Evidence from the CSI 300 Index Additions and Deletions

Asymmetric Responses to Stock Index Reconstitutions: Evidence from the CSI 300 Index Additions and Deletions

Ching-Ting Lina,*

a Department of Money and Banking, National Chengchi University, 64, Sec. 2, Zhi-Nan Road, Wenshan District, Taipei 11605, Taiwan R.O.C.

Wei-Kuang Chenb,†

b Department of Money and Banking and Risk and Insurance Research Center, National Chengchi University, 64, Sec. 2, Zhi-Nan Road, Wenshan District, Taipei 11605, Taiwan R.O.C.

*Corresponding author. Tel: 886-2-2939-3091ext.81248. Email: [email protected]. †Tel: 886-2-2939-3091ext.81220. Email: [email protected]. Asymmetric Reponses to Stock Index Reconstitutions: Evidence from the CSI 300 Index Additions and Deletions

Abstract

This study investigates constituent changes to the CSI 300 index, which is scheduled semiannually in accordance with clearly-stated selection methodology. We find that stocks experience a permanent price increase and receive optimistic EPS forecasts from analysts following their addition to the index. These optimistic earnings expectations are supported by increased capital-raising activities and capital expenditure. Conversely, we do not find any significant results for index deletions. Evidence in the form of changes in the number of shareholders and shadow costs are consistent with the investor awareness theory. Increased investor awareness and monitoring forces newly-added firms to perform effectively, resulting in the attraction of more newly-issued capital from investors due to the firms’ lower cost of capital. Monitoring and management efficiency, however, would not lessen sharply for deletions.

JEL Classification: G12, G14, G20.

Keywords: CSI 300 index; stock index reconstitution; investor awareness; analyst EPS forecast; capital expenditure; asymmetric market response. 1. Introduction

China’s equity markets have developed tremendously since the Shanghai Stock Exchange and the Shenzhen Stock Exchange were launched in 1990. The total market capitalization was more than USD 6 trillion at the end of 2014, ranked as the second largest in the world.1 The CSI 300 index was launched on April 8, 2005 and is composed of the 300 largest and most liquid A-shares listed on the two stock exchanges. The CSI 300 index has been used as the basis for many financial products around the world and is also used by investors to develop and benchmark their portfolios.2 The CSI 300 index reconstitution methodology is relatively transparent, in contrast with the S&P 500 and MSCI indices. For example, changes to the S&P 500 index are made on an as-needed basis. Market capitalization, liquidity, public float, and many other factors determine the constituents of the S&P 500 index, but the selection methodology is not clearly-stated to the public. As for the MSCI indices, MSCI implements regular semiannual and quarterly reviews, but does not target any specific number of firms. This means that a deletion from one of the MSCI indices does not guarantee a corresponding addition to that index. In other words, additions and deletions in the MSCI indices are not symmetrical, and as such reconstitutions of the MSCI indices are not predictable. By contrast, reconstitutions for the CSI 300 index are regular and made in accordance with a clearly-stated selection methodology. The components of the CSI 300 index are reviewed twice a year in June and December. Determined by the ranking of market capitalization and liquidity, the largest and the most liquid 300 stocks are selected as the index components. Any component adjustment is effective on the first trading day in July and January. With a regular review schedule and clearly-stated selection methodology, the change of index constituents could be predictable and the abnormal returns for newly-added stocks

1 At the same year end, the market capitalization of NYSE stood at USD 19 trillion, the Tokyo Stock Exchange comprised USD 4.38 trillion and the Hong Kong Exchanges stood at USD 3.23 trillion. Market capitalization in other emerging markets was USD 1.21 trillion in the Korea Exchange, USD 1.52 trillion in the National Stock Exchange in India, and USD 0.08 trillion in the Taiwan Stock Exchange. 2 There are 22 exchange traded funds (ETFs), 31 index funds, and 2 feeder funds based on the CSI 300 index. The total assets of the ETFs trading the CSI 300 index are estimated around $89.96 billion globally. More than $19.10 billion in fund assets are estimated to be benchmarked to the CSI 300 index. The CSI 300 index futures launched on April 16, 2010. According to data retrieved from Thomson Reuters, they already surpassed the S&P 500 futures in terms of turnover. 1 around the announcement date should be smaller than those found in the literature pertaining to the S&P 500 and MSCI indices. However, we find that stocks experience a permanent price increase post- addition. The average announcement date abnormal return is a significantly positive 0.45%. The cumulative excess returns from the announcement date to the effective date, 20 days after the effective date, and 60 days after the effective date are 1.17%, 1.53%, and 2.97%, respectively. By contrast, there is only a weak abnormal return decrease post-deletion. Deleted firms have a temporary price decrease initially, but this loss is then recouped in the long run. The asymmetric response to reconstitutions is in line with findings documented by Chen et al. (2004), who propose that investor awareness is the main driver of asymmetric response for stock index reconstitutions. Investor awareness increases post-addition. However, investors do not neglect deleted firms immediately so that investor awareness does not decline immediately post- deletion. We conduct tests on changes in the number of shareholders and shadow cost, and our empirical results are consistent with the view of the investor awareness hypothesis. If the investor awareness hypothesis is held to be true, additions to the index would attract more investors, who would exert pressure on the added firms to improve their performance (Denis et al., 2003). With better corporate governance, firms are expected to lower their cost of capital and attract more newly issued capital from investors. By contrast, investors do not neglect deleted firms immediately, so investor monitoring and capital should exhibit a weak change post-deletion. Following Denis et al. (2003), we use analysts’ EPS forecasts as a proxy to capture changes in earnings expectations. Findings in this paper show that firms added to the CSI 300 index receive optimistic EPS forecasts. Analysts systematically revise their earnings forecasts upwards post-addition. Conversely, we don’t find any significant change for firms that have been deleted from the CSI 300 index. Results are in line with evidence from Denis et al. (2003) and Chen et al. (2004) that reconstitutions are not information-free and market response is asymmetric for additions and deletions due to changes in investor awareness. The next question concerns whether added firms improve management efficiency due to increased investors awareness. We use capital-raising activities and capital expenditure as proxies to examine if reconstitutions have any impact on management

2 efficiency. To our best knowledge, this is the first paper using capital expenditure to provide a direct examination of changes in firms’ management efficiency for stock index reconstitutions. The evidence shows that firms that are newly added to the index conduct more capital-raising activities, especially debt financing. The increased capital-raising activities lead to the expansion of capital expenditure, as hypothesized. For deletions, on the other hand, the changes in capital-raising activities and capital expenditure are mild and insignificant. In contrast to Li et al. (2009), who indicate that increases in capital expenditure destroy firm value, we find that increases in capital expenditure can drive up firm value and result in improved earnings forecasts by financial analysts. This paper contributes to our understanding of at least two aspects of stock index reconstitutions. First, existing theories on the price effects of stock index reconstitutions are mixed. Harris and Gurel (1986) propose the price pressure hypothesis and indicate that price change is temporary, which is supported by Mase (2007), Biktimirov and Li (2014), and others. On the contrary, Shleifer (1986) indicates that stocks have a long-term downward sloping demand curve so that price change is permanent, which is confirmed in several subsequent studies (Beneish and Whaley, 1996; Lynch and Mendenhall, 1997; Wurgler and Zhuravskaya, 2002; Yun and Kim, 2010). Chen et al. (2004), departing from the previous literature, find an asymmetric market response to reconstitutions and attribute this phenomenon to investor awareness. Most prior empirical studies on stock index reconstitutions, however, focus on indices in developed countries (Biktimirov and Li, 2014; Chen et al., 2004; Denis et al., 2003; Harris and Gurel, 1986; Liu, 2006, 2000; Lynch and Mendenhall, 1997; Mase, 2007) rather than emerging markets. Reconstitutions for major indices (e.g., the S&P 500) do not have a regular review schedule or clearly- stated selection criteria. Component changes for those indices may contain publicly unavailable information. This paper contributes to the literature on market response to reconstitutions in the top bourses in emerging markets. The data used in this work includes a complete list of constituent changes from the inception of the CSI 300 index to the most recent review. The CSI 300 index reconstitution is scheduled semiannually by clearly-stated selection methodology so that the settings for the CSI 300 index reconstitutions should have the least information content, which provides an alternative way to examine price impact on constituent changes. Similar to the

