The investment behavior of Qualified Foreign Institutional Investors in China

The investment behavior of Qualified Foreign Institutional Investors in China

Journal Pre-proof The Investment Behavior of Qualified Foreign Institutional Investors in China Ningyue Liu, Don Bredin, Huijuan Cao PII: S1042-444X(...

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Journal Pre-proof The Investment Behavior of Qualified Foreign Institutional Investors in China Ningyue Liu, Don Bredin, Huijuan Cao

PII:

S1042-444X(20)30003-7

DOI:

https://doi.org/10.1016/j.mulfin.2020.100614

Reference:

MULFIN 100614

To appear in:

Journal of Multinational Financial Management

Received Date:

27 November 2019

Revised Date:

16 January 2020

Accepted Date:

25 January 2020

Please cite this article as: Liu N, Bredin D, Cao H, The Investment Behavior of Qualified Foreign Institutional Investors in China, Journal of Multinational Financial Management (2020), doi: https://doi.org/10.1016/j.mulfin.2020.100614

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier.

The Investment Behavior of Qualified Foreign Institutional Investors in China1 Ningyue Liu, Don Bredin, Huijuan Cao Author information: Ningyue Liu:Corresponding author. School of Management and Economics, Beijing Institute of Technology, email: [email protected] Don Bredin: Graduate School of Business School, University College Dublin, email: [email protected]

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Huijuan Cao: Business School, Sun Yat-sen University, email: [email protected]

authors would like to thank Ronan Powell, Gary Tian and Wang Mu-Shua for valuable comments and

conference participants at the 30th Australasian Finance and Banking Conference, December 2017, Sydney. The authors also thank the funding support of the National Natural Science Foundation of China (No. 71502014,

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No.71672007 and No.71672010) and Science Foundation Ireland (SFI) under Grant Number 16/SPP/3347.

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Highlights  QFIIs in China represent small overall holdings, with shareholdings below blockholder thresholds. QFIIs have a preference for large well known firms with some element of state ownership, lower liability levels and greater turnover capacity.



The presence of QFIIs is associated with better corporate operating performance. This positive relationship is more pronounced in firms with greater QFIIs’ commitment, in terms of investment size and horizon.



QFIIshave an incremental influence oncorporateperformance compared to domestic counterparts.

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Abstract

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Using data from Chinese listed firms, this paper examines the investment behavior of Qualified Foreign Institutional Investors (QFIIs). QFIIs have a preference for large wellknown firms with some element of state ownership, lower liability levels and greater turnover capacity. Furthermore, the presence of QFIIs in Chinese firms is associated with better levels of corporate operating performance. We show that this positive relationship is more pronounced, the greater the international investors level of commitment, in terms of investment size and horizon. Our results highlight the heterogeneity of foreign institutional investors and support the view that foreign institutional investors in emerging markets provide an important source of managerial oversight. Our results are particularly noteworthy given all QFIIs in China represent small overall holdings, with shareholdings below blockholder thresholds. 1

Keywords: Qualified Foreign Institutional Investors, Investor Heterogeneity, Investment Preference, Shareholder Activism

JEL classifications: G11; M41; G2

1. Introduction

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Since China became a member of the World Trade Organization (WTO) in December 2001, it has implemented numerous measures to liberalize its economy and improve its investment environment. An example is the Qualified Foreign Institutional Investor (QFII) scheme. It has been designed to encourage and facilitate the largest overseas institutions access to China’s debt and equities markets. The QFII scheme represents a significant departure from China’s traditional approach of strict capital controls. Up to January 2019, more than 280 QFIIs have been approved by the China Security Regulatory Commission (CSRC). The total investment quota of QFIIs has grown to 100 billion US dollars by the end of January 2019.1 While the growth in QFIIs has been quite spectacular, there is little understanding of their investment preferences and in particular, whether the benefits associated with institutional investment are applicable to the case of China.

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Existing studies on foreign institutional investors mainly answer two questions, regarding the investment preference and investment consequence. For the first question, the home bias hypothesis argues that foreign investors have less local knowledge than domestic investors (Kang and Stulz, 1997; Huang and Shiu, 2009; Liu et al., 2014; Roque and Cortez, 2014; Zou et al., 2016; Ferreira et al., 2017). To overcome this issue, foreign institutional investors prefer to invest in firms with a more transparent information environment (Chou et al., 2014), good corporate governance structure (Chung and Zhang, 2011; Miletkov et al., 2014; McCahery et al., 2016), firms located in countries with stronger investor protection (Aggarwal et al., 2005), or with higher levels of social trust (Jin et al., 2016). Besides, foreign investors will choose a concentrated investment strategy to reduce information processing costs (Kang and Stulz, 1997; Fedenia et al., 2013; Liu et al., 2014).

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The role of foreign institutional investors in corporate governance has also attracted much attention in the literature. Compared with domestic investors, foreign institutional investors enjoy the advantage of independence, as they are less likely to have existing business connections with the invested firm (Huang and Shiu, 2009; Aggarwal et al., 2011; Bena et al., 2017). Besides, foreign institutions are likely to have more experience and suitable expertise to deploy superior monitoring technologies (Kim et al., 2016). With these advantages, they will exert influence on corporate governance voting by hand (Aggarwal et al., 2015; Lel, 2019) or by feet (Hartzell and Starks, 2003; Parrino et al., 2003). Some studies document that foreign institutional investors will create value for the firm (Ferreira and Matos, 2008; Huang 1

QFII data is from the State Administration of Foreign Exchange website, http://www.safe.gov.cn/. 2

and Shiu, 2009; Kim et al., 2016; Bena et al., 2017). In addition to the monitoring role, foreign investors also have advisory functions. Andriosopoulos and Yang (2015) find with increased foreign investment, there is greater likelihood of cross border investment.

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However, there are still some remaining open questions in terms of investment preference and consequence. For the investment preference, existing research tends to prioritize the question of whether institutional investors invest in certain firms or not. However, the equally important question concerns the investment size and the investment horizon. To clarify how to attract long-term or concentrated-holding foreign institutional investors is important. Capital market openness exposes firms to global risk premium, which brings more volatility (Chen et al., 2013). Long-term foreign institutional investors help to reduce such risk. Callen and Fang (2013) find institutional investor stability is negatively associated with future stock price crash risk. The Chinese government is acutely aware of such risk, and claim explicitly in the QFII regulation document that medium and long-term QFIIs are encouraged. In addition, the different kinds of institutional investors, have different influences on the invested firms. For example, Liu et al. (2018) find short-term investors exacerbate CEO’s myopia. For the investment consequence of foreign institutional investors, the exiting studies focus less on the investor heterogeneity, which is fundamental to determine the monitoring incentive of the institutional investors (Bushee, 1998; Gaspar et al., 2005; Cornett et al., 2007; Burns et al., 2010). Chen et al. (2007) find only long term and concentrated holding institutions have the motivation and the ability to engage in corporate governance. Similar studies show that institutions with a long-term horizon are active in corporate governance (McCahery et al., 2016), while short-term institutions exacerbate CEO myopic behavior (Liu et al., 2018).

