1

Analyst forecasts, firm asymmetric information and audit quality

Chee Cheong* and Ralf Zurbruegg

University of Adelaide Business School

Abstract

This paper examines the role that audit quality has on the type of information analysts impound

onto stock prices across a sample of developed and emerging markets. Specifically, we

investigate the amount of firm-specific versus market-wide information analysts reveal by

analyzing stock return synchronicity. We find that even under a similar disclosure regime, if the

enforcement of the accounting standards is weak then less firm-level information reaches the

market. Supporting this, we also find information asymmetries between the firm and the market

is heightened when the audit regime is weak.

Keywords: financial analysts, information asymmetry, audit quality, emerging markets

JEL Classification: G14

The authors are grateful for the feedback and guidance provided by Ferdinand A. Gul,

Vernon J. Richardson, Keshab Shrestha, participants at both the 2015 JCAE conference and

seminar at Monash University, Malaysia. All errors, however, remain solely that of the authors.

* Corresponding Author. Email: Chee.Cheong@adelaide.edu.au, Tel: +61 8 8313 0356,

University of Adelaide Business School, University of Adelaide, SA 5005, AUSTRALIA

2

1. Introduction

In this paper we examine how important a country’s propensity to comply with financial

auditing and reporting standards affects the type of information analysts impound into stock

prices. We achieve this by first endogenously controlling for differences in disclosure rules

across markets and then look at the impact analysts have on stock return synchronicity. In

particular, we examine how the above relationship is moderated for firms that have a high level

of asymmetric information. A good audit environment should limit asymmetric information

problems. However, if the enforcement standards are weak then the costs to analysts of extracting

firm-specific information will be high, limiting the type of information that is revealed to the

market.

From both a theoretical and empirical perspective, there is evidence to suggest that the

information content that is incorporated into stock prices is a function of a country’s institutional

features. Morck, Yeung and Yu (2000) argue that weaker property rights discourage informed

trading and therefore limit the amount of firm-specific information being produced. Supporting

this argument, Chan and Hameed (2006) provide similar findings for a sample of emerging

markets, but also demonstrate that the impact of analysts following a stock leads to increased

stock return synchronicity, implying they are impounding market-wide information into stock

prices. Congruent with Morck et al. (2000), they argue that in countries with low disclosure

requirements and weak enforcement of them, analysts are more likely to focus on producing

market-wide information as the costs of collecting firm-specific information are too high.

One drawback from the earlier work of Morck et al. (2000) and Chan and Hameed (2006)

is that they are not able to separate the impact of disclosure requirements from the actual

compliance regime within the market. For example, Morck et al. (2000) find no evidence that the

3

development of a country’s accounting standards has an impact on stock return synchronicity.

This may, in part, be a result of their inability to separate good accounting standards from the

actual compliance with them. It therefore remains an open, empirical question of how important

the enforcement regime is, separate to the stated reporting standards, in influencing the type of

information that analysts impound into stock prices. In this paper we explicitly examine this

issue.

On a firm level, information asymmetries arising from the characteristics of a company are

also likely to impact the information analysts try to collect. We consider two firm characteristics

that may lead to increased information asymmetries that are also particularly related to the quality

of the accounting compliance regime within a country. The first is the proportion of intangible

assets a firm has. Barth, Kasnik and McNichols (2001) highlight the fact that firms with

substantial, intangible assets are more likely to have a greater information asymmetry between

managers and investors, leading to more uncertainty on the fundamental value of the firm. This

may lead to the costs of analysts acquiring firm-specific information for these companies to

change. Additionally, how intangibles are reported is directly related to the accounting and audit

quality environment. Accounting for intangibles is a complex matter and more often involves a

substantial amount of managers’ professional judgement in recognizing and estimating intangible

assets. For example, under IFRS goodwill undergoes an annual impairment test, which requires

managers to make a number of assumptions to determine impairment. In the absence of a strong

audit environment, it is therefore possible for managers to exercise such discretion

opportunistically by withholding significant private information, thereby leading to higher

information asymmetries between managers and investors.

The second measure we examine is the ownership structure of the company. Research

dating back to Morck, Shleifer, Vishny (1988) and Jiambalvo, Rajgopal and Venkatachalam

4

(2002) note that firms with differing levels of strategic investors has an impact on the amount of

firm-specific information that is released to the market. With good accounting standards, private

information advantages such as this should be reduced. However, if the enforcement and

compliance regime is weak then regardless of the accounting standards, the information

asymmetry between the strategic investors and the rest of the market will remain.

In both of the above cases where firms may have a large amount of intangible assets or that

their ownership structure may lend itself to greater information asymmetries, we postulate that

the impact this will have on analysts impounding firm-specific information will be largely

dependent on how well accounting standards are enforced, separate to whether or not the state

claims to have adopted high disclosure standards.

While controlling for differences in disclosure practices, we use a sample of firms within

16 developed and emerging markets to show that the amount and type of information analysts

reveal for firms that they follow varies with the financial reporting and audit regime within the

country. In particular, we show that the financial reporting and audit regime has a strong impact

on the type of information analysts collect for firms that have greater information asymmetries. In

other words, the importance of having institutional environments that encourage compliance with

accounting standards is particularly prominent for firms where there is less information

transparency at the firm level.

Our paper contributes to the literature in several ways. First, we contribute to the theoretical

work dating back to Grossman and Stiglitz (1980) who examine the incentives analysts have to

collect private information on firms. Although studies have examined the impact intangible assets

(Barth et al., 2001 and Matolcsy and Wyatt, 2006) and strategic investors (Moyer, Chatfield and

Sisneros, 1989; Ball, Kothari and Robin, 2000) have on analyst coverage, the influence it has on

the type of information analysts collect, based on different compliance regimes, has not been

5

investigated. In this paper we examine the impact these inter-related factors have on analysts’

ability to impound useful information into stock prices.