3 results from Chen et al. (2004), we find that stocks experience a permanent price increase post-addition and a weak decrease post-deletion. These results are in line with the investor awareness theory. Second, existing empirical studies on stock index reconstitutions have neglected the role of capital expenditure on stock index reconstitutions. Denis et al. (2003) use analysts’ EPS forecasts and used EPS as a proxy for earnings expectations to show that firms newly added to the S&P 500 experience an increase in earnings expectations as well as realized earnings. Chen et al. (2004) indicate that changes in investor awareness are the main drivers of abnormal returns for reconstitutions. However, the existing empirical studies do not provide direct evidence that membership in the index would benefit firms to attract more newly issued capital from investors. To the best of our knowledge, this is the first study to investigate changes in capital expenditure to capture the relationship between investor awareness and management efficiency. This paper adds to the literature by providing direct evidence that in the presence of increased investor awareness and close monitoring post-addition, firms perform more efficiently. Due to their improved management performance, their cost of capital decreases and they therefore attract more newly issued capital from investors. The results indicate that reconstitutions are not information-free. This paper proceeds as follows. Section 2 describe the CSI 300 index and its selection methodology. Section 3 illustrates sample data. Price effects are analyzed in Section 4. Potential explanations for price effects is demonstrated in Section 5. Section 6 discusses capital-raising activities and capital expenditure. We perform regression analyses in Section 7. Some robustness checks are provided in Section 8. We conclude in Section 9.

2. Description of the CSI 300 and its Selection Methodology

The CSI 300 index was launched by China Securities Index Co., Ltd. on April 8, 2005. It tracks the largest and the most liquid 300 A-shares of the approximately 2700 companies listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange. The CSI 300 index has grown tremendously over the past decade with a market

4 capitalization of USD 4.5 trillion at the end of 2014, about 16 times since its inception in 2005. It captures approximately 75% of the equity market capitalization, and covers over 10 sectors of the Chinese economy. The index is the basis for many financial products around the world and is also used by investors to develop and benchmark their portfolios. Although the CSI 300 index has experienced tremendous growth, investment in the index is not as heavy as for the S&P 500 and MSCI indices. There is USD 0.1 trillion benchmarked to the CSI 300 index, which accounts for approximately 2.2% of the market capitalization. By comparison, there is over USD 7.8 trillion benchmarked to the S&P 500 (approximately 41% of the S&P 500 market capitalization) and there is USD 9.5 trillion benchmarked to the MSCI with ETF assets comprising approximately USD 380 billion. The assets held by index funds, or ETFs, that track the CSI 300 is relatively small. By contrast, individual and non-financial domestic institutional investors are the major investor types in terms of average holding percentage across stocks in the CSI 300 index. The average holding percentages by individual and non-financial domestic institutional investors are 46.04% and 43.56% at the end of 2014, respectively. The CSI 300 index reconstitution methodology is relatively transparent. The CSI Committee reviews the CSI 300 components regularly twice a year in June and December. From the index universe that contains all A-shares listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange, stocks with adequate liquidity and size are selected as index components. To be specific, candidates in the universe are ranked by daily average trading value during the most recent year. The top 50% of stocks are kept and ranked again by daily average market value during the most recent year. The top 300 stocks are selected as the index components. The rebalanced list is effective on the first trading day in July and January. Different from the S&P and the MSCI indices, the CSI Committee creates a reserve list with 15 stocks at each periodical review. Any deletion or temporary adjustment brought about by events such as IPOs, mergers and acquisitions, spin-offs, trading suspension, temporary delisting and delisting, and bankruptcy, will incur a corresponding addition from the reserve list. Once the number of stocks in the reserve list is less than a half of the original number, the CSI will complement the list and publicize it timeously.

3. The Sample

This study analyzes changes in the CSI 300 index components over the period June 2005 to December 2014. The sample period covers all CSI 300 index component adjustments since from the index’s inception until the end of 2014. The list of added and deleted stocks is collected from official announcements on the China Securities Index Co., Ltd. website. Data for each addition and deletion includes announcement date, effective date, official ticker, and company name. The initial complete list consists of 432 additions. Any deletion would have a corresponding addition, so we obtain 432 deletions in the initial sample as well. To facilitate computation, this paper only includes stocks with at least 90 trading days before the announcement dates and 60 days after the effective dates. With this restriction imposed, additions and deletions drop 15 and 3 firms, respectively, due to insufficient data. Moreover, stocks which have no price information on the announcement or effective date are eliminated in order to compare returns on event dates. There are 20 additions and 53 deletions that are eliminated because their price information is not available on either the announcement or effective date due to their being suspended or delisted from the index. For contemporaneous events, such as IPOs, mergers and acquisitions, spin-offs, trading suspension, temporary delisting and delisting, and bankruptcy, are excluded from the initial sample. With some overlaps in each screening stage, the final sample includes 398 additions and 378 deletions as shown in Table 1.

< Insert Table 1 >

4. Analysis of Price Effects

In this section, we analyze the price effects as a result of changes to the CSI 300 index. We report findings based on abnormal and cumulative abnormal returns measured relative to the CSI 300 index. Following Lynch and Mendenhall (1997) and Chen et al. (2004), we calculate abnormal returns by using the market adjusted

6 model.3 To be specific, abnormal return is the difference between returns of sample firms and the CSI 300 index. We report abnormal return on the announcement date. Several event windows are considered to calculate cumulative abnormal returns, including periods from the announcement date to the effective date, and 20 and 60 days after the effective date. Stock and index price information is retrieved from the Datastream database. As Table 2 shows, additions exhibit a permanent positive price change. The average abnormal return of additions on the announcement date is 0.45% at a 1% significance level. The average cumulative excess returns remain positively significant in the various event windows post-addition. The average cumulative abnormal returns are 1.17%, 1.53%, and 2.97% over the periods between the announcement date and the effective date, and 20 and 60 days after the effective date, respectively. By contrast, there is only a weak price decrease post-deletion. Deletions exhibit a temporary price decrease initially, which is then recouped in the long run. The average abnormal return on announcement date is -0.39%. The average cumulative abnormal returns are -3.46% and -1.35% over the period from the announcement date to the effective date and 20 days after the effective date. However, these losses reverse 60 days after the effective dates and deletions experience a 2.44% cumulative return. Consistent with findings from Chen et al. (2004), there is evidence of an asymmetric response to the index component adjustment in that there is a permanent price increase in the case of additions but a weak decrease in the case of deletions.

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5. Potential Explanations for the Asymmetric Price Effects

In the previous section, we find that the price increase for additions to the CSI 300 is permanent but there is only a weak price decrease for deletions. In this section, we discuss four hypotheses that have been documented to support potential explanations

3 We also compute abnormal returns by using market model. The method gives conclusions similar to those reported using the market adjusted model. Details are available upon request. 7 of the price effects: price pressure, downward sloping long-run demand curve, operating performance, and investor awareness.

5.1 Price pressure The price pressure hypothesis posits that stocks would only experience a temporary price change due to portfolio rebalancing requirements for index-tracking funds. Once the trading pressure from index-tracking funds diminishes, the stock price returns to its normal level. The price pressure hypothesis is provided by Harris and Gurel (1986) who study stocks added to the S&P 500. Elliott and Warr (2003) also find evidence that trading pressure only causes a temporary departure from equilibrium around the effective date. In addition to the S&P 500, the hypothesis is supported by evidence from other markets, such as FTSE 100 (Mase, 2007) and FTSE SmallCap (Biktimirov and Li, 2014). The empirical finding in this paper is not in line with the temporary price change implied by price pressure. Instead, the evidence shows a permanent positive price change for additions. The average announcement date abnormal return is 0.45% at a 1% significance level. The cumulative excess returns remain significantly positive until 60 days after the effective date. The results are robust and consistent with some alternative specifications. As we previously discussed, index funds/ETFs only hold a small percentage of the assets in China’s equity markets, which indicates that the potential for demand shocks to be created by index funds is limited.

