org/10.101/j.jbankfin.2013.08.027

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Chen, H., Noronha, G., &Singal, V. (2004). The price response to S&P 500 index additions and deletions: Evidence of asymmetry and a new explanation. The Journal of Finance, 59(4), 1901-1930. https://doi.org/10.111/ j.1540-6261.200.006.x

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De, S., Gondhi, N., &Sarkar, S. (2011). Behavioral biases, investor performance, and wealth transfers between investor groups. https://doi.org/10.213/ssrn.2022992 Denis, D. K., McConnell, J. J., Ovtchinnikov, A. V., & Yu, Y. (2003). S&P 500 index additions and earnings expectations. The Journal of Finance, 58(5), 1821-1840. https://doi.org/10.111/1540-6261.005 Dhillon, U., & Johnson, H. (1991). Changes in the Standard and Poor's 500 List. Journal of Business, 75-85. https://doi.org/10.108/296526 Elliott, W. B., Van Ness, B. F., Walker, M. D., &Warr, R. S. (2006). What drives the S&P 500 inclusion effect? An analytical survey. Financial Management, 35(4), 31-48. 22

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Harris, L., &Gurel, E. (1986). Price and volume effects associated with changes in the S&P 500 list: New evidence for the existence of price pressures. The Journal of Finance, 41(4), 815-829. https://doi.org/10.111/j.1540-6261.198. tb04550.x Hegde, S. P., & McDermott, J. B. (2003). The liquidity effects of revisions to the S&P 500 index: An empirical analysis. Journal of Financial Markets, 6(3), 413-459. https://doi.org/10.101/s13864181(02)00046-0

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Liu, Q., Luo, T., &Tian, G. G. (2015). Family control and corporate cash holdings: Evidence from China. Journal of corporate finance, 31, 220-245.https://doi.org/10.101/j.jcorpfin.2015.02.007 Loderer, C., Cooney, J. W., & Van Drunen, L. D. (1991). The price elasticity of demand for common stock. The Journal of Finance, 46(2), 621-651.https://doi.org/10.2307/2328839

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Lynch, A. W., & Mendenhall, R. R. (1997). New evidence on stock price effects associated with changes in the S&P 500 index. The Journal of Business, 70(3), 351-383. Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete information. The journal of finance, 42(3), 483-510. https://doi.org/ 10.111/j.15406261.198.tb04565.x Morck, R., & Yang, F. (2001). The mysterious growing value of S&P 500 membership (No. w8654). National Bureau of Economic Research. https:// doi.org/10.338/w8654 Parrino, R., Sias, R. W., & Starks, L. T. (2003). Voting with their feet: Institutional ownership changes around forced CEO turnover. Journal of financial economics, 68(1), 3-46. 23

Petajisto, A. (2009). Why do demand curves for stocks slope down?. Journal of Financial and Quantitative Analysis, 44(5), 1013-1044. Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing approaches. The Review of Financial Studies, 22(1), 435-480. https://doi.org/10.109/rfs/hhn053 Pruitt, S. W., & Wei, K. J. (1989). Institutional Ownership and Changes in the S&P 500. The Journal of Finance, 44(2), 509-514. https://doi.org/ 10.111/j.1540-6261.198.tb05070.x Scholes, M. S. (1972). The market for securities: Substitution versus price pressure and the effects of information on share prices. Journal of business, 45(2), 179. Seasholes, M. S., & Wu, G. (2007). Predictable behavior, profits, and attention. Journal of Empirical Finance, 14(5), 590-610.https://doi.org/10.101/j.jempfin.2007.03.002

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Sias, R. W., & Starks, L. T. (1997). Institutions and individuals at the turn‐of‐the‐year. The Journal of Finance, 52(4), 1543-1562.

24

Figures

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Figure 1: Structure of NSE NIFTY indices

In-Scope

Out of Scope

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18 16 14 12 10 8 6 4 2 0

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

ur

Number of Index Funds and ETFs

Figure 2: Number of ETFs and Index Funds benchmarking Nifty Indices

Nifty50 (ETF)

NiftyNext50(Indexfund)

Jo

Nifty50(IndexFund)

Year

25

NiftyNext50(ETF)

Year ly Events

Nifty 50 Nifty Next 50

26

ro of

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Jo 2002 Add 2002 Delete 2003 Add 2003 Delete 2004 Add 2004 Delete 2005 Add 2005 Delete 2006 Add 2006 Delete 2007 Add 2007 Delete 2008 Add 2008 Delete 2009 Add 2009 Delete 2010 Add 2010 Delete 2011 Add 2011 Delete 2012 Add 2012 Delete 2013 Add 2013 Delete 2014 Add 2014 Delete 2015 Add 2015 Delete 2016 Add 2016 Delete

Number of Event

Figure 3: Number of Additions and Deletions across Nifty Indices

The Figure-2shows the stock additions and deletions for Nifty 50 and Nifty Next 50 is for period 2002-2016. 18 16 14 12 10 8 6 4 2 0

Tables and Figures

Table 1. Stock categorization based on Events The table represents stock categorization based on events of Nifty Indices from period 2002-2016. The Column(1) reports the event categorization as per NSE, column (2) the events are sub-categorized further, the additions are divided into three categories of Emergent, Heavy Weight and Upgrade and deletions into Loser and Demote. Column (3) and Column (4) shows details and number of events occurred for each stock category. Event (2) Emergent Additions

Heavy Weight Upgrader

Deletion

Loser

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Demote

Details (3) Stock get added to Nifty Next 50, resulting entry to Nifty 100 A Stock directly moves to Nifty 50 i.e. before the stock gets added to Nifty Next 50. These stocks are high market cap after big IPO Stock gets added to Nifty 50 but after Nifty Next 50 entry Stock deleted from Nifty Next 50 and Nifty 50 resulting in exit from Nifty 100 Stock moves from Nifty 50 to Nifty Next 50.

