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Financial liberalization can contribute to the growth & the development of liquid financial markets- illustrate & explain.
In order to meet the needs of a rapidly growing economy, the Mainland Government has introduced ambitious and wide-ranging reforms in recent years to liberalize its financial system. These reforms include, among others, deregulating the banking sector, refining the inter bank funding markets, opening up the domestic financial markets to foreign investors, allowing domestic investors to participate in financial markets overseas, and introducing a new mechanism to allow greater exchange rate flexibility. In this paper, we study an important aspect of the reforms, i.e. their impact on financial market liquidity with a focus on stock trading.
In the literature, the relationship between financial liberalization and economic growth has been extensively studied and so there is no shortage of empirical studies supporting that the former positively impacts the latter.<href=”#_ftn1″ name=”_ftnref1″ title=””> However, the evidence is less apparent as to how a more liberalized financial system delivers growth. In theory, more liquid stock markets can induce more researches on firms, given the incentives to profit from new information (Boot and Thakor, 1997); more liquid stock markets can stimulate greater corporate control, leading to more efficient resource management of firms (Stein, 1988); and more liquid markets can better facilitate the channeling of savings into long-term investment, increasing capital allocation efficiency (Levine, 1991). Hence, a possible channel that financial liberalization can contribute to growth is that it helps develop more liquid financial markets.<href=”#_ftn2″ name=”_ftnref2″ title=””>
. FINANCIAL REFORMS
I. FINANCIAL REFORMS IN CHINA
Prior to the adoption of the open-door policy in 1978, the financial system in China was a closed and centralized one (Chan, 1993). In the absence of financial markets, the financial system was equivalent to the banking system, one that consisted of only one bank, the People’s Bank of China (PBoC). Below the national headquarters of PBoC in Beijing was a network of regional headquarters, branches and rural credit cooperatives spreading over the country. The responsibility of the Bank was to perform banking functions in a centrally-planned economy, such as allocating credit, and granting subsidies and aids to nationalized industries and other production units.
Four commercial banks were subsequently established after 1978 and the PBoC was formally given the central bank status in 1984. The banking system then started resembling that of a modern economy. Following this major evolution, trading or exchange of funds among the commercial banks became necessary, which gave rise to an informal interbank money market in 1986. This was followed by the establishment of other financial markets and infrastructure over the subsequent ten years, a period that can be referred to as phase one of financial liberalization. In this phase, stock exchanges were established in 1990, the foreign exchange market in 1994, the primary government bond market in 1996, and the interbank bond market in 1997.
Financial reforms continued in the second phase from 1997 to 2007. This round of financial reforms differs from those in phase one as it focused more on (i) deregulation of the banking sector; (ii) refinement to and enhancement of the markets and infrastructure established earlier; and (iii) integration of the domestic financial markets with the rest of the world. Measures in this phase ranged from recapitalizing the state-owned commercial banks, restructuring their operations, introducing strategic investors to these banks, listing their shares in the stock exchange, allowing Chinese firms to open foreign exchange accounts, permitting domestic residents to buy B shares, introducing the QFII and, more recently, the QDII, refining the interbank money market, introducing flexibility to the exchange rate system, and encouraging domestic entities to issue bonds. Table 1 presents a chronology of the most significant financial reforms over the past two decades.
As a whole, the reforms in phase two aimed at increasing efficiency through reducing regulations as well as increasing interactions between local and overseas market participants. As these reforms are the ones that had really made financial markets in China more liberalized, phase two is the focus of this study.
II. FINANCIAL REFORMS IN INDIA
India’s financial and capital market reforms since the early 1990s have had a positive impact on both the banking sector and capital markets. Nevertheless, the capital markets remain shallow, particularly when it comes to differentiating high-quality firms from low-quality ones (and thus lowering capital costs for the former compared with the latter). While some high-quality firms (e.g., large firms) have substituted bond finance for bank loans, this has not occurred to any significant degree for many other types of firms (e.g., old, export-oriented and commercial paper-issuing ones). This reflects the fact that most bonds are privately placed, exempting issuers from the stringent accounting and disclosure requirements necessary for public issues. As a result, banks remain major financiers for both high- and low-quality firms. The paper argues that India should build an infrastructure that will foster sound capital markets and strengthen banks’ incentives for better risk management.