Also, by using a set of markets that have adopted the same disclosure rules by focusing on

countries that have mandatory adoption of IFRS, we are able to highlight the importance of the

compliance and enforcement regime is in influencing the type of information analysts impound

into stocks. Previous work examining return synchronicity across international markets have not

been able to explicitly separate the effects of differing disclosure rules from the type of

enforcement regime (Morck, et al., 2000 and Chan and Hameed, 2006). Additionally, and despite

our paper not explicitly dealing with IFRS issues, we do contribute to the growing literature that

shows how the mandatory adoption of a particular disclosure regime alone, such as IFRS, does

not imply that the benefits of adoption will be uniform (see Horton, Serafeim and Serfafeim,

2013 and Byard, Li and Yu, 2011). In particular, we find that accounting compliance is, itself, a

significant indicator of how effective improved mandatory disclosures in countries will be,

insofar as it encourages analysts to seek and collect firm-specific information. Our results can

therefore partially explain why analyst forecasts are not as accurate in environments with weaker

reporting quality as we relate the empirical evidence from papers showing poorer forecast

performance for these environments to be a function of a reduced incentive for analysts to collect

firm-specific information.

The rest of the paper is organized as follows. Section 2 reviews previous work on the

impact that the information environment has on stock return synchronicity and analyst

performance, along with our hypothesis development. Section 3 discusses the data and model

development while Section 4 provides the empirical results. Finally, Section 5 summarizes our

paper with some concluding remarks.

6

2. A review of the related literature and hypothesis development

Early work by Roll (1988) uses the R2 from a market model regression as an indicator of

stock return synchronicity. The higher the R2, the greater the stock is synchronous with general

market movements. In examining stocks in the U.S., Roll finds that they have low stock return

synchronicity, leading him to conclude firm-specific information must be incorporated into these

stocks. More recently, Morck et al. (2000) show that a negative relationship exists between stock

return synchronicity and the extent to which the government protects private property rights.

Using a good government index, they show that stocks in countries where the index has a low

value is associated with stocks not impounding as much firm-specific information. Their

explanation for this is that weaker property rights discourage informed arbitrage and weaken the

benefit of analysts collecting firm-level information. Supporting this finding Wurgler (2000)

shows that the efficiency of capital allocations in a country is also negatively associated with

stock return synchronicity. Furthermore, Durnev, Morck, Yeung and Zarowin (2003) show that a

positive relationship exists between a number of accounting measures of stock price

informativeness and firm-specific price variation. In other words, stock return synchronicity is

directly related to the degree firm-specific information is incorporated into stock prices.

Additionally, from a behavioral perspective, there is evidence to suggest there is a cultural

dimension to explaining stock return synchronicity differences across countries. Both Hope

(2003) and Nguyen and Troung (2013) use similar measures to capture behavioral biases and

differences in risk preferences to show its impact on analyst forecasts and return synchronicity,

respectively.

Studies have also shown analyst coverage to be a significant item that can influence the

degree of stock return synchronicity. Analysts, as information intermediaries, provide earnings

forecasts on firms and therefore should have the ability to produce firm-specific information. At

7

the same time, as Piotroski and Roulstone (2004) note, they are outsiders to the company and

may, in fact, be contributing to the amount of industry-wide information that is relevant for

pricing the stock. With analysts following more than one company at a time, they have a relative

advantage in terms of exploiting intra-industry information. Supporting the latter argument, Chan

and Hameed (2006) find that stock return synchronicity rises for firms with greater analyst

coverage in emerging markets.

The above research does not, however, differentiate between a market’s information

disclosure standards relative to the enforcement of them. Chan and Hameed (2006), for example,

do not directly measure the level of information disclosure and corporate transparency across

countries, but rather just imply these factors can explain why analysts generate forecasts based on

market-wide information, as the costs of collecting firm-specific information become too high in

regimes with low quality information environments. Likewise, the earlier work by Morck, et al,

(2000) found a country’s accounting standards had little impact on the level of firm-specific

information that is impounded into the stock markets. However, this may be due to the inability

to segregate a country’s accounting standards relative to the enforcement of them.

This leads us to our first hypothesis where we focus on determining the difference that a

disclosure regime has on analysts impounding information, as opposed to the compliance regime.

Simply put:

H1: While controlling for differences in disclosure rules, regimes that encourage quality

auditing and financial reporting will motivate analysts to impound more firm-specific

information.

We postulate that analysts will only invest time in collecting firm-specific information if

some degree of reliability can be placed on a firm’s financial reports. Otherwise, an analyst may

be better off focusing on collating and relying on market-wide information to base earnings

8

forecasts. Whether the stated disclosure standards are of good quality or not is immaterial if the

actual enforcement of them is weak. Supporting this line of reasoning, work by Ball (2006), Hope

(2003) and Barniv, Myring and Thomas (2005) all show that incentives for corporate disclosure

are just as important as the purported accounting standards a regime claims to follow.

In order for us to control for the level of disclosure rules across countries, we exploit the

fact that since 2008, 16 markets have adopted mandatory IFRS reporting. Work, to date, has

shown that mandatory adoption of IFRS has led to the improvement in analyst forecasts. Tan,

Wang and Welker (2011) show mandated IFRS adoption leads to a general increase in analyst

coverage and a significant increase in foreign analysts’ forecast accuracy. They suggest this is

due to the comparability benefits across countries that accounting harmonization brings, further

increasing the usefulness of accounting information. Likewise, Horton et al. (2013) provide

evidence there is an overall improvement in the information environment. They find this is

particularly true in countries whose previous GAAP regime differs from IFRS, as analyst

forecasting performance improves significantly in these markets. Additionally, both Ball (2006)

and Wysocki (2011) note that although reporting standards may now be uniform under IFRS in

countries that adopt it, the reporting quality is likely to vary dependent on national institutional

features. This is further highlighted by Barth, Landsman and Lang (2005) who argue accounting

quality is of vital importance in determining the benefits of adopting a better disclosure regime.

The implication of this is that the variation in the performance of analyst forecasts in IFRS

countries may, at least, be partially a function of the audit and compliance regime within the

country.

At a firm level, Hutton, Marcus and Tehranian (2009) show that U.S. firms with a higher

degree of opacity, as measured by earnings management, also have a higher R2, implying less

firm-specific information is being impounded in stocks with less transparent financial statements.

9

Likewise, Jin and Myers (2006) examine the degree of opacity in firms across a number of

countries and find a similar result: namely, for countries where firms are able to be more opaque,

the level of return synchronicity increases. They also find the likelihood of market crashes

increases in more opaque markets.