Number of Events (4) 118 21

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Event Type (1)

27

34

143 12

Table 2: Data Descriptive for all the variables This table presents the full sample (2002-2016) descriptive statistics of sample stocks that are added or deleted from Nifty Indices for the dataset. Column (1) contains the variables described in Table-A.1 and Column (2) shows number of observations for final dataset, column (3) shows means values of variables followed by Column (4) shows median values, column (5) standard deviation, Column (6 & 7) show minimum and maximum values respectively. Observations

Mean

Median

STD

Min.

Max.

(2)

(3)

(4)

(5)

(6)

(7)

Stock Price (INR)

20,629

491.13

190.00

4,034.63

2.19

7,497.45

Nifty 500 Index level

20,629

4,344.81

4,329.10

1,822.66

1,138.55

9,490.65

Market Size Nifty (INR Bn)

20,629

27,689.35

28,363.66

13,638.23

3,627.55

65,338.99

Stock Market Cap (INR Bn)

20,598

238.40

125.31

426.73

231.2

6,926.08

Age of firm in Event year

20,613

39.34

30.00

27.21

1.00

123.00

Number of Shareholders ('000)

20,418

261.83

95.94

572.45

1200

4,937.66

Shadow Cost

18,975

1.45

0.61

4.83

0.00

127.13

Share Traded (Mn)

20,567

11.62

9.28

6.08

4.08

35.78

Nifty P/E Ratio

20,241

20.46

21.09

4.42

13.30

32.55

Stock EPS

19,489

54.22

15.90

281.91

-35.66

583.31

Stock P/E Ratio

14,535

55.93

14.69

771.40

-4.17

336.08

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(1)

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Variable

28

Table 3: Descriptive Statistics for ownership structure This table presents the full sample (2002-2015) descriptive statistics of institutional ownership and other variables across index rebalancing events. Panel A and Panel B present descriptive of institutional ownership for Additions and Deletions category events respectively. Panel C presents descriptive for computed variables. Column (1) contains the variables described in Table-A.1 and Column (2) shows number of observations for each stock category, column (3) shows means values of each stock category followed by Column (4) presents median values column (5) standard deviation, Column (6 & 7) show minimum and maximum values respectively. Panel A: Stock Addition Events StdDev (5)

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29

Min (6) 0.00 0.00 0.00 0.00

Max (7) 86.88 20.99 41.51 69.74

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15.06 3.54 6.49 12.33 14.51 3.68 6.54 12.47