. TESTING THE RELATIONSHIP BETWEEN LOANS AND
Prior to the 1992 market reforms, the pricing and volume of corporate securities were controlled by the Government; IPO requirements were loose in the absence of adequate accounting, disclosure and listing requirements; and all securities were treated at par regardless of firm size, liquidity, floating stock, trading volume, performance, etc. In order to improve the infrastructure needed to develop a sound capital market, the Government empowered the Securities and Exchange Board of India (SEBI) as a regulatory body in 1992. In the same year, SEBI published guidelines on equity issues that enabled issuers to price their primary issues freely, generating the first stock market boom in 1993-1995. Moreover, the National Stock Exchange (NSE), the first nationwide screen-based stock exchange, was established in 1994, intensifying competition among the existing 22 stock exchanges. In 1995, NSE formed the National Securities Clearing Corporation Ltd. to eliminate counterparty and payment risks. The National Securities Depository Ltd., set up in 1996, dispensed with the need for physical share certificates by setting up a system of computer records of ownership of securities. SEBI allowed the entry of foreign institutional investors to the capital market in 1992 and introduced the Takeover Code in 1994 as well as further deregulation subsequently. Stricter entry and disclosure norms were introduced in 1996. Compared with the equity market, whose market capitalization accounts for more than 50 per cent of GDP, the corporate bond market remains small, with the share of outstanding corporate bonds issued standing only at about 3 per cent even today. Further, private placements account for 90 per cent of public debt issues.
I. RELATIONSHIP BETWEEN BANK LOANS AND BOND FINANCE
The existing literature on finance suggests that compared with bank loans, bond finance are less effective at minimizing agency problems and improving corporate control. This is because even if bond covenants are inefficient (for example, allowing unprofitable projects to continue or profitable projects to be terminated), corporate bondholders, unlike banks, lack the ability to respond flexibly to ensure better resource allocation (Berlin and Loeys, 1988). The bond market disciplines issuers mainly through bond covenants, which are written in terms of readily observable indicators of the firm’s ability to repay. Further, renegotiation of corporate bond agreements is difficult and costly compared with bank loan agreements, since a change in covenants must be approved by bondholders through collective representation clauses (for example, in the United States, changes are permitted to covenants if two thirds of bondholders agree). The wider the bond ownership, the more difficult renegotiation becomes. Generally, calls for renegotiation are considered to be less likely for high-quality firms than low-quality ones because the former are perceived to have better performance and management. Also, greater access to the bond market is assured by a high reputation as a diligent payer of debt services based on previous bank-borrower relationships and the readiness to standardize information. To test whether high-quality firms, compared with low-quality ones, have easier recourse to bond issuance and have shifted away from bank loans, the model uses each firm’s borrowings from banks as a percentage of total liabilities as a dependent variable. As explanatory variables, the interaction-variables are used between outstanding bonds issued by firms as a percentage of total liabilities BOND and dummy variables derived from the firms’ characteristics. Dummy variables are related to the following high-quality firms: large HASSET, profitable HROA, low-risk LRISK, older OLD, CP-issued CPD, publicly listed, and export-oriented HEXPORT. HASSET is equal to 1 if a firm has above-average assets and 0 otherwise; HROA is equal to 1 if a firm has above-average profitability and 0 otherwise; LRISK is equal to 1 if a firm has below-average variance of ROA and 0 otherwise; OLD is equal to 1 if a firm was incorporated before 1991 and 0 otherwise; and HEXPORT is equal to 1 if a firm has above-average exports as a percentage of sales and 0 otherwise. As high-quality firms are expected to be more active in substituting bond finance for bank loans than low-quality ones, the signs of these interaction variables are expected to be negative, given that declining interest rates make it attractive for firms to issue bonds. In addition, two proxies for firms with large intangible assets are introduced. HDEPSALE is equal to 1 if a firm has above-average depreciation expenditure as a share of sales (thus, above-average tangible assets) and 0 otherwise. LADVSALE is equal to 1 if a firm has a below-average sum of marketing and advertising expenditures as a share of sales (thus above-average tangible assets) and 0 otherwise. As for the signs of the coefficients of BOND X HDEPSALE and BOND X LADVSALE, existing theories do not say much about the relationship between bank loans and bond finance. Banks may require fixed assets as collateral on firms, while bond investors may pay higher prices for issuers with sufficient collateral. In such a case, the coefficients of BOND X HDEPSALE and BOND X LADVSALE are positive since firms with a large amount of tangible assets may have access to both bank loans and bond finance, compared with those that have a small amount of tangible assets.
