Institutional Development and Stock Price Synchronicity...

January 8, 2011

Institutional Development and Stock Price Synchronicity: Evidence from China

Iftekhar Hasan

Rensselaer Polytechnic Institute and Bank of Finland

Liang Song Rensselaer Polytechnic Institute

Paul Wachtel

Stern School of Business New York University

Abstract

China has experienced dramatic institutional development such as the establishment of secure property rights, the enhancement of law enforcement, and the liberalization of political institutions over the last 20 years. The variation across China’s provinces in the level of institutional development provides a unique opportunity to compare the amount of firm-specific information incorporated into share prices, as measured by stock price synchronicity, across distinct institutional environment, but still within one country. Based on a sample of 31 Chinese provinces for the period 1998-2007, we show that stock price synchronicity is lower when there is sounder institutional development in term of property rights protection, rule of law, and political pluralism. We also find that better institutions have a more pronounced effect on stock price informativeness for firms with poor corporate governance measured by higher government ownership and lower foreign ownership.

JEL Classification Numbers: G14; G15; G24; G38

Keywords: Institutions; China; Stock price synchronicity.

Institutional Development and Stock Price Synchronicity: Evidence from China

1. Introduction

A large literature has shown that high firm-specific stock return variation or low stock

price synchronicity can reflect more firm-specific information to investors, which will increase

the efficiency of capital allocation and decrease the cost of capital (Durnev, Morck, and Yeung,

2004; Wurgler, 2000). Further there is a cross country literature indicating that economies with

poor institutional environments have relatively high stock price synchronicity because reliable

firm specific information is not readily available (e.g., Morck, Yeung, and Yu, 2000, Jin and

Myers, 2006, and Fernandes and Ferreira 2008; 2009). However, a potential drawback of these

empirical studies is that firms operating in distinct legal and information environments are also

affected by other country specific characteristics such as diversity in historical experiences. This

complicates the task of isolating the effect of the institutions that provide investor protection

from the effects of other country characteristics. In our study, we overcome this obstacle by

holding constant national characteristics and allowing investor protection provided by better

institutions to vary by focusing on the effect of province-level institutional characteristics such as

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property rights protection and the rule of law on firm-specific return variation within a single

country - China.

More importantly, the exiting literature only focuses on the effects of investor protection

from the perspective of legal and information environment, (e.g., Morck, Yeung, and Yu, 2000,

Jin and Myers, 2006, and Fernandes and Ferreira 2008; 2009). However, political environment is

more important because politicians can change other investor protection characteristics, if they

choose to do so (Pagano and Volpin, 2005). For example, an authoritarian political party can

enact a new legislation anytime and completely change the existing legal environment related to

investor protection. It is especially true in emerging markets because their political environment

is still evolving. In addition, political environment has a direct effect on firms’ stock price

synchronicity because political institutions determine economic policies. Thus, in our paper, we

also try to employs variation across China’s provinces in the level of political environment and

examine how political pluralism influences stock price synchronicity.

Grossman and Stiglitz (1980) argue that the level of informed trading and informative

pricing, which is a central determinant of the level of firm-specific stock variation, is determined

by the cost–benefit trade-off on information collection. Following this rationale, we reason that

the poor institutional development increase the cost of information collection and reduce

investors’ incentives to collect private information. This reduces the information content of stock

prices and increases stock price synchronicity. A priori, there are several reasons why poor

institutional development could lead to less private information collection. First, in provinces

that provide poor property protection for investors, the risk of expropriation by corporate insiders

could make firm-specific information less valuable and informed risk arbitrage is unattractive.

Second, even good regulations regarding information disclosure are often not fully enforced

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when there is poor rule of law. The entrenched managers are able to withhold relevant

information to cover their own self-serving behavior.1 Third, liberal democracy is positively

associated with successful economic liberalization (Zack-Williams, 2001). Thus, the absence of

political pluralism will discourage the entry of investors from other countries or other provinces

who would be willing to collect private information. Fourth, in regions with less democracy,

political events, or even rumors about political events have a significant impact on the overall

economy which will reduce the value of firm-specific information and increase stock price

synchronicity (Morck, Yeung, and Yu, 2000).

Rapid development over the last 20 years has both improved the development of Chinese

institutions but also increased the variation among China’s provinces in the level of institutional

development. This provides a perfect research setting to answer our research questions. In this

paper we explore the link between stock price synchronicity and province-level institutional

characteristics regarding property rights protection, the rule of law and political pluralism. We

use data for 31 Chinese provinces for the period 1998-2007 and show that a firm’s stock price

synchronicity is lower (i.e. the information content of its price is higher) when the firm is located

in a province with a better environment.

In addition, the Chinese stock market has some unique features because there are both

shares issued to domestic investors (A-shares) and two types of shares issued to foreign investors

(B-shares that trade on the Shanghai or Shenzhen stock exchanges and H-shares that are traded

in Hong Kong). Our result is still robust when we use different specifications of stock price

synchronicity measures that include the influence of other markets.

Also, Morck, Yeung, and Yu (2000) find that the relationship between investor protection

and stock price synchronicity is different in emerging markets as compared to developed 1 For a discussion of these issues in emerging markets see Chan and Hameed (2006), Fan and Wong (2005).

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countries. To see if there are any similar threshold effects within China, we divide our sample

into developing provinces with below median GDP per capita and developed provinces and the

regressions are estimated for these two sub samples separately. We find that our results are not

driven by the differences between the sub samples.

Finally, some alternative explanations may drive our main results. For instance, it is

possible that the poor provincial institutions may enhance the development of business group,

industry vertical integration, and operating diversification, which further increase stock price

synchronicity (Khanna and Thomas, 2009). To deal with these issues, we find that better

institutions have a more pronounced effect on stock price informativeness for firms with poor

corporate governance measured by higher government ownership and lower foreign ownership.

These results suggest that the negative relationship between institutional development and stock

price synchronicity is because that the poor institutional development provides less investor

protection. This increases the cost of information collection and reduces investors’ incentives to

collect private information. This further reduces the information content of stock prices and

increases stock price synchronicity.

Our study contributes to the literature in several ways. First, as noted earlier, by using

province level data from one country, we overcome the problem faced by all cross country

studies regarding omitted country characteristics. Our study provides a unique focus on the

effect of province-level investor protection in the single most important emerging country -

China. The measures of institutional development in China at the provincial level were first

developed by Hasan, Wachtel and Zhou (2009) who relate use the data to explain difference in

the growth rate across provinces. Second, in addition to the effects of property rights and law

enforcement, we also examine the effect of another important aspect of institutional development,

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political pluralism, a measure of the strength of democracy, on stock price informativeness

which has not been investigated before. Third, China has the quota system to select companies

from each province for listing or raising additional equity on a stock exchange. Du and Xu (2008)

demonstrate that provinces in which listed firms have more firm-specific information

incorporated into stock prices were rewarded with more stock quotas in the subsequent periods.