0.00 0.00 0.00 0.00

82.82 20.99 41.51 69.74

12.45 3.33 5.70 7.53

2.49 0.04 0.02 0.42

56.49 15.79 28.27 32.15

16.47 3.21 6.96 12.39

3.25 0.05 0.00 0.00

86.88 16.99 36.53 52.94

14.90 4.50 6.80 12.19

0.00 0.00 0.00 0.00

88.20 28.77 30.96 79.65

15.19 4.63 6.98 12.53

0.00 0.00 0.00 0.00

88.20 28.77 30.96 79.65

9.87 2.35 4.29 7.95

5.30 0.03 0.06 0.66

45.30 11.50 17.46 31.77

0.21 27.21 0.20 13,166.79

-2.18 1.00 0.00 3,627.55

2.21 123.00 4.72 65,338.99

0.22 28.79 0.24 13,805.31

-2.23 1.00 0.00 3,627.55

2.21 123.00 7.97 65,338.99

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Variable Observations Mean Median (1) (2) (3) (4) All Additions IO (%) 8,574 26.50 24.80 MF (%) 8,574 3.98 2.96 Bank (%) 8,574 5.76 3.85 FII (%) 8,574 16.76 14.97 Emergent (Additions to Nifty Next 50) IO (%) 5,734 25.83 24.12 MF (%) 5,734 4.15 3.17 Bank (%) 5,734 5.89 4.00 FII (%) 5,734 15.80 13.65 Heavy Weight (Additions to Nifty 50 Directly) IO (%) 1,147 19.67 16.78 MF (%) 1,147 2.99 2.00 Bank (%) 1,147 5.33 3.61 FII (%) 1,147 11.35 9.84 Upgrade (Additions to Nifty 50 shifted from Nifty Next 50) IO (%) 1,693 32.14 29.12 MF (%) 1,693 4.20 3.70 Bank (%) 1,693 5.55 3.35 FII (%) 1,693 22.39 21.42 Panel B: Stock Deletion Events All Deletions IO (%) 7,645 26.99 25.70 MF (%) 7,645 4.21 2.96 Bank (%) 7,645 9.12 8.19 FII (%) 7,645 13.65 10.84 Losers (Deletions from Nifty Next 50) IO (%) 7,154 27.65 26.45 MF (%) 7,154 4.35 3.04 Bank (%) 7,154 9.26 8.33 FII (%) 7,154 14.03 11.33 Demote (Deletions from Nifty 50 but to Nifty Next 50) IO (%) 491 21.89 20.06 MF (%) 491 2.61 1.79 Bank (%) 491 7.06 6.64 FII (%) 491 12.22 10.18 Panel-C All Additions and All Deletions All Additions AR (%) 8,574 0.01 0.00 Firm Age 8,574 39.34 30.00 Volatility 8,574 0.06 0.02 Nifty 500 Mcap (INR Bn) 8,574 23,552.57 22,498.88 All Deletions AR (%) 7,645 -0.01 -0.01 Firm Age 7,645 45.17 36.00 Volatility 7,645 0.07 0.02 Nifty500 Mcap (INR Bn) 7,645 23,990.48 22,498.88

Table 4 Correlation Matrix – Ownership Patterns This table presents correlation matrix for various institutional ownership variables as defined in table-A.1 in Appendix. Panel A: Stock Additions

IOMF

IOBank

IOFII

IOMF

All Additions

IOBank

IOFII

IOMF

Emergent

IOBank

IOFII

IOMF

Heavy Weight

IOBank

IOFII

Upgrade

0.45

0.44

0.83

0.41

0.40

0.82

0.84

0.63

0.71

0.48

0.57

0.90

IOMF

1.00

0.20

0.14

1.00

0.15

0.09

1.00

0.64

0.39

1.00

0.15

0.28

IOBank

0.20

1.00

-0.07

0.15

1.00

-0.14

0.64

1.00

-0.06

0.15

1.00

0.20

IOFII

0.14

-0.07

1.00

0.09

-0.14

1.00

0.39

-0.06

1.00

0.28

0.20

1.00

Panel B: Stock Deletions

IOBank

IOFII

All Deletions

IOMF

IOBank

IOFII

Loser

IOMF

IOBank

IOFII

Demote

0.45

0.46

0.80

0.44

IOMF

1.00

0.10

0.16

1.00

IOBank

0.10

1.00

-0.08

0.09

IOFII

0.16

-0.08

1.00

0.16

0.46

0.80

0.46

0.43

0.77

0.09

0.16

1.00

0.39

-0.01

1.00

-0.09

0.39

1.00

-0.17

1.00

-0.01

-0.17

1.00

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IO

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IOMF

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IO

30

-0.09

Table 5: Calendar time abnormal return This table report regression results for Fama-French three factors and Carhartfactor following Chan et al., (2013) study. The regression model is estimated for equal-weighted portfolios of added and deleted stocks in the event quarter and examine the portfolio performance in the following three years. Rpt is a portfolio’s return for month t, with A and D in the superscript indicating stocks added to index and deleted from index, respectively; R ft is the risk-free interest rate; (RmtRft) is the market excess return; SMBt is the difference in the returns of portfolios of small and large-cap stocks; HMLt is the difference between the returns of portfolios of high and low book-to-market ratio stocks; and MOMt is the highest monthly portfolio return. Symbol ∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, or 1% level, respectively. 𝐴 𝑜𝑟 𝐷 𝑅𝑝𝑡 = 𝛼 + 𝛽𝑚 (𝑅𝑚𝑡 − 𝑅𝑓𝑡 ) + 𝛽𝑠 𝑆𝑀𝐵𝑡 + 𝛽ℎ 𝐻𝑀𝐿𝑡 + 𝛽𝑘 𝑀𝑂𝑀𝑡 + 𝜀𝑡

SMB

HML

MOM

Upgrade (5) 0.038

Deletions (6) -0.095

Demote (7) -0.022

Loser (8) 0.051***

(0.014)

(0.008)

(0.008)

(0.024)

(0.066)

(0.018)

(0.017)

0.267***

0.595***

0.578***

0.153***

0.541***

0.591***

0.607***

(0.274)

(0.16)

(0.162)

(0.926)

(0.115)

(0.15)

(0.248)

0.828***

1.023***

1.002***

1.763***

1.035***

0.938***

1.770***

(0.34)

(0.207)

(0.216)

(0.531)

(0.161)

(0.204)

(0.487)

0.234

-0.057

-0.131

1.520***

-0.072

-0.141

-0.317

(0.289)

(0.161)

(0.165)

(0.652)

(0.124)

(0.158)

(0.328)

1.104***

0.619***

0.630***

0.589***

0.995***

0.874***

1.433***

(0.247)

(0.146)

(0.152)

(0.47)

(0.111)

(0.141)

(0.305)

0.0786

0.0526

0.0475

0.0809

0.0598

0.1632

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Adj R sqr

HeavyWeight (4) 0.014***

31

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Rm-Rf

Emergent (3) 0.012***

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Intercept

Additions (2) 0.034**

re

Parameter (1)