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In this paper, we discuss the dynamic investment decision of QFIIs, including what will influence QFIIs to decide whether to invest, how much to invest and how long to invest. We also examine the investment consequence of different types of QFIIs. We predict that QFIIs may act as value creators if they hold more shares and for a long time. The mechanism used by QFIIs to improve invested firms’ performance is information gathering and active participation. Compared to domestic investors, QFIIs enjoy the advantage of independence and global investment experience (Ferreira and Matos, 2008, Huang and Zhu, 2015). They have fewer potential business ties with local firms (Ferreira and Matos, 2008), and face less pressure from government (Huang and Zhu, 2015). All indications point to these investors playing an active role. Under the QFII scheme, a foreign institutional investor must receive the qualification before they make investments in Chines capital market. The approval process selects foreign investors who are more professional and experienced. Even if information asymmetry will restrict QFIIs’ ability to play a monitoring role, a concentrated and long-term holding will help QFIIs to overcome this shortcoming. For example, in the 2011 Annual Shareholders Meeting of Gree Electric Appliances Incorporation (Gree), Yale University (a QFII) played a pivotal role in electing the 3

new director. Jiyong Feng was a candidate for director, who was supported by Yale University. Beyond all expectations, he defeated the other candidate recommended by the State-owned Assets Supervision and Administration Commission. Yale University was on the top 10 shareholder list of Gree, and also held the Gree’s stock for more than one year. Thus, Yale University had the opportunity and ability to influence the invested firm’s corporate governance.

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Following the classification framework in Chen et al. (2007), we classify all the QFIIs into three investor types. The complete set of QFII (total QFII) represents all the institutional investors that have received the qualified quota and have completed an investment in a Chinese firm. The second type of investor are those that have a considerable holding, top ten shareholders, in a Chinese firm and are referred to as concentrated holding (CH QFII). Finally, the last type of investor examined, has been a top ten shareholder in a firm continuously for four quarters, at the end of the 4th quarter and is referred to as long-term (LT QFII).

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Based on this classification system, our paper simultaneously evaluates both the investment preferences and the potential investment consequences (Ferreira and Matos, 2008; Aggarwal et al., 2011). Specifically, we address two questions. Firstly, what firm attributes determine the dynamic change of QFIIs’ investment? Secondly, can the particular types of QFIIs influence management and enhance the invested firms’ performance? Not only do we consider the key attributes contributing to whether a firm receives QFIIs’ investment, but we also examine the question of what might influence a QFII’s decision to invest larger amounts or for a longer horizon. This requires a dynamic analysis. We also consider QFIIs’ heterogeneity, when we consider the consequences of QFIIs’ investment.

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First, we investigate the dynamic investment preference of QFIIs. Our results indicate that total QFIIs prefer firms who are more recognized, with low liability levels, large in size, high turnover ability and are state owned. CH QFIIs, especially prefer low-liability level firms. Compared to total QFIIs and CH QFII, LT QFIIs invest less in firms that grow faster or with concentrated ownership, but they prefer recognized firms and large-scale firms.

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When we focus on the ex-post activities of QFIIs. Our results indicate that the presence of QFIIs in the Chinese firm is associated with better operating performance, and this benefit is primarily as a result of LT QFII. Our results are consistent with the literature highlighting the important role played by foreign institutional investors, e.g. Edmans (2014) and Bena et al. (2017). However, an important distinction here is that the investors examined are not block-holders. Over 90% of the QFIIs are non-blockholders. We further examine how government ownership influences the positive effect of QFII. Political connections may reduce the effectiveness of corporate governance (Chen et al., 2013). We find the QFIIs’ positive effect is less pronounced for SOE, which means that the government ownership interrupts QFIIs’ participation in corporate governance. We also implement a number of robustness tests. To control the endogenous 4

problems, we use the instrumental variable method to reexamine the relationship between QFIIs’ ownership and corporate operating performance. We use the change of quotas granted by the government as the instrumental variable. The investment quota is the result of the government’s decision. In China, to control the openness process of the capital markets, the government determines how many investment quotas to grant to QFIIs. QFIIs can make investments within the scope of the granted quotas. While the investment quota is exogenous from firm’s operation, it is related to QFIIs’ investment. Using the instrumental variable method, we document consistent findings that additional QFII involvement is associated with better corporate performance. We also use the PSM method, control the domestic institutional investors’ behavior and use alternative performance measures to further improve the robustness of our findings.

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The main contributions of the paper are as follows. First, our paper enriches the literature on the behavior of foreign institutional investor in emerging markets, especially in China, the world's second largest economy after the United States. Foreign capital is expected to play an important role in promoting economic growth in countries with developing financial systems (Aggarwal et al., 2005).

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Second, instead of pooling all the QFIIs together, we emphasize the heterogeneity of foreign institutional investors. Our findings highlight the importance of investor size and investment horizon. Greater commitment from foreign institutional investors translates to improved operating performance. Previous literature shows that certain types of, but not all, institutional investors exert influence on R&D investment decisions (Bushee, 1998) and CEO compensation (Hartzell and Starks, 2003). Garspar et al. (2005) show that short-term shareholders have weaker monitoring effects on managers. Cornett et al. (2007) document the positive association between independent institutional investors and operating performance. Our empirical results support the heterogeneous nature of institutional investors, and find long-term foreign institutional investors with concentrated holdings play a more important role in corporate governance.

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Finally, our results highlight that even those with relatively small shareholdings can play a role, as reflected in the improved operating performance. This is particularly noteworthy taking into account the relatively strict criteria governing access to Chinese security markets. An important distinction here relates to the size of the institutional investor in China. As highlighted by Edmans (2014) large shareholders (block-holders), given their size, are more likely to play a monitoring role and so greater involvement in relation to managerial oversight and corporate governance. With less than 5% ownership, QFIIs are generally representative of relatively small investment shareholdings in Chinese firms. However, as indicated by Huang and Zhu (2015), QFIIs tend to punch above their weight. In particular, Huang and Zhu (2015) find that QFIIs have much more influence than their domestic counterparts and can even have an influence on controlling shareholders. Survey evidence during the early stages of the QFII scheme, highlighted that 33% of respondents have actively promoted corporate governance by exercising their rights as shareholders. While over half of all QFIIs participants provided management support 5

to the invested companies, in the form of dialogue session, seminars and training sessions (see Tan, 2009). The rest of paper is organized as follows. In section 2, the institutional background is discussed. Section 3 describes the data and methodology. We present the empirical results of our tests in section 4, with robustness tests presented in section 5. Finally, section 6 provides some concluding remarks.

2. Institutional Background

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The openness of Chinese equity market is a gradual process. The QFII scheme was launched in November 2002, when CSRC and People's Bank of China (PBOC) issued ‘Interim Procedures for the Administration of Investment in Domestic Securities by Qualified Foreign Institutional Investors’. The interim procedure clearly stated that if a foreign institutional investor was approved by CSRC and received the quota from the State Administration of Foreign Exchange (SAFE), the investor would be qualified and so could invest in the Chinese security market. Under the approval scheme, QFIIs must satisfy certain criteria, including large value of assets under management, good credit standing, a sound governance structure and internal control systems. For example, commercial banks are required to have a minimum of $10 billion in security assets under management in their previous financial year.