We provide an important mechanism to improve stock price informativeness and further increase

firms’ access to capital market by conducting province-level legal reform.

The remainder of the paper proceeds as follows. Section 2 discuses the related literature

and develops our hypotheses. Section 3 presents our sample, measures, data sources, and reports

descriptive statistics. Section 4 describes our empirical methodology and provides the primary

evidence about the relationship between institutional development and stock price

informativeness. Section 5 shows our robustness tests and the final section summarizes our

conclusions.

2. Related Literature and Hypothesis development

From the 1980s, China has followed an incremental and experimental approach to

reforming its centrally planned economy into a market system which has resulted in remarkably

high growth rates sustained for almost three decades (Prasad and Rajan, 2006). Rapid economic

growth both demands and facilitates the development of legal institutions and the evolution of

political systems (Williamson, 1996). There are a few empirical studies of the role of

institutional development in China showing, for example, that the institutional environment

matters for Chinese firms’ reinvestment decisions and for fostering entrepreneurship (Cull and

Xu, 2005; Djankov, Qian, Roland, and Zhuravskaya, 2006).

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An important feature of the evolution of the legal environment in China is that many of

the laws adopted are enacted locally following national legislation so that their implementation

varies from province to province (Krug and Hendrischke, 2003). Such variation is found for

laws and regulations that ensure the protection of property rights (see, Hasan, Wachtel, and Zhou,

2009 for details). Thus, there is information available to examine the effect of property rights on

stock price synchronicity.

Studies with cross country data have established the importance of intellectual property

rights protection for economic growth (Gould and Gruben, 1996; Park and Ginarte, 1997).

Hasan, Wachtel, and Zhou (2009) use the number of trade mark applications in Chinese

provinces to proxy the awareness of property rights and show that it is related to growth rates.

The link between property right protection and the information in stock prices has been

discussed in Morck, Yeung, and Yu (2000). They argue that poor protection for property rights

could discourages informed risk arbitrage because expropriation risk will make such behavior

less valuable and increase their cost of collecting firm-specific private information. The reduced

informed trading will impede the capitalization of firm-specific information into stock prices and

decreases firm-specific return variation. Morck, Yeung, and Yu (2000) provide evidence that the

stocks of firms in economies with poor country-level property rights have relatively high

synchronicity. In our paper, we focus on the effect of province-level property right protection

and our first hypothesis is that:

H1: Stock price synchronicity is lower for firms in provinces with better property rights.

Another notable feature of the evolution of the modern Chinese legal system was the

spread of the rule of law and, specifically, institutional structures for the enforcement of rules

and the settlement of disputes. For instance, in 1995, the State Compensation Law allowed

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citizens to sue the state (Burns, 1999). The functions of the Party and government in the

people’s congress system are separated to strengthen the rule of law (Burns, 1999). In this

process, many members of the legal profession such as lawyers and judges were rehabilitated

which significantly improved the quality of law enforcement (Hasan, Wachtel, and Zhou, 2009).

The effect of rule of law and related issues on firm-specific return variation was explored

by Ball (2001) and Chan and Hameed (2006). Even when there are adequate laws on the

disclosure of corporate information, they are often not fully enforced when there is poor rule of

law. Entrenched managers might find it in their interest to withhold information which increases

the cost of collecting private information and leads to less firm-specific information being

reflected into stock prices. The cross country study by Morck, Yeung, and Yu (2000) related

aspects of the rule of law such as an efficient judiciary to stock price synchronicity. In our study,

we focus on the effect of rule of law across provinces in China and hypothesize that:

H2: Stock price synchronicity is lower for firms in provinces with higher quality of law

enforcement.

Besides the development of the legal system, China’s leaders have long recognized the

need for political reform. While China is still a largely authoritarian communist state, there have

been significant movements towards political pluralism (White, 1993, Chapter 8). For example,

non-party members such as professional experts and members of minority political parties

occupy approximately one third of the seats in the National People’s Congress (NPC), the

highest legislative body in China (Hasan, Wachtel, and Zhou, 2009). This significantly

contributes to elements of pluralism in Chinese political structures and decision making. The

delegates to the NPC are elected by the provincial people’s congresses and the degree of

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pluralism varied across provinces. Thus we will be able to examine the effect on stock price

synchronicity of a more open and pluralistic political structure.

The cross country literature has shown that more democratic political environments are

associated with economic growth (Rodrik and Wacziarg, 2006; Borner, Brunetti, and Weder,

1995). Shleifer and Vishny (1993) and Mauro (1995) document that poor quality of political

institutions is associated with substantial economic costs, especially in developing economies.

Further, the literature shows a positive association between liberal democracy and successful

economic liberalization (e.g., Zack-Williams, 2001).

In the China context, we suggest that greater political pluralism in a province will attract

investors from elsewhere who are willing to collect private information which decreases

synchronicity. In regions with less democracy, political events, or even rumors about political

events will dominate market-wide stock price variation and lead to more stock price

synchronicity. Morck, Yeung, and Yu (2000) linked one aspect of the country-level quality of

government, an absence of corruption, to stock price synchronicity. In our paper, we focus on the

effect of pluralism across provinces in China and hypothesize that:

H3: Stock price synchronicity is lower for firms in provinces with more political pluralism, ceteris paribus.

3. Data

3.1. Sample

Our sample covers the period 1998-2007 and includes all non-financial firms in China

with available stock returns data from Datastream for at least 200 trading days in a particular

year.2 Altogether, the sample includes 1012 firms and 5719 firm-year observations. Firms are

2 We begin in 1998 because the province level data on the institutional environment are not available earlier.

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assigned to the geographical region where the company headquarters are located. The location

of each firm’s headquarters was collected by hand from the prospectus. The distribution of firms

in the sample across the 31 geographical areas is shown in Panel A of Table 1.3 Firms are

dispersed almost evenly across the provinces and not concentrated in a few regions.

3.2. Measurement of stock price synchronicity

Stock price synchronicity is based on market model regressions for each firm of daily

stock returns on the returns of an industry index and the market index. The Chinese market has

some unique features because there are both shares issued to domestic investors (A-shares) and

two types of shares issued to foreign investors (B-shares that trade on the Shanghai or Shenzhen

stock exchanges and H-shares that are traded in Hong Kong).

To begin, we estimate the following market model for each stock and each year using

daily return data:

Returnit = α + β1 IndustryReturnt + β2 MarketReturnt +εit (1)

where Returnit denotes the daily return on A-shares for firm i and day t. IndustryReturnt is the

industry return calculated as a value-weighted stock return of all other firms within the same

industry as firm i (with Returnit omitted); MarketReturnt is the value-weighted A-share market

return for all firms; and εit represents unspecified random factors.