0.1783

Table 6: Regression of Stock Returns and Institutional Ownership Table below presents regression estimates for the model given in equation (1). Results are presented in two panels-A and B. The panel-A Shows Pre-event using twelve quarters and Panel-B shows post-event using twelve quarters (3 year) regression where the dependent variable is abnormal return (AR), the key independent variable is ΔIO (change in Institutional ownership) followed by control variables. Other control variables, MktRet is NIFTY 500 index return, Log(Age) is natural log of firm age, Volatility is quarterly variance of daily stock return, Dum-Value is dummy which takes value equals to 1 if stock is cheaper, 0 otherwise. The observations in stock categories vary due to adjustment of dataset with survivorship or M&A. The Column (1) presents the parameters followed by Column (2) showing the regression of results of addition categories. The column (3), Column (4) and Column (5) shows regression results of addition subcategories as Emergent, Heavy Weight and Upgrade respectively. The Column (6) shows deletion category regression results followed by column (7) of Loser and column (8) of Demote category. Symbol ∗, ∗∗, and ∗∗∗ denote significance at the 10%, 5%, or 1% level, respectively. 𝐴𝑅𝑖,𝑡 = 𝛼 + 𝛽1 𝛥𝐼𝑂𝑖,𝑡 + 𝛽2 𝑀𝑘𝑡𝑅𝑒𝑡𝑖,𝑡 + 𝛽3 𝐿𝑜𝑔(𝐴𝑔𝑒)𝑖,𝑡 + 𝛽4 𝐿𝑜𝑔(𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒)𝑖,𝑡 + 𝛽5 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 + 𝛽6 𝐷𝑢𝑚𝑉𝑎𝑙𝑢𝑒 𝑖,𝑡 + 𝜀𝑖,𝑡

MktRet Log(Age) Log(FirmSize) Volatility Dum_Value

2,076

1,416

252

na

Adj R Sqr Observations

Upgrade (5) 0.050 (0.042) 0.054*** (0.012) 1.002*** (0.057) -0.057* (0.027) 0.269*** (0.051) -0.041** (0.019) 0.050 (0.042) 0.377

Deletions Category All Deletions Loser Demote (6) (7) (8) -0.031 -0.028** -0.027** (0.027) (0.012) (0.011) 0.011*** 0.007** 0.537*** (0.003) (0.003) (0.139) 1.120*** 1.102*** 1.379*** (0.032) (0.033) (0.092) -0.048** 0.007 0.071 (0.017) (0.017) (0.058) -0.046*** -0.028 0.181*** (0.019) (0.02) (0.061) 0.029** 0.028* -0.015 (0.011) (0.011) (0.033) -0.031 -0.028 -0.027 (0.027) (0.029) (0.08) 0.404 0.402 0.4372

-p

ΔIO (%)

Additions Category Emergent Heavy Weight (3) (4) 0.047* -0.007 (0.026) (0.068) 0.023** 0.209** (0.010) (0.103) 0.919*** 0.781*** (0.035) (0.087) -0.056*** -0.086* (0.016) (0.042) -0.046 0.685*** (0.03) (0.106) -0.032** -0.035 (0.011) (0.027) 0.047* -0.007 (0.026) (0.068) 0.306 0.517

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(1) Intercept

All Addition (2) 0.040* (0.021) 0.029** (0.013) 0.960*** (0.028) -0.066*** (0.013) 0.063** (0.024) -0.030*** (0.009) 0.040* (0.021) 0.330

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Parameter

ro of

Panel A: Institutional Holding: Pre- Index Rebalancing Event

408

1,860

1,716

144

Panel B: Institutional Holding: Post Index Rebalancing Event

(1) Intercept

Jo

ΔIO (%)

All Addition (2) -0.002 (0.02) 0.091*** (0.031) 1.088*** (0.038) -0.010 (0.012) -0.312*** (0.021)

Additions Category Emergent Heavy Weight (3) (1) 0.018 -0.085 (0.027) (0.049) 0.068** 0.298** (0.039) (0.111) 0.998*** 1.173*** (0.05) (0.084) -0.016 -0.078** (0.016) (0.036) -0.387*** -0.183*** (0.025) (0.057)

Upgrade (2) 0.031 (0.04) 0.064 (0.063) 1.240*** (0.085) -0.029 (0.022) -0.168*** (0.046)

-0.017 (0.012) -0.018*** (0.007)

-0.036 (0.021) -0.031** (0.014)

ur

Parameter

MktRet

Log(Age)

Log(FirmSize) Volatility

Dum_Value

-0.014*** (0.009) -0.052** (0.023)

0.029 (0.031) -0.085* (0.049)

32

Deletions Category All Deletions Loser Demote (3) (1) (2) 0.008 0.017 -0.225*** (0.033) (0.034) (0.095) -0.077* -0.081* -0.211*** (0.048) (0.048) (0.071) 1.039*** 1.004*** 1.671*** (0.058) (0.058) (0.229) -0.069** -0.015 0.137*** (0.02) (0.021) (0.059) -0.205*** -0.058** 0.859*** (0.034) (0.029) (0.216) -0.013 0.110*** -0.108*** (0.014) (0.015) (0.036) 0.008 -0.017 -0.225*** (0.033) (0.014) (0.095)