5.2 Downward sloping long-run demand curve A downward sloping long-run demand curve indicates that excess demand resulting from index adjustment will not move the price permanently if and only if stocks have perfect substitutes. In the absence of perfect substitutes, a permanent price change must arise due to imperfect substitutability or a downward sloping demand curve. Shleifer (1986) proposes the imperfect substitutes hypothesis as an explanation. The permanent price change of the S&P 500 has been confirmed by many studies (Beneish and Whaley, 1996; Lynch and Mendenhall, 1997; Wurgler and Zhuravskaya, 2002). The hypothesis is also supported in other markets, such as TSE 300 index (Kaul et al. 2000), Nikkei 500 (Liu, 2000), Nikkei 225 (Liu, 2006), and KOSPI 200 index (Yun and Kim, 2010).

However, the empirical finding in this paper does not support a symmetric permanent price change implied by downward sloping long-run demand curve. Instead, the evidence shows a temporary negative price change for deletions. The average announcement date abnormal return is -1.066% at a 1% significance level. The cumulative excess returns remain significantly negative only until the effective date. Price losses reverse after the effective date and are recouped in the long run.

5.3 Operating performance The operating performance hypothesis states that index inclusion provides new information about companies’ future prospects. Firms are more likely to perform better post-addition because they are more closely monitored by investors who exert pressure on the firms to improve their performance. Denis et al. (2003) use analysts’ EPS forecasts as a proxy for earnings expectations and show that firms newly added to the S&P 500 experience an increase in earnings expectations as well as improved realized earnings. Hrazdil and Scott (2009) extend the analysis and investigate the source of earnings improvements. They find that the main drivers are relatively large discretionary accruals and support the view that inclusion to the S&P 500 is not information-free. By contract, Tu and Chang (2012) find no information effect with additions to the MSCI Taiwan index.

To examine the operating performance hypothesis, we follow Denis et al. (2003) to evaluate and report the change in analysts’ EPS forecasts as the proxy for expected earnings.

5.3.1 Analysts’ EPS forecasts We include both current-year and one-year-ahead EPS forecasts. For firms adjusted in June, the earnings forecast for the same fiscal year are taken as the current year’s forecast. Earnings forecast for the next fiscal year are classified as the one- year-ahead forecast. For firms adjusted in December, the earnings forecast in the next fiscal year are taken as current-year forecast. Earnings forecast in the following year are taken as the one-year-ahead forecast. For example, a firm is announced to be added to the index in June 2010. Earnings forecast for the year 2010 are considered as current-year. Earnings forecast for the year 2011 are classified as one-year-ahead. If it

9 is announced that a firm will be added to the index in December 2010, the earnings forecast for the year 2011 will be considered as current-year and earnings forecast for the year 2012 are classified as one-year-ahead. The fiscal year end in China is December 31. The median EPS forecasts in the period 4 months before the announcement month are compared with the median EPS forecasts in the period 4 months after the announcement month. For pre-announcement forecasts, each analyst’s most recent forecast before the announcement month is used. For post-announcement forecasts, each analyst’s first forecast after the announcement month is used. New analysts are excluded.4 The average number of analysts per added firm is 23.46 with a median of 21, a maximum of 106, and a minimum of 1. The average number of analysts per deleted firm is 20.04 with a median of 17, a maximum of 106, and a minimum of 1. Two benchmarks are employed to compare with the sample firms. The first benchmark includes all other firms listed on the Shanghai Stock Exchange or the Shenzhen Stock Exchange. The second benchmark, called ISL-benchmark, includes stocks matched with the sample firms on the basis of industry, market capitalization, and liquidity.5 Analysts’ earnings forecasts and industrial classification are obtained from Institutional Brokers’ Estimates System International, Inc. (I/B/E/S).

5.3.2 Results: Changes in Analysts’ EPS Forecasts We calculate raw and standardized changes in analysts’ EPS forecasts and compare these changes with the two groups of matched benchmark firms for current- year and one-year-ahead EPS forecasts. Raw change in EPS forecast is the difference between post-announcement forecast and pre-announcement forecast. Furthermore, we divide raw change in EPS forecast by pre-announcement forecast, which is defined as standardized change in EPS forecast. To avoid the reverse effect, only positive pre- announcement EPS forecasts are considered. In this test, we include 255 current-year addition forecasts, 221 one-year-ahead addition forecasts, 181 current-year deletion forecasts including 2 negative EPS forecasts, and 143 one-year-ahead deletion forecasts.

4 We follow Denis et al. (2003) to exclude new analysts who make EPS forecast after the announcement date but have no coverage for a specific firm during the 12 months before the announcement date. 5 Refer Denis et al. (2003) for details of matching process. 10

As the first row in Panel A in Table 3 shows, the mean change in current-year forecasts for additions is an insignificant negative of -CNY0.0165, with a median of - CNY0.009. However, consistent with previous studies (Denis et al., 2003), the results of the two groups of benchmark firms show that analysts systematically revise their earnings forecasts downwards as the fiscal year progresses. For example, the average raw change for all other firms is -CNY0.0313, with a median of -CNY0.0215 at a 1% significance level. The average raw change for ISL benchmark firms is -CNY0.0385, with a median of –CNY0.0243 at a 1% significance level. We compare the changes for additions and their benchmarks. The results show a significant positive difference between sample and the benchmark firms’ forecasts. The mean difference with all other firms is 0.0148, with a median of 0.0204. The mean difference with ISL- matched firms is 0.0220, with a median of 0.0083. The standardized change in EPS forecast presents parallel evidence. The evidence indicates that when a firm is added to the index, analysts are optimistic about the future performance so the added firms receive significant increases in analysts’ earnings forecasts. The findings of one-year-ahead EPS forecasts are similar to those for current- year forecasts. However, we do not find any statistically significant evidence compared with the ISL-matched firms. The weak findings may be explained by extremely high volatility in China’s equity markets which results in future predictions being less systematic in the long run. Results for deletions are presented in Panel B in Table 5. In contrast to a systematic revision of earnings forecasts upward post-addition, we do not find any significant change for firms deleted from the CSI 300 index. The evidence illustrates an asymmetric response to index reconstitutions with regards to analysts’ earnings forecasts.

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The results are consistent with the operating performance hypothesis that index inclusion provides new information about companies’ future prospects. Firms are expected to perform better post-addition because they are more closely monitored by investors who exert pressure on the firms to improve performance. By contrast,

11 investors do not neglect deleted firms immediately, so performance expectation exhibits a weak change post-deletion.

5.4 Investor awareness Different from previous studies, Chen et al. (2004) investigate price response to the S&P 500 component change and find an asymmetric result that there is a permanent price increase with additions but a weak decrease with deletions. They attribute the asymmetric phenomenon to changes in investor awareness. Investor awareness is an idea developed originally in Merton (1987). According to Merton (1987), investors exist in a segmented market, so their portfolio is not well diversified because of incomplete information. A premium, called shadow cost, is required to compensate for unsystematic risks. Kadlec and McConnell (1994) further decompose shadow cost. Chen et al. (2004) use it as a proxy for investor awareness. Chen et al. (2004) document that shadow cost decreases with additions because of the decrease of required rate of return. Investor awareness, however, does not diminish immediately with deletions. The evidence is robust with the change in number of shareholders, which is another proxy for investor awareness in Chen et al. (2004). Elliott et al. (2006) present an analytical survey of the explanations for the increase in stock value associated with inclusion in the S&P 500 index. They find that increased investor awareness is the primary factor behind the cross-section of abnormal announcement returns.