Next, following the approach introduced by Gul, Kim, and Qiu (2010), we estimate

extended market models to account for differences among the various markets where Chinese

shares are traded. For firms with only domestic A-share, the extended model is shown in

equation (2a) which adds the world market returns:

Returnit = α + β1 IndustryReturnt + β2 MarketReturnt +

3 There are 22 provinces, 5 autonomous regions and 4 municipalities. The Special Administrative Regions are not included in the sample.

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β3 WorldMarketReturnt + εit (2a)

WorldMarketReturnt is the world market return that is calculated using the MSCI World index

for day t.

For firms with A and B-shares, we also add the value-weighted B-share market return

BShareMarketReturnt as shown in equation (2b):

Returnit = α + β1 IndustryReturnt + β2 MarketReturnt + β3 BShareMarketReturnt + β4

WorldMarketReturnt + εit (2b)

And for firms with A and H-shares, we add the world market factor and the H-share market

returns as shown in equation (2c):

Returnit = α + β1 IndustryReturnt + β2 MarketReturnt + β3 HShareMarketReturnt + β4

WorldMarketReturnt + εit (2c)

where HShareMarketReturnt is the value-weighted Hong Kong market return.

These market models are estimated for each firm for each year. We use the Ri2 of the

market model estimated for a particular firm in a particular year to measure its stock price

synchronicity. A higher value of Ri2 means higher stock price synchronicity and less firm-

specific return variation. A higher value of Ri2 is also associated with larger estimates of β from

the market model which is often interpreted as risk. Thus, increased synchronicity is the same as

increased risk.

Because Ri2 is bounded within [0, 1], we also use i , a logistic transformation of Ri

2, as

our preferred measure of the annual stock price synchronicity for firm i:

)1

log(2

2

i

ii R

R

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We will denote synchronicity from the original model in equation (1) as Ri2(1) and synchronicity

from the extended models in equations (2a-b-c) by Ri2(2). The corresponding logistic

transforms are )1( and )2( .

3.2. Measures of institutional development

We measure three aspects of province-level institutional development in China: property

rights protection, rule of law and political pluralism. A major challenge in our study is that

direct measures of such institutional development are not available and only imperfect proxies

exist. We follow Hasan, Wachtel, and Zhou (2009) and use similar hand collected proxy

measures from a variety of Chinese sources.

The variable PropertyRights is the number of domestic trademark applications per firm

for a certain province and year. Firms will make use of trade marking if they are confident that

the institutional environment in the province is good enough to actually protect the property

rights that come with the trademark. Thus, a higher value of PropertyRights represents a higher

level of property rights protection. The data on domestic trademark applications is from the

annual issues of the Almanac of China’s Property Rights and the Yearbook of China’s Industrial

and Commercial Administrative, annual provincial yearbooks, and the government-sponsored

trademark website, China Trademark Online. When the data is missing in a certain year, we use

the product between the national data at that year and the proportions of applications in the

province in 1998.

The variable RuleofLaw is the number of lawyers per 10,000 people for a certain

province and year. An increased presence of legal professionals in a province is associated with

both the development of legal institutions and of the mechanisms for law enforcement. Thus, a

higher value of the variable RuleofLaw represents a higher level of law enforcement. The

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number of lawyers in each province is taken from the Statistics Yearbook of China’s Legislation

and the annual issues of the Statistics Yearbooks of each province.4 It is supplemented by

additional information from web based resources, such as the China Law of Lawyering (china-

lawyering.com), China Lawyers Investigation website (www.007cn.cn), and China Lawyers

website (www.chineselawyer.com.cn). If the data is missing, we use the interpolated value based

on nationwide growth in the number of lawyers. Population data are from the National Bureau of

Statistics of China.

The variable PoliticalPluralism as the proportion of non-Communist party members in

the provincial People’s Congress relative to the proportion in the National People’s Congress for

a certain province and year. If the provincial proportion of non-Communist Party members is

higher than the national benchmark at that time, then the province arguably has a more open or

pluralistic political environment. Thus, a higher value of the variable PoliticalPluralism indicates

a higher level of political pluralism. The data on political pluralism is taken from the regional

People’s Congress Yearbooks of each province in China and the Examination and Approval

Reporting Document issued by the Examination Committee of the People’s Congress.

Information on the membership structures of the People’s Congress in six provinces is not

available and data from neighboring provinces with similar political characteristics is used as an

estimate.

3.3. Other variables

The set of control variables includes various characteristics of firms and the macro

environment that are known to influence synchronicity (Chan and Hameed, 2006; Gul, Kim, and

Qiu, 2010). Specifically, we include the proportion of equity held by the government and by

4 They provide data for 1990, 1995 and 2000–2002.

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http://www.007cn.cn/

http://www.chineselawyer.com.cn/

foreign owners (GovernmentOwn and ForeignOwn) for each firm year. The government and

foreign ownership data is from the NUS Business School's database of Chinese listed firms’

ownership structure. We also include dummy variables for the share structure (BShare and

HShare if the firm has issued B shares and H shares respectively). Firm characteristics included

are: a) the annual trading turnover (Volume) which is the total number of shares traded in a year

divided by the total number of shares outstanding at the end of fiscal year; b) Size, the logarithm

of total assets; (c) Leverage, total liabilities divided by total assets; (d) EarningVolatility, the

standard deviation of a firm’s ROA for the preceding five-year period including the current year;

(e) MatketToBook, the ratio of market value of assets to the book value of assets where the

market value of assets is defined as the book value of assets minus the book value of equity plus

the market value of equity; and (f) Log(IPOAge), the logarithm of the firm age since IPO. We

also include two measures of industry size: (a) the logarithm of the number of firms in the

industry to which a firm belongs (IndustryByNumber) and (b) the logarithm of the total assets of

all firms in the industry that are in our sample (IndustryBySize). The stock turnover data are

from Datastream and the firm accounting data are from the Worldscope database. Macro

variables in the set of controls are (a) the annual province-level growth rate in per capita real

GDP (GDPGrowth), and (b) the per capita real GDP (GDPPerCapita). The real annual per

capita GDP for each province is from China Economic Information Network Database. We

provide a table of data definitions in the appendix.

3.4. Descriptive statistics

Summary statistics by province are shown in Table 1. Provinces are ranked by the values

of the three institutional variables in Panels A through C.5 Panel D shows the provinces ranked

5 The values are the mean values for all the available observations of companies in the province.

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by the mean value of stock price synchronicity for the firms from the province measured by

R2(1). Most of the provinces with higher institutional development have low stock price

synchronicity. For example, Gan’Su province has the highest stock price synchronicity.

Moreover it has the lowest value for PropertyRights and the second lowest value for RuleofLaw

and PoliticalPluralism. Bei’Jing has the lowest stock price synchronicity; it has the highest value

of PropertyRights and RuleofLaw and the fourth highest value of PoliticalPluralism.

