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Investment Preferences On A Shares Market In China

The Qualified Foreign Institutional Investor (QFII) regime is a successful policy allowing the opening to the world and at the same time preventing foreign investment risks and protecting the financial system.

With the rising of economic globalization, integration between different capital markets gets much promoted. China as now world’s second largest economy, however has a relative low GDP per capita and weak-form efficiency of capital market. Therefore, China is in urgent need of advanced techniques and experience in financial industry. In recent years, developing countries and emerging markets have been further opened up their capital markets in order to increase their competitive power by attracting foreign investments. The Qualified Foreign Institutional Investor (QFII) regime is a successful tactic allowing the opening to the world and at the same time preventing foreign investment risks and protecting the financial system.

Analyse QFII’s investing strategy may help domestic investors with better understanding and skills of investment and management on the A-share market.

This paper analysis QFII’s investing preference in the aspects of corporate performance, capital structure, industries and regions.

1. Introduction

1.1 Motivation

The Qualified Foreign Institutional Investor (QFII) regime is a successful tactic allowing the opening to the world and at the same time preventing foreign investment risks and protecting the financial system.

Analyse QFII’s investing strategy may help domestic investors with better understanding and skills of investment and management on the A-share market.

1.2 Research Background

With the rising of economic globalization, integration between different capital markets particularly the emerging markets gets much promoted. China as now world’s second largest economy yet has a relative low GDP per capita-US$3,744 compare with japan US$39,738 in 2009 (World Bank, 2009) and weak-form efficiency of capital market. Therefore, China is in urgent need of advanced techniques and experience in financial industry.

In recent years, developing countries and emerging markets have been further opened up their capital markets in order to increase their competitive power by attracting foreign investments. After more than 20 years of reform and development, the equity market in China has played an important role in the country’s financial system and corporate development.

China has been attracting substantial foreign direct investment (FDI) ever since 1978. However, with foreign exchange controls over the country’s capital account in place, foreign investors were not allowed to enter the domestic Chinese shares market. Stock exchanges were closed off to foreign investors due to its exercise of tight capital controls which restrict the movement of assets in-and-out of the country. Despite record high levels of inward FDI in recent years, portfolio investment in Chinese companies by foreign investors has been restricted to Chinese company shares listed in Hong Kong or other overseas stock exchange such as New York and London and within China to B shares.

Meanwhile, China has faced a more rigorous international competitive circumstance in its financial system, given its WTO commitments to open up by 2006. Thus, with a lackluster stock market that had often been regarded as highly speculative with poor corporate governance and weak protection for minority shareholders, the Chinese authorities decided to allow selected Qualified Foreign Institutional Investor (QFII) to enter the A-share market under a limited quota system. The aim was to provide a scheme for limited relaxation of foreign exchange controls over the country’s capital market, as well as to leverage the investment and management skills of successful foreign financial institutions to raise the standards of the Chinese market. The expectation of introducing QFII was to bring with greater market stability and longer-term investment, modern investment notions and a more rational approach to share investment, to provide some relief to the prevailing short-term speculative behavior nevertheless preventing foreign investment risks and protecting the financial system at the same time.

A-Shares market is opened to international institutional investors in December 2002 under the Qualified Foreign Institutional Investor (QFII).

The QFII program was launched in 2002 to allow licensed foreign investors to buy and sell Yuan-denominated "A" shares in China's mainland stock exchanges in Shanghai and Shenzhen. As of September 2010, a total of 94 foreign institutional investors have been approved under the QFII program. Foreign access to China's Yuan-denominated "A" stocks are still limited, with quotas placed under the QFII program amounting to US$30 billion.

Regulations of the QFII program were based on "Temporary Regulation on Domestic Securities Investment by Qualified Foreign Institutional Investor”, which was publicized on 5th Nov 2002 and ceased to be in effect on 1st Sep 2006, and "Regulation on Domestic Securities Investment by Qualified Foreign Institutional Investor", which is publicized on 24th Aug 2006, and came into effect on 1st Sep 2006.