5.4.1 Evidence in support of investor awareness We follow Chen et al. (2004) in using the change in the number of shareholders and shadow cost as proxies for investor awareness. If the investor awareness hypothesis is held to be true, there should be a significant increase in number of shareholders post-addition but a weak decrease post-deletion; a significant decrease in shadow cost post-addition but a weak increase post-deletion. Information about the number of shareholders, firm size, and index market capitalization is retrieved from the Datastream database. First, we compare the number of shareholders before the announcement date and the number of shareholders after the effective date. The number of shareholders

12 before the announcement date is the most recent number of shareholders listed prior to the announcement date. The number of shareholders after the effective date is at least 9 months after the effective date to allow data updates. This test uses a sample of 363 additions and 345 deletions. As the results show in Panel A in Table 4, there is a significant positive change for additions but only a weak decrease for deletions, as hypothesized. The average paired change in number of shareholders for additions is 21,003, with a median of 7,129 at a 1% significance level. The increase rate of mean is 34.10% and 49.60% for median. However, the average paired change for deletions is only -1,011, with a median of -3,973. The decrease rate of mean change is -1.09% and -1.53% for median change. Second, we compare the shadow cost before the announcement date and shadow cost after the effective date. Following Kadlec and McConnell (1994) and Chen et al. (2004), shadow cost is defined as follows:

푆ℎ푎푑표푤퐶표푠푡 = (푅푒푠𝑖푑푢푎푙푆푡푎푛푑푎푟푑퐷푒푣𝑖푎푡𝑖표푛⁄퐶푆퐼300푀푎푟푘푒푡퐶푎푝) × (퐹𝑖푟푚 푆𝑖푧푒⁄푁푢푚푏푒푟 표푓 푆ℎ푎푟푒ℎ표푙푑푒푟푠) where market value of equity is used as a proxy for firm size. Firm size and the CSI 300 index market capitalization are the values taken on the announcement date. Residual standard deviation is the standard deviation of the abnormal return. The pre- announcement residual standard deviation is the standard deviation of the difference between stock return and index total return in the 250-day period before the announcement date. The post-announcement residual standard deviation is the standard deviation of the difference in the 250-day period after the effective date. Number of shareholders are defined previously. There are 269 additions and 337 deletions in this test. As the results show in Panel B in Table 4, there is a significant negative change for additions but only a weak increase for deletions, as hypothesized. The average paired change in shadow cost for additions is -1.06x10-9, with a median of -0.25610- 9 at a 1% significance level. By contrast, the average paired change for deletions is only -0.03510-9, with a median of 0.00710-9. The magnitude of shadow cost change for deletions is small and insignificant.

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According to the findings, the asymmetric evidence is consistent with the investor awareness hypothesis that investor awareness increases post-addition. But investors do not neglect firms immediately when they are excluded from the index. It is interesting that given the transparency of the CSI 300 reconstitution process, it should be easy for the investors to predict new adjusted firms. However, the evidence indicates that investors become aware of new additions only when the firms are included in the index. Different from the S&P 500 and the MSCI indices, individual investors dominate China’s equity markets. Compared with financial institutional investors such as fund managers who are well-trained, individual investors tend to be less informed and have less resources to follow the markets closely. They are too naive to predict the potential addition of firms from the index universe, so we observe a significant increase in investor awareness post-addition despite the relative transparency of the CSI 300 reconstitution process.

6. Capital-Raising Activities and Capital Expenditure

Given the above findings that firms experience an optimistic earnings expectation and an increase in investor awareness post-addition, a related question concerns whether the event of being added to the index leads to an improvement in management efficiency. According to Denis et al. (2003), additions are more closely monitored by investors, who exert pressure on management to perform effectively. If the investor awareness hypothesis is held to be true, additions to the index would attract more investors who monitor the firms closely and exert pressure on the firms to improve their performance. With better corporate governance, firms are expected to lower their cost of capital and attract more newly issued capital from investors. By contrast, investors do not neglect deleted firms immediately, so investor monitoring and capital investment should exhibit a weak change post-deletion. To test the above statement, we compare capital-raising activities and capital expenditures before and after index reconstitutions.

6.1 Capital-raising activities Two popular capital-raising mechanisms have been widely used in the markets: corporate debt issuance and seasoned equity offerings (SEOs). A corporate debt issuance is a financial obligation that allows issuers to raise money from the lenders to expand the set of positive NPV projects or new markets in accordance with the terms of the contract. SEOs involve issuing additional new securities for a firm whose securities already trade in the secondary market. The relationship between capital-raising activities and management efficiency has been well-documented in many studies. For example, Jensen (1986) indicates that debt creation reduces agency costs since managers are forced to effectively meet their debt obligation. Aghion and Bolton (1992) show how debt serves as a mechanism through which managers are disciplined by the transfer of control rights from managers to creditors in the event of default. Given the above discussion, we expect to see an increase in capital-raising activities post-addition. For deletions, on the other hand, the change in capital-raising activities should be limited due to a weak change of investor awareness. We compare corporate debt financing and SEOs conducted by firms around reconstitutions. First, the proportion of sample firms that conducts capital-raising activities within three years prior to the announcement date is compared with the proportion within three years after the effective date. Capital-raising data are obtained from Securities Data Corporation (SDC) Platinum database over the period from January 2000 to February 2016. As Table 5 shown, more firms conduct capital-raising activities after being added to the CSI 300 index. Among the addition sample firms, 48% conduct capital-raising activities within three years after the effective date while only 28% do so within three years before the announcement date. The major increase in capital raising comes in the form of debt financing; 15% of the sample firms are new debt issuers after being included into the index. For SEOs, 9% of the sample firms utilized this capital-raising mechanism for the first time post-addition. For the deletions, the change in proportion of firms conducting capital-raising activities around reconstitutions is small, as expected. On average, there are only 1% more sample firms new to debt financing and 4% more new to SEOs after being excluded from the index.

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The analysis above demonstrates that firms are more likely to conduct capital- raising activities post-addition than post-deletion. A related question concerns the frequency and the size of the capital-raising activities. We address the question in terms of frequency and proceeds. The frequency of capital-raising activities within three years before the announcement date is compared with the frequency of such activities within three years after the effective date. Proceeds are standardized by total assets at previous fiscal year end. The average standardized proceed ratio within three years before the announcement date is compared with the average standardized proceed ratio within three years after the effective date. Moreover, we compare these changes with the ISL-matched benchmark firms. We expect to see an increase in frequency and proceed ratio post-addition but a weak change post-deletion. As Panel A in Table 6 shows, firms conduct capital-raising activities more frequently after being included in the CSI 300 index. On average, additions conduct capital-raising activities 0.523 more times post reconstitution at a 1% significance level. Compared with the benchmark firms, the mean difference in frequency change is a significantly positive 0.343. The major increase comes from debt financing, for which the mean difference is 0.327 at a 1% significance level. The proceed ratio shows a significant positive change as well. On average, the mean difference of proceed ratio between additions and benchmark firms is 0.809 at a significant level. The major source of proceed ratio increase is SEOs, for which the mean difference is 0.831. For deletions, the magnitude of change in frequency and proceed ratio is relatively weak compared to additions. The average frequency change is 0.122 and the average proceed ratio change is -0.016. Compared with ISL-matched firms, the average frequency difference is -0.003 and the average proceed ratio difference is 0.062. Moreover, the results presented for deletions are not significant.

< Insert Table 6>

The results again support the expectation that additions improve management efficiency so that they have greater access to the capital markets owing to their lower cost of capital. By contrast, investors do not neglect deleted firms immediately, so there is no significant difference for firms excluded from the index.

6.2 Capital expenditures In a manner similar to that for capital-raising activities, an increase in the capital expenditure for additions is expected because they have a greater access to capital markets. On the other hand, deletions will experience a negative or weak change in capital expenditure after being excluded from the index. The capital expenditure before the announcement date is compared with the capital expenditure after the effective date. The capital expenditure before the announcement date is obtained as late as possible but still prior to the announcement date. The capital expenditure after the effective date is obtained at least one year after that date, so as to allow updates. To provide more information, we standardize capital expenditure as a percentage of total assets. Total assets is obtained from the last fiscal year end report. The changes in capital expenditure and standardized capital expenditure are compared with all other firms and ISL-matched firms following methods in previous sections. This test includes 350 additions and 329 deletions. Capital expenditure and total asset data is retrieved from Datastream. As Panel A in Table 7 shows, additions significantly expand their capital expenditure after being added to the CSI 300 index. The mean change in capital expenditure is a significantly positive CNY288,437, with a median of CNY32,758. Consistent with the fact that China’s equity markets have developed tremendously, the mean change of other firms is significantly positive as well. Although the markets grow systematically, additions exhibit a stronger increase in capital expenditure compared with the market as a whole. The mean difference of change in capital expenditure between additions and all other firms is a significantly positive CNY348,929, with a median of CNY29,334 at a 1% significance level. The comparison with ISL-matched firms shows parallel evidence that additions expand capital expenditure more than ISL-matched firms, with a mean difference of CNY278,857 and a median of CNY593.