0.3853

0.3728

0.41

0.2529

0.2558

0.4485

0.4621

Observations

2,076

1,416

252

408

1,860

1,716

144

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Adj R Sqr

33

Table 7: Descriptive Statistics for Investor Recognition This table presents the descriptive statistics of variable used for shadow cost computation for the full sample. Column (1) contains the variables details and Column (2) shows number of observations for each stock category, column (3) shows means values of each stock category followed by Column (4) showing median values, column (5) standard deviation, Column (6 & 7) showing minimum and maximum values respectively. Variable

Obs

Mean

Median

Stdev

Min

Max

(1)

(2)

(3)

(4)

(5)

(6)

(7)

All Additions Category Residualstdev

8,574

0.016

0.014

0.023

-0.358

0.393

Firm Size (in Bn)

8,574

335.98

193.57

555.32

2.84

6,926.08

NumShareholders (‘000)

8,574

289.21

105.86

587.64

4.54

4,937.66

Emergent (Additions to Nifty Next 50) 5,734

0.017

0.014

0.027

-0.358

0.393

Firm Size (in Bn)

5,734

220.24

143.70

263.33

2.84

2,481.38

NumShareholders (‘000)

5,734

247.13

92.71

563.83

4.54

4,937.66

Heavy Weight (Additions to Nifty 50 Directly)

ro of

Residualstdev

Residualstdev

1,147

0.014

0.012

0.005

0.006

0.037

Firm Size (in Bn)

1,147

557.97

403.15

448.24

34.56

2,068.79

NumShareholders (‘000)

1,147

683.54

195.80

1,041.87

24.81

4,827.33

-p

Upgrade (Additions to Nifty 50 after Nifty Next 50) 1,693

0.016

0.013

0.017

0.000

0.204

Firm Size (in Bn)

1,693

608.39

299.23

981.37

43.15

6,926.08

NumShareholders (‘000)

1,693

238.35

134.80

re

Residualstdev

271.75

23.71

1,424.06

All Deletions 7,645

Firm Size (in Bn)

7,645

NumShareholders (‘000)

7,645

0.025

0.015

0.085

0.001

1.654

169.30

101.21

221.20

0.75

2,071.83

346.51

143.76

645.46

4.54

4,937.66

lP

Residualstdev

7,154

0.026

0.015

0.090

0.001

1.654

Firm Size (in Bn)

7,154

145.30

91.84

207.39

0.75

2,071.83

NumShareholders (‘000)

7,154

257.65

123.54

434.10

4.54

4,425.10

ur

Residualstdev

na

Loser (Deletions from Nifty Next 50)

Demote (Deletions from Nifty 50 but to Nifty Next 50) 491

0.016

0.013

0.011

0.003

0.095

Firm Size (in Bn)

491

318.79

262.47

212.04

57.72

1,226.30

491

884.96

367.53

1,175.71

10.91

4,425.10

Jo

Residualstdev

NumShareholders (‘000)

34

Table 8: Shadow Cost: Mean t-Statistics Pre and Post Index Rebalancing

ro of

Table below presents mean value of Shadow cost across the different event categories for pre (3-years prior) and post-event (3year post) period . The table is divided into two panels. Panel-A presents mean descriptive for shadow cost three-year pre-index rebalancing event. Panel B shows mean descriptive for shadow cost three-year post-index rebalancing event. Panel- C presents mean difference t-test statistics between post and pre-event period. The Column (1) shows event categories followed by Column (2) number of observations for three years post or pre-event. Column (3) shows the institutional ownership percentage, Column (4) shows the shadow cost, followed by the number of shareholders in column (5) and abnormal return (AR) in column (6). Panel A: Pre Event (3-years) stock addition and deletion events Shadow Number of Abnormal Event Category Observations IO (%) Cost (x1009) Share Holder Return (%) (1) (2) (3) (4) (5) (6) Stock Additions Events All Additions 2,638 25.43 1.91 170,344 0.025 Emergent 1,719 24.74 1.41 176,654 0.019 Heavy Weight 175 21.98 4.51 135,772 0.029 Upgrade 635 28.31 2.80 140,965 0.041 Stock Deletions Events All Deletions 2,009 26.94 1.32 290,673 -0.015 Losers 1,813 27.92 1.18 228,937 -0.013 Demote 305 20.42 1.92 659,787 -0.011

Panel B: Post Event (3-years) Stock Addition and Deletion Event

Event Category

Observations

IO (%)

(1)

(2)

(3)

2,009 1,813 305

Jo

(5)

253,807 189,293 549,196 238,088

251,325 176,213 882,707

na

Panel C: Mean Difference Test (Post – Pre Event) IO (%) Shadow Cost Number of (x1009) Share Holder 0.784*** -0.305*** 83,463*** 1.373** 0.089* 12,639*** -2.946** -3.542*** 413,424*** 3.301** -0.457 97,123**

ur

Event Category All Additions Emergent Heavy Weight Upgrade Stock Deletion Events All Deletions Losers Demote