Pursuant to "Regulation on Domestic Securities Investment by Qualified Foreign Institutional Investor",to qualify as a QFII, the candidate must:

has stable finance, good credibility, meet the minimum asset scale set by China Securities Regulatory Commission (CSRC)

the number of staffs meet the requirement set by the authority in its own country or area

has healthy governing structure and complete internal control system, received no significant punishment in the last 3 year

candidate’s home country has complete legal and supervision system, and its home country or home area has signed Memorandum of Understanding (MOU) with CSRC, and maintains effective supervision cooperation

Other requirements set by CSRC based on prudence (Wikipedia, 2011).

Since QFII first launched in 2002, it developed quickly both on the aspect on amount and the size of quota. The number of QFII increased from initially 12 to 88 in the year 2009 and the quota of QFII rose dramatically from 2003 to 2010 (US$0.35 billion to US$18.97 billion, respectively).

Figure 1.1

Data from CSRC (

Figure 1.2

Data from SAFE (

2 Literature Review:

2.1 Financial Market Integration

The pace of capital account liberalization in China has slowed sharply since the Asian financial crisis in 1997–98, which caused growing skepticism in both academic and policy circles about the wisdom of free capital mobility (Bhagwati 1998). Fred Hu (2005) suggested that China has sought to maintain extensive control over portfolio investment, including equities, bonds, loans, currencies, commodities, and derivative instruments. Nevertheless China has conducted cautious experiments aimed at gradual and orderly relaxation of capital controls. The most significant initiative launched by far is the introduction of the QFII scheme in 2002.

Most studies show that financial integration among international markets has increased since the 1997 Asian financial crisis. A number of researchers have applied co-integration technique within mainland China as well as between the Chinese and Hong Kong markets (Kim and Singal, 2000; Sjoo and Zhang, 2000). Kwan and Reyes (1997) introduced a regression model to analysis the relationship between QFII and market stability in Taiwan. After comparing 3-year data before and after the introduction of QFII (1988–1994), they detected a diminishing trend in the volatility of stock returns following the introduction. Kim and Singal (2000) examined macroeconomic ramifications from the introduction of QFII in newly emerging economies and found empirical evidence showing that the opening up of the stock market through QFII has contributed positively to the development of capital market, with negative impact on inflation or violent exchange rate fluctuation.

According to Grossman and Stiglitz (1980) pointed that economic theory generally suggests that speculative activity enhances the informational and allocational role of asset markets thereby making markets more efficient.

Geert Bekaert and Campbell R. Harvey (1995 and 2000) found that foreign speculative activity in emerging markets can play a particularly important role. First, the potential of market manipulation is acute in small emerging markets and liquidity is often poor. Although there are many policy initiatives that could increase liquidity and reduce the degree of collusion among large traders, there may not be a sufficient mass of domestic speculators to ensure market liquidity and efficiency. Second, opening the market to foreign speculators may increase the valuation of local companies, thereby reducing the cost of equity capital. The intuition is straightforward.

Geert Bekaert and Campbell R. Harvey (2000) concluded that the capital market integration process reduces the cost of capital but perhaps by less than they expected.

Levine and Zervos (1996, 1998) found that opening stock markets also promote the development of equity markets-it is positively related to long-run economic growth, which is supported by Boyd and Smith (1996) on theoretical.

E. Han Kim and Vijay Singal (2000) pointed that opening stock markets to foreign investors can provide several potential benefits for emerging economies. Opening markets represent an important opportunity to attract foreign capital to finance economic growth.

2.2 Foreign Investment

According to E. Han Kim and Vijay Singal (2000), foreign investors will demand transparency and improved disclosure rules that are crucial for improved allocational efficiency of capital. Furthermore, they will require accountability of management and shareholder rights in order to protect themselves against expropriation of wealth by controlling investors. A convincing and satisfactory response to these demands will decrease the risk of holding stocks and in turn lower the cost of capital.

La Porta et al. (1997) suggested that protection of foreign investors has a positive impact on market development.

Aggarwal et al. (2008) find that policies both on the country-level such as better accounting disclosures stronger shareholder rights and legal framework and firm-level related to greater transparency and disclosure are positively associated with mutual fund investment in emerging markets in the post-crises period.