As for standardized capital expenditure, we find a negative change for additions. The average change in standardized capital expenditure is -0.7%, with a median of - 0.44%. However, after subtracting the change of benchmark firms, the difference is still in line with our prediction. The median difference between additions and all other firms is 0.13%. The mean difference between additions and ISL-matched firms is 0.23% For deletions, the capital expenditure change is positive but with a smaller magnitude compared with additions. The mean change is CNY121,397 with a median of CNY13,784 – less than a half of the change for additions. Comparing with the benchmarks, we do not find any significant difference between deletions and the two benchmark firms. For standardized capital expenditure, there is a significantly negative -1.75%, with a median of -1.37% for deletions. We also find a significant negative difference between deletions and benchmarks. For example, the mean difference with all other firms is -1.40%, with a median of -0.81%. The mean difference with ISL-matched firms is -1.52%, with a median of -1.09%.

< Insert Table 7 >

7. Regression analysis

We now perform regression analysis to establish the relationships between the results reported previously. First, we run the regression analyses by combining the variables relating to price effect. Secondly, we combine changes in capital expenditure with the event of reconstitution.

7.1 Price effect As illustrated in previous sections, the price response to the CSI 300 index reconstitutions is asymmetric, which is supported by the operating performance and investor awareness hypotheses. We run regressions using the cumulative abnormal return from the announcement date to 60 days after the effective date (CAR60) as the dependent variable, which would represent the permanent price effect from reconstitutions. We use change in shadow cost (ΔShadowCost) and change in number of shareholders (ΔShareholdersNumber) as proxies for investor awareness. For

18 operating performance hypothesis, we use change in EPS forecast (ΔEPS) and change in EPS forecast ratio (ΔEPS%) to capture the correlation between price effect and earnings expectation. Following Chen et al. (2004), we include some control variables that may have an influence on abnormal returns. Control variables are the number of years a stock has been listed on Datastream (Age), exchange dummy (1 if stock is listed on Shanghai Stock Exchange, 0 otherwise), and firm size divided by the CSI 300 index market capitalization (Size).

퐶퐴푅60 = 훼 + 훽1훥푆ℎ푎푑표푤퐶표푠푡 + 훽2훥푆ℎ푎푟푒ℎ표푙푑푒푟푠푁푢푚푏푒푟 + 훽3훥퐸푃푆 + 훽4훥퐸푃푆% + 훽5퐴푔푒 + 훽6퐸푥푐ℎ퐷푚푦 + 훽7푆𝑖푧푒 + 휀

As shown in Panel A in Table 8, the coefficient of ΔShadowCost in model 1 is - 0.028 at a 1% significance level, as hypothesized. The negative coefficient suggests that after firms are added to the index, shadow cost reduces and firms experience a positive cumulative abnormal return at the first quartile. In model 2, we use ΔShareholdersNumber as a proxy for investor awareness, but the result is not significant. For earnings expectations, the coefficient of ΔEPS is a significantly positive 0.144 in model 3 and the coefficient of ΔEPS% is a significantly positive 0.114 in model 4. The evidence is consistent with the operating performance hypothesis that analysts systematically revise their earnings forecasts upwards post- addition and firms experience a positive cumulative abnormal return at the first quartile. In models 5 and 6, we combine both investor awareness and EPS forecasts in the regressions. The results are consistent with previous models in that after being included into the CSI 300 index, firms with a greater abnormal return experience a reduction in the shadow cost and gain optimistic earnings expectations. The only exception is that the coefficient of ΔEPS in model 6 is negative, but the significance level of the coefficient is weak. For the deletions shown in Panel B, the coefficient of ΔShadowCost in model 1 is 0.062 but only at a weak significance level, as hypothesized. We do not find any significant results for ΔShareholdersNumber. This finding is consistent with the expectation that investors do not neglect deleted firms immediately upon deletion from the index, so investor awareness does not decline immediately in this period. In model 3, the positive and significant coefficient of ΔEPS is consistent with the hypothesis of positive correlation between earnings expectation and abnormal return.

We do not, however, find any significant results for ΔEPS% as shown in model 4. Combing the explanations of investor awareness and earnings expectation together in model 5 and 6, the result is still weak, showing no significant result for investor awareness. Control variables, including age, exchange dummy, and firm size, are not consistently significant across the regressions. Age and exchange dummy are not significant. Firm size is negatively correlated with abnormal return. This is in line with the findings in Chen et al. (2004) that large firms’ abnormal returns tend to be less affected by the event of reconstitutions. < Insert Table 8>

7.2 Capital expenditure To examine the causes of the fact that firms added to the CSI 300 tend to expand a greater capital expenditure, we perform regression analyses by combining the variables relating to capital expenditure, including the event of reconstitution. We run the regressions using the change of capital expenditure ratio (ΔCapexRatio) as the dependent variable. Capex ratio is defined as the percentage of capital expenditure in the current year divided by total assets of the end of previous year. To test the causality of capital expenditure in the context of index reconstitution, a dummy variable is included in the regression that it is equal to 1 for added stocks and 0 for deleted stocks (EventDmy). In addition to the event dummy, we add variables that may affect the level of capital expenditure. Vogt (1997) shows that capital spending is positively related to the level of cash flow. We include the change in free cash flow (ΔCashFlow) to control for the variation of capital expenditure. According to Koch and Shenoy (1999), dividend and capital structure policies interact to provide significant predictive information about future cash flow. The change in dividends (ΔDividends) is added to capture the tradeoff between dividends and capital expenditure. Besides these, the cumulative abnormal return (CAR60), the change in capital-raising frequency (ΔFreq), and the change in proceed ratio (ΔProceedRatio) are considered in the regression as well.

훥퐶푎푝푒푥푅푎푡𝑖표 = 훼 + 훽1퐸푣푒푛푡퐷푚푦 + 훽2훥퐶푎푠ℎ푓푙표푤 + 훽3훥퐷𝑖푣𝑖푑푒푛푑푠 + 훽4퐶퐴푅60 + 훽5훥퐹푟푒푞 + 훽6훥푃푟표푐푒푒푑푅푎푡𝑖표 + 휀

Regression results are reported in Table 9. As shown in model 1, the coefficient of the event dummy is a significantly positive 1.054, as hypothesized. The positive coefficient suggests that a firm added to the CSI 300 index increases its capital expenditure. All else being equal, added firms expand their capital expenditure about 1.054% higher than the deleted firms within three years after adjustments are made. In models 2 to 7, we add ΔCashflow, ΔDividends, CAR60, ΔFreq, and ΔProceedRatio as control variables in the regressions and the coefficients of event dummy are consistently positive and significant. In the multiple regression, control variables are not consistently significant across the regressions. ΔCashflow, ΔDividends, and ΔProceedRatio are not significant. CAR60 is negatively related to the change of capital expenditure but only at a weak significance level. ΔFreq is positively and significantly correlated with the capital expenditure expansion, as shown in model 5, in which its coefficient is 0.408. The intuitive result is that the more frequently a firm conducts capital-raising activities, the more capital expenditure is expected.