The regression estimation is performed using the OLS method based on the same database for the two periods. The estimation results as reported in the coefficient of BOND X HASSET turned from being statistically insignificant in 1992-1996 to being statistically significant and negative for 1997-2001. This suggests that bank loans and bond finance have become substitutes for each other for large firms compared with small firms in recent years – evidence of differentiation of firms by quality. Second, the coefficients of BOND X HROA turned out, however, to be statistically significant but positive for both periods. This suggests that bank loans and bond finance are complementary for profitable firms relative to unprofitable ones. Moreover, the coefficient of BOND X HEXPORT was statistically insignificant in 1992-1996, but became statistically significant and positive in 199-2001. This indicates that complementarities’ has been strengthened recently for export-oriented firms relative to less export-oriented firms. The complementary relationships for profitable and, recently, for export-oriented firms occur, because banks tend to provide shorter-term working capital, which is not a direct substitute for relatively longer-term bond finance (of between five and seven years). Third, the coefficients of BOND X HDEPSALE and BOND X LADVSALE were statistically significant and negative in 1992-1996, but turned out to be statistically insignificant in 1997-2001. Thus, bank loans and bond finance were substitutes for each other at an earlier stage for firms with greater tangible assets, but no difference was observed between firms with different levels of tangible assets in later periods.
With respect to firms’ borrowings from financial institutions, the main results reported in table 2 are as follows: first, the coefficients of BOND X HASSET and BOND X HROA turned from being statistically insignificant for 1992-1996 to being statistically significant and negative for 1997-2001. This suggests that loans from financial institutions and bond finance have become substitutes for each other for large and profitable firms in recent years – evidence that SEBI’s efforts to improve the market infrastructures have had a positive impact. Second, in addition, the coefficients of BOND X LRISK and BOND X LISTED were statistically significant and negative for both periods. These results suggest that borrowings from financial institutions and bond finance have functioned as substitutes for each other for low-risk firms and publicly listed firms for both periods – evidence of quality differentiation. The fact that financial institutions provide longer-term loans relative to banks may explain why loans from these institutions and bond finance tend to be more interchangeable for large, profitable, low-risk and listed firms. Third, the coefficient of BOND X OLD was statistically insignificant in 1992-1996 but became significant and positive in 1997-2001, suggesting that loans from financial institutions and bond finance have been complementary for old firms relative to new firms in recent years. Moreover, the coefficient of BOND X HEXPORT turned from being statistically significant and negative for 1992-1996 to being statistically significant and positive for 1997-2001. Last, the coefficient of BOND X LADVSALE turned out to be statistically significant and positive in 1997-2001, while that of BOND X HDEPSALE was statistically significant and positive in 1992-1996. Thus, the results for firms with large tangible assets have been mixed.
While some evidence of quality differentiation has been observed in the reform period, the overall weak relationship between loans from both banks and financial institutions and bond finance may indicate that the latter has not yet succeeded in distinguishing high-quality firms from low-quality ones to a substantial degree. This may be closely associated with the fact that most bonds are issued in the private placement market, to which even low-quality firms have access. According to the existing literature, private placement bond finance lies between bank loans and publicly issued bond finance, since private market borrowers tend to be less transparent with respect to their information (Carey et al., 1993). Therefore, the differences between loans and bond finance are subtle. This also suggests that there has been insufficient infrastructure building needed for a sound bond market, so few high-quality firms have qualified to act as public issuers.