The level of firm opacity will also have an impact on the type of information analysts

collect on the company and reveal to the market. Importantly, this will also be influenced by the

accounting compliance regime of the country. Even though a country may impose a set of good

quality accounting standards, such as IFRS, it does not mean that individual firms will adhere to

them unless there is a good compliance regime to back up the standards. This leads us to our

second hypothesis. We expect that the incentives to produce quality accounting reports and

financial statements will be positively related to the strength of the audit regime that is prevalent

within a market. As such, the costs for analysts of extracting firm-specific information will be

more pronounced for firms that might take advantage of firm-level information asymmetries in

weak enforcement environments. If this is the case, then we should observe that analysts working

in weaker compliance regimes are more likely to impound market-wide information, as opposed

to firm-level information, for firms with potentially high levels of information asymmetries:

H2: Dependent on the compliance regime, the level of a firm’s information asymmetries

will significantly affect the type of information analysts impound onto the market.

In this paper we consider two sources of information asymmetries that the stakeholders of

the firm have some control over. They are the proportion of intangible assets a firm has and the

degree of strategic investors that are in a firm. In the case of intangibles, Barth, Kasnik and

McNichols (2001) highlight the fact that firms with substantial, intangible assets are more likely

10

to have a greater information asymmetry between managers and investors, leading to more

uncertainty on the fundamental value of the firm. Additionally, how intangibles are reported is

directly related to the accounting and audit quality environment which will also have an impact

on how easy analysts can extract firm-specific information for these firms.

The second measure, the proportion of strategic investors within a firm, focuses on the

ownership structure of the company. Jiambalvo, Rajgopal and Venkatachalam (2002) note that

firms with a greater proportion of strategic investors discourages the release of private

information. Supporting this point, Fan and Wong (2002) show that greater corporate ownership

can discourage disclosure as it is not to the advantage of the incumbent investors. This will

increase the cost to the analyst in extracting firm-specific information. In the context of our

paper, we would expect that there is a likely reduction in the private information advantage these

strategic investors have if the auditing environment is strong. If it is weak, then their ability to

withhold price-sensitive information will grow.

It is also possible that a nonlinear relationship exists. Morck, Shleifer, Vishny (1988)

provide evidence that an inverted U-shape relationship can materialize between the proportion of

strategic investors and firm information asymmetries. Not only can a large proportion of strategic

investors lead to a limitation of firm-relevant information being released to the market, but also if

there are too few strategic investors. The incentive to monitor the firm diminishes if ownership is

spread very thinly between minority shareholders. In either case, we expect to find that in weaker

compliance regimes the ownership structure of the firm plays an important role in the likelihood

of analysts collating and revealing firm-specific information to the market. The weaker the

compliance regime, the more likely an unfavorable ownership structure will lead to a firm

holding price-relevant information to the market.

11

We also examine a third measure based on the work of Dasgupta, Gan and Gao (2010).

They show that the more time the market has to learn about a firm’s time-invariant

characteristics, the larger the stock return synchronicity will be. However, we expect this to be

the case only for firms located in high quality audit environments, as the benefit of learning over

time about a firm’s intrinsic quality, for example, can only happen if investors can trust the

quality of the financial reports. Following Dasgputa, et al. (2010), we use firm age as our

measure and expect that its role in explaining a firm’s level of return synchronicity is only

significant in a strong audit environment.

3. Data and research method

Our sample consists of all 15 countries and one special economic zone that are part of

either the G-20 list of nations or the International Monetary Fund (IMF) list of emerging markets1

that have proceeded with mandatory adoption of IFRS by 2008. The reason for picking only

countries that have fully adopted IFRS is to ensure that the accounting standards regime is, as

much as possible, the same across our sample of countries so that any differences in reporting

quality will be a result of institutional features prevalent within the market, separate to the

disclosure standards.

Our sample consists of five developed markets (Australia, France, Germany, Italy and the

United Kingdom) plus five emerging markets (Brazil, China, South Africa and Turkey) from the

G-20 list and an additional seven emerging economies from the IMF list (Chile, Hong Kong

(which we treat separately to mainland China), Israel, Philippines, Poland, Qatar and the United

Arab Emirates (U.A.E.). The cut-off date of 2008 was chosen for a specific reason. Choosing a

1 We obtain the adoption year of each country from the IFRS Foundation and IASPlus websites.

12

date earlier than this limits the breadth of the countries we can use and choosing a date after this

limits the time series properties of our dataset. With the exception of Turkey, all countries will

have had at least one year of mandatory IFRS.2

We use Worldscope and Thomson Reuters to identify firms within these markets. We are

able to identify a total of 13,766 stocks from 2008 until 2013. We delete (i) 2,800 firms which do

not have Institutional Brokers Estimate System (IBES) codes (as we cannot match analyst

coverage with them); (ii) another 5,265 firms that do not fully adopt IFRS during our study

period; and (iii) 435 firms that we cannot adequately track appropriate accounting standards for.

As we intend to measure analyst coverage by the number of analysts in the IBES database that

provide one-year ahead earnings per share (EPS) forecasts on a firm, we further reduce the

sample of stocks by another 1,287 firms3 with no earnings forecasts matched to them. We also

delete another 238 firms which do not have a minimum of 40 consecutive trading weeks of stock

data to enable our calculation of stock return synchronicity. Additionally, we lose 54 firms due to

missing observations for at least one of our firm-specific variables. The final sample of firms

equates to 3,684. This amounts to 16,374 firm-year observations with 69,620 EPS forecasts from

37,096 analyst-year observations. Panel A of Table 1 provides details of the year each country in

our sample adopted IFRS, the total number of firms in our sample from each country and a break-

down of analyst coverage over time. Chile has the fewest number with 13 analyst-year

observations, while the United Kingdom has the largest (9,262). Although dominated by the

2 Dasgupta, et al. (2010) suggests that return synchronicity may increase if the information environment improves as

there should be a reduction in price-sensitive surprises for the firm in the future. From our perspective, we want to

ensure that our analysis is not influenced by any possible change in synchronicity levels from the transition phase of

adopting IFRS, per se, if this leads to an improved information environment. As such, with the exception of Turkey,

all of the markets we examine have at least one year of mandatory adoption preceding our chosen sample time

period. Also, whether we include Turkey within our sample or not makes no qualitative difference to our results. 3 Our empirical analysis uses three-way interaction terms that produces high multicollinearity if a large proportion of

firms have no analysts following.

13

developed markets, our sample consists of a sizable minority of analysts following the emerging

markets (approximately a quarter) which ensures cross-sectional variation in the explanatory

variables we intend to use. Panel B provides a breakdown of firms and analyst coverage by

industry classification.