< Insert Table 9>

8. Robustness checks

As stated previously, the selection criteria of the CSI 300 index components are based on market capitalization and liquidity such that only the largest and most liquid stocks are included in the index. A related issue concerns the size of the adjusted firms; sample firms may be relatively smaller than the typical CSI 300 stocks. If the adjusted firms are smaller than the typical firms in the CSI 300, it may induce the problem of bid-ask bounces. Accumulating such biases over time may overestimate the true cumulative abnormal returns over the event window. We first compare firm size for the sample firms and all stocks in the CSI 300. As shown in Table 10, the evidence is consistent with our suspicion that the adjusted firms are smaller than the typical CSI 300 stocks. The average market capitalization of all stocks in the CSI 300 is CNY41.85 billion, with a median of CNY16.48 billion. For additions, the average size is CNY18.66 billion with a median of CNY14.51 billion. For deletions, the average size is CNY9.66 billion with a median of CNY8.22 21 billion. As for turnover volume, adjusted firms is less liquid than the typical CSI 300 stocks as well. The average turnover volume of all stocks in the CSI 300 is CNY49.95 million. For additions, the average turnover volume is CNY19.74 million. For deletions, the average turnover volume is CNY11.77 million. Another observation is that deleted firms’ size is only half that of added firms. Deleted firms’ turnover is also smaller than that of added firms. This finding is consistent with the fact that the selection criteria of the CSI 300 index components are based on market capitalization and liquidity. The smallest and the least liquid firms are excluded from the index. Although turnover is small for sample firms, sample firms’ bid-ask spread exhibits a similar pattern to typical stocks in the CSI 300 index. The average spread of all stocks is CNY0.0139 and the average relative spread is 0.18%. The average spread of additions is CNY0.0131 and the average relative spread is 0.15%. The average spread of deletions is CNY0.0116 and the average relative spread is 0.19%.

< Insert Table 10>

In order to investigate the influence of bid-ask bounces on stock returns, we calculate abnormal returns by using the midpoints of daily closing bid and ask prices instead of daily closing prices. We apply the same multiple event windows that we use to calculate abnormal return on the announcement date and accumulated abnormal return over the periods from the announcement date until the effective date, 20, as well as 60 days after the effective date. The results are reported in Table 11.6 Similar to previous findings, additions exhibit a permanent positive price change after the implementation of reconstitutions. The average abnormal return of additions on the announcement date is 0.035% at a 1% significance level. The average cumulative excess returns remain positively significant in the long run. The average cumulative abnormal return is 2.23%, 1.94%, and 1.63% over the periods of the announcement date to the effective date, 20, and 60 days after the effective date, respectively. Although the cumulative abnormal returns do not increase with accumulation window as shown in Table 2, the conclusion is still held that stocks experience a permanent positive price change post-addition.

6 We also compute abnormal returns by using market model. The method gives conclusions similar to those reported using the market adjusted model. Details are available upon request. 22

By contrast, there is only a weak abnormal return decrease post-deletion. Deleted firms have a temporary price decrease, which is then recouped in the long run. The average abnormal return on the announcement date is -0.044%. The average accumulated abnormal returns are -3.47% and -2.89% over the period from the announcement date to the effective date and 20 days after the effective date. However, price fully reverses 60 days after the effective dates and we do not find any significant result in the long run.

< Insert Table 11>

In short, although adjusted firms are smaller than the typical CSI 300 stocks, the asymmetric price effects are robust when we use the midpoints of closing bid and ask prices to calculate abnormal returns.

9. Conclusion

Early studies on the discussion of market response to stock index reconstitutions document an asymmetric effect due to investor awareness. Increases in investor awareness of added firms could improve a firm’s management effectiveness so that firms would be expected to perform more efficiently. Therefore, previous studies have concluded that reconstitutions are not information-free. However, the methodology of component changes employed in most major indices, such as the S&P 500, is not transparent and may contain information that is unavailable to the public. We therefore shed some light on this issue by utilizing data retrieved from the CSI 300 index which has a clearly-stated reconstitution methodology and provide an alternative way to examine price impact on constituent changes. Our evidence supports the investor awareness and operating performance hypotheses around index reconstitutions. Consistent with our prediction, additions not only receive optimistic earnings expectations but also raise more capital from investors. Conversely, deletions exhibit only weak changes in terms of earnings expectations, and capital raising. The evidence indicates that stock index reconstitutions are not information-free.

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Table 1: Additions and Deletions Sample

This table reports initial and final sample of regular additions to and deletions from the CSI 300 index over the period of June 2005 to December 2014. To be included in the final sample, stocks have at least 90 trading days prior to and 60 post announcement dates. Stocks which have no price information on announcement or effective dates are eliminated. Stock adjustments due to IPOs, mergers and acquisitions, spin-offs, trading suspension, temporary delisting and delisting, and bankruptcy are excluded.

Additions Deletions Year Month Initial Final Initial Final 2005 Jun. 14 13 14 12 Dec. 14 13 14 8 2006 Jun 30 24 30 18 Dec 30 28 30 27 2007 Jun 28 24 28 25 Dec 30 25 30 24 2008 Jun 19 18 19 17 Dec 18 16 18 16 2009 Jun 24 23 24 22 Dec 16 15 16 16 2010 Jun 18 15 18 16 Dec 26 24 26 22 2011 Jun 23 22 23 20 Dec 24 24 24 23 2012 Jun 18 18 18 18 Dec 15 15 15 14 2013 Jun 16 16 16 16 Dec 21 21 21 18 2014 Jun 26 23 26 25 Dec 22 21 22 21 Total 432 398 432 378

Table 2: Price Effects

This table reports price effects of additions to and deletions from the CSI 300 index over the period of June 2005 to December 2014. Abnormal returns are calculated relative to the CSI 300 index’s total return. Anndate is the average abnormal return on announcement date. Anndate~Effdate+20 (+60) is the average cumulated abnormal return from announcement to 20 (60) trading days after effective date. Abnormal and cumulative abnormal returns are reported in percentage. The significance of the mean is tested with a standard t-test. *, **, and *** denote significance at the 10%, 5% or 1% level, respectively.

Addition Deletion Sample Size 398 378 Anndate 0.454 *** -0.394 ** Anndate~Effdate 1.165 ** -3.456 *** Anndate~Effdate+20 1.527 ** -1.352 * Anndate~Effdate+60 2.974 *** 2.439 **

Table 3: Analysts’ EPS Forecasts

The table reports changes in analysts’ EPS forecast for sample and matched firms over the period of June 2005 to December 2014. Sample firms include regular additions to (Panel A) and deletions from (Panel B) the CSI 300 index. The first benchmark include all other firms listed at Shanghai Stock Exchange or the Shenzhen Stock Exchange. The second benchmark group firms, call ISL-matched firms, considers characteristics of each sample firm according to its industry, size, and liquidity. EPS forecast change is defined as the differences between post-announcement EPS forecast and pre-announcement EPS forecast. EPS forecast change standardized by EPS is divided EPS forecast change by pre-announcement EPS forecast. Positive pre-announcement EPS forecast are considered only. Current-year and one-year-ahead EPS forecast are reported. Differences between sample and matched firms are calculated with contemporaneous samples. Local currency, CNY, is used for EPS forecast change. The significance of the mean (median) is tested with a standard t-test (sign- test). *, **, and *** denote significance at the 10%, 5% or 1% level, respectively.

Comparison with All Other Firms Comparison with ISL-Matched Firms 1 2 3 4 5 Mean Mean Mean (Median) (Median) (Median) Mean Changes of EPS Mean Changes of EPS Changes of EPS (Median) Forecasts for (Median) Sample Forecasts for Forecasts for of Difference ISL-Matched of Difference Size Sample Firms All Other Firms (col.1-col.2) Firms (col.1-col.4) Panel A: Additions Current-Year EPS Forecasts EPS forecast 255 -0.0165 -0.0313 *** 0.0148 -0.0385 *** 0.0220 * -0.009 * -0.0215 *** 0.0204 *** -0.0243 *** 0.0083 ** EPS forecast 255 2.00% -5.74% *** 7.74% *** -1.72% 3.72% * Standardized by EPS -1.76% * -3.63% *** 3.99% *** -5.37% *** 1.52% * One-Year-Ahead EPS Forecasts EPS forecast 221 -0.0245 -0.0259 *** 0.0014 -0.0199 * -0.0046 -0.003 -0.0152 *** 0.0245 ** -0.0198 *** 0.0117 EPS forecast 221 3.03% -3.65% *** 6.68% *** 0.73% 2.30%

standardized by EPS -0.46% -1.89% *** 3.27% *** -3.51% *** 0.03% Panel B: Deletions Current-Year EPS Forecasts EPS forecast 181 -0.0333 *** -0.0295 *** -0.0038 -0.0161 * -0.0171 -0.0167 *** -0.0187 *** 0.009 -0.0188 *** -0.0019 EPS forecast 179 0.20% -5.00% *** 5.20% -0.97% 1.17% standardized by EPS -4.37% *** -4.76% *** 1.26% -2.62% *** -2.86% One-Year-Ahead EPS Forecasts EPS forecast 143 -0.0285 * -0.0236 *** -0.0049 0.0021 -0.0306 -0.005 -0.0152 *** 0.017 -0.007 0.0032 EPS forecast 143 7.16% -3.00% *** 10.16% * 3.54% 3.63% standardized by EPS -1.15% -1.89% *** 2.75% -1.72% 1.18%