II. RELATIONSHIP BETWEEN BOND AND EQUITY FINANCE
When a solid infrastructure allows outside shareholders to distinguish high-quality firms from low-quality ones, the former are likely to increase equity over debt. There are several reasons for this. First, high-quality firms do not need to increase debt in order to signal their truly favourable (e.g., profitable) position to outside shareholders. If the equity market is unable to differentiate between high-quality and low-quality firms because of inadequate disclosure systems, the former have an incentive to increase debt over equity. This is because they know that low-quality firms would not follow them given that the higher marginal expected bankruptcy costs for any debt level would prevent the latter from increasing debt (Harris and Raviv, 1991).
. MODELLING THE RELATIONSHIP BETWEEN FINANCIAL REFORMS AND MARKET LIQUIDITY
The concept of financial liberalization is very broad. It covers a wide range of issues from how freely capital can move in and out of a country to who can buy and sell a certain financial asset. Financial market liquidity is much more specific; it refers to the level of sensitivity of price changes to the level of trading activity. In a liquid market, financial assets are bought and sold without a significant change in prices. Juxtaposing these definitions of financial liberalization and market liquidity does not immediately suggest a direct causal link from the former to the latter. However, we would argue that there is an indirect link between the two concepts. Once people have more freedom to make their own financial choices and decisions, the cost (monetary and non-monetary) of funding economic and financial activities should also be lower than otherwise. We assume hypothesis that a lower cost would stimulate financial market activities and thereby lead to an increase in market liquidity. We choose the share market in Shanghai, one of the most important financial markets, to test this hypothesis. We implement the test by regressing measures of market liquidity on a number of variables we believe are indicators of financial liberalization. We try to cover most aspects of liberalization of the financial system including the capital account, the banking system and the stock market itself. The model is estimated by means of fixed-effect panel regression:<href=”#_ftn3″ name=”_ftnref3″ title=””>
LQ =? + ?KFLOW + ?M2 +?DUAL +?MARKET +?RISK +? +? ———- (A)
LQ =? + ?QFII + ?M2 +?DUAL +?MARKET +?RISK +? +? ————– (B)
Where LQ denotes liquidity, KFLOW short-term capital flow, QFII the investment quota
approved under the qualified foreign institutional investor scheme, M2 money supply M2 as a ratio of GDP, DUAL the market capitalization of the dual-listed A -shares as a share of the total A-share market capitalization, MARKET overall market condition, RISK profile, and subscripts i and t stock i and time t respectively.
LQ is measured in three ways as commonly defined in literature (Kyle, A., 1985). The first is the tightness of the market or the cost of transaction, which can be represented by the size of the bid-ask spread.<href=”#_ftn4″ name=”_ftnref4″ title=””> The second is the depth of the market or the degree of participation, which can be measured by the transaction volume. The third is the resilience of the market, which can be viewed as the power of the market to pull the price back to its, previous or new, equilibrium after a shock; or, alternatively, the speed at which price fluctuations dissipate after a shock (Amihud, 2002). In addition, the fourth measure of liquidity is the composite indicator, which is computed by taking simple average of the inverse of the normalized components of the three liquidity measures discussed above.<href=”#_ftn5″ name=”_ftnref5″ title=””> <href=”#_ftn6″ name=”_ftnref6″ title=””>
KFLOW measures the short-term capital flowing in and out of the economy. This is proxied by the sum of the inflow and outflow of portfolio investment as a share of GDP.<href=”#_ftn7″ name=”_ftnref7″ title=””> Short-term capital flow is an important de facto measure of financial openness of an economy, which should reflect the degree of financial liberalization in terms of how integrated it is with the outside world (Prasad, et al, 2003). This is irrespective of how restrictive the economy may be on a de jure basis. We expect the effect of this variable on market liquidity to be positive.