Our dependent variable in our regressions is the stock return synchronicity measure,

SYNCH. We calculate it following Piotroski and Roulstone (2004) by estimating the linear

regression:

Ri,t = β0 + β1Rm,t + β2Rm,t-1 + β3Rindustry,t + β4Rindustry,t-1 + εi,t (1)

where Ri,t is the return of stock i at week t, Rm,t is the market return at week t and Rindustry,t is the

industry return at week t. The industry return Rindustry,t for week t is created using all firms with

the same two-digit GICS sector code within a country. As with other papers, we also include one

period lags into the model. We estimate this regression for each firm-year using weekly

observations over a minimum of 40 weeks. We define synchronicity as:

(2)

where R2 is the coefficient of determination from the estimation of Eq. (1) for firm i in year t. A

high SYNCH indicates that the firm is highly correlated with the market. Table 2 shows the

descriptive statistics, sorted by country, of the stock return synchronicity measure. All the

developed markets in our sample, with the exception of Italy, have return synchronicity measures

that are smaller than the average SYNCH across our sample of countries (-0.7). This indicates

14

that, on average, stock prices in these developed markets are less likely to follow market-wide

trends. The table also shows the dispersion of our analyst coverage variable across countries. We

use one plus the natural logarithm of the number of analysts (ANALYST) covering company i in

year t. For our sample the average number of analysts following stocks in our sample is 1.18. The

number varies across countries, based not only on the size of the overall market but also the

number of stocks we use in a country. For some of the smaller markets this explains why analyst

coverage may be high.

Associated with each country in the table are also the two measures we use to assess the

likelihood that a country complies with the auditing and accounting standards of the country. Our

direct measurement is AUDITING STRENGTH that comes from a survey question contained in

the World Economic Forum Database (WEFD) that asks businesses ‘In your country, how would

you assess financial auditing and reporting standards regarding company financial performance?’.

The answer is based on a Likert scale, with one implying a negative answer to the question, and

seven very positive (in terms of auditing strength and quality of financial reporting). This

provides us with a measure from businesses of their perception towards the auditing strength

within their country.

To complement this, and to capture corporate governance factors, we use a measure called

INVESTOR PROTECTION taken from the Doing Business Database.4 The indicator reflects the

average score obtained from three dimensions of investor protections, which deal with (i) the

transparency of related party transactions (measuring the extent of disclosure); (ii) director’s

liability, and (iii) shareholders’ ability to sue directors for misconduct. The data is, again, taken

4 Information about the database can be found at http://www.doingbusiness.org/. The Doing Business Project

provides objective measures of business regulations and enforcement of them in 189 countries around the world.

15

from a questionnaire provided to corporate and securities lawyers and is based on securities

regulations, company laws, civil procedure codes and court rules of evidence.

Table 2 further shows the descriptive statistics for our firm-specific variables of interest and

our control variables. The first three are our information asymmetry variables. INTANGIBLES is

the total value of intangibles to total assets in a firm, STRATEGIC HOLDINGS is the percentage

of total shares in issue of 5% or more, held strategically and not available to common investors,

and AGE is the number of years a firm has been listed on an exchange.

The remaining six variables form the control set. DIVERSITY is a count of the number of

Standard Industrial Classification (SIC) codes a firm’s operations are associated with. The larger

the number, the greater its diversity of business operations. We expect a positive relationship to

exist between this and stock return synchronicity as a business with operations across a number

of industries will share more features with the general trend of the market than a firm which is

highly concentrated in a particular industry. Secondly, we control for the SIZE of the firm as the

natural logarithm of the total assets within a company. The larger the size of the company the

more proportionally weighted it will be to the market index. We therefore expect a positive

relationship between stock return synchronicity and SIZE. Third, we calculate the natural

logarithm of company i’s year-t trading volume. We expect the coefficient associated with this

measure, VOLUME, to have a positive sign as more actively traded companies are able to react

to information more rapidly and in a synchronous manner (see Alford and Berger, 1999). We also

calculate the annualized weekly stock return volatility (COMPANY RISK). Private information

is more valuable for firms with higher return volatility and therefore this may affect the level of

return synchronicity for these firms, as well as the number of analysts following the firm

(Bhushan, 1989). We expect a positive relationship between COMPANY RISK and stock return

synchronicity. Furthermore, we control for industry concentration by creating a Herfindahl

16

revenue-based index, H-INDEX. Piotroski and Roulstone (2004) show that the more

concentrated the industry is, the more likely individual firms in the industry will be synchronous

with each other in respect to their returns. Finally, GNI is the logarithm of the gross national

income of a country on a per capita basis. We use this as a general proxy for the macro-economic

wealth of a country.

To see if there is a difference in the variables across institutional environments, Table 3

shows the means, medians and results from conducting difference tests in splitting the data by

institutional features, rather than by country. Firms are sorted into low and high INVESTOR

PROTECTION and AUDIT STRENGTH categories based on whether a firm is located in an

environment that is below or above the respective medians of these two variables. Both t-tests on

the means and Wilcoxon tests on the medians reveal that there are significant differences across

the categories. In particular, and in alignment with our expectations, even when the accounting

standards are similar across countries, SYNCH is significantly lower when there is higher

investor protection and stronger auditing strength.

Table 4 provides a correlation matrix. None of the pairwise correlations are particularly

high, with ANALYST and SIZE being the largest at 0.59. Also, it is worth highlighting that none

of the variables we are using to capture the institutional features of a country are highly collinear,

implying they are measuring different attributes of the overall information environment within a

country. The correlation between INVESTOR PROTECTION and AUDITING STRENGTH is

only 0.36. Even if we split the sample by high and low median values for these two variables, as

in Table 3, pairwise correlations between these two variables are no larger than 0.7.

When analyzing the correlations between our dependent variable, SYNCH, and the

explanatory variables, we notice they hold the correct signs for those measures where we predict

a sign. In particular, both institutional variables have a negative relationship with SYNCH,

17

indicating the better the institutional features of a country the more firm-specific information is

likely to be produced. With analyst coverage, SYNCH has a positive relationship (a correlation of

0.36), similar to the results obtained in other studies (see Chan and Hameed, 2006).