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In June 2006, the CSRC, PBOC and SAFE jointly issued new regulations to replace the interim measures. The qualifying criteria for QFII applicants have been reduced. In July 2012, the CSRC modified the rules governing the QFII scheme, issuing ‘Provisions on Issues Concerning the Implementation of the Administrative Measures for Domestic Securities Investment by Qualified Foreign Institutional Investors’. The lowering of the qualification requirements represented a continuation of the policy of openness to Chinese security markets. The QFII investment limit also changed. The ratio of all the QFIIs’ holdings in one firm has also been permitted to increase from 20% to 30% in 2012(Robinson et al., 2013). In January 2016, the SAFE indicated plans to further expand the domestic securities market and the approval administration was to be simplified. Under the new regulations of QFIIs issued by the SAFE on February 2016, the lock-up period has been reduced from 1 year to 3 months. In 2018, the SAFE went even further and cancelled the regulation on the lock-up period. In February 2016, the PBOC announced that additional foreign financial institutions, including pension funds, charitable funds, endowment funds and other long-term investors could participate in China's interbank bond market, with no investment limit. To date, QFIIs’ investing in China are primarily large internationally renowned funds and investment banks (see Huang and Zhu, 2015).

3. Data and Methodology 3.1 Measures of QFIIs Rather than examining the pooled QFIIs’ behavior, we classify all the QFIIs into three types, total QFIIs (total QFII), concentrated holding QFIIs (CH QFII) and longterm QFIIs (LT QFII). Total QFIIs are all the QFIIs who have received the qualified 6

quota and complete an investment in a Chinese firm. In cases where a QFII makes a concentrated investment in one Chinese firm and becomes one of the top 10 shareholders, we define this category of QFII as a CH QFIIs. Finally, if a CH QFII has been one of the top 10 shareholders in the firm continuously for 4 quarters, at the end of the 4th quarter, we define it as a LT QFIIs. It is important to note, while CH and LT QFIIs represent the top 10 shareholders, but with less than 5% ownership. Under Chinese regulation, QFIIs’ investment is limited and represent relatively small shareholdings. Detailed indicators are provided below2.

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Our definition of the three categories of QFIIs highlight that QFIIs could migrate between different categories over the complete sample period, 2009 to 2017. Classifying various categories of QFIIs relies on firms’ quarterly statements. For example, from the quarterly statement of Gree, a large Chinese appliance manufacturer, Yale University (a QFII) is one of the top 10 shareholders from 2014 Q4 to 2015 Q3. As of 2015 Q3, we can define Yale University as a LT QFII. 3.2 Data

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Our sample consists of A-share listed firms in the China equity market during the period 2009 to 2017. The sample starts from 2009 in order to avoid the influence of global financial crisis in 2008. Existing studies show that during the financial crisis, investors may change their investment strategies (Lins et al., 2017). Corporate financial and governance data is sourced from the China Securities Markets and Accounting Research (CSMAR) database. The data of QFIIs’ stock holdings is obtained from the Wind Financial Database, a leading financial information provider in China.3 We implement the following process for sample selection. First, we exclude financial companies such as banks, insurance companies, and investment trusts. Second, firms who suffer from financial losses for two consecutive fiscal years are also dropped from our sample, because there is institutional difference to treat such firms between Chinese capital market and other main capital markets. In China, there is Special Treatment regulation to treat such firms (ST firms), which doesn’t exist in the main capital markets. To reduce the influence from the institutional difference, we drop these samples4. In addition, we winsorized all the continuous variables before computing the statistics.

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Figure 1 reports the overall number of CH, LT and total QFIIs in different firms from 2009 to 2017. The number of firms invested by the specific QFIIs is on the vertical axis. From figure 1, we can see the numbers of total QFIIs and CH QFIIs are very similar, which indicates that when a QFII makes an investment, its investment is 2

The primary focus of our paper is whether particular types of QFIIs have the incentive and the ability to exert influence on corporate governance. Following the referee’s suggestion, we also capture this point using other classification methods. In particular, we classify QFIIs from the perspective of QFIIs’ origin, independence and general portfolio turnover. We expect QFIIs from strong-enforcement countries, who are independent or with lower portfolio turnover have greater incentives and ability to exert positive effects on corporate governance. The empirical results support our expectations. Given the length of the paper, these additional results are located in the online appendix B. 3 The CSMAR database and Wind Financial Database have been adopted by a number of recent studies, including Lennox et al. (2014), Guan et al. (2016) and Firth et al. (2013). 4 We also implement the empirical analysis including ST samples or 2007-2008 samples (financial crisis period), and the results remain unchanged. 7

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large enough to be one of the top 10 shareholders in the invested firm. Under the QFII framework, investment is closely related to the quota approved by the SAFE. Any relaxation in the approval quota is likely to lead to increased investment in China. In 2012, the CSRC modified the rules governing the QFII scheme leading to lower qualification requirements and a broader range of investments. As a result, during 2012 to 2014, the number of CH QFIIs increased, with LT QFIIs remaining more stable. Figure 2 presents the industry allocations of QFIIs in China. The horizontal axis indicates the particular industry over time, while the vertical axis presents the number of firms within each industry that QFIIs invest in. Figure 2 illustrates that QFIIs hold disproportionately more investments in manufacturing industries. Finally, Figure 3 presents the level of QFIIs investments in different Chinese provinces. The horizontal axis represents the provinces in China, while the vertical axis presents the number of firms invested by QFIIs. As expected, QFIIs’ investments are dominant in the eastern regions of China (e.g. Beijing, Shanghai and Guangdong), where are resource-rich. [Insert Figure 1 here] [Insert Figure 2 here]

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[Insert Figure 3 here]

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Table 1, Panel A presents a description of the holding size and investment horizon for total, CH QFIIs and LT QFIIs. There are 7,327 firm-quarter observations with QFIIs’ investment, of which 7,166 of them are invested by CH QFIIs. On average, CH QFIIs hold 1.356% of the firms’ shares, with 2,115 observations representing LT QFIIs. The average holding percentage of LT QFIIs is 2.129%. For CH QFIIs and LT QFIIs, the holdings are very small by international standards and generally representative of non-block-holders. In one quarter, the maximum number of CH QFIIs in a firm is 9 while the number of LT QFIIs is 7. In addition, one Chinese firm has on average 1.331 CH QFIIs and 1.217 LT QFIIs. Panel B further shows that less than 3% of CH QFIIs can be defined as block-holders, while it is closer to 6% for LT QFIIs. [Insert Table 1 here]

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3.3 Model

We adopt two models to examine the investment preference and the consequences of QFIIs’ investment in China.

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INSi,t = α+α1 Recognitioni,t-1+α2 Financiali,t-1 + α3 Governancei,t-1+∑αk Industry + ∑αk YEAR+ εi,t

(1)

Performancei,t = β+β1 INSi,t-1+β2 Financiali,t +β3 Governancei,t+ ∑βk Industry +∑βkYEAR+θi,t

(2)

where i, t refers to firm i and year t. In model (1), ε is a random disturbance and INS is a dummy variable. INS is replaced by T_QFII when we analyze the total QFIIs. T_QFII is a dummy variable which is equal to 1 if there is a QFII shareholding in the firm i and year t, otherwise 0. INS is replaced by CH_QFII (LT_QFII) when we analyze the CH (LT) QFIIs. CH_QFII (LT_QFII) is a dummy variable which is equal 8

to 1 if there is a CH_QFII (LT_QFII) in a firm i and year t, otherwise 0.