The trend in synchronicity is shown in Figure 1 which plots the mean value of R2(1) of

Chinese firms in each year. Synchronicity is clearly decreasing over our sample period which is

consistent with the evidence in Morck, Yeung, and Yu (2000) and Campbell, Lettau, Malkiel,

and Xu (2001). Generally, stock price synchronicity decreases as financial markets develop.

Summary statistics for the whole sample are shown in Table 2. All variables are

winsorized at the 1 and 99 percentiles to exclude possible outliers. As shown in the table, the

mean value of R2(1) are 0.491, respectively, while the mean value of R2(2) are 0.433,

respectively. These statistics are similar to the results reported in the sample of Morck, Yeung,

and Yu (2000) and Gul, Kim, and Qiu (2010). The large standard deviations of both R2 and

indicate that there is considerable cross-sectional variation in synchronicity. All of the

institutional variables exhibit a reasonable amount of variation across time and province. Table 2

also shows that, on average, the state shareholder holds 31.0 percent of shares outstanding and

the foreign shareholders hold 6.7 percent of shares outstanding, suggesting that the government

still has a dominant impact on Chinese listed firms.

Table 3 presents the correlations between our province-level and firm-level variables.

The correlation between the two synchronicity measures, Ψ(1) and Ψ(2) is 0.976. Measures of

synchronicity are negatively correlated with property rights (PropertyRights), rule of law

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(RuleofLaw), and political pluralism (PoliticalPluralism). A firm’s stock price synchronicity is

lower when they are located in a province in which there is sounder institutional development in

term of property rights protection, rule of law, and political pluralism. The simple correlations

are consistent with our three hypotheses (H1, H2, and H3).

4. Regression results

4.1. Estimates of synchronicity model

To test for the effects on synchronicity of property rights (H1), of rule of law (H2), and of

political pluralism (H3), we estimate the following equation:

it = α + β1 PopertyRightsit + β2 RuleofLawit + β3 PoliticalPluralismit + (CONTROLit)

+ (YearDummies) + (IndustryDummies) + εit (3)

where it is the measure of stock synchronicity for firm i and year t. The institutional

environment variables PropertyRights, RuleofLaw and PoliticalPluralism are our primary

interests and we expect that β1, β2 and β3 < 0. CONTROL denotes a set of control variables

including the variables GovernmentOwn , ForeignOwn, BShare, HShare, Volume, Size, Leverage,

EarningVolatility, MarketToBook, Log(IPOAge), IndustryByNumber, IndustryBySize,

GDPGrowth, and Log(GDPPerCapita). Finally, the regressions include fixed effects for the

year (YearDummies) and industry (IndustryDummies).

Government related shareholders are more likely to be associated with expropriation risk

and information asymmetry problems compared to foreign shareholders. Thus, we expect the

coefficient on GovernmentOwn to be positive and the coefficient on ForeignOwn to be negative.

Since foreign investors are presumably more skilled at collecting and trading on firm-specific

information, we expect that the variables BShare and HShare are negatively correlated with

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stock price synchronicity. We expect that the coefficient on Volume is negative because more

trading will help incorporate more firm-specific information. We expect that the coefficient on

the variable Size is positive because stocks of large firms are more likely to be aligned with the

whole market. The coefficient on the variable Leverage is likely to be positive because the cost

of collecting private information may be higher for the firms with greater risk of financial

distress. We expect that the coefficient on EarningVolatility is negative because stocks of firms

with higher earning uncertainty have more firm-specific variation. The coefficient of the variable

MarketToBook is expected to be negative because stocks of firms with higher growth potential

will incorporate more firm-specific information. We expect that the coefficient on Log(IPOAge)

is positive because more of the time-invariant firm-specific information such as managerial

quality is already incorporated in the stock price for older firms and firms-specific information

that can be impounded in to the stock price is less available (Dasgupta, Gan, and Gao, 2009).

Estimates of Equation (3) with )1( as the dependent variable are shown in Table 4. The

measures of institutional development are entered separately in the first three columns and are all

included in the last column. Reported t-values are calculated using robust standard errors

corrected for firm-level clustering. This addresses potential biases that may arise from serial

dependency in the data. The measures of institutional development all have the expected

negative impact on synchronicity, either when entered separately or all together. Moreover, the

coefficients are all significant.

The effect of these institutional factors on stock price synchronicity is not only

statistically significant, but also economically relevant. We use the results reported in the last

column of Table 4 to assess the impact of the institutional variables on stock price synchronicity.

A one standard deviation increase in PropertyRights decreases stock price synchronicity (i.e.

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)1( ) by 0.184*0.496 or 0.091 which is about 3 percent of the range of synchronicity (the

difference between the largest and smallest values of )1( is 2.993). A one standard deviation

increase in RuleofLaw decreases stock price synchronicity by 0.088*0.730 or 0.064, roughly 2.1

percent of the range. Finally, a one standard deviation increase in PoliticalPluralism decreases

stock price synchronicity by 0.301*0.119 or 0.156, just over 1.2 percent of the range. Taken

together, these examples underline the importance that different institutional factors have for


All the control variables have the expected signs. The coefficient on GovernmentOwn is

significantly positive suggesting that higher state ownership is associated with less use of firm-

specific information in determining stock prices. The coefficient on the variable ForeignOwn is

significantly negative suggesting that higher foreign ownership increases the use of firm-specific

information. The coefficients of BShare and Hshare are both significantly negative, as

anticipated. This implies that more foreign traders involved increases the information content of

stock prices. The coefficient of the variable Volume is significantly negative. The coefficients of

the variable MartketToBook are significantly negative. This suggests that firms with high growth

opportunity tend to commove less. The coefficient estimates of the remaining variable have the

expected signs, but are insignificant.

4.2. Alternative stock price synchronicity measures?

As mentioned above, the Chinese stock market has some unique features because there

are both shares issued to domestic investors (A-shares) and two types of shares issued to foreign

investors (B-shares that trade on the Shanghai or Shenzhen stock exchanges and H-shares that

are traded in Hong Kong). A-share firms that simultaneously issue B-shares or H-Shares need to

be compliant with International Financial Reporting Standards or Hong Kong Generally accepted

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Accounting Principles instead of domestic accounting standards. Thus, compared with firms that

exclusively issued A-shares, they have stricter financial reporting regulation and their financial

statements are more likely to be audited by international auditing firms. In addition, foreign

investors are more likely to have superior capabilities, resources, and skills to collect and process

firm-specific information (Gul, Kim, and Qiu, 2010). Thus, synchronicity is also measured with

the alternative market model that includes the influence of other markets. Table 5 presents the

regression results using )2( as the dependent variable. The coefficient estimates in Table 5 are

qualitatively identical to those reported in Table 4 and thus support our previous conclusions.