Based on the above list of variables, our empirical framework that we use to test our

hypotheses has our dependent variable, SYNCH, to be a function of several factors:

SYNCHi,t = f (ANALYSTi,t, institutional variables, information asymmetry variables, (3)

interaction variables, control variables, fixed effects).

where the institutional variables are our measures that capture the auditing strength and investor

protection within a country, the information asymmetry variables are our measures for intangible

assets within a firm, share ownership structure and firm age, and the interaction terms are cross-

product variables from multiplying analyst coverage with one of our information asymmetry

variables to examine the impact information asymmetry has on the type of information analysts

collect and impound into the market.

Additionally, in all our analyses we incorporate both country and period fixed effects to

account for the possibility of any omitted variable bias that we have not explicitly controlled for

using our set of independent variables. Also, when generating the results from our panel

regressions we report heteroskedasticity and contemporaneous correlation corrected standard

errors.

18

4. Empirical analysis

4.1. The impact of the audit regime and investor protection on return synchronicity

In order to facilitate the ease of interpretation of the cross-product terms that we use we

transform both our firm asymmetry variables and our institutional variables into dichotomous

values, taking the value of one if a particular observation is above the median value of our

sample, and zero otherwise. This ensures that all our interaction terms comprise of one dummy

variable and one continuous variable.

Table 5 focuses on the importance of AUDIT STRENGTH in influencing the type of

information analysts impound into the market. Regression 5.1 provides a base-line result using

the full sample of data to address our first hypothesis. As with Chan and Hameed (2006) we find

that ANALYST coverage increases stock return synchronicity. However, in alignment with the

descriptive statistics and correlations presented in Tables 3 and 4, we also find that both AUDIT

STRENGTH and INVESTOR PROTECTION reduce this value. Specifically, the coefficient for

the dummy variable of INVESTOR PROTECTION (-1.228) is significantly negative at the 10%

level, while the interaction term for AUDIT STRENGTH with ANALYST is also negative and

significant at the 1% level. In the latter case, the result supports our first hypothesis that in

environments with high auditing strength and quality, analysts are more likely to impound firm-

specific information into the market and thereby lower stock return synchronicity.

The remaining set of regressions in Table 5 are based on splitting the data into high and low

audit strength environments in order to examine how this affects the type of information analysts

reveal when dealing with firms with differing levels of information asymmetries. Regressions 5.2

and 5.5 show the results from including variables to account for INTANGIBLES and firm AGE.

In the case of INTANGIBLES it is negative and significant in the strong audit strength

environment (a coefficient of -0.1325), but positive and significant in the weak audit strength

19

environment (0.0807). This result is interesting. Given that we expect the valuation of firms that

have more intangible assets to be more complex and dependent on the reliability of their financial

statements for guidance; these results suggest that the audit environment does have a substantial

impact on the type of information that is revealed to the market for these types of firms. Where

audit quality is good the ability to extract firm-specific information will be easier, and this is

reflected in a reduction in stock return synchronicity. Conversely, in a weak audit environment

the costs of extracting firm-level information will be much higher if the reliability of a firm’s

financial reports are questionable, leading to stock returns following more general industry and

market trends.

Supporting the previous findings, regressions 5.3 and 5.6 include an interaction term for

INTANGIBLES with ANALYST which shows that only in the strong audit strength environment

does return synchronicity reduce as a result of analysts following a stock. If the financial

statements of a firm are unreliable, such as is more likely to be the case in the weak audit strength

environment, then the ability for analysts to reveal firm-specific information from these reports

will be less likely.

In regards to firm age, we find that the coefficient values for the parameter AGE are always

insignificant, regardless of the audit strength environment. However, in Regressions 5.4 and 5.7

where we include interaction terms for AGE with ANALYST, we do find the coefficients to be

positive and significant, at the 5% levels, regardless of the quality of the audit regime. This

would be in agreement with Dasgupta (2010) that older firms will have higher levels of return

synchronicity associated with it as the market is more familiar with the intrinsic, time-invariant,

properties of the firm. In our case, it would seem this is impounded through analyst coverage

with the audit environment making little difference to the result.

20

Table 6 presents the same set of regressions as in Table 5, but this time with a focus on

INVESTOR PROTECTION. Most of the results are similar to Table 5, but there are some

interesting differences. Focusing on the impact that AGE has on synchronicity we find that it

only has a significant, positive impact when the investor protection environment is high. Either

the coefficient values for AGE are positive (Regressions 6.2 and 6.3) or the combined effect of

AGE and its interaction with ANALYST leads to a significant, positive outcome (Regression

6.4). We find no significant relationship when examining the regression results for the low

investor protection environment. This result is interesting as it indicates that firm age may only

be useful in explaining stock return synchronicity under a beneficial institutional environment.

This would, to some extent, be expected given that the ability for firm age to provide investors

with a better gauge of a firm’s intrinsic value and reduce uncertainty about future surprises can

only occur if the compliance regime is strong enough to ensure price-relevant information on the

stock is revealed to the market. This would not be the case in a low investor protection

environment.

Tables 7 and 8 replicate the regressions listed in Table 5 and 6, but now focus on the impact

that share ownership structure has on stock return synchronicity. To account for possible

differences in the relationship between STRATEGIC HOLDINGS and SYNCH depending on

whether there are a lot or just a few strategic investors, we create two dummy variables to

represent low strategic holdings (DD_SH_BOTTOM25 is equal to one if the proportion of

strategic holdings for the firm is within the bottom quartile of our sample of firms, and zero

otherwise) and high strategic holdings (DD_SH_TOP25 is equal to one if the proportion of

strategic holdings for the firm is within the top quartile of our sample, and zero otherwise) in our

analysis.

21

Focusing on Table 7 that looks at weak and strong auditing strength environments, we find

that the proportion of strategic investors has no impact on the type of information analysts

impound into the market when audit quality is strong. The interaction terms in Regressions 7.1

and 7.2 are always insignificant. The individual effects, however, do reveal differences in stock

return synchronicity relating to the concentration of strategic holdings of a firm. Firms with a

high proportion of strategic investors have a significantly reduced return synchronicity, whilst

firms with relatively low levels of strategic investors have a higher return synchronicity. Our

view is that in a good quality audit environment, the ability for the market to extract firm-specific

information from companies that have concentrated shareholdings will be easier, leading to a

reduction in synchronicity. The ability for larger strategic investors to withhold price-relevant

information (Jiambalvo, et al., 2002) will be substantially reduced where the audit quality

environment is strong. However, the audit regime cannot reveal additional information if none is

collected to begin with. Where there are very few strategic investors, the incentive for

stakeholderes to collect firm information is limited if their holdings are small. This will lead, as

Morck, et al. (1988) argue, to a general rise in information asymmetries between the firm and the

market, and hence the rise in return synchronicity that we observe.