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A probit regression is adopted to estimate model (1). We include firm’s recognition variables (Recognition), financial variables (Financial) and governance variables (Governance) which may influence QFIIs’ investment behavior. Home bias hypothesis argues that investors has less information about foreign markets (e.g. Chou et al., 2014). Foreign investors rely on some positive signals when making investment decisions and firm’s recognition is a clear signal for foreign investors. Following Covrig et al. (2006), we use several measures to proxy firm’s recognition. SS300index is a dummy variable which is equal to 1, if the firm’s stock is a member of ShanghaiShenzhen 300 index. Since 2005, the Shanghai-Shenzhen 300 index has been jointly issued by the Shanghai stock exchange and Shenzhen stock exchange to reflect stock market trend. Previous studies have documented that firm’s recognition is an important determinant of foreign institutional investment. Greater visibility and recognition results from firms being listed on such exchanges, see Covrig et al. (2006). In our complete sample, about 13.3% of firms are components of the Shanghai-Shenzhen 300 index. This ratio increases to 36.7% for firms that LT QFIIs invest in, which further emphasizes that LT QFIIs prefer to invest in firms with more recognition. We also adopt analyst following (AnalystFollow) to measure firm’s recognition. Firms with more analysts following are generally associated with greater visibility in the market.

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We also examine the influence of financial characteristics and corporate governance characteristics on foreign institutional investors’ assets allocation. Drawing on previous studies (e.g. Aggarwal et al., 2005, Liu et al., 2014), we include firm’s growth (Growth), liability level (Lev), turnover ability (Turnover) and firm size (Size) in model (1). Furthermore, institutional investors prefer to invest in firms with sound corporate governance structure (McCahery et al., 2016; Chung and Zhang, 2011; Miletkov et al., 2014). Given the dominant role of SOE in China, we also include state owned property (SOE) variable in model (1). Board independence (independence) and the ownership of the largest shareholders (Large) are also included. In Chinese listed firms, a third of the board members are required to be independent. We define board independence (Independence) as a dummy variable. It is equal to 1 if the firm has more independent directors than required. In our sample, only 9% of the listed firms are deemed to have board independence.

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In model (2), we examine the impact of QFIIs on operating performance. θ is the random disturbance. Following Boubakri and Cosset (1998), Gompers et al. (2003), ROA (return on assets) are used to measure corporate performance (Performance). In our robustness tests, we also use return on equity to measure corporate performance. The primary explanatory variable in model (2) are the different kinds of QFIIs (INS) in each firm. We adopt the one-yearlag value of INS to measure the potential effect of QFIIs on the corporate performance. Drawing on the literature (e.g. Ferreira and Matos, 2008), control variables include financial and governance attributors influencing corporate performance, including firm’s size (Size), leverage (Lev), inventory turnover (Turnover), growth ability (Growth), ownership concentration (Large), board 9

independence (Independence) and state-owned property (SOE). The definitions of all variables are presented in Appendix A. 3.4 Descriptive Statistics

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Table 2 presents descriptive statistics of the firm’s characteristics for our sample. Our full sample includes 13,365 firm-year observations, while 597 of them are firms with LT QFIIs. Column (1) and (2) report mean and standard deviations for each indicator, for the complete sample. Column (3) focuses on samples without LT QFIIs, while column (4) describes the indicators for firms with LT QFIIs. The last column reports p-values for t-tests of equality between column (3) and (4). During 2009 to 2017, the firms held by LT QFIIs perform better, with larger scale and have better inventory turnover ability. In addition, LT QFIIs hold more Shanghai-Shenzhen 300 stocks. Finally, in terms of corporate governance, LT QFIIs would appear to have a preference for firms with state involvement and more board independence. Overall, the results presented in Table 2 highlight, with the exception of Growth, the statistically significant differences between LT QFIIs and remaining QFIIs for all firm characteristics.5 [Insert Table 2 here]

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4. Empirical Results 4.1 QFIIs Investment Preference

[Insert Table 3 here]

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To examine drivers of QFIIs’ investment, we adopt a probit regression of model (1). The results for total QFIIs, CH QFIIs and LT QFIIs are reported in column (1), (2) and (3) respectively in table 3.

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As expected, our proxy for firm recognition (SS300 index and Analysts Following) would appear to indicate that recognition is an important driver. In all cases QFIIs prefer low-debt enterprises. Large scale firms with flexible turnover ability are also preferred by QFIIs. Only LT QFIIs invest less in firms with growth potential.

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Our corporate governance indicators also highlight a number of important relationships. For ownership concentration (Large), the coefficients are statistically significant for both total QFIIs and CH QFIIs, but not LT QFIIs. While the finding that QFIIs have a preference for SOE in China, they clearly value the ability to be involved. As indicated by Huang and Zhu (2015), QFIIs tend to play a much more active role compared to domestic institutional investors. This is noteworthy, given their quite low overall shareholding. Consistent with our findings for ownership concentration, SOE certainly have a positive influence on QFIIs’ investment. However, the influence dilutes with greater institutional investor involvement. Heterogeneity in the determinants does manifest itself with respect to deeper investor involvement, represented by LT QFIIs. Clearly institutional investors with a long-term 5

We also report pairwise correlation matrix. QFIIs’ investment is associated with better operating performance. To save space, we put this table as online appendix A. 10

focus, favor dispersed ownership and so is consistent with QFIIs’ playing an active role, despite their small shareholding.

4.2 QFIIs and Corporate Operating Performance

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Column (4) and (5) report results of a dynamic analysis. Column (4) examines what factors will influence a move from the regular QFIIs into CH QFIIs. The sample of column (4) is all firms with regular QFIIs but without CH QFIIs in the last period (T_QFIIi,t-1=1 & CH_QFIIi,t-1=0). If the firm has a CH QFII in the current year (CH_QFIIi,t=1), the dependent variable is equal to 1, otherwise 0. Column (5) investigates the case of likely determinants as investors move from CH to LT QFIIs. Results for column (4) and (5) emphasize the heterogeneity of QFIIs investor types. There are statistically significant incremental effects for investors opting for CH QFIIs (from regular QFIIs) and for LT QFIIs (from CH QFIIs). The results in column (4) indicate that there is additional information contained within financial and corporate governance to indicate investor movement from regular QFIIs to CH QFIIs. Results from column (5) again highlight incremental information and general consistency for both recognition and financials for explaining investor movement from CH to LT QFIIs.

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The results of the impact of QFIIs on corporate operating performance are reported in Table 4. The results indicate that the presence of total, CH and LT QFIIs are all positively associated with corporate performance. The increase is greatest for LT QFIIs, 0.016, while the positive effect for total and CH QFII is respectively 0.007 and 0.008, which emphasizes the importance of distinguishing the particular types of investors. The positive association with performance grows with investor commitment, this is evident in particular from results reported in column (4) and (5) where the incremental performance improvement is highlighted.

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The column (4) and (5) examine if there is a regular QFII or CH QFII in the last period, whether CH and LT QFII (respectively) are still positively associated with corporate performance (ROA). All financial indicators have a statistically significant effect on performance, with consistent findings irrespective of the level of investor commitment. The results of control variables show that ownership concentration (Large) is associated with better performance. In countries with less investor protection, ownership concentration is found to be an efficient corporate governance strategy (La Porta et al., 2000). In particular, empirical evidence has found that concentrated ownership leads to improved firm performance especially in emerging markets (Boubakri et al., 2005). Table 4 indicates a (statistically significant) negative relationship between government ownership (SOE) and performance (ROA). Our results highlight that in China, government ownership not only indicates political connection but also government intervention (see Firth et al., 2010). Cao et al. (2017) document that political connections may reduce the effectiveness of corporate governance. We find consistent evidence that this intervention leads to a negative impact on performance. The negative effect of board independence on performance suggests that in China the independent director system is not particularly effective in relation to corporate governance. 11

[Insert Table 4 here]

4.3 Further Analysis

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We further examine how ownership by QFIIs interacts with government ownership. In SOE, the government is the actual controller of the firm. On the one hand, the government will provide SOE political resources, which contributes to the firms’ development (Chen et al., 2010; Liu et al., 2013; Cao et al., 2017). On the other hand, the government may potentially interrupt firms’ operation. Instead of maximizing shareholders' interests, SOE also undertake some social responsibility (Bai et al., 2000), for example, to increase employment (Shleifer and Vishny, 1994) and maintain social stability, which will reduce shareholders’ interests. The government intervention may also reduce the positive corporate governance effect of QFII.