4.3. Is there a threshold effect?

Morck, Yeung, and Yu (2000) find that the relationship between investor protection and

stock price synchronicity is different in emerging markets as compared to developed countries.

To see if there are any similar threshold effects within China, we divide our sample into

developing provinces with below median GDP per capita and developed provinces. To test

whether our results hold within both sub samples or mainly describe differences between the

groups, we estimate synchronicity regressions for sub samples as shown in Table 6. The

regressions are estimated for the high and low GDP per capita provinces separately. The

coefficient estimates are qualitatively identical to those reported in Table 4 across these two sub

samples, which thus support our previous conclusions and indicate that our results are not driven

by the differences between the sub samples.

4.4. Alternative explanations

In this paper, we argue and show that the poor institutional development increase the cost

of information collection and reduce investors’ incentives to collect private information. This

reduces the information content of stock prices and increases stock price synchronicity.

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However, there may be other alternative explanations to drive our main results. For example,

Khanna and Thomas (2009) use a unique dataset in Chili and show that two firms with more

connections are more likely to have synchronized stock returns. Thus, it is possible that the poor

provincial institutions may enhance the development of business group, industry vertical

integration, and operating diversification, which further increase stock price synchronicity. In

addition, it is possible that the regions with stronger institutions have a bigger investor pool. The

investors are more likely to invest in their home province. Thus, the stock price synchronicity is

lower.

To formally address these concerns, we add interaction terms between our institutional

development variables and the state ownership variable to the synchronicity regressions. Gul,

Kim, and Qiu (2010) argue that the state owners are more likely to be related to inefficient

corporate governance and provide less protection for minority shareholders. If, in fact, the higher

investor protection provided by the better institutional environment decreases the cost of

information collection and increases investors’ incentives to collect private information, which

contributes to the incorporation of information into stock prices of firms, we expect that the

effect of institutional development on synchronicity is more pronounced for firms with higher

government ownership and there is a negative coefficient on this interaction variable.

The results in Table 7 show that the interactions between government ownership and the

variables PropertyRights and RuleofLaw are negative and significant. This is true when the

interactions with the three institutional variables are considered individually or when we put all

three variables and the interaction terms into the regression together (column (4) of Table 7).

That is the (negative) effect of institutional quality on synchronicity is more pronounced for

firms with higher government ownership.

19

Next we test whether provincial institutional development has a more significant impact

on synchronicity for firms with lower foreign ownership. To do so, we add interaction terms

between our institutional development variables and the foreign ownership variable to the

synchronicity regressions. Foreign shareholders are less likely to be associated with

expropriation risk and information asymmetry problems (e.g., Kang and Stulz, 1997; Jiang and

Kim, 2004). If, in fact, the higher investor protection provided by the better institutional

environment decreases the cost of information collection and increases investors’ incentives to

collect private information, which contributes to the incorporation of information into stock

prices of firms, we expect find that the effect of institutional development on synchronicity is

more pronounced for firms with lower foreign ownership and there is a positive coefficient on

this interaction variable. The estimates shown in columns (5) to (8) of Table 8 show that the

coefficients on the interactions between foreign ownership and the three institutional

development variables are all positive and significant. This is true when the interactions with

the three institutional variables are considered individually or when all three variables and the

interaction terms are entered into the regression together in column (8) of Table 7. That is

increased foreign ownership decreases the size of the negative effect of institutional quality on

synchronicity.

In summary, we find that better institutions have a more pronounced effect on stock price

informativeness for firms with higher government ownership and lower foreign ownership.

These results suggest that the poor institutional development provides less investor protection.

This increases the cost of information collection and reduces investors’ incentives to collect

private information. This further reduces the information content of stock prices and increases


20

5. Other robustness checks

In this section we preformed on several additional robustness tests that were preformed to

give us confidence that the results already discussed are reliable. In the interest of space the

results are not shown although they are available from the authors on request.

First, estimates of the market model are based on daily returns and a potential bias can be

introduced by non-synchronous trading because some stocks do not trade every day (Scholes and

Williams 1977; French, Schwert, and Stambaugh 1987). In order to account for this, we also

estimated the market models with lagged industry and market returns without any noticeable

effect on the results. In addition, we estimated the market models with weekly rather than daily

data which does not alter our results and conclusions, either.

Second, we varied the windows used to winsorize the data to 2 and 98 percentiles or 5

and 95 percentiles. Our results are still robust to such variation in the size of the data set.

Third, some variables such as Ruleoflaw have interpolated values for certain years. To

ensure that interpolation of this variable does not affect our results, we use the firm-year

observations without those interpolated values to estimate our synchronicity regression. We

obtain similar results and the same conclusions.

Fourth, four large municipalities (Beijing, Chongqing, Shanghai, and Tianjin) are under

much stronger control by the central government than the other provinces. Thus, we re-estimated

the models without these four municipalities, and find that our results are robust.

6. Conclusions

China provides a unique opportunity to examine the influence different institutional

environments regarding investor protection on stock price synchronicity in an area where other

21

country-level characteristics are shared. Specifically, we investigate whether and how stock

price synchronicity is associated with province-level institutional characteristics unique to China.

The province-level institutional variables we examine include property rights, rule of law,

and political pluralism. Our panel study of 31 Chinese provinces for the period 1998-2007

suggests that the development of awareness of property rights, law enforcement, and political

pluralism are associated with more stock price informativeness. We also find that better

institutions have a more pronounced effect on stock price informativeness for firms with higher

government ownership and lower foreign ownership.

Our evidence has some clear policy implications for emerging economies with high stock

price synchronicity. We know from Wurgler (2000) and Durnev, Morck, and Yeung (2004)

among others that the efficiency of capital allocation and investment will be improved if stock

prices incorporate more firm-specific information (i.e. synchronicity is reduced). And our results

show convincingly that institutional improvements are associated with reduced stock price

synchronicity. Thus, our results show a clear channel by which improvements in the institutional

environment are associated with improved economic outcomes.

22

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25

Appendix

Variable Definitions R2(1) The R2 of the market model in equation (1).

R2(2) The R2 of the market models in equation (2).

Ψ(1) Logarithmic transformation of R2(1)

Ψ(2) Logarithmic transformation of R2(2) PropertyRights The number of trademark applications per firm in a province; proxy for the

awareness of property rights.

RuleofLaw The number of lawyers per 10,000 people in a province; proxy for rule of law.