When comparing the above results to the weak audit environment there is one main

difference that emerges. First, as we would expect, high levels of firm concentration do not lead

to a reduction in return synchronicity, as is the case in the strong audit environment. As for the

case with low levels of ownership concentration, the result does not change, with return

synchronicity rising for the same reasons we expect it does within the strong audit environment.

For Table 8, where we split our sample between high and low investor protection, we

broadly find the results similar to Table 7. The only significant difference that we observe is that

when there are a lot of strategic investors (SH_TOP25) the overall effect is to reduce

22

synchronicity in both the low and high investor protection environments. However, the role that

analysts play is different. In the high investor protection regime the coefficient for the interaction

term of SH_TOP25 with ANALYST is negative and significant (-0.0576), whilst it is positive

and significant (0.1226) within the low investor protection environment. We interpret this to

imply that analysts can only reveal industry and market-wide information on the stocks when the

investor protection environment is weak, but when it is strong they have the ability to extract

firm-level information.

4.2 Robustness tests

We test a number of alternate model and sample specifications to see if our results are

robust to changes in the regression framework. In Table 9 we present the results from a Placebo

test where we randomly assign a firm to a country other than where it truly resides. If the

institutional environment of a firm is important in determining the return synchronicity of a

company, then we should find that randomly placing a firm in another country leads to the

importance of that country’s institutional features to be irrelevant. Table 9 records the number of

positive and negative coefficient values that are also significant (at the 10% level) from

conducting the randomization 100 times on Regressions 5.1 and 6.1 on the full sample of data.

What we find is that 100% of the time ANALYST is significant and positive. We expect this to

be the case as it should not matter where the firm resides that analyst coverage increases a firm’s

return synchronicity. It is a factor relating to the firm, not the country. When we turn our

attention to the institutional variables we find that there is no particular outcome that dominates

the coefficient results. At best, we find that 76% of the time there is a significant and positive

AUDITING STRENGTH coefficient. However, the sign is, in fact, opposite to what we should

expect. Furthermore, the interaction terms do not deliver any strong results either, implying that

23

the outcomes are randomly drawn. From this we conclude that a country’s institutional features

are unique in affecting a firm’s return synchronicity domiciled in the country, and is not a result

of our sample of firms being stochastically related to the compliance regime.

Table 10 is split into three panels and provides the results from either changing the control

variables that we use for our regressions or the sample of countries that we include. For brevity,

we provide the results only for analyzing the impact that INTANGIBLES has on firm

synchronicity. Starting with Panel A, we select an alternative set on controls. For firm size we

use the log of the market capitalization of the firm instead of total assets. Instead of firm

volatility, we measure general stock market volatility and use a dummy variable that equals one if

the firm has business operations spanning three or more SICs and zero otherwise, to represent the

diversity of business operations within a company. We also employ a variable called that

measures trading volume by total outstanding shares as a measure of liquidity. Finally, we

include a macroeconomic variable which is the logarithm of a country’s GDP per capita.

Our results are not substantially different from Table 5. Regression 10.1 reveals that in a

strong audit quality environment, analyst coverage of a firm reveals further firm-specific

information to the market. The same is true within a weak audit environment as the interaction

term between ANALYST and INTANGIBLES is negative and significant (a coefficient of -

0.038), although only at the 10% level, relative to the 1% significance the coefficient obtained in

the strong audit regime. However, the coefficient for INTANGIBLES by itself is positive with a

value of 0.1334. This implies that within a weak audit regime the impact of intangibles does still

lead to an increase in return synchronicity, as would be expected, although analysts are still able

to extract firm-level information from the firms. In the case of Regressions 10.3 and 10.4, the

results are more clear-cut. Only in the high investor protection environment is there any evidence

that return synchronicity is reduced in the presence of analysts following a firm.

24

Panel B repeats the above exercise, but this time we remove the largest five firms in each

country. It can be argued that in emerging markets a few large firms dominate the stock market

and therefore our results may show a bias due to favoring higher return synchronicity from these

companies in our sample, despite us already accounting for firm size as one of our controls. The

results from this set of regressions provide complement the outcomes that we observe in Panel A,

with little deviation. In Panel C of Table 10 we show the results for when we remove countries

with less than one hundred firms in our sample (Brazil, Chile, Hong Kong, Israel, Philippines,

Qatar, Turkey and U.A.E.) in case a bias originates from there. The overall results are not

significantly different from our previous regressions, indicating that our findings are not

influenced by a small country sample bias. Only in strong audit and high investor protection

environments do we find evidence that analysts are able to impound firm-specific information

into stock prices.

We also perform a number of other tests to see what happens if we do not include industry

returns for our cohort of emerging markets in equation (1) when calculating SYNCH because in

smaller markets a few large companies may be driving a whole industry and therefore there will

be a high covariance between industry returns and large firms dominating the industry. Although

not tabulated, our results, again, do not substantially change. The same is true if we focus on

replicating the results from analyzing strategic investors with our alternate set of controls and

changes to the sample of firms and countries we select. The core results remain qualitatively the

same.

25

5. Conclusion

As information intermediaries, financial analysts play an important role in disseminating

timely information into the financial markets on the current and future value of a stock. In this

paper we show that the type of information they collect and then impound into stock prices is

dependent on the quality of the audit and financial reporting environment. We also show that

audit quality is particularly important where there is more likely to be information asymmetries

between the firm and market. Specifically, we find that the compliance regime moderates the

type of information analysts impound dependent on the ownership structure of the firm and the

level of intangible assets a firm has.

Our results demonstrate that a good compliance regime, through the maintenance of a

strong audit and financial reporting environment, is extremely important in influencing the type

of information analysts impound into the market. From a policy perspective our results provide

impetus for regulators to pursue the enforcement of accounting standards to encourage market

efficiency and assist the role analysts can play as financial intermediaries within these markets in

revealing firm-level information.

26

References

Alford, A. W., & Berger, P. G. (1999). A simultaneous equations analysis of forecast accuracy,

analyst following, and trading volume. Journal of Accounting, Auditing and Finance,

14(3), 219-240.