Han and Signal (2000) also pointed that policy makers of emerging markets require benefits weighed against various uncertainties associated with the opening of markets. According to Signal (2000), one issue of major concern is the movement of so called hot money which is an international flow of funds with highly sensitivity to differences in interest rates, expectations of future economic growth, and expected returns from holding stocks. Given the sensitivity of these investments, even a small shock to the economy can lead to a volatile change in fund flows, which exacerbates the shock and destabilizes the domestic economy. Additionally, Signal (2000) concluded that opening markets means an exposure to foreign influence. If the stock prices of foreign markets are for some reason more volatile than domestic market, domestic stock prices may also become more volatile. A greater volatility in stock prices would make investors more averse to holding stocks and lead them to demand a higher risk premium therefore implies a higher cost of capital and less investment.

As an important part of QFII, multinational financial institutions have disastrous losses in the sub-prime crisis. In the year of this crisis, their short-term behaviour impact significantly to China’s stock market and there is no evidence that such impact will reduce (L. Sun, 2008).

2.3 Investment Preference

Tang et al. (2008) studied the listed companies from 2004 to 2007 in Shenzhen stock exchange. They found that companies with high proportion and quantity of institutional shareholders usually have high transparency of information. However, these institutional investors prefer short-term investment since their long-term investment do not base on the level of transparency.

Lei (2009) found that institutional investors have herd behavior during their decision making.

2.3.1 Corporate Performance

Ye (2009) pointed that QFII prefer to focus on corporate performance and are good at finding blue chip companies. They are ‘Value Discoverer’. However, QFII are reluctant to participant in corporate governance. So they are not ‘Value Creator’.

Jiang (2008) compared QFII with domestic funds and individual investors and found that in the promoting period, their investing preferences contain significant differences. Domestic funds prefer large cap stocks yet QFII tend to small cap. He also found that investors from North America and South-east Asia prefer firms with large total assets, low debt ratio while European investors prefer small capital size with high liquidation.

Pan (2010) suggested that QFIIs prefer high leverage companies in China’s A-share market, yet they require high quality cash flows therefore reduce the liquidity risks generated through the financial leverage. Nevertheless, QFII do not concern much about companies’ return on assets but require a higher net margin than the market index. Profitability on main business is a more important criterion for share selection compare with total assets and profitability of net assets. Operation efficiency is another factor that influences QFII’s decision on an investment.

Furthermore, companies invested by QFII have a strong sustainable growing capability which reflected in high growing of equity, main business and earing after tax rather than the growing of total assets. QFII’s holding companies have high cash-to-debt ratio which means they maintain sufficient cash flows.

According to Yang (2008), there are four characteristics of QFII’s selected firms. Firstly, they have high market value and sound liquidity, usually they are large firms yet small and medium enterprises are still under QFII’s consideration. Secondly, the firms have high transparency of the management and good communication with the investors. Thirdly, they have strong profitability and reasonable share price respect to their profitability. Finally, they usually in the industry which has a high correlation to economic growth and the companies are in the leading position in their industries.

In his research, Yang (2008) discovered six findings:

The investing performance of QFII is much ahead to the market index. However, the superior return of QFII is not significant compared to the domestic equity funds.

Accurate predict of major trends and the hit point of the market.

Due to the performance based investing strategy and With the pursuit of market dominance, target companies hold by QFII have much better earning per shares than the market average and .

As the overall level of profitability in the market decrease, earning per share of QFII’s holdings also decline nevertheless the downward trend is much smoother than the index.

QFII always make simultaneous response to the changes of government policies and macro-economic environment which are closely related to the movements of stock price.

Most of the QFII’s business are long-term investments yet still contain some short-term speculates. They prefer combination of investment and speculative strategies.

2.3.2 Industries

According to the published history information of QFII and listed companies, the traditional periodic industries are QFII’s first preference. Buying at the highest P/E ratio and selling at the lowest P/E ratio, QFII therefore make profit based on the periodical volatility of the stock.

Tang’s research (2008) showed that the trading strategies of QFII in China’s A-share market do not fully adhere to the concept of long-term investment. QFII neither improved investment philosophy in China’s A-share market nor reduce the speculative habits of domestic investors. On the contrary, QFII gradually adopt China’s domestic investment environment and made some changes in trading strategies and investment tactics.