Table 4: Investor Awareness

This table reports changes in the number of shareholders and shadow costs of additions to and deletions from the CSI 300 index over the period of June 2005 to December 2014. The pre- event number of shareholders is the most updated number before the announcement date. The post-event number of shareholders is the number at least 9 months after the effective date. Shadow cost is calculated as follows:

푆ℎ푎푑표푤퐶표푠푡 = (푅푒푠𝑖푑푢푎푙푆푡푎푛푑푎푟푑퐷푒푣𝑖푎푡𝑖표푛⁄퐶푆퐼300푀푎푟푘푒푡퐶푎푝) × (퐹𝑖푟푚 푆𝑖푧푒⁄푁푢푚푏푒푟 표푓 푆ℎ푎푟푒ℎ표푙푑푒푟푠) where firm size uses market value of equity as proxy. Firm size and the CSI300 index market capitalization are the data on the announcement date. Residual standard deviation is the standard deviation of the abnormal return in the 250-day period before the announcement date for the pre-event period. For post-event period, residual standard deviation is the standard deviation of the abnormal return in the 250-day period after the effective date. The significance of the mean (median) is tested with a standard t-test (sign-test). *, **, and *** denote significance at the 10%, 5% or 1% level, respectively.

1 2 3 Mean Mean Mean (Median) (Median) (Median) Sample Before After of Paired Changes Size Change Change (col.2-col.1) Panel A: Number of Shareholders Additions 363 61,600 82,603 21,003 *** 30,299 45,328 7,129 *** Deletions 345 92,973 91,961 -1,011 70,956 69,868 -3,973 *** Panel B: Shadow Cost (x 10-9) Additions 269 2.191 1.131 -1.06 *** 0.969 0.623 -0.256 *** Deletions 337 0.402 0.367 -0.035 0.154 0.163 0.007

Table 5: Proportion of Sample Firms Raising Capital

This table reports proportion of firms raising capital for additions to and deletions from the CSI 300 index over the period of June 2005 to December 2014. Capital-raising activities includes corporate debt issuance (Debts) and seasoned equity offerings (SEOs). Proportion of Firms Raising Capital Before Change is the proportion of sample firms ever raising capital within three years before the announcement date. Proportion of Firms Raising Capital After Change is the proportion of sample firms ever raising capital within three years after the effective date. Changes in Proportion of Firms Raising Capital is the difference of Proportion of Firms Raising Capital After Change and Proportion of Firms Raising Capital Before Change.

1 2 3 Change in Proportion of Proportion of Proportion of Firms Firms Firms Raising Capital Raising Capital Raising Capital Sample Size Before Change After Change (col.2-col.1) Additions Total 398 0.28 0.48 0.20 Debts 398 0.11 0.27 0.15 SEOs 398 0.21 0.30 0.09 Deletions Total 378 0.27 0.31 0.04 Debts 378 0.18 0.19 0.01 SEOs 378 0.14 0.18 0.04

Table 6: Capital-raising Activities

The table reports change in capital-raising activities, including corporate debt issuance (Debts) and seasoned equity offerings (SEOs), for sample and matched firms over the period of June 2005 to December 2014. Sample firms include regular additions to (Panel A) and deletions from (Panel B) the CSI 300 index. Capital-raising frequency within three years before the announcement date is compared with capital-raising frequency within three years after the effective date. Proceed ratio is proceeds over last fiscal end total assets. The average proceed ratio within three years before the announcement date is compared with the average proceed ratio within three years after the effective date. The ISL-matched firms consider characteristics of each sample firm according to its industry, size, and liquidity. The significance of the mean is tested with a standard t-test. *, **, and *** denote significance at the 10%, 5% or 1% level, respectively.

1 2 3 Mean Changes Mean Changes Mean of Sample for Sample for ISL-Matched Difference Size Firms Firms (col.1-col.2) Panel A: Additions Frequency Total 398 0.523 *** 0.182 *** 0.343 *** Debts 398 0.427 *** 0.102 *** 0.327 *** SEOs 398 0.095 ** 0.080 *** 0.016 Proceed Ratio Total 398 0.032 -0.777 *** 0.809 *** Debts 398 0.012 *** -0.076 ** 0.088 *** SEOs 398 0.027 -0.805 *** 0.831 *** Panel B: Deletions Frequency Total 378 0.122 * 0.125 *** -0.003 Debts 378 0.053 0.082 *** 0.029 SEOs 378 0.069 * 0.043 *** 0.026 Proceed Ratio Total 378 -0.016 -0.078 0.062 Debts 378 -0.006 -0.030 * 0.024 SEOs 378 -0.012 -0.074 0.062

Table 7: Capital Expenditure (Capex)

The table reports changes in capital expenditure for sample and matched firms over the period of June 2005 to December 2014. Sample firms include regular additions to (Panel A) and deletions from (Panel B) the CSI 300 index. The first benchmark include all other firms listed at Shanghai Stock Exchange or the Shenzhen Stock Exchange. The second benchmark group firms, call ISL-matched firms, considers characteristics of each sample firm according to its industry, size, and liquidity. The most updated but before the announcement date capex is compared with the capex at least one year after the effective date to allow updates. Capex is standardized by total assets at previous fiscal end. Differences between sample and matched firms are calculated with contemporaneous samples. Local currency, CNY, is used for changes in capital expenditure. The significance of the mean (median) is tested with a standard t- test (sign-test). *, **, and *** denote significance at the 10%, 5% or 1% level, respectively.

Comparison with All Other Firms Comparison with ISL-Matched Firms 1 2 3 4 5 Mean Mean Mean Mean Mean (Median) (Median) (Median) (Median) (Median) Sample Change for Change for Difference Change for ISL- Difference Size Sample Firms All other Firms (col.1-col.2) Matched Firms (col.1-col.4) Panel A: Additions Capex 350 288,437 ** 9,074 *** 348,929 *** 58,211 *** 278,857 ** 32,758 *** 8,968 *** 29,334 *** 11,291 *** 593 Capex standardized 350 -0.70 * -0.44 *** -0.12 -0.74 *** 0.23 ** by assets -0.44 ** -0.38 *** 0.13 *** -0.16 *** 0.00 * Panel B: Deletions Capex 329 121,397 9,187 *** 132,830 59,166 *** 80,658 13,784 ** 8,968 *** 8,496 2,875 867 Capex standardized 329 -1.75 *** -0.43 *** -1.40 *** -1.07 *** -1.52 *** by assets -1.37 *** -0.37 *** -0.81 *** -0.22 *** -1.09 ***

Table 8: Regression Analysis –Price Effects

This table reports the following multivariate regression results:

퐶퐴푅60 = 훼 + 훽1훥푆ℎ푎푑표푤퐶표푠푡 + 훽2훥푆ℎ푎푟푒ℎ표푙푑푒푟푠푁푢푚푏푒푟 + 훽3훥퐸푃푆 + 훽4훥퐸푃푆% + 훽5퐴푔푒 + 훽6퐸푥푐ℎ퐷푚푦 + 훽7푆𝑖푧푒 + 휀 The dependent variable is the cumulative abnormal return from the announcement date to 60 days after the effective date (CAR60). Independent variables include: the log difference between post-change shadow cost and the pre-change shadow cost as more fully described in Table 4 (ΔShadowCost), the log difference between post-change number of shareholders and the pre-change number of shareholders as more fully described in Table 4 (ΔShareholdersNumber), the log difference between post-change EPS forecasts and the pre- change EPS forecasts as more fully described in Table 3 (ΔEPS), the log difference between post-change EPS forecast ratio and the pre-change EPS forecast ratio as more fully described in Table 3 (ΔEPS%), log number of years listing on Datastream (Age), a dummy set to 1 for ShangHai stocks, and 0 otherwise (ExchDmy), and log firm size ratio (Size) which is divided firm size by the market capitalization of the CSI 300 index. *, **, and *** denote significance at the 10%, 5% or 1% level, respectively.