Ball, R. (2006). International Financial Reporting Standards (IFRS): pros and cons for investors.

Accounting and Business Research, 36(3), 5-27.

Ball, R., Kothari, S. P., & Robin, A. (2000). The effect of international institutional factors on

properties of accounting earnings. Journal of Accounting and Economics, 29(1), 1-51.

Barniv, R., Myring, M. J., & Thomas, W. B. (2005). The association between the legal and

financial reporting environments and forecast performance of individual analysts.

Contemporary Accounting Research, 22(4), 727-758.

Barth, M.E., Kasznik, R., & McNichols, M. F. (2001). Analyst coverage and intangible assets.

Journal of Accounting Research, 39(1), 1-34.

Barth, M.E., Landsman, W.R. & Lang, M.H. (2008). International Accounting Standards and

Accounting Quality. Journal of Accounting Research, 46(3), 467-498.

Bhushan, R. (1989). Firm characteristics and analyst following. Journal of Accounting and

Economics, 11(2), 255-274.

Byard, D., Li, Y., & Yu, Y. (2011). The effect of mandatory IFRS adoption on financial analysts’

information environment. Journal of Accounting Research, 49(1), 69-96.

Chan, K., & Hameed, A. (2006). Stock price synchronicity and analyst coverage in emerging

markets. Journal of Financial Economics, 80(1), 115-147.

Dasgupta, S., Gan, J. & Gao, N. (2010). Transparency, Price Informativeness, and Stock Return

Synchronicity: Theory and Evidence. Journal of Financial and Quantitative Analysis,

45(5), 1189-1220.

27

Durnev, A., Morck, R., Yeung, B., & Zarowin, P. (2003). Does greater firm‐specific return

variation mean more or less informed stock pricing?. Journal of Accounting Research,

41(5), 797-836.

Fan, J. P., & Wong, T. J. (2002). Corporate ownership structure and the informativeness of

accounting earnings in East Asia. Journal of Accounting and Economics, 33(3), 401-425.

Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient

markets. The American Economic Review, 70(3), 393-408.

Hope, O. K. (2003). Disclosure practices, enforcement of accounting standards, and analysts’

forecast accuracy: An international study. Journal of Accounting Research, 41(2), 235-272.

Horton, J., Serafeim, G., & Serafeim, I. (2013). Does mandatory IFRS adoption improve the

information environment?. Contemporary Accounting Research, 30(1), 388-423.

Hutton, A. P., Marcus, A. J., & Tehranian, H. (2009). Opaque financial reports, R2, and crash

risk. Journal of Financial Economics, 94(1), 67-86.

Jiambalvo, J., Rajgopal, S., & Venkatachalam, M. (2002). Institutional ownership and the extent

to which stock prices reflect future earnings. Contemporary Accounting Research, 19(1),

117-145.

Jin, L., & Myers, S. C. (2006). R2 around the world: New theory and new tests. Journal of

Financial Economics, 79(2), 257-292.

Matolcsy, Z., & Wyatt, A. (2006). Capitalized intangibles and financial analysts. Accounting &

Finance, 46(3), 457-479.

Morck, R., Shleifer, A., & Vishny, R. W. (1988). Management ownership and market valuation:

An empirical analysis. Journal of Financial Economics, 20, 293-315.

28

Morck, R., Yeung, B., & Yu, W. (2000). The information content of stock markets: Why do

emerging markets have synchronous stock price movements? Journal of Financial

Economics, 58(1), 215-260.

Moyer, R. C., Chatfield, R. E., & Sisneros, P. M. (1989). Security analyst monitoring activity:

Agency costs and information demands. Journal of Financial and Quantitative Analysis,

24(4), 503-512.

Nguyen, N. H., & Truong, C. (2013). The information content of stock markets around the world:

A cultural explanation. Journal of International Financial Markets, Institutions & Money,

26, 1-29.

Piotroski, J. D., & Roulstone, D. T. (2004). The influence of analysts, institutional investors, and

insiders on the incorporation of market, industry, and firm-specific information into stock

prices. The Accounting Review, 79(4), 1119-1151.

Roll, R. (1988). The stochastic dependence of security price changes and transaction volumes:

Implications for the mixture‐of‐distributions hypothesis. The Journal of Finance, 43(3),

541-566.

Tan, H., Wang, S., & Welker, M. (2011). Analyst following and forecast accuracy after mandated

IFRS adoptions. Journal of Accounting Research, 49(5), 1307-1357.

Wurgler, J. (2000). Financial markets and the allocation of capital. Journal of Financial

Economics, 58(1), 187-214.

Wysocki, P. (2011). New institutional accounting and IFRS. Accounting and Business Research,

41(3), 309-328.

29

Table 1: Analyst Coverage This table reports the IFRS adoption year, number of firms and analyst coverage by country (Panel A) and industry (Panel B). The IFRS adoption year is obtained from the IFRS Foundation and IASPlus websites. The number of firms which adopt IFRS during the study period are obtained from the Thomson Reuters database. Analyst coverage data is provided by the Institutional Brokers' Estimate System database. Panel A: Country statistics