Tang (2009) suggested two reasons for these changes. Firstly, currently QFII in China’s market are lack of the power and the market has some limitations for them to achieve their value-based trading philosophy. Secondly, the major part of QFII in this market is investment banks and commercial banks which pursuing maximizes benefits rather than those truly professional investors who focus on operating assets.

Main holdings of QFII distributed in industrials, manufactories, metals, transportation industry, petrochemical, retailing and food industry. In his 5 years’ observation, Wang (2010) found QFII prefer these industries and their holdings’ proportions in such industries are stable. This indicates QFII have continually and consistently preference of these industries.

Characters of the holding companies:

large market value associated with strong competitive ability, high liquidation and leadership in the industry

strong profitability

valuation discount: main holdings of QFII have 15%~20% relative valuation discounts therefore obtain superiority of relative valuation

main holdings reflect QFII’s concerning on firms’ growth

QFII do not concern on firms’ solvency

Wang (2010) concluded that QFII’s industry allocation concentrates in finance and Insurance, industrials, metals and transportation industry. Three major industries rise from 50% at the beginning of observation to 70% at the ending. QFII also concern firms’ profitability, growth ability, capital structure and valuation. Large listed companies therefore are QFII’s first preference.

2.3.3 Capital Structure and Location

Zhou (2007) suggested that capital structure has significant influence on QFII’s investment. He pointed that establishment of the Audit Committee, board size, proportion of managerial ownership, the proportion of the big shareholder and the company size have a significantly positive impact on the QFII’s investment. However, the proportion of independent directors and meetings of board of directors do not influence QFII’s investment.

Harri Kinnunen (2004) found that the criteria for institutional investors to choose a capital fund is not the necessarily of diversification benefits. Instead the geographical reason is the most important.

3. Data and Methodology

3.1 Data collection

Data was collected from CCER database and the website of Jinrongjie (

The sample size is 1281. The data was consisting of 3 parts. The first part of the data focused on the 1281 stocks and can be divided into 2 subsets. One subset is the top 10 stocks on each quarter hold by 5 QFII which have been continually investing in A-share market in the time interval of 16 quarters (from September 2004 to September 2008). The 5 QFII are Citigroup Global Markets Limited, Morgan Stanley & Co. International Limited, Merrill Lynch International, the Hong Kong and Shanghai Banking Corporation Limited and Credit Suisse (Hong Kong) Limited. Over the 16 quarters these 5 QFIIs hold 183 different stocks in total. Excluding 27 stocks no longer existing or lack of reliable data the subset size therefore is 156. The other subset is stocks that not hold by these 5 QFII (1125 in total). The sample size therefore is 1281.

The second part of the data indicated the financial ratios of the 1281 companies in the A-share market. This part focused on the firms’ performance including total assets, profitability, debt ratio, current ratio and risk ratio.

The third part focused on the industries, capital structures and areas of these firms in the sample.

The classification of the industries is based on Global Industry Classification Standard (GICS) which is an industry taxonomy developed by Morgan Stanley Capital International (MSCI), and Standard & Poor's (S&P) for use by the global financial community (Wikipedia, 2011). The GICS structure consists of 10 major sectors which are Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Utilities and Telecommunication Services.

The capital structure is classified as State-owned, Private-owned and other based on the CCER’s categories of ultimate controller type.

The locations of the firms are geographically divided into three major areas (Eastern China, Middle China and Western China, respectively).

3.2 Methodology

The major issue is to analyse the factors that influence QFII’s decision making when investing in A-share market. The decision making process of QFII contains 2 steps.

1. Decide whether or not to invest in a specific stock

2. Decide how much to invest in this stock

The main idea is using regression analysis to test the relationship between the dependent variable and the independent variables. The factors can be classified by 4 major aspects which are corporate performance, capital structure, industries and locations as mentioned before. Since there are 2 steps in QFII’s decision making process, the sample therefore was divided into 2 groups: one is stocks that hold by these 5 QFII and another is stocks not hold by QFII in the sample.