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Table 5 reports the results of the moderating role of government ownership in the relation between QFIIs’ ownership and corporate performance. As we predict, the positive effect of QFIIs on corporate performance is less pronounced for SOE. This is consistent with previous findings that political connection will reduce the effectiveness of corporate governance (Chen et al., 2013). [Insert Table 5 here]

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5. Robustness Tests

5.1 Instrumental Variables Regressions

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Can we conclude that the relatively small group (in terms of overall shareholding) of international investors add value? Clearly our approach to examine performance does suffer from endogeneity problems. First, there may be selection bias; e.g. equally likely that QFIIs invest in more profitable firms and this is what our performance results indicate. Second, it is still possible that endogeneity arising from unobserved omitted variables. For instance, we are unable to observe the existence of managerial-specific relationships between QFIIs and the invested firms. An instrumental variable regression can be adopted to control such endogeneity. We next examine the robustness of our findings using an instrumental variables approach.

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We employ the yearly growth of investment quota granted by SAFE (INSTRUMENT) as the instrumental variable of QFIIs’ investment. A valid instrumental variable must satisfy two conditions (Dhaliwal et al., 2016). First, the relevance condition requires the instrumental variable is related to the QFIIs’ investment in the firm after controlling for control variables in our main model. Second, the exclusion restriction requires the instrumental variable is unrelated to corporate performance conditioning on the full set of control variables. The Chinese approval system of QFIIs provides a unique setting to find such a valid instrumental variable. As we mentioned in the institutional background of QFII scheme, before QFIIs can make investments in the Chinese mainland capital market, they must receive the investment quota from the government. Under this approval system, the 12

investment quota granted by the government will influence QFIIs’ investment, but is unrelated to the micro-level corporate operating performance. The policy is certainly influenced by the aggregate of all firm’s operations. However, any possible influence here would be the aggregate performance and not firm specific performance, which is at the heart of the exclusion restriction, so the exclusion restriction will not be violated.

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The 2-Stage Least Squares regression results are reported in table 6. The yearly growth of investment quota granted by SAFE (INSTRUMENT) is highly correlated with the YEAR dummy. As a result, we drop the year fixed effect in the regression. The first-stage results are presented in columns (1), (2) and (3). As we predict, additional levels of investment quota are significantly positively related to QFIIs’ investment. This shows that the selected instrumental variable meets the relevance condition. The second-stage results in columns (4), (5) and (6) show a positive relation between QFIIs’ investment and corporate operating performance. Again, we find that deeper investor involvement levels (LT QFIIs) are associated with improved levels of performance. In terms of information gathering, LT QFIIs are more likely to gather firms’ internal information and are more familiar with the firms’ condition. While in terms of shareholder activism, LT QFIIs with relatively large shareholding percentage and longer holding time have incentives and abilities to participate in corporate governance, as the monitoring benefits are higher than the costs (Chen et al., 2007).

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[Insert Table 6 here]

5.2 Propensity Score Matched (PSM) Analysis

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Our results have indicated a positive relationship between the presence of QFIIs and corporate performance. To improve the robustness of our conclusion, we use the PSM method to further control the observed omitted variable and reexamine the extent of performance differences between firms with and without total QFIIs, CH QFIIs or LT QFIIs. In non-experimental studies, the PSM method is used to select a subset of comparison units to the treatment units. The selection criterion is the propensity score, which is calculated by the pretreatment characteristics (Dehejia and Wahba, 1998 and 2002).

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In our study, the treatment group is firms invested by total QFIIs, CH QFIIs or LT QFIIs. The pretreatment characteristics are the factors significantly influencing QFIIs’ investment decisions in Model (1), including firm’s recognition (SS300index, AnalystFollow), size (Size), liability level (Lev), turnover ability (Turnover), sales growth (Growth), SOE (SOE), ownership concentration (Large) and board independence (Independence). The propensity score is calculated via these variables. Based on the calculated propensity score, we match each treatment unit (firm with QFIIs) with control groups (firms without QFIIs) one by one. Finally, we can compare the outcome difference between these two groups. Results are presented in table 7. Panel A in Table 7 reports the results of the data balancing test. After PSM, there are almost no significant differences in the pretreatment characteristics, which shows that we have controlled the observed differences between the treatment group and the 13

control group well. Based on the matched samples, we compare the operating performances between the treatment group and the control group. Panel B reports the results. ROA is used to measure corporate performance. In all cases, there is a statistically significant and positive difference in corporate performance (ROA) between the treatment group and the control group, especially for the LT QFII. After matching, the ROA difference between the firms invested by LT QFIIs and the control group without LT QFIIs is the largest. This is consistent with our findings to date in the paper that the improvement in performance increases with the level and the investment time horizon. [Insert Table 7 here] 5.3 Domestic Investors vs QFIIs

[Insert Table 8 here] 5.4 Other Robustness Tests

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Domestic institutional investors dominate the Chinese market, and they have more information about the market compared to QFIIs. There could be an omitted variable problem, where QFIIs follow the domestic investors’ investment strategy. To solve this endogenous problem, we control the effect of domestic investors. r_Domestic represents the domestic institutional ownership. We do not use the dummy variable to measure domestic investors, for almost every listed firms in China are held by domestic institutional investors. So, there is no variation if we use dummy variable instead of continuous ownership to measure the existence of domestic institutional investors. We still use dummy variable here to measure QFIIs. The results in table 8 highlight the incremental influence of foreign institutional investors on performance compared to domestic counterparts. Further, consistent with our main findings, the presence of LT QFIIs is associated with a 0.011 increase in ROA, which is higher than the effect of CH QFIIs.

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Furthermore, we also use QFII’s continuous ownership, instead of the dummy variable, to measure different kinds of QFIIs. The results are presented in table 9. Greater levels of QFII ownership is associated with better corporate performance. The positive relation between QFII ownership and corporate operating performance is more pronounced, the greater the international investors level of commitment, in terms of investment size and horizon. We also use different corporate performance measures, such as return on equity (ROE) and ROA change (△ROA) rather than the level value of ROA. All the results are consistent with our main results presented earlier. 6 [ Insert Table 9 here ] [Insert Table 10 here]

6. Conclusions The results in our study depict a complete and dynamic picture of QFII’s ex-ante 6

We also implemented our model with firm fixed effects, however the results were not statistically significant. 14

and ex-post behavior in a firm. Heterogeneity of institutional investors forms the basis of our analysis. We divide all the QFIIs into three types according to their investment size and investment horizon. Furthermore, we examine QFIIs’ investment preferences and the consequences of their investment. To avoid the self-selection bias when estimating the QFIIs’ effect on corporate performance, we use the instrumental method to control the potential bias.