PoliticalPluralism The proportion of non-Communist party members in the provincial People’s Congress relative to the proportion in the National People’s Congress

GovernmentOwn The percentage of shares held by government owner(s) at year beginning ForeignOwn The percentage of shares held by foreign owner(s) (Hong Kong, Taiwan,

other countries) at year beginning Bshare An dummy variable which equals 1 if a firm issues B-shares and 0 otherwise

Hshare An dummy variable which equals 1 if a firm issues H-shares and 0 otherwise

Volume The total number of shares traded in a year, scaled by the total number of shares outstanding at the end of fiscal year

Size The logarithm of total assets Leverage Total liabilities divided by total assets EarningVolatility The standard deviation of a firm’s ROAs over the preceding five-year

period, including the current year

MarketToBook Market value of assets over book value of assets. Market value of assets are measured as book value of assets minus book value of equity plus market value of equity

Log(IPOAge) The logarithm of the firm age since IPO IndustryByNumber The logarithm of the number of firms in the industry to which a firm belongsIndustryBySize The logarithm of year-end total assets of all sample firms in the industry to

which a firm belongs

GDPGrowth Annual growth rate in per capita real GDP GDPPerCapita The per capita GDP deflated to the base year of 1998 Log(GDPPerCapita) The logarithm of per capita GDP deflated to the base year of 1998

26

Table 1

Institutional Development and Stock Price Synchronicity

Provinces are ranked by the mean value of PropertyRights in Panel A. In panel B, provinces are ranked by the mean value of Ruleoflaw. In panel C, provinces are ranked by the mean value of PoliticalPluralism. In panel D, provinces are ranked by stock price synchronicity, measured by the mean value of R2(1). All variables are as defined in the appendix.

Panel A Panel B Panel C Panel D

Province Number of Firms PropertyRights Province RuleofLaw Province PoliticalPluralism Province R2(1)

Gan'Su 27 0.125 Xi'Zang 0.228 Ji'Lin 0.913 Gan'Su 0.812

Hei'Long'Jiang 27 0.168 Nei'Meng'Gu 0.231 Gui'Zhou 0.927 Jiang'Xi 0.756

Xin'Jiang 22 0.168 Gui'Zhou 0.231 Liao'Ning 0.927 He'Nan 0.735

Jiang'Su 57 0.194 Gan'Su 0.231 Gan'Su 0.927 Qing'Hai 0.707

Qing'Hai 22 0.223 Si'Chuan 0.340 Tian'Jing 0.934 Yun'Nan 0.678

Guang'Xi 25 0.225 Yun'Nan 0.340 Guang'Xi 0.981 Guang'Xi 0.657

Gui'Zhou 16 0.248 An'Hui 0.378 Jiang'Xi 0.981 He'Bei 0.645

Hu'Nan 55 0.252 He'Nan 0.395 An'Hui 0.990 Gui'Zhou 0.624

An'Hui 23 0.258 Shan'Dong 0.403 Hu'Bei 0.990 Hu'Nan 0.601

Hu'Bei 21 0.268 He'Bei 0.432 Hu'Nan 0.990 An'Hui 0.589

Jiang'Xi 39 0.281 Hei'Long'Jiang 0.466 He'Bei 0.991 Hu'Bei 0.567

Ji'Lin 19 0.286 Guang'Xi 0.479 Ning'Xia 0.991 Xi'Zang 0.547

Tian'Jing 21 0.287 Jiang'Xi 0.479 Jiang'Su 1.007 Jiang'Su 0.526

Ning'Xia 19 0.324 Hu'Nan 0.483 Zhe'Jiang 1.007 Tian'Jing 0.516

Yun'Nan 37 0.325 Hu'Bei 0.486 Qing'Hai 1.020 Ji'Lin 0.501

He'Bei 22 0.335 Jiang'Su 0.570 Xi'Zang 1.020 Liao'Ning 0.498

Shan'Dong 22 0.364 Fu'Jian 0.609 Xin'Jiang 1.020 Ning'Xia 0.488

Shaan'Xi 28 0.372 Hai'Nan 0.609 Nei'Meng'Gu 1.020 Nei'Meng'Gu 0.478

Liao'Ning 20 0.403 Shan'Xi 0.617 Yun'Nan 1.033 Shan'Dong 0.467

Nei'Meng'Gu 25 0.407 Shaan'Xi 0.617 Guang'Dong 1.045 Si'Chuan 0.453

He'Nan 13 0.423 Qing'Hai 0.630 Hai'Nan 1.045 Hei'Long'Jiang 0.425

Si'Chuan 14 0.512 Guang'Dong 0.705 Chong'Qing 1.045 Shan'Xi 0.421

Shan'Xi 28 0.522 Zhe'Jiang 0.718 Si'Chuan 1.045 Shaan'Xi 0.421

Zhe'Jiang 63 0.570 Ning'Xia 0.793 Fu'Jian 1.045 Fu'Jian 0.413

Xi'Zang 24 0.724 Liao'Ning 0.797 Shan'Xi 1.050 Chong'Qing 0.407

Chong'Qing 27 0.730 Ji'Lin 0.827 Shaan'Xi 1.050 Xin'Jiang 0.376

Guang'Dong 62 0.920 Xin'Jiang 0.892 Hei'Long'Jiang 1.066 Hai'Nan 0.356

Hai'Nan 48 0.944 Chong'Qing 0.905 Bei'Jing 1.079 Zhe'Jiang 0.333

Fu'Jian 64 1.069 Tian'Jing 1.414 He'Nan 1.085 Guang'Dong 0.280

Shang'Hai 62 1.189 Shang'Hai 1.787 Shang'Hai 1.114 Shang'Hai 0.278

Bei'Jing 60 1.512 Bei'Jing 2.886 Shan'Dong 1.171 Bei'Jing 0.278

27

Table 2 Descriptive Statistics

All variables are as defined in the appendix and winsorized at the 1 and 99 percentiles.

Variables Mean Std dev Min Max

R2(1) 0.491 0.177 0.133 0.821

R2(2) 0.433 0.175 0.132 0.830

Ψ(1) -0.230 0.342 -1.921 1.072

Ψ(2) -0.331 0.336 -1.921 1.042

PropertyRights 0.472 0.496 0.002 5.011

RuleofLaw 0.677 0.730 0.101 7.451

PoliticalPluralism 1.016 0.119 0.651 1.434

GovernmentOwn 0.310 0.132 0.000 0.716

ForeignOwn 0.067 0.046 0.000 0.415

Hshare 0.039 0.193 0.000 1.000

BShare 0.131 0.338 0.000 1.000

Volume 2.081 0.713 0.211 3.511

Size 20.670 1.037 19.120 22.812

Leverage 0.385 0.219 0.121 0.962

EarningVolatility 0.203 0.139 0.000 0.492

MarketToBook 2.764 1.565 0.726 5.491

IPOAge 9.458 5.190 1.000 18.000

IndustryByNumber 5.236 1.189 3.261 6.693

IndustryBySize 26.773 1.165 24.145 28.410

GDPGrowth (%) 7.200 4.526 -17.100 21.574

GDPPerCapita (RMB) 1704.574 1005.678 413.000 12613.000

28

29

Table 3

Correlation Matrix

This table presents the correlation between the measure of stock price synchronicity and province-level (Panel A) and firm-level (Panel B) variables. All variables are as defined in the appendix. Numbers in parentheses represent p-values. The superscripts, ***, **, and * denote the 1%, 5%, and 10% levels of significance, respectively.