To analysis the 1st step, the significance level of the independent variables between the groups will be tested. And to analysis the 2nd step, factors within the group of stocks hold by QFII will be tested.

3.2.1 Model and Dependent Variables

In order to find out the solution, a regression model is employed as following:

This regression model was employed for both of the 2 steps with different definition of Y. In step 1, in order to discover the relationship of these independent variables and whether or not QFII hold the stock, Y was defined as 0 and 1 dummies.

In step 2, to investigate deep further within the group of stocks which hold by QFII, Y was defined as the natural logarithm of holding size of the stock.

is the constant, are the coefficients of the independent variables.

3.2.2 Independent Variables

The 1st to the 5th factors are associated with corporate performance in the aspects of total assets, profitability, debt ratio, current ratio and risk ratio (LogTA, ROA, Beta, DR and CR coefficient respectively). The following table represents the meanings and specifications of the first five independent variables.

Table 3.1




Capital Size of the firm

natural Logarithm of Total Assets

Profitability of the firm

Risk ratio of the firm

sensitivity of the stock's return to market returns

Debt ratio of the firm

Current ratio of the firm

The 6th to 14th independent variables represent the industry of the firm which are Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology and Utilities.

The GICS structure consists of 10 major sectors but the industry factors in this model contain 9 of them to avoid the dummy trap.

In addition, if stock is in the industry of Telecommunication Services, then

The 15th and 16th factors stand for capital structures which are State-owned and Private-owned. The 17th and 18th independent variables represent the location of the firm which are Eastern China and Middle China. Same as in the industry part, 2 of the 3 capital structure factors and location factors are included in this model to avoid the dummy trap (the other structure of capital can be present as =0 and =0, the location of Western China can be represented as =0 and =0).

3.3 Hypothesis:

3.3.1 Hypothesis for the first step

When deciding whether or not to invest the stock:

1. QFII prefer firms with large total assets

2. QFII do concern about the target firms’ performance: they prefer high profitability and low risk

3. They prefer some specific industries

4. They prefer private-owned enterprises

5. QFII like to invest in Eastern China

3.3.2 Hypothesis for the second step

Similar with hypothesis in the section 3.3.1, when considering the holding size of stock:

1. QFII prefer firms with large total assets

2. QFII do concern about the target firms’ performance: they prefer high profitability and low risk

3. They prefer some specific industries

4. They prefer private-owned enterprises

5. QFII like to invest in Eastern China

The hypothesis will be tested using the software STATA. For step 1 the Logistic Regression will be employed and for step 2 the software will do a linear regression.

4. Results

4.1 Data Analysis

Table 4.1: Industries capital structures and locations












































Results of data analysis in step 1

Table 4.2: Statistics of corporate performance factors





































Results of data analysis in step 2

Table 4.3: Statistics of corporate performance factors





































4.2 Factors’ Correlations

Table 4.4: Correlation of corporate performance factors






















































































































Table 4.4: (cont.)












































































Table 4.4: (cont.)


































4.3 Regression Results

Result of the regression analysis of step 1 and 2 in STATA (logistic regression and linear regression):

Table 4.5: coefficients and significance

Dependent Variables: selections of stocks in step 1 and holdings in step 2


Step 1

Logistic Regression

Step 2

Linear Regression






























































Observation Number



Note 1: step 1 employed logistic regression, step 2 employed linear regression

Note 2: * represent 10% level of significant, ** represent 5% level of significant and *** represent 1% level of significant

5. Discussion

5.1 Data Analysis

From the year 2002 China introduced the policy of QFII, both quantity and quota of QFII promoted significantly. Figure 5.1 shows that the number and quota of QFII have increased by nearly ten times since the initial date.

Figure 5.1

The following graph shows the top ten popular locations among the 5 QFII’s holding companies in China’s A-share market. These ten locations share more than 80% of total investments from the 5 QFII.

As shown in Figure 5.2, Guangdong and Shanghai are the two most well liked provinces among QFII’s investments.