Number of Share Holder

-p

All Deletions Losers Demote

re

2,638 1,719 175 635

(4) Stock Additions Event 26.21 1.61 26.12 1.50 19.04 0.97 31.61 2.34 Stock Deletion Events 26.06 0.86 26.46 0.89 22.74 0.80

lP

All Additions Emergent Heavy Weight Upgrade

Shadow Cost (x1009)

-0.880** -1.459*** 2.321**

-0.457** -0.289* -1.125**

35

-39,347** -52,724*** 222,920***

Abnormal Return (%) (6)

-0.013 -0.014 -0.022 -0.006 -0.003 -0.002 0.000

Abnormal Return (%) -0.037** -0.033 -0.051** -0.047** 0.012*** 0.012* 0.011**

Table 9: Computation of Shadow Cost Pre and Post Twelve Quarters

Pre Event (A) Post Event (B) Post-Pre

e-

HeavyWeight Upgrade Delete Mean Median Mean Median Mean Median (4) (5) (6) 3.332 3.927 1.056 0.571 1.344 0.341 2.490 3.020 1.181 1.093 1.223 0.324 2.815 3.268 2.152 0.582 1.150 0.528 2.143 2.259 2.030 0.634 1.049 0.603 1.743 1.888 1.953 0.742 0.934 0.570 1.700 1.828 1.802 0.858 1.087 0.540 1.840 1.891 1.938 0.985 0.955 0.493 1.671 1.619 2.542 0.920 1.053 0.534 1.709 1.224 2.295 0.936 0.861 0.404 1.918 1.738 2.857 1.079 0.792 0.353 1.981 1.577 2.350 1.290 0.766 0.334 2.122 1.408 2.934 1.575 0.757 0.351 1.800 1.519 2.191 1.331 0.624 0.317 1.500 1.237 2.852 1.291 0.616 0.283 1.454 1.000 1.258 1.303 0.706 0.275 0.932 0.824 2.386 1.113 0.614 0.286 1.171 0.852 1.630 1.340 0.825 0.320 1.072 0.881 1.138 1.226 0.836 0.311 0.979 0.687 1.156 1.191 1.013 0.316 0.743 0.701 1.559 0.965 0.960 0.384 0.688 0.638 1.575 0.947 1.006 0.417 0.895 0.701 0.981 0.793 0.924 0.340 0.908 0.490 0.981 0.709 0.971 0.298 0.864 0.527 0.970 0.647 1.166 0.321 0.871 0.472 0.903 0.867 0.565 0.358 PANEL-B: Mean Difference t-test Mean Median Mean Median Mean Median 2.122 2.137 2.091 0.939 0.998 0.326 1.007 0.751 1.449 1.033 0.850 0.448 -1.116*** -1.386*** -0.642* 0.094 -0.147 0.122*

Pr

Emergent Mean Median (3) 0.980 0.675 1.216 0.817 1.173 0.920 1.091 0.814 1.110 0.785 1.335 0.793 1.383 0.884 1.438 0.800 1.317 0.841 1.272 0.792 1.359 0.896 2.275 1.064 1.897 0.982 2.127 0.932 2.406 1.013 1.722 0.737 1.866 0.678 1.797 0.623 1.411 0.647 1.231 0.638 1.187 0.664 1.127 0.611 1.088 0.591 1.034 0.559 0.912 0.570

Jo ur

(1) -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12

Addition Mean Median (2) 1.106 0.675 1.757 0.846 1.978 0.907 1.615 0.834 1.589 0.803 1.462 0.817 1.507 0.892 1.710 0.833 2.749 0.881 2.607 0.840 2.719 0.967 3.284 1.160 2.641 1.074 2.240 1.039 2.490 1.031 1.783 0.855 1.934 0.858 1.944 0.726 1.585 0.709 1.440 0.705 1.206 0.701 1.070 0.631 1.049 0.632 1.288 0.605 1.194 0.564

na l

Quarter

pr

oo

f

This table shows quarterly mean and median values of Shadow Cost variable for pre and post-index rebalancing events. The Panel-A shows mean and median shadow cost for twelve quarters pre and post event across additions, deletions and their respective stock categories. The Panel-B report difference in the means and medians values between post and pre-event of index rebalancing. Column (1) shows the quarter wise data pre-event and post event. The Column (2) presents additions stock category followed by sub-category of Emergent Column (3), Heavy Weight and Upgrade are in column (4) and column (5) respectively. The Column (6) is Deletions category followed by sub-category of Loser and Demote in Column (7) and Column (8). Symbol ***, **, and * indicate level of significance at 1%, 5% and 10% respectively. The figure in parenthesis represents standard errors. Panel A: Pre and Post 12 Quarter around index rebalancing Shadow Cost

Mean 2.007 1.602 -0.405**

Median 0.871 0.755 -0.117**

Mean 1.329 1.492 0.163

Median 0.840 0.689 -0.151*

36

Loser Mean Median (7) 1.352 0.424 1.196 0.386 1.158 0.532 1.071 0.603 0.880 0.553 1.045 0.478 0.910 0.348 0.997 0.503 0.895 0.399 0.839 0.299 0.822 0.306 0.810 0.346 0.663 0.317 0.636 0.283 0.744 0.284 0.617 0.300 0.853 0.338 0.850 0.345 1.054 0.338 0.991 0.392 1.033 0.418 0.971 0.365 1.019 0.321 1.196 0.343 0.601 0.383 Mean 0.880 0.998 0.118*