QFII is the accumulated level of approved QFII investment quota. The quota indicates the degree of freedom as in the maximum amount of investment that foreign investors can make in purchasing and holding domestic financial assets if they consider fit.<href=”#_ftn8″ name=”_ftnref8″ title=””>It is therefore important to note that it does not necessarily reflect how much foreign investor invests in China’s financial markets, let alone in the stock market. QFII is used as an alternative variable to KFLOW because of their co linearity and the fact that it should capture an aspect of the de jure capital mobility. Like KFLOW it should have a positive impact on market liquidity.
M2 is the ratio of money supply M2 to GDP. Reform and deregulation have gained tremendous momentum in the banking sector over the past decade. Both at the wholesale level (e.g., money and bond markets) and retail level (deposit and lending business), the financial system has deepened considerably as a result of greater efficiency of financial intermediation. Due to the lack of a consistent set of data for both money and bond markets and the potential problem of multi co linearity, money supply M2 is our preferred variable of choice. It should have a positive impact on market liquidity.
DUAL is the market capitalization of the dual-listed A-shares as a share of the total A-share market capitalization.<href=”#_ftn9″ name=”_ftnref9″ title=””>The dual-listed A- H shares, representing those companies that have an additional channel to raise capital outside the Shanghai market, are more able to gain investor recognition and access a broader investor base. We take this variable to be an indicator of the degree of integration between the Shanghai and Hong Kong markets and it should therefore impact market liquidity positively.
MARKET is proxies by the level of the Shanghai 180 index and RISK is measured by the 30-day price volatility of the stock concerned. Market liquidity may change as a result of other factors such as market conditions or volatility. Bullish market sentiment itself may lead to more trading activity, while higher volatility may keep investors on the sideline, assuming that they are risk-averse. Hence, the purpose of introducing these two variables to the model is to control for the potential effects of cyclical factors on liquidity.
. EMPIRICAL RESULTS
Models A and B are estimated with monthly data of the 50 constituent stocks of the SSE50 index, covering the period from October 2002 to December 2007.<href=”#_ftn10″ name=”_ftnref10″ title=””> Stock price, volume, number of outstanding shares and volatility data are extracted from Bloomberg; all other data are extracted from CEIC and EMED.<href=”#_ftn11″ name=”_ftnref11″ title=””> Around 4% of observations of selected stocks are missing in the beginning of the period (because some companies were not yet listed) or towards the end (because some stocks were suspended for trading for various reasons). However, this is not a problem from the estimation point of view, as the fixed-effect panel regression model can accommodate unbalanced panel data in the computation of standard errors by incorporating unequal group sizes in the disturbance variance estimator and within-group estimators such that statistics are correctly weighted in the estimation.<href=”#_ftn12″ name=”_ftnref12″ title=””>
The liquidity of the 50 stocks are together with their respectively. As before mid-2005, there was no obvious change in the median measure for the stocks except for the bid-ask spread, which showed an upward trend from around the beginning of 2004. Between mid-2005 and mid-2007, the average liquidity increased significantly on all measures before tapering off towards the end of the period under study (or even fell in terms of transaction volume). Overall, it is quite clear that the liquidity of the stocks, on average, increased in the period as a whole.
In the estimation, cross-section weight is applied to correct for the presence of cross-section heteroskedasticity. Robust standard errors are computed by the White diagonal method so that the estimator is robust to different error variances in each stock or time period.
The three different liquidity measures, highlighting different aspects of market liquidity, respond similarly to the financial reforms under study. All explanatory variables are significant and have the hypothesized signs for most definitions. Only when liquidity is defined as the average bid-ask spread as a percentage of the stock price, the coefficient of KFLOW displays a wrong sign but is insignificant.<href=”#_ftn13″ name=”_ftnref13″ title=””> As a whole, the results support the notion that financial liberalization, as indicated by variables KFLOW, QFII, M2 and DUAL, positively impact the liquidity of the stock market in China. In other words, a closer financial linkage with the rest of the world and rapid financial deepening enhance the liquidity and efficient functioning of its stock market.