a. b. c. d. e.

a. Ψ(1) 1

b. PropertyRights -0.457*** 1

c. RuleofLaw -0.355*** 0.385*** 1

d. PoliticalPluralism -0.295*** 0.224*** 0.233*** 1

e. GDPGrowth -0.127*** 0.092*** 0.034 0.167*** 1

f. Log(GDPPerCapita) -0.088*** 0.035*** 0.026* 0.114*** 0.726***

Panel A: Province-level variables

a. b. c. d. e. f. g. h. i. j. k. l. m.

a. Ψ(1) 1

b. Ψ(2) 0.976*** 1

c. GovernmentOwn 0.618*** 0.615*** 1

d. ForeignOwn -0.390*** -0.391*** -0.489*** 1

e. Hshare -0.253*** -0.211*** -0.172*** 0.118*** 1

f. BShare -0.406*** -0.386*** -0.283*** 0.261*** 0.179*** 1

g. Volume -0.312*** -0.313*** -0.305*** 0.421*** 0.045*** 0.174*** 1

h. Size -0.011 -0.008 -0.007 0.004 -0.014 0.011 0.005 1

i. Leverage 0.006 0.005 -0.029* 0.006 0.008 -0.0005 -0.013 0.007 1

j. EarningVolatility 0.006 0.005 0.006 -0.002 0.008 -0.005 -0.0005 -0.013 0.006 1

k. MarketToBook 0.065*** 0.065*** 0.171*** -0.143*** -0.012 -0.044*** -0.158*** 0.013 -0.011 -0.018 1

l. IPOAge -0.006 -0.005 -0.007 -0.006 0.009 0.005 0.001 -0.008 -0.010 0.001 0.006 1

m. IndustryByNumber 0.008 0.007 0.006 -0.016 -0.005 -0.008 -0.008 0.0005 -0.019 -0.005 -0.012 -0.004 1

n. IndustryBySize 0.004 0.002 0.014 -0.0058 0.009 0.004 -0.010 -0.007 -0.004 -0.017 -0.004 -0.004 0.434***

Panel B: Firm-level variables

30

31

Table 4 The Effect of Property Rights, Rule of Law, and Political Pluralism on Stock Price Synchronicity

All variables are defined in the appendix. The dependent variable is Ψ(1). Each equation also includes year and industry dummies. Numbers in parentheses represent t-values that are adjusted using standard errors corrected for clustering at the firm level. The superscripts, ***, **, and * denote the 1%, 5%, and 10% levels of significance, respectively.

(1) (2) (3) (4)

PropertyRights -0.184*** -0.146***

(-15.185) (-11.988)

RuleofLaw -0.088*** -0.048***

(-15.839) (-8.221)

PoliticalPluralism -0.301*** -0.104**

(-6.899) (-2.266)

GovernmentOwn 1.105*** 1.182*** 1.241*** 1.082***

(20.503) (22.836) (23.233) (20.207)

ForeignOwn -0.331*** -0.304*** -0.232** -0.362***

(-3.501) (-3.144) (-2.349) (-3.862)

BShare -0.121*** -0.145*** -0.188*** -0.104***

(-6.634) (-8.434) (-11.299) (-5.596)

HShare -0.159*** -0.167*** -0.184*** -0.153***

(-14.529) (-15.188) (-16.390) (-14.110)

Volume -0.048*** -0.050*** -0.053*** -0.047***

(-11.575) (-12.425) (-12.843) (-11.608)

Size -0.003 -0.003 -0.002 -0.003

(-0.959) (-1.114) (-0.546) (-1.064)

Leverage 0.027* 0.030** 0.035** 0.029**

(1.925) (2.107) (2.399) (2.092)

EarningVolatility 0.006 0.002 0.012 0.006

(0.285) (0.111) (0.552) (0.276)

MarketToBook -0.010*** -0.011*** -0.011*** -0.010***

(-4.834) (-5.192) (-5.114) (-4.882)

Log(IPOAge) -0.000 -0.001 -0.002 0.000

(-0.084) (-0.131) (-0.405) (0.027)


(0.272) (0.935) (0.804) (0.552)

IndustryBySize -0.004 -0.006 -0.004 -0.005

(-0.553) (-0.846) (-0.574) (-0.763)

GDPGrowth -0.002 -0.005*** -0.004*** -0.002

(-1.518) (-3.337) (-2.739) (-1.566)

Log(GDPPerCapita) -0.037 0.007 -0.009 -0.027

(-1.374) (0.239) (-0.324) (-1.033)

Sample size 5719 5719 5719 5719

Adj. R2 0.567 0.547 0.523 0.575

32

Table 5 The Effect of Property Rights, Rule of Law, and Political Pluralism on an Alternative Measure of Stock Price Synchronicity


(1) (2) (3) (4)

PropertyRights -0.164*** -0.130***

(-13.683) (-10.998)

RuleofLaw -0.079*** -0.043***

(-14.120) (-7.401)

PoliticalPluralism -0.288*** -0.112**

(-6.542) (-2.468)

GovernmentOwn 1.104*** 1.173*** 1.224*** 1.083***

(20.754) (22.868) (23.218) (20.436)

ForeignOwn -0.370*** -0.346*** -0.282*** -0.398***

(-3.952) (-3.630) (-2.910) (-4.282)

BShare -0.055*** -0.077*** -0.115*** -0.040***

(-3.903) (-5.928) (-9.627) (-2.759)

HShare -0.143*** -0.151*** -0.166*** -0.137***

(-13.690) (-14.293) (-15.386) (-13.292)

Volume -0.048*** -0.050*** -0.053*** -0.047***

(-11.713) (-12.561) (-12.981) (-11.729)

Size -0.002 -0.002 -0.001 -0.002

(-0.566) (-0.720) (-0.206) (-0.645)

Leverage 0.026* 0.028** 0.033** 0.028**

(1.821) (1.988) (2.280) (1.990)

EarningVolatility 0.001 -0.002 0.007 0.002

(0.067) (-0.081) (0.325) (0.073)

MarketToBook -0.010*** -0.011*** -0.011*** -0.010***

(-4.676) (-4.996) (-4.926) (-4.708)

Log(IPOAge) -0.000 -0.000 -0.001 0.000

(-0.036) (-0.078) (-0.327) (0.064)


(0.445) (1.045) (0.932) (0.712)

IndustryBySize -0.005 -0.007 -0.005 -0.007

(-0.768) (-1.043) (-0.794) (-0.970)

GDPGrowth -0.004*** -0.007*** -0.006*** -0.004***

(-4.069) (-5.502) (-4.981) (-4.080)