Median 0.343 0.431 0.089**

Demote Mean Median (8) 1.105 0.341 1.239 0.242 1.127 0.535 0.965 0.584 1.346 0.855 1.393 0.951 1.154 0.743 1.361 0.780 0.660 0.533 0.568 0.447 0.574 0.454 0.678 0.416 0.663 0.343 0.767 0.389 0.857 0.471 0.883 0.341 0.886 0.303 0.882 0.311 0.755 0.250 0.756 0.384 0.896 0.645 0.689 0.282 0.740 0.231 0.968 0.394 0.247 0.033 Mean 0.777 1.014 0.237***

Median 0.336 0.573 0.237***

Table 10: Shadow cost and institutional ownership

of

This table presents cross-sectional regression result for the post-index rebalancing event. The dependent variable is abnormal return across the models. Main variable of interest is ΔShadowCost and ΔIO measured computing changes between quarter after and before the index rebalancing event. Other interest variables, ΔIO, ΔIOMF, ΔIOBank, and ΔIOFIIareover all institutional ownership, mutual fund ownership, Banks ownership and Foreign institutional ownerships respectively. Other control variables, MktRet is NIFTY 500 index return, Log(Age) is natural log of firm age, Volatility is quarterly variance of daily stock return, Dum-Value is dummy which takes value equals to 1 if stock is cheaper and 0 otherwise. The column (1) presents the parameter followed by Column (2) that runs regression for full sample containing additions and deletions. Column (3) is regression of additions category followed by column (4) for deletions. Standard Errors are reported below the estimates and are adjusted by the Rogers standard errors clustered at firm level (Petersen, 2009). Symbol ***, **, and * indicate level of significance at 1%, 5% 1nd 10% respectively. The figure in parenthesis represents standard errors. 𝐴𝑅𝑖 = 𝛼 + 𝛽1 𝛥𝑆ℎ𝑎𝑑𝑜𝑤𝐶𝑜𝑠𝑡𝑖 + 𝛽2 𝛥𝐼𝑂𝑖 + 𝛽3 𝑀𝑘𝑡𝑅𝑒𝑡𝑖 + 𝛽4 𝐿𝑜𝑔(𝐴𝑔𝑒)𝑖 + 𝛽5 𝐿𝑜𝑔(𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒)𝑖 + 𝛽6 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖

ro

+ 𝛽7 𝐷𝑢𝑚_𝑉𝑎𝑙𝑢𝑒𝑖 + 𝜀𝑖,𝑡

Parameter

ΔShadow Cost ΔIO

(3)

-0.2163**

-0.2778***

(0.1172)

(0.1072)

(0.1425)

(0.1539)

-0.0024***

-0.004***

0.0082***

0.0080***

(0.0007)

(0.0002)

(0.0028)

(0.0027)

Jo

Log(FirmSize) Volatility Dum_Value Adj R Sqr

-0.3454***

-0.3931***

(0.0262)

(0.0032)

ΔIOFII

Log(Age)

(5)

0.0226***

ΔIOBank

MktRet

(4)

0.0862***

ur na

ΔIOMF

(2)

re

Intercept

Deletions

lP

(1)

Addition

-p

𝐴𝑅𝑖 = 𝛼 + 𝛽1 𝛥𝑆ℎ𝑎𝑑𝑜𝑤𝐶𝑜𝑠𝑡𝑖 + 𝛽2 𝛥𝐼𝑂𝑀𝐹 + 𝛽3 𝛥𝐼𝑂𝐵𝑎𝑛𝑘 + 𝛽3 𝛥𝐼𝑂𝐹𝐼𝐼 + 𝛽5 𝑀𝑘𝑡𝑅𝑒𝑡𝑖 + 𝛽6 𝐿𝑜𝑔(𝐴𝑔𝑒)𝑖 + 𝛽7 𝐿𝑜𝑔(𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒)𝑖 + 𝛽8 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦𝑖 + 𝛽9 𝐷𝑢𝑚_𝑉𝑎𝑙𝑢𝑒𝑖 + 𝜀𝑖,𝑡

0.4157***

0.2529**

(0.1278)

(0.1031)

-0.0615**

0.0626**

(0.036)

(0.0339)

0.0749**

-0.0233

(0.0384)

(0.0416)

0.2575***

0.2549***

0.1917***

0.1905***

(0.0757)

(0.0758)

(0.0829)

(0.0827)

-0.0077

-0.0080

0.0025

-0.0045

(0.0172)

(0.017)

(0.0188)

(0.0187)

0.0204**

0.0256**

0.0289***

0.0341***

(0.0119)

(0.0128)

(0.0124)

(0.0139)

-0.2135***

-0.2150***

-0.2721***

-0.2703***

(0.0849)

(0.0852)

(0.0718)

(0.0722)

-0.0179*

-0.0157

0.0203**

0.0210**

(0.0102)

(0.0101)

(0.0119)

(0.0117)

0.07823

0.08267

0.08396

0.08628

37

Observation

173

173

155

155

Jo

ur na

lP

re

-p

ro

of

Appendix

38

Table A.1: Variable Construction Institutional Ownership and Control Variables Variable

IOMF

IOBank

Followed Study

Quarterly data of combined percentage ownership held by nonpromoter

Pruitt, S. W., & Wei, K. J. (1989).