Figure 5.2

The following three figures (Figure 5.3 to Figure 5.5) show the distributions of industries, capital structures and locations of the firms in the sample (the large circle) and firms hold by the five QFII (the small circle). Clearly, firms from the sample and the subset have similar distributions in all these aspects. Major industries are materials, industrials and consumer discretionary (more than 70% in total). Over 75% of the firms in sample are state-owned and 66% in subset. Most firms located in Eastern China (more than 60%) and the proportion of firms from central and west are close (around 20%).

Figure 5.3

Figure 5.4

Figure 5.5

Note: the inner circles represent the subset of stocks hold by QFII and the outer circles represent the sample.

The average total assets of the firms in the sample are 9.96 billion Yuan however the average total assets of the firms hold by QFII in the sample are surprisingly lower (9.957 billion Yuan).

5.2 Factor Analysis

Table 4.2 shows the statics of corporate performance factors including mean, median, max and min value and standard deviation in the model of step 1. Since the differences of means and medians are relatively small, all these factors especially LogTA, ROA and Beta can be regarded as symmetric. The standard deviation of debt ratio is relatively large compare with other factors (all close to 1).

Table 4.3 is similar to Table 4.3 but with the statistics from step 2. Only the value of LogTA, ROA and Beta changed due to the sharp decrease in observations. However, the changes are extremely small that the statistics of factors in step 2 can regard the same as in step 1.

As shown in Table 4.4, all the correlations between the independent variables are all smaller than 30% therefore these factors can be regarded as independent to each other therefore are acceptable for the model.

5.3 Regression Analysis

Table 4.5 presents the results of the regression including coefficients, standard deviation and level of significance of the independent variables. Observations in the model of step 1 are 1281 and in model of step 2 are 156.

5.3.1 Analysis of Step 1

In the first step, since the dependent variable Y is binomial, the logistic regression was employed. The result turns out that neither the industry part nor the location part in this model has any significance. Two out of five factors in the section of corporate performance are significant. The factor of total assets is significant at 1% level and ROA has 10% level of significance. The coefficients of these two factors are positive yet small. In the part of capital structure, factors of state-owned and private-owned have significant level of 10% and 5%, respectively. The coefficient of determination (R-square) of this model is not very big (12.04%).

The regression results of step 1 indicate the following situation: before QFII make decisions on whether to invest in a firm in A-share market they would consider capital size, profitability and capital structure of this firm. On the contrary, QFII do not care about the firm’s risk ratio, debt ratio and liquidity. The firm’s industry and location are also ignored by QFII when they assessing the investment.

Therefore, in section 3.3.1, Hypothesis 2 does not hold since they do not care about the risk level of target firms. QFII also ignored industrial and locational factors. Although QFII do concern firms’ capital structure, they do not like private-owned enterprise since the coefficient is negative.

To conclude, in section 3.3.1, only Hypothesis 1 is hold.

5.3.2 Analysis of Step 2

In the second step, the dependent variable is the natural logarithm of the capital size hold by QFII. The holding size (capital size hold by QFII) is carried out by multiplying the total assets of the target firm and the percentage of shares hold by QFII.

In step 2, the R-square rise to 25.52% therefore the model becomes much reliable and is able to explain the data perfectly. In the second model, LogTA no longer significant while ROA increase its level from 10% to 5% yet still with a small coefficient. Beta in step 2 gains great importance (1% significance) and has a negative coefficient. Current ratio also becomes significant with negative coefficient. In the section of industry, the factors of financials and utilities get significant at 5% and 10% with positive coefficients. The part of locations is still not significant at all.

These results demonstrate that under the circumstance of choosing a company to invest but have not decided how much to invest yet, QFII would consider the firm’s profitability, risk sensitivity, and current ratio. They also prefer firms from the industries of financials and utilities. However, they do not concern about firms’ capital size or locations.

Therefore, in section 3.3.2, Hypothesis 2 and 3 hold.

5.4 Summarize

To summarize, the results from the two steps of regression analysis imply the following outcomes: firstly, when QFII are like to invest in China’s A-share market, they will first to examine the target firms’ corporate performance, capital size and structure. QFII prefer firms with large total assets and strong profitability but not concern about the firm’s risk ratio in this process.