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Our findings on QFIIs’ investment preference show that total QFIIs prefer firms with higher level of recognition, lower liability level, larger size, higher turnover ability and state ownership. However, QFIIs tend to decrease their investment size and investment time in firms with more concentrated ownership. These results indicate the heterogeneity associated with QFIIs preferences. Moreover, we examine whether QFIIs contribute to higher corporate performance. After controlling for the potential endogenous problem, we find that LT QFIIs are associated with much more value creating effects.

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Our findings highlight the heterogeneity in terms of the relationship between investor types and any potential influence on firm’s operating performance. Greater commitment from foreign institutional investors translates into improved operating performance. Ferreira and Matos (2008) document that firms with higher foreign institutional ownership have better operating performance. We go further and highlight the importance of the holding time and investment horizon of foreign institutions, consistent with Chen et al. (2007). In particular, our results highlight that this relatively small-scale international shareholdings, as represented by QFIIs, have a positive impact on operating performance. This is noteworthy taking into account the relatively strict criteria governing access to Chinese security markets. Our empirical findings also will be of value to policy-makers in emerging markets, and China in particular, in gauging the important drivers of corporate performance. The holding size and holding duration of QFIIs are essential.

15

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Management, 2016. 37-38: 12-28.

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Number of Firms

450 400 350 300 250 200 150 100 50 0

CH QFII

LT QFII

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total QFII

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Figure 1 The number of QFIIs from 2009 to 2017

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Number of Firms

7000 6000 5000 4000 3000 2000 1000

total QFII

CH QFII

LT QFII

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Figure 2 Industry allocations of QFIIs in China

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0

20

Number of Firms

1600 1400 1200 1000 800 600 400 200

total QFII

CH QFII

LT QFII

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Figure 3 Province allocations of QFIIs in China

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Table 1 Characteristics of QFIIs Panel A The holding size and horizon of QFIIs Total QFII

CH QFII

LT QFII

Average holding percentage/ quarter

1.346%

1.356%

2.129%

Average number in one firm/quarter

1.323

1.331

1.217

Maximum(minimum) number in one firm/ quarter

9(1)

9(1)

7(1)

Firm-quarter observations

7,327

7,166

2,115

Panel B Blockholders in CH and LT QFIIs Firm-quarter-CH QFII observations

Percent

Firm-quarter-LT QFII observations

Percent

Non Blockhoder

9,313

97.66%

2,417

93.90%

Blockholder

223

2.34%

157

6.10%

Total

9,536

100%

2,650

100%

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Table 2 Descriptive Statistics

N=

Total Sample

LT_QFII=0

LT_QFII=1

(13,365)

(12,768)

(597)

(2)

(3)

(4)

(5)

Mean

Std. Dev.

Mean

Mean

Mean Difference

ROAi,t

0.035

0.055

0.034

0.063

-0.029***

T_QFIIi,t

0.172

0.377

0.134

0.983

-0.849***

CH_QFIIi,t

0.163

0.369

0.124

1.000

-0.876***

SS300indexi,t

0.133

0.340

0.122

0.367

-0.245***

AnalystFollowi,t

1.568

1.137

1.526

2.459

-0.933***

Levi,t

0.442

0.212

0.443

0.427

0.016*

Sizei,t

22.16

1.252

22.120

22.973

-0.854***

Growthi,t

0.176

0.441

0.177

0.172

0.004

Turnoveri,t

12.130

40.050

11.615

23.018

-11.403***

Largei,t

34.610

14.870

34.389

39.362

-4.973***

SOEi,t

0.422

0.494

0.415

0.571

-0.156***

Independencei,t

0.0905

0.287

0.089

0.122

-0.033***

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(1)

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The column (1), (2), (3) and (4) show the descriptive statistics of the total sample, the firms without long-term QFIIs and the firms with long-term QFIIs respectively. The last column (5) is the p-value of t-test between column (3) and (4). The definitions of variables are presented in the Appendix A. *, **, and *** denote significance at the 10%, 5%, and 1% levels respectively

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Table 3 QFII Preference Dependent Variable

CH_QFIIi,t

LT_QFIIi,t

CH_QFIIi,t

LT_QFIIi,t

(1)

(2)

(3)

(4)

(5)

T_QFIIi,t-1=1 &CH_QFIIi,t-1=0

CH_QFIIi,t-1=1 <_QFIIi,t-1=0

0.091*

0.256***

1.024

0.227*

(1.892)

(1.920)

(3.877)

(1.248)

(1.816)

0.266***

0.267***

0.310***

0.394

0.156***

(17.856)

(17.801)

(12.613)

(1.610)

(3.412)

-0.295***

-0.285***

-0.483***

-3.966**

-0.210

(-3.459)

(-3.321)

(-3.511)

(-2.486)

(-0.802)

0.086***

0.073***

0.092***

-0.055

0.093*

(4.613)

(3.915)

(3.173)

(-0.205)

(1.708)

0.002***

0.002***

0.002***

0.008

0.002**

(4.729)

(4.927)

(4.165)

(0.412)

(2.186)

-0.010

-0.013

-0.134**

0.654

-0.238*

(-0.295)

(-0.398)

(-2.163)

(1.068)

(-1.873)

0.006***

0.006***

0.002

-0.026

-0.001

(5.928)

(6.054)

(1.482)

0.270***

0.269***

(8.420)

(8.333)

0.028

0.026

(0.601)

(0.557)

-3.401***

-3.078***

(-8.443) Year effect Industry effect

Sizei,t-1 Turnoveri,t-1 Growthi,t-1 Largei,t-1 SOEi,t-1

Constant

Pseudo

R2

Observations

(-1.611)

(-0.405)

0.262***

0.747

-0.055

(5.289)

(1.239)

(-0.577)

0.019

1.060*

-0.000

(0.275)

(1.810)

(-0.002)

-4.807***

1.427

-3.776***

(-7.606)

(-6.956)

(0.240)

(-3.166)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

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Independencei,t-1

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Levi,t-1

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AnalystFollowi,t-1

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0.089*

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SS300indexi,t-1

T_QFIIi,t

0.084

0.084

0.135

0.275

0.061

13,359

13,359

13,291

79

1,735

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This table reports the results of QFIIs’ preference on corporate characteristics. The coefficient is the marginal effect. We examine not only the factors influencing the QFIIs’ investment, but also their investment size and holding time. The column (1), (2) and (3) are results for total QFIIs (investment or not), CH QFIIs (investment size) and LT QFIIs (investment holding time) respectively. Z-statistics are in parentheses. The definitions of variables are presented in Appendix A. *, **, and *** denote significance at the 10%, 5%, and 1% levels respectively.