Data source: CMIE Prowess

Chan et.al (2013)

Quarterly data of ownership percentage held by non-promoter Non-Promoter Mutual Funds

Pruitt, S. W., & Wei, K. J. (1989).

Data source: CMIE Prowess

Chan et.al (2013)

Quarterly data of ownership percentage held by non-promoter

Pruitt, S. W., & Wei, K. J. (1989).

Bank, Insurance companies and other Financial Institutes

Chan et.al (2013)

ro

Data source: CMIE Prowess IOFII

Quarterly data of ownership percentage held by non-promoter Non Promoter FII ownership

-p

Quarterly Calculated based on P/E ratio of Stock less then Nifty 500 P/E

Market Return (MrkRet)

Quarterly returns of Nifty 500.

Log(Age)

Natural log of firm age from year of incorporation

re

Dum_value

lP

Data source: CMIE Prowess and NSE Website

Data source: CMIE Prowess

Residualstdev

Created by authors Brown and Warner (1985) Gompers and Metric (2001)

Natural Log of Market capitalization of ithstock of quarter‘t’, Quarterly values from CMIE

ur na

Volatility

Pruitt, S. W., & Wei, K. J. (1989). Chan et.al (2013)

Data source: CMIE Prowess

Log(FirmSize)

of

IO

Frequency/ Measurement

Quarterly variance of daily stock return. Difference between yearly returns standard deviation of market and stocks. Stddev (m, i)  m   i

NumShhoder

Number of shareholders of the firm. Yearly basis Raw variable from CMIE

MrkCap

Quarterly Nifty500 market capitalization

Shadow Cost

Yearly basis Shadow Cost computation.

Jo

Shareholder Base



Chen et al. (2004)

Stddev (m, i) FirmSize X TotalMCAP NumberofShareholders

39

Table A.2: List of Stocks with corporate actions like delisting, and M&A

36 37 38 39 40

Delete Add Delete Add

41 42 43 44

17 18 19 20 21 22 23 24 25 26 27 28

Digital Globalsoft Ltd Indian Petrochemicals Corporation Ltd Burroughs Welcome (India) Ltd GTL Ltd (Global Telesytems Ltd) ITC Hotel Ltd Videsh Sanchar Nigam Ltd Aptech Ltd Videsh Sanchar Nigam Ltd Aventis Pharma Ltd Kotak Mahindra Finance Ltd NagarjunaFertilisers& Chemicals Ltd Silverline technology Ltd.

Delete Delete Delete Add Delete Add Delete Delete Add Add Delete Delete

45 46 47 48 49 50 51 52 53 54 55 56 57 58

Event

SPIC Ltd United Phosphorous Ltd I-Flex Sloution Ltd Hughes Telecom Ltd Madras Cement Ltd Mphasis BFL Ltd Orchid Chemicals & Pharmaceuticals Ltd UTI Bank Ltd EMerck (India) Ltd ICI India Ltd Jindal Vijaynagar Steel Ltd Motor Industries Ltd

Delete Delete Add Add Delete Add Delete

Motor Industries Co Ltd Jindal Vijayanagar Steel Ltd Ingersoll Rand (India) Ltd Mundra Port and Special Economic Zone Ltd Aventis Pharma Ltd United Phosphorus Ltd HCL INFOSYS Ltd Penta Software Ltd Global Trust Ltd Tata Info telecommunication ltd FSS Ltd Bongai Refinery Limited IBP Ltd Reckit and Colemen Ltd. Wartsila Limited German Remedies Ltd Sesa Goa Ltd Cairn India Ltd

Delete Delete Delete Add

lP

Add Delete Delete Add Add

of

Delete Delete Delete Add Delete

Delete Add Delete Delete Delete Delete Delete Delete Delete Delete Delete Delete Delete Delete

Jo

8 9 10 11 12

Stock

ro

Delete Delete Delete Delete Delete Delete Add

13 14 15 16

Indian Aluminium Co Ltd Madras Refineries Ltd Nagarjuna Fertilizers & Chemicals Ltd Brooke Bond Lipton India Ltd ICICI Ltd Essar Gujarat Ltd Smithkline Beecham Consumer Healthcare Ltd Andhra Valley Power Supply Co Ltd Indo Gulf Corporation Ltd Ponds (India) Ltd Reckitt & Colman India Ltd Great Eastern Shipping Company Limited Indian Rayon & Industries Ltd Digital Equipment (india) Ltd ICICI Ltd National Aluminium Co Ltd

No . 29 30 31 32 33 34 35

-p

Event

re

Stock

ur na

No . 1 2 3 4 5 6 7

--------------------------------End of Paper-------------------------------

40