The two control variables, MARKET and RISK, also capture a positive impact on the liquidity. Our results are consistent with the argument that better market performance promotes market participation, which in turn increases the trading activity and market liquidity. The direction of the trading order flow can also be explained by the relative riskiness of the stocks. Based on our results, higher market activity and liquidity are associated with stocks that experience a higher volatility. This suggests that Mainland investors tend to trade more heavily on riskier stocks, which is consistent with the finding of Fong, et al (2007).
. STOCK DUAL-LISTING AND STOCK MARKET INTEGRATION
As most overseas markets are more liberalized and cross-border capital flows are generally unrestricted, other empirical studies do not share exactly the same focus as ours. However, a growing volume of empirical studies on the effects of stock exchange mergers and acquisitions on market efficiency and liquidity has produced similar results.
In Europe, research has found that the creation of the pan-European exchange, Euronext, for instance, has been followed by significant improvements in the market liquidity and efficiency.<href=”#_ftn14″ name=”_ftnref14″ title=””>Using bid-ask spread, trading volume and volatilities as liquidity measurements, Pagano and Padilla (2005) show that the resulting integration of cash trading and clearing platform has contributed to an improvement in liquidity. Nielsson (2008) also finds that the integration had significant and positive effects on (i) the liquidity of big firms and firms with foreign sales and (ii) the market share of Euronext.
In the case of China, there are no mergers and acquisitions between stock exchanges. However, the A-H dual-listing mechanism can be seen as the first step that the two markets work together. A wider range of securities, namely the A-shares, available for Hong Kong’s investors and an additional source of capital for Chinese enterprises are the practical outcome under this arrangement. Compared with the forming of Euronext in the European case, the dual listing of A and H-shares can be considered as a weak-form integration to that effect. Our finding that the A-H dual listing mechanism, as proxied by variable DUAL, positively influences all four measures of stock market liquidity is in agreement with what is found in Europe, i.e., integration can lead to improvement in liquidity.
This paper has found that financial and capital market reforms have had positive impacts on these markets. However, the financial and capital markets remain shallow for several reasons. First, firms characterized as being of high quality have increasingly substituted bond finance for bank loans, but this behavior was more prevalent for the relationship from loans from financial institutions to bond finance. The weaker substitution relationship for bank loans reflects their short-term nature as a result of the intervention policies of previous governments. As the reforms make further progress, banks should be expected to lengthen the maturity of credit as they diversify. Thus, a greater substitution relationship is likely to emerge for high-quality firms than for low-quality ones.
Empirical literature on financial liberalization tends to study its impact from the macroeconomic perspective, assessing, for instance, its contribution to economic growth. This paper focuses on the impact of financial liberalization on the liquidity of financial markets. It is important, because an increase in market liquidity not only signifies improvement in economic efficiency as it reduces transaction cost (measure of tightness), but also indicates reduction in vulnerability as it has a price stabilizing effect (measure of resilience). Evidence of positive impact on market liquidity also contributes to the understanding about the linkage between financial liberalization and economic growth.
Second, the overall weak substitution relationship between loans (both from banks and financial institutions) and bond finance for high-quality firms suggests the failure of the largely privately funded bond markets to differentiate firms by quality, because SEBI exempts public issues from stringent accounting and disclosure requirements. Indeed, tighter regulations in the public capital market have encouraged some firms to shift from the equity market to the private placement bond market. Such a regulatory arbitrage merits greater attention and a further improvement of the infrastructure.
Third, while equity finance has become one of the most important financing sources next to loans, the equity market has not proved a stable source during 1990-2001. Firms appear to have taken advantage of the two stock market booms in order to raise funds cheaply, but have shifted away from the market once the boom petered out. Therefore, there has been no steady shift among high-quality firms from loans from banks and financial institutions to equity. This reflects an inadequate infrastructure for a sound capital market despite SEBI’s efforts to strengthen accounting, auditing and disclosure requirements, thereby failing to differentiate between firms of different quality and to enable high-quality firms to issue shares at higher prices than low-quality ones regardless of the boom-bust cycles of stock prices. The poor infrastructure is evidenced by the frequent cases of malpractice and price manipulation. The results of this study reinforce the need for further financial and capital market reforms with an emphasis on infrastructure building.