24

Table 4 Corporate Operating Performance and QFIIs Dependent Variable

ROAi,t (1)

T_QFIIi,t-1

(2)

(3)

(4)

(5)

T_QFIIi,t-1=1

CH_QFIIi,t-1=1

0.007*** (6.609)

CH_QFIIi,t-1

0.008***

0.010***

(7.696)

(2.895)

LT_QFIIi,t-1

Growthi,t Largei,t SOEi,t

(4.567)

-0.133***

-0.133***

-0.133***

-0.153***

-0.152***

(-42.871)

(-42.891)

(-42.916)

(-22.265)

(-21.882)

0.012***

0.012***

0.012***

0.014***

0.014***

(24.129)

(24.162)

(24.080)

(13.481)

(12.788)

0.000***

0.000***

0.000***

0.000***

0.000***

(4.304)

(4.223)

(3.981)

(3.444)

(2.583)

0.022***

0.022***

0.022***

0.024***

0.023***

(17.201)

(17.186)

(17.167)

(6.582)

(6.496)

0.000***

0.000***

0.000***

0.000***

0.000***

(8.873)

(8.873)

(9.162)

(2.842)

(2.930)

-0.007***

-0.007***

-0.007***

-0.008***

-0.008***

(-7.549)

(-7.613)

-0.004***

ro of

Turnoveri,t

(7.837)

-p

Sizei,t

0.010***

re

Levi,t

0.016***

(-3.268)

(-3.417)

-0.004***

-0.005***

-0.004

-0.007*

(-3.226)

(-3.208)

(-3.257)

(-1.261)

(-1.922)

-0.171***

-0.170***

-0.170***

-0.192***

-0.205***

(-11.977)

(-11.941)

(-11.027)

(-6.553)

(-5.429)

Year effect

YES

YES

YES

YES

YES

Industry effect

YES

YES

YES

YES

YES

13,337

13,337

13,337

2,402

2,304

0.288

0.289

0.289

0.334

0.338

Constant

Observations R-squared

na

Independencei,t

lP

(-7.607)

Jo

ur

This table reports the results of the impact of QFIIs on corporate performance. Columns (1) to (3) focus on full samples for ROA. Columns (4) to (5) focus on dynamic analysis for ROA. Standard errors are clustering at the firm level (t-statistics are in parentheses). The definitions of variables are presented in Appendix A. *, **, and *** denote significance at the 10%, 5%, and 1% levels respectively.

25

Table 5 The Moderating Role of Government Ownership Dependent Variable

ROAi,t (2)

(3)

INS=

T_QFII

CH_QFII

LT_QFII

INSi,t-1

0.009***

0.009***

0.019***

(8.893)

(9.080)

(8.067)

-0.004***

-0.003*

-0.006**

(-2.812)

(-1.816)

(-1.983)

-0.103***

-0.103***

-0.103***

(-57.590)

(-57.655)

(-58.034)

0.008***

0.008***

0.008***

(27.889)

(27.940)

(28.137)

0.000***

0.000***

0.000***

(5.266)

(5.103)

0.018***

0.018***

(22.152)

(22.160)

0.000***

0.000***

(11.069)

(11.108)

-0.006***

-0.006***

-0.006***

(-9.578)

(-9.897)

(-10.491)

-0.004***

-0.004***

-0.004***

Levi,t Sizei,t Turnoveri,t Growthi,t Largei,t Independencei,t

(-4.020) Constant

0.000*** (11.540)

-0.095***

(-9.204)

(-14.080)

YES

YES

YES

YES

YES

YES

11,916

11,916

11,902

0.378

0.379

0.380

na

Observations

(22.002)

-0.098***

lP

Industry effect

0.017***

(-3.955)

-0.096***

Year effect

(4.626)

(-4.032)

(-14.007)

R-squared

-p

SOEi,t-1

re

INSi,t-1*SOEi,t-1

ro of

(1)

Jo

ur

This table reports the results of the impact of government ownership on the relation between QFIIs and corporate performance. Standard errors are clustering at the firm level (t-statistics are in parentheses). The definitions of variables are presented in Appendix A. *, **, and *** denote significance at the 10%, 5%, and 1% levels respectively.

26

Table 6 Instrumental Variables Regressions Dependent Variable

T_QFIIi,t-1

CH_QFIIi,t-1

LT_QFIIi,t-1

First stage regression (1)

(2)

ROAi,t Second stage regression

(3)

Fit_T_QFIIi,t-1

(4)

(5)

(6)

0.282*** (11.759)

Fit_CH_QFIIi,t-1

0.202*** (11.759)

Fit_LT_QFIIi,t-1

0.745*** (11.759)

Sizei,t

(5.086)

(6.957)

(3.517)

-0.194***

-0.182***

-0.089***

-0.078***

-0.096***

-0.067***

(-11.308)

(-10.734)

(-9.530)

(-14.636)

(-22.954)

(-10.837)

0.064***

0.059***

0.029***

-0.006***

-0.000

-0.010***

(19.481)

(18.338)

(14.098)

(-4.091)

(-0.160)

(-5.353)

0.000***

0.000***

0.000***

-0.000***

-0.000**

-0.000***

(3.370)

(3.807)

(4.657)

(-3.744)

(-2.520)

(-8.674)

-0.024***

-0.016**

-0.006**

(-3.810)

(-2.566)

(-2.133)

0.001***

0.001***

0.000

(5.219)

(4.552)

(1.174)

0.035***

0.036***

(4.654)

0.025***

0.026***

(20.681)

(19.306)

(20.001)

-0.000

0.000**

0.000***

(-1.486)

(2.115)

(5.922)

0.019***

-0.016***

-0.014***

-0.021***

(4.898)

(4.739)

(-12.228)

(-11.571)

(-12.791)

0.010

0.006

0.008

-0.007***

-0.005***

-0.010***

(0.856)

(0.478)

(1.226)

(-4.913)

(-3.747)

(-6.814)

-1.074***

-0.990***

-0.452***

0.132***

0.029

0.166***

(-5.195)

(-4.826)

(-2.939)

(4.895)

(1.446)

(5.629)

Year effect

NO

NO

NO

NO

NO

NO

Industry effect

YES

YES

YES

YES

YES

YES

Observations

13,337

13,337

13,337

13,337

13,337

13,337

0.040

0.281

0.281

0.281

SOEi,t Independencei,t Constant

0.049

0.045

This table reports 2-Stage Least Squares regression results using instrumental variables. We use the yearly growth of the granted quota to QFIIs (INSTRUMENT) as the instrumental variable of QFIIs’ investment. We report first-stage results in column (1), (2) and (3), and report secondstage results in column (4), (5) and (6). We use the predicted values from the first-stage in the second-stage regressions. The dependent variables in columns (1)– (3) are T_QFII, CH_QFII and LT_QFII, respectively. The dependent variable in columns (4) - (6) is corporate operating performance (ROA). Standard errors are clustering at the firm level (t-statistics are in parentheses). The definitions of variables are presented in Appendix A. *, **, and *** denote significance at the 10%, 5%, and 1% levels respectively.

Jo

R-squared

na

Largei,t

lP

0.029***

ur

Growthi,t

0.004***

re

Turnoveri,t

0.014***

ro of

Levi,t

0.010***

-p

INSTRUMENT

27

Table 7 Propensity Score Matching Analysis Panel A Data balancing test Difference (Treatment group - Control group) (2)

(3)

T_QFII

CH_QFII

LT_QFII

SS300indexi,t-1

-0.026*

-0.009

0.002

AnalystFollowi,t-1

0.008

-0.004

0.058

Sizei,t-1

-0.014

-0.030

-0.022

Levi,t-1

0.002

0.000

0.001

Turnoveri,t-1

0.625

-2.706*

0.172

Growthi,t-1

0.014

0.006

0.009

Largei,t-1

-0.583

0.392

0.303

SOEi,t-1

-0.010

-0.002

-0.025

Independencei,t-1

0.009

0.013

ro of

(1)

-0.004

Panel B Corporate performance in treatment group and control group ROA (2)

Treatment group

Control group

T_QFII

0.049

0.041

CH_QFII

0.049

0.042

LT_QFII

0.063

0.051

re

Group

(3)

-p

(1)

Difference 0.008*** 0.007*** 0.012***

Jo

ur

na

lP

In this table, we use PSM to compare the extent of treatment effect by QFIIs. Panel A reports the results of data balancing tests. Columns (1) - (3) report the mean difference between treatment group and control group after match. Panel B reports the corporate performance in treatment group and control group. ROA is used as outcome variable to measure corporate performance. Treatment group in column (1) means the firms with different kinds of QFIIs. Control group in column (2) means the firms without the particular kind of QFIIs. The definitions of variables are presented in Appendix A. *, **, and *** denote significance at the 10%, 5%, and 1% levels respectively.