Secondly, after deciding to invest, QFII will assess the target firms’ profitability, risk sensitivity and current ratio. They prefer strong profitability associated with low risk and current ratio. During this process, firms in financial and utility industry will gain more investment from QFII than in other industries.

6. Conclusion

6.1 Conclusion

China has been making efforts to increase freedom and efficiency of capital market. The introduction of QFII is the most significant initiative allowing the opening of the market and at the same time preventing foreign investment risks and protecting the financial system. Since 2002 China first launched the policy of QFII, both the quantity and quota of QFII increased remarkably. The quantity of QFII increased from 12 to 88 (from 2002 to 2009) and the quota of the QFII rise from 0.35 billion in 2003 to 18.97 billion in 2010 (in US dollar).

Various literatures investigated the relationship of QFII and the market such like whether QFII help improving China’s market efficiency, QFII and market integration and regulation environment. Other papers also studied QFII’s selection preferences in different subjects such like corporate performance, industries and shareholder characteristics. However, all of these literatures researched these factors separately. Nevertheless, few of them did quantitative analysis on aspects of corporate performance, capital structure, locations and industries altogether.

This paper investigated QFII’s investing preference of firms listed on China’s A-share market and focused on the four major aspects as mentioned before by the regression method. The total sample is 1281 stocks including 156 stocks hold by 5 QFII in the time period of 16 quarters (from September 2004 to September 2008). All these 5 QFII have continuously invested in the A-share market.

According to the original data, the most popular major industries among QFII’s investment are materials, industrials and consumer discretionary (over 60% in total). Also, 60% of QFII’s holding companies are state-owned and located in Eastern China.

From the two regression steps, it can be concluded that QFII consider profitability, capital size and capital structure as standards of selecting an investment. After this procedure, for those firms under QFII’s consideration, strong profitability, low risk sensitivity and low current ratio can attract QFII’s investment.

To conclude, QFII have some preferences on whether to invest in a firm or how much to invest in a firm. But the standards of these two procedures are different. In the previous decision making process QFII concentrate on the firm’s profitability, capital size and structure. In the second decision making process QFII concern more about the firm’s profitability, risk ratio, current ratio and the firm’s industry. These results show profitability is the only standard that used in both these two processes of QFII’s decision making.

6.2 Limitation and Recommendation

Although the model in the second step is reliable, the first model is relatively unreliable due to its small R-square. This may cause by outliers in the data. Furthermore, the dependent variable in the second model is the average value. Therefore, the link of the dependent variable to independent variables in the time serious may be reduced. The original data may also contain some error due to the different sources. Nevertheless, the main problem is that QFII itself is a relatively new issue in China and the database therefore is small.

For further investigation, it will be better if the dependent variable is produced as the lag of holding size in small time intervals. For instance, let. However, this method requires improvement on quality and quantity of the data which currently is impossible due to shortage of QFII’s data. Nevertheless, with QFII continuously investing in the market the database of QFII will be enlarged. Future research can get benefit from this improvement.


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Tian, G. (2007) “Are Chinese stock markets increasing integration with other markets in the greater China region and other major markets?” Working Paper, University of Wollongong.

Wang, W (2009) “Sector allocation and stock preference of QFII” Modern Management Science No. 4: 51-53.

Wang, Y. (2009) “Comparison Study on Investment Behaviours of QFII and Domestic Fund in China” Working Paper. Shanghai Jiao Tong University.

Wikipedia (2011) “Global Industry Classification Standard” [online] (Updated 15 December 2010) Available at: [Accessed 25 April 2011].

Wikipedia (2011) “Qualified Foreign Institutional Investor” [online] (Updated 18 April 2011) Available at: [Accessed 10 April 2011].

World Bank (2011) “GDP (current US$)” [online] (Updated 12 February 2011) Available at: [Accessed 15 March 2011]

Yang, S. (2008) “Analysis and evaluation of QFII’s investment style” Economic and Trace Update Vol. 6 No. 114: 119-201.

Ye, D. (2009) “QFII Ownership and Corporate Performance” Financial Accounting (March): 87-89.

Yin, L. (2009) “An Empirical Research on the Relationship between Institutional Investors and Stock Price Synchronicity” Working Paper, University of Xiamen.

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