Log(GDPPerCapita) 0.013 0.052** 0.038 0.022

(0.551) (2.035) (1.462) (0.898)

Sample size 5719 5719 5719 5719

Adj. R2 0.539 0.522 0.503 0.546

33

Table 6 The Effect of Property Rights, Rule of Law, and Political Pluralism on Stock Price Synchronicity in Subsamples


Developed Province Sub sample Developing Province Sub sample

(1) (2) (3) (4) (5) (6) (7) (8)

PropertyRights -0.173*** -0.139*** -0.214*** -0.189***

(-12.550) (-9.910) (-12.764) (-11.845)

RuleofLaw -0.076*** -0.023*** -0.109*** -0.087***

(-12.209) (-3.011) (-13.430) (-12.496)

PoliticalPluralism -0.402*** -0.147*** -0.174*** -0.099

(-7.931) (-2.727) (-2.680) (-1.637)

GovernmentOwn 1.136*** 1.217*** 1.229*** 1.124*** 1.033*** 1.102*** 1.213*** 0.958***

(18.512) (19.719) (19.541) (18.431) (14.823) (16.903) (17.881) (14.170)

ForeignOwn -0.258** -0.182 -0.091 -0.281** -0.400*** -0.458*** -0.425*** -0.431***

(-2.096) (-1.392) (-0.718) (-2.282) (-3.313) (-3.756) (-3.298) (-3.732)

BShare -0.078*** -0.128*** -0.175*** -0.073*** -0.191*** -0.180*** -0.200*** -0.173***

(-3.248) (-5.770) (-8.696) (-3.050) (-7.206) (-6.844) (-7.577) (-6.449)

HShare -0.132*** -0.147*** -0.164*** -0.129*** -0.186*** -0.192*** -0.201*** -0.181***

(-9.922) (-10.331) (-11.793) (-9.594) (-12.698) (-13.701) (-13.589) (-12.869)

Volume -0.055*** -0.061*** -0.062*** -0.055*** -0.041*** -0.040*** -0.045*** -0.036***

(-9.012) (-9.547) (-9.650) (-9.177) (-6.905) (-6.849) (-7.443) (-6.405)

Size 0.001 0.002 0.003 0.001 -0.007* -0.009** -0.007 -0.008*

(0.371) (0.485) (0.724) (0.375) (-1.661) (-2.034) (-1.502) (-1.911)

Leverage 0.006 0.003 0.009 0.007 0.045** 0.056*** 0.059*** 0.048**

(0.307) (0.176) (0.455) (0.357) (2.116) (2.602) (2.666) (2.324)

EarningVolatility -0.003 0.000 0.012 0.003 0.012 -0.001 0.011 0.004

(-0.094) (0.007) (0.415) (0.093) (0.398) (-0.046) (0.341) (0.136)

MarketToBook -0.013*** -0.014*** -0.014*** -0.013*** -0.007** -0.008** -0.008** -0.006**

(-4.777) (-5.030) (-4.906) (-4.918) (-2.200) (-2.437) (-2.574) (-2.017)

Log(IPOAge) -0.004 -0.004 -0.005 -0.004 0.005 0.004 0.003 0.006

(-0.677) (-0.699) (-1.023) (-0.699) (0.891) (0.649) (0.489) (1.054)

IndustryByNumber 0.006 0.008 0.010 0.007 -0.002 0.008 0.005 0.002

(0.634) (0.784) (0.934) (0.770) (-0.230) (0.809) (0.437) (0.167)

IndustryBySize -0.002 -0.002 -0.002 -0.003 -0.005 -0.010 -0.006 -0.008

(-0.310) (-0.285) (-0.282) (-0.379) (-0.512) (-1.114) (-0.660) (-0.896)

GDPGrowth 0.002 -0.001 0.001 0.002 -0.005** -0.009*** -0.008*** -0.004*

(1.532) (-0.555) (0.537) (1.290) (-2.042) (-3.342) (-3.169) (-1.831)

Log(GDPPerCapita) -0.032 -0.066 -0.080* -0.060 -0.009 0.053 0.049 -0.014

(-0.767) (-1.549) (-1.904) (-1.464) (-0.191) (1.071) (0.963) (-0.295)

Sample size 2910 2910 2910 2910 2809 2809 2809 2809

Adj. R2 0.593 0.568 0.555 0.596 0.545 0.529 0.494 0.569

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Table 7 The Effect of State (Foreign) Ownership on the Relation between

Institutional Development and Stock Price Synchronicity

All variables are defined in the appendix. The dependent variable is Ψ(1). Each equation includes the standard set of control variables and year and industry dummies. However, only the coefficients on institutional and ownership variables and the interactions are shown in the table. Numbers in parentheses represent t-values that are adjusted using standard errors corrected for clustering at the firm level. The superscripts, ***, **, and * denote the 1%, 5%, and 10% levels of significance, respectively.

(1) (2) (3) (4) (5) (6) (7) (8)

PropertyRights -0.036 0.001 -0.307*** -0.245***

(-1.211) (0.031) (-12.669) (-9.866)

RuleofLaw -0.006 0.048*** -0.147*** -0.095***

(-0.326) (2.592) (-10.992) (-6.693)

PoliticalPluralism -0.456*** -0.245** -0.584*** -0.309***

(-4.288) (-2.376) (-7.142) (-3.969)

PropertyRights* GovernmentOwn -0.698*** -0.712*** - - - -

(-5.251) (-5.735) - - - -

RuleofLaw * GovernmentOwn -0.374*** -0.437*** - - - -

(-4.008) (-4.952) - - - -

PoliticalPluralism * GovernmentOwn 0.586 0.532 - - - -

(1.608) (1.551) - - - -

PropertyRights* ForeignOwn - - - - 1.567*** 1.286***

- - - - (7.250) (5.854)

RuleofLaw * ForeignOwn - - - - 0.762*** 0.602***

- - - - (5.814) (4.436)

PoliticalPluralism * ForeignOwn - - - - 3.873*** 2.920***

- - - - (4.549) (3.978)

GovernmentOwn 1.374*** 1.396*** 0.647* 1.066*** 1.085*** 1.174*** 1.242*** 1.063***

(17.187) (17.244) (1.728) (3.047) (20.163) (22.876) (23.290) (19.989)

ForeignOwn -0.276*** -0.290*** -0.234** -0.293*** -1.037*** -0.802*** -4.143*** -4.282***

(-2.881) (-2.987) (-2.379) (-3.110) (-7.510) (-6.074) (-4.861) (-5.938)

Sample size 5719 5719 5719 5719 5719 5719 5719 5719

Adj. R2 0.572 0.550 0.524 0.587 0.572 0.550 0.526 0.583

Figure 1 Stock Price Synchronicity by Year

This figure plots the average stock price synchronicity of Chinese listed firms measured by R2(1)

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Institutional Development and Stock Price Synchronicity...

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