28

Table 8 Domestic Investors vs QFIIs Dependent Variable

ROAi,t

r_Domestici,t-1

(1)

(2)

0.000***

0.000***

(11.607)

(11.356)

CH_QFIIi,t-1

0.006*** (4.343)

LT_QFIIi,t-1

0.011*** (4.533)

Turnoveri,t Growthi,t Largei,t

-0.130***

(-38.742)

(-37.883)

0.011***

0.011***

(19.781)

(18.459)

0.000***

0.000***

(4.405)

(3.734)

0.022***

0.022***

(15.317)

(15.477)

0.000***

0.000***

(3.893) SOEi,t

-0.010***

re

(-9.038) Independencei,t

Year effect

R-squared

na

Industry effect

(3.689)

-0.010*** (-9.432)

-0.005***

-0.005***

(-3.157)

(-3.290)

-0.122***

-0.110***

(-6.327)

(-5.645)

YES

YES

YES

YES

10,600

10,600

0.287

0.292

lP

Constant

Observations

ro of

Sizei,t

-0.132***

-p

Levi,t

Jo

ur

This table reports the results of the impact of domestic institutional investors and QFIIs on corporate performance. r_Domestic represents the domestic institutional ownership. Standard errors are clustering at the firm level (t-statistics are in parentheses). The definitions of variables are presented in Appendix A. *, **, and *** denote significance at the 10%, 5%, and 1% levels respectively.

29

Table 9 Corporate Operating Performance and the Average Ownership of QFIIs Dependent Variable

ROAi,t (1)

r_T_QFIIi,t-1

(2)

(3)

0.022*** (8.037)

r_CH_QFIIi,t-1

0.003*** (10.327)

r_LT_QFIIi,t-1

0.009*** (10.894)

Growthi,t Largei,t SOEi,t Independencei,t

-0.130***

(-38.113)

(-37.628)

(-37.721)

0.012***

0.012***

0.012***

(21.722)

(21.098)

(21.221)

0.000***

0.000***

(4.101)

(3.990)

0.021***

0.021***

(14.935)

(14.969)

0.000***

0.000***

(8.206)

(8.200)

(8.229)

-0.007***

-0.007***

-0.007***

(-6.916)

(-6.871)

(-6.799)

-0.005***

R-squared

(14.921)

0.000***

(-3.395)

-0.127***

-0.128***

(-6.641)

(-6.338)

(-6.355)

YES

YES

YES

YES

YES

YES

10,600

10,600

10,600

0.283

0.286

0.286

lP

Observations

0.021***

(-3.374)

na

Industry effect

(4.047)

-0.005***

-0.132***

Year effect

0.000***

-0.005***

(-3.322) Constant

ro of

Turnoveri,t

-0.130***

-p

Sizei,t

-0.131***

re

Levi,t

Jo

ur

This table reports the results of the impact of QFIIs on corporate performance, using the continuous ownership rather than the dummy variable to measure QFII. r_T_QFII, r_CH_QFII and r_LT_QFII are measured as the average ownership for different kinds of QFIIs over 4 quarters for year t and firm i. Standard errors are clustering at the firm level (t-statistics are in parentheses). The definitions of variables are presented in Appendix A. *, **, and *** denote significance at the 10%, 5%, and 1% levels respectively.

30

Table 10 Alternative Corporate Operating Performance Measures

(1)

(2)

(3.788)

(0.927) 0.013***

0.002

(5.006)

(1.352)

Largei,t SOEi,t Independencei,t Constant

(6.882)

(2.622)

-0.163***

-0.162***

-0.162***

0.040***

0.040***

0.041***

(-14.705)

(-14.690)

(-14.677)

(9.017)

(9.046)

(9.091)

0.023***

0.022***

0.022***

-0.005***

-0.005***

-0.005***

(14.811)

(14.779)

(14.683)

(-6.744)

(-6.790)

(-6.877)

0.000***

0.000***

0.000**

0.000

0.000

0.000

(2.703)

(2.631)

(2.404)

(0.251)

(0.226)

(0.108)

0.045***

0.045***

0.045***

-0.009***

-0.009***

-0.009***

(14.369)

(14.370)

(14.357)

(-6.131)

(-6.132)

(-6.136)

0.000***

0.000***

0.000***

0.000**

0.000**

0.000**

(6.563)

(6.533)

(6.718)

-0.015***

-0.015***

(-6.088)

(-6.149)

-0.009**

-0.009**

(-2.451)

(-2.446)

-0.335***

-0.333***

(-9.607) YES

Industry effect

YES

Observations

13,337 0.126

(1.988)

(1.979)

(2.021)

-0.015***

-0.000

-0.000

-0.000

(-6.164)

(-0.232)

(-0.250)

(-0.288)

-0.009**

0.001

0.001

0.001

(-2.482)

(0.628)

(0.629)

(0.598)

-0.332***

0.081***

0.081***

0.082***

(-9.525)

(-9.015)

(5.900)

(5.935)

(6.019)

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

13,337

13,337

10,622

10,622

10,622

0.127

0.127

0.036

0.037

0.037

na

Year effect

R-squared

0.005***

ro of

Growthi,t

(6)

0.029***

lP

Turnoveri,t

(5)

0.001

LT_QFIIi,t-1

Sizei,t

(4)

0.010***

CH_QFIIi,t-1

Levi,t

(3)

-p

T_QFIIi,t-1

△ROAi,t

ROEi,t

re

Dependent Variable

Jo

ur

This table reports the results of the impact of QFIIs on corporate performance. We use alternative corporate performance measures. We use ROE to measure corporate performance, as shown in columns (1) to (3). We use ROA change to measure corporate performance, as shown in columns (4) to (6). Standard errors are clustering at the firm level (t-statistics are in parentheses). The definitions of variables are presented in Appendix A. *, **, and *** denote significance at the 10%, 5%, and 1% levels respectively.

31

Appendix A Variable Definitions

CH_QFII

INS

LT_QFII

Recognition

Financial

SS300index AnalystFollow Lev Size Turnover Growth Large SOE

Governance

Jo

ur

na

lP

Independence

ro of

T_QFII

Definitions Net income / asset Dummy variable. If there is a QFII, the value is 1; Otherwise, 0. Dummy variable. If there is a QFII on the list of top 10 shareholders in the year, the value is 1; Otherwise, 0. Dummy variable. If there is a long term QFII with concentrated holdings in the year, the value is 1; Otherwise, 0. Dummy variable. If the stock is the component of Shanghai-Shenzhen 300 Index, the value is 1; Otherwise, 0. Ln (analyst numbers + 1) Total liability/ total assets Ln (Assets) Ln (Costs/inventory) The yearly growth of operating revenue. The share ratio of the largest shareholders. If the ultimate controller is government, it is 1; Otherwise, 0. Dummy variable. If the firm has more independent directors in the board than the regulation, it is 1; Otherwise, 0.

-p

Variables ROA

re

Category Performance

32