Panel A: Additions 1 2 3 4 5 6 Intercept 0.203 0.258 -0.022 -0.038 0.311 0.182 Investor awareness ΔShadowCost -0.028 *** -0.033 *** ΔShareholdersNumber -0.024 -0.023 EPS forecasts ΔEPS 0.144 ** -0.295 -0.480 * ΔEPS% 0.114 ** 0.335 * 0.603 ** Control variables Age -0.028 -0.003 0.023 0.027 -0.089 -0.003 ExchDmy 0.055 0.028 0.023 0.029 0.124 * 0.062 (ShangHai=1) Size -1.424 *** -0.147 -0.037 -0.038 -1.623 *** -0.003

Sample Size 148 148 148 148 148 148 Adj. R2 0.059 -0.008 0.016 0.010 0.118 0.005 F-test 5.21 0.50 1.91 1.54 4.29 1.13 Panel B: Deletions Intercept -0.025 -0.253 -0.010 -0.023 -0.180 -0.113 Investor awareness ΔShadowCost 0.062 * 0.043 ΔShareholdersNumber -0.005 -0.010 EPS forecasts ΔEPS 0.175 ** 0.056 0.921 ** ΔEPS% 0.076 0.229 -0.419 Control variables Age 0.054 0.162 0.020 0.021 0.044 -0.018 ExchDmy 0.020 0.107 0.053 * 0.059 * 0.103 * 0.270 ** (ShangHai=1) Size -0.566 -1.318 * -0.701 *** -0.672 *** 0.083 0.617

Sample Size 128 128 128 128 128 128 Adj. R2 0.012 0.033 0.105 0.079 0.046 0.198 F-test 2.03 2.07 5.15 4.03 2.08 3.39

Table 9: Regression Analysis – Capital Expenditure

This table reports the following multivariate regression results:

훥퐶푎푝푒푥푅푎푡𝑖표 = 훼 + 훽1퐸푣푒푛푡퐷푚푦 + 훽2훥퐶푎푠ℎ푓푙표푤 + 훽3훥퐷𝑖푣𝑖푑푒푛푑푠 + 훽4퐶퐴푅60 + 훽5훥퐹푟푒푞 + 훽6훥푃푟표푐푒푒푑푅푎푡𝑖표 + 휀 The dependent variable is the difference between post-change capital expenditure ratio and the pre-change capital expenditure ratio as more fully described in Table 7 (훥퐶푎푝푒푥푅푎푡𝑖표). Independent variables include: a dummy set to 1 for added stocks, and 0 for deleted stocks (EventDmy), the log difference between the average free cash flow within three years post- change and the average free cash flow within three years pre-change (ΔCashFlow), the log difference between the sum dividends within three years post-change and the sum dividends within three years pre-change (ΔDividends), the cumulative abnormal return from the announcement date to 60 days after the effective date (CAR60), the log difference between post-change total capital-raising frequency and the pre-change total capital-raising frequency as more fully described in Table 6 (ΔFreq), and the log difference between post-change average proceed ratio and the pre-change average proceed ratio as more fully described in Table 6 (ΔProceedRatio). *, **, and *** denote significance at the 10%, 5% or 1% level, respectively.

1 2 3 4 5 6 7 Intercept -1.750 *** -1.861 *** -1.892 *** -1.693 *** -1.850 *** -1.851 *** -1.965 *** EventDmy 1.054 ** 1.038 * 1.048 ** 1.072 ** 0.894 * 1.001 * 0.895 * (Addition=1) ΔCashflow 0.015 0.016 ΔDividends 0.021 0.011 CAR60 -2.398 * -2.522 * ΔFreq 0.408 ** 0.438 ** ΔProceedRatio -0.187 0.041

Sample size 660 660 660 660 660 660 660 Adj. R2 0.005 0.003 0.003 0.008 0.010 0.004 0.009 F-test 3.96 2.04 2.13 3.50 4.29 2.38 2.03

Table 10: Summary Statistics

This table reports summary statistics of all stocks in the CSI 300 index, additions to and deletions from the CSI 300 index over the period of June 2005 to December 2014. Daily average closing price (P), turnover volume (TurnVol), market capitalization (MV), the difference between daily closing bid and ask prices (Spread), and Spread over daily closing bid-ask midpoint (Relative Spread) are reported. Data for all stocks in the CSI 300 is the information at year end. Data for adjusted firms is drawn from the 240 trading day interval [-250,-11] prior to the announcement dates.

N Mean Std. Min p25 Median p75 Max Skew Kurt Panel A: All stocks in CSI 300 P (in CNY) 2981 10.92 10.94 0.49 4.67 8.08 13.73 175.97 5.25 51.04 TurnVol (million CNY) 2981 49.95 60.47 0.34 14.54 33.45 61.99 778.40 3.73 23.38 MV (billion CNY) 2981 41.85 131.72 0.95 9.26 16.48 30.59 2,848.21 10.54 146.23 Spread (in CNY) 2262 0.0139 0.0193 0.0100 0.0100 0.0100 0.0100 0.4200 12.11 196.39 Relative Spread (in %) 2262 0.1801 0.1701 0.0073 0.0800 0.1307 0.2225 2.6144 4.02 32.64 Panel B: Additions P (in CNY) 398 11.74 10.29 1.12 4.90 8.96 15.16 95.56 3.16 16.86 TurnVol (million CNY) 398 19.74 30.73 0.62 5.65 11.17 20.87 422.87 7.01 77.76 MV (billion CNY) 398 18.66 22.41 1.18 7.82 14.51 19.14 269.67 5.49 47.00 Spread (in CNY) 398 0.0131 0.0112 0.0023 0.0076 0.0105 0.0139 0.0994 4.09 22.51 Relative Spread (in %) 398 0.1525 0.0893 0.0190 0.0872 0.1268 0.2034 0.5131 1.13 1.18 Panel C: Deletions P (in CNY) 377 7.59 5.69 0.52 3.97 5.92 8.85 39.52 2.25 6.98 TurnVol (million CNY) 376 11.77 12.62 0.74 5.33 8.99 14.52 130.67 5.26 40.77 MV (billion CNY) 377 9.66 9.22 0.95 5.05 8.22 10.46 84.66 3.86 21.41 Spread (in CNY) 377 0.0116 0.0088 0.0021 0.0088 0.0105 0.0117 0.1024 6.38 52.29

Relative Spread (in %) 377 0.1942 0.0952 0.0485 0.1256 0.1749 0.2459 0.6473 1.21 2.06

Table 11: Price Effects – Bid-Ask Midpoints

This table reports price effects of additions to and deletions from the CSI 300 index over the period of June 2005 to December 2014 by using the midpoints of daily closing bid and ask prices. Abnormal returns are calculated relative to the CSI 300 index’s total return. Anndate is the average abnormal return on the announcement date. Anndate~Effdate, Anndate~Effdate+20, and Anndate~Effdate+60 are the average cumulated abnormal return from the announcement date to the effective date, 20, and 60 days after the effective date. Returns are reported in percentage. The significance of the mean is tested with a standard t- test. *, **, and *** denote significance at the 10%, 5% or 1% level, respectively.

Addition Deletion Sample size 398 378 Anndate 0.035 *** -0.044 ** Anndate~Effdate 2.226 *** -3.470 *** Anndate~Effdate+20 1.938 *** -2.891 *** Anndate~Effdate+60 1.625 ** -1.512