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<href=”#_ftnref1″ name=”_ftn1″ title=””> See, For instance, in a recent comprehensive study by Hermes and Lensink (2005) covering 25 developing countries, financial liberalization is found to be positively related to private investment and per capital GDP growth. Ranciere, et al (2006) evaluate, for 60 countries, the costs and benefits of financial liberalization to the economy and concludes that the positive impact of financial liberalization on economic growth far outweighs its negative impact associated with the triggering of financial crises potentially.
<href=”#_ftnref2″ name=”_ftn2″ title=””> See, further discussion can be found in Levine (2001).
<href=”#_ftnref3″ name=”_ftn3″ title=””> See, we performed the Hausman test and for six out of our eight models, the results rejected the null hypothesis that there is no misspecification when the random-effect model is used. Although this is only marginally significant for the remaining two models, the fixed-effect model is chosen in the estimation for all our models for consistency sake. The fixed and random-effect panel regression models are discussed in more detail in the Appendix.
<href=”#_ftnref4″ name=”_ftn4″ title=””> See, It should be noted that this variable, which has to be expressed a percentage of the stock price during estimation, may not be a satisfactory measure in the case of China. Due to the presence of institutional control, the bid-ask spread is almost fixed. As a result, this variable often reflects stock price movements; rather than changes in the bid-ask spread itself.
<href=”#_ftnref5″ name=”_ftn5″ title=””> See, The Bank of England (2007) has also calculated a similar summary indicator of market liquidity by taking the average of nine component indicators across five markets (stock, corporate bonds, Gilt, foreign exchange and money markets).
<href=”#_ftnref6″ name=”_ftn6″ title=””> See, the components are the average bid-ask spread over stock price, total outstanding shares over trading volume and absolute value of price change over trading volume.
<href=”#_ftnref7″ name=”_ftn7″ title=””> Portfolio investment and GDP data are available on a semi-annually and quarterly basis respectively. Monthly observations for these variables are obtained by linear interpolation. For consistency with other variables, portfolio investment, which is originally denominated in US dollars, is converted to renminbi in our estimation.
<href=”#_ftnref8″ name=”_ftn8″ title=””> For consistency with other variables, the QFII quota, which was originally denominated in US dollars, is converted to renminbi in our estimation.
<href=”#_ftnref9″ name=”_ftn9″ title=””>See, the total A-share market capitalization is the sum of the market capitalization of the Shanghai A-share market and Shenzhen A-share market.
<href=”#_ftnref10″ name=”_ftn10″ title=””> See, the total A-share market capitalization is the sum of the market capitalization of the Shanghai A-share market and Shenzhen A-share market.
<href=”#_ftnref11″ name=”_ftn11″ title=””>See, www.bloomberg.com, www.ceicdata.com and www.emed.com.hk.
<href=”#_ftnref12″ name=”_ftn12″ title=””> See, See Appendix for more discussions on adjustment for unbalanced panel data.
<href=”#_ftnref13″ name=”_ftn13″ title=””> See, however, this should be viewed with caution. See footnote 4.
<href=”#_ftnref14″ name=”_ftn14″ title=””>
 See, the monetary union in 1999 has since intensified cross-border financial cooperation and integration. Based on the information published in Euronext and NYSE’s websites, Euronext was formed when the stock exchanges in Paris, Brussels and Amsterdam merged. The merged exchange subsequently also integrated with the Portuguese exchange and expanded its operations through acquiring London International Financial Futures and Options Exchange (LIFFE) in 2002. Euronext integrated with New York Stock Exchange (NYSE) in 2007. This cross-continent integration created a combined group, called NYSE Euro next.