Year of IFRS

adoption No. of Firms

No. of analysts in each country

2008 2009 2010 2011 2012 2013 Total

Australia 2005 702 537 505 551 548 491 420 3,052

Brazil 2007 12 35 36 38 58 58 40 265

Chile 2005 5

4 4 5 13

China 2007 141 429 499 549 537 460 329 2,803

France 2005 383 1,278 1,205 1,262 1,172 986 880 6,783

Germany 2005 465 1,190 1,182 1,223 1,142 963 836 6,536

Hong Kong 2005 48 232 241 275 299 262 221 1,530

Israel 2006 53 34 35 37 43 37 36 222

Italy 2005 217 590 588 599 576 471 398 3,222

Philippines 2005 16 25 27 26 27 23 19 147

Poland 2005 157 118 160 161 160 137 117 853

Qatar 1995 22 23 37 56 65 44 37 262

South Africa 2005 174 174 200 205 187 162 130 1,058

Turkey 2008 23 85 93 98 113 98 87 574

United Arab Emirates 2003 50 59 95 101 115 78 66 514

United Kingdom 2005 1,216 1,602 1,683 1,695 1,666 1,435 1,181 9,262

Total 3684 6411 6586 6876 6712 5709 4802 37096

30

Table 1: Analyst Coverage (continued) Panel B: Industry statistics

Industry No. of Firms No. of analyst in each industry

2008 2009 2010 2011 2012 2013 Total

Energy 269 470 511 564 561 505 403 3014

Materials 507 791 825 860 891 783 634 4784

Industrials 660 1313 1375 1396 1373 1122 904 7483

Consumer Discretionary 580 1245 1224 1265 1199 1014 858 6805

Consumer Staples 192 602 605 623 582 494 384 3290

Health Care 250 456 455 458 410 352 306 2437

Financials 628 1034 1082 1056 990 837 677 5676

Information Technology 453 776 743 766 761 616 515 4177

Telecommunication Services 58 342 345 345 314 277 245 1868

Utilities 87 274 283 279 269 224 184 1513

Total 3684 7303 7448 7612 7350 6224 5110 41047

Note: The total number of analyst-year observations in panel B (41,047) is greater than panel A (37,096) because some analysts track multiple firms across multiple industries.

31

Table 2: Descriptive Statistics This table presents the descriptive statistics of the dependent variable and independent variables. SYNCH is the measure of annual stock return synchronicity calculated from equation (2) using the R2 obtained from the industry and market model regression of equation (1) from weekly observations. ANALYST is the natural logarithm of one plus the number of analysts. INVESTOR PROTECTION reflects the average score of transparency of related party transactions, director’s liability and ability to sue directors for misconduct. AUDITING STRENGTH reflects the auditing strength and quality of financial reporting of each country. INTANGIBLE is the total intangible assets relative to total assets of a firm. STRATEGIC HOLDINGS is the percentage of total strategic holdings relative to total number of shares. AGE is the number of years the firm has been listed on an exchange. DIVERSITY is a count of the number of SIC codes a firm’s operations are associated with. SIZE is the natural logarithm of company total assets. VOLUME is the natural logarithm of company yearly trading volume. COMPANY RISK is the annualised weekly stock return volatility. H INDEX is a revenue-based industry Herfindahl index. GNI is the logarithm of the gross national income of a country on a per capita basis. Panel A

Variables SYNCH ANALYST INVESTOR

PROTECTION AUDITING

STRENGTH INTANGIBLES AGE

Source of Data I/B/E/S database Doing Business

Database World Economic Forum Database

Thomson Reuters database

Thomson Reuters database

Country Average Std Dev Average Std Dev Average Std Dev Average Std Dev Average Std Dev Average Std Dev

Australia -1.42 0.92 1.20 1.06 5.70 0.00 6.03 0.16 0.06 0.14 14.96 18.08

Brazil -0.85 1.03 1.26 0.98 5.30 0.00 4.89 0.14 0.05 0.10 3.12 7.52

Chile -0.79 1.61 0.32 0.52 6.15 0.15 5.48 0.21 0.07 0.09 6.44 12.56

China -1.38 1.16 1.26 1.17 5.00 0.00 4.50 0.33 0.03 0.06 3.57 4.64

France -0.98 1.07 1.60 1.14 5.30 0.00 5.67 0.28 0.06 0.09 13.43 7.00

Germany -1.38 1.17 1.47 1.17 5.00 0.00 5.78 0.34 0.07 0.11 11.17 10.74

Hong Kong -1.22 1.06 1.42 1.28 8.95 0.11 6.05 0.13 0.04 0.12 6.75 8.71

Israel -0.91 1.11 0.55 0.63 8.30 0.00 5.63 0.25 0.04 0.09 7.24 8.98

Italy -0.58 0.97 1.50 1.12 6.00 0.00 4.20 0.19 0.09 0.14 9.71 6.40

Philippines -0.62 0.93 1.06 0.82 4.30 0.00 4.92 0.19 0.03 0.11 33.20 28.60

Poland -0.93 0.96 0.94 0.97 6.00 0.00 4.89 0.31 0.05 0.10 6.89 10.11

Qatar 1.95 5.39 1.08 1.01 4.30 0.00 5.70 0.30 0.01 0.06 8.99 11.04

South Africa -1.39 1.03 1.31 0.95 8.00 0.00 6.34 0.15 0.04 0.10 16.20 19.83

Turkey -0.21 0.87 1.41 1.25 5.63 0.15 4.48 0.21 0.04 0.09 6.56 10.00

United Arab Emirates 1.13 6.12 1.19 1.00 4.00 0.00 5.32 0.08 0.03 0.08 8.30 10.79

United Kingdom -1.55 1.08 1.29 1.07 8.00 0.00 5.89 0.23 0.10 0.17 19.94 24.31

Simple Average# -0.70 1.66 1.18 1.01 6.00 0.03 5.36 0.22 0.05 0.10 11.03 12.46

Simple Median# -0.92 1.07 1.26 1.04 5.67 0.00 5.56 0.21 0.05 0.10 8.64 10.43 # Simple averages/medians are country equally-weighted statistics.

32

Table 2: Descriptive Statistics (continued) Panel B

Variables STRATEGIC HOLDINGS

DIVERSITY SIZE VOLUME COMPANY RISK H INDEX GNI

Source of Data Datastream database

Datastream database

Thomson Reuters database

Datastream database

Datastream database

Thomson Reuters database

World Bank database

Country Average Std Dev Average Std Dev Average Std Dev Average Std Dev Average Std Dev Average Std Dev Average Std Dev

Australia 0.27 0.20 2.98 1.90 19.01 2.28 11.30 2.05 0.63 0.38 0.17 0.06 10.83 0.16

Brazil 0.33 0.30 3.92 2.23 21.60 1.89 10.91 1.98 0.40 0.20 0.18 0.14 9.18 0.17

Chile 0.04 0.12 5.80 2.07 24.03 3.31 12.85 1.87 0.25 0.12 0.25 0.15 9.39 0.17

China 0.47 0.29 3.53 1.87 22.59 2.69 13.48 2.41 0.51 0.24 0.10 0.09 8.42 0.26

France 0.49 0.26 3.65 2.10 20.51 2.39 8.34 2.92 0.38 0.18 0.16 0.12 10.68 0.01

Germany 0.38 0.30 3.62 1.96 19.66 2.41 6.05 1.88 0.45 0.28 0.19 0.08 10.72 0.03

Hong Kong 0.47 0.27 4.15 2.04 22.00 2.08 12.32 3.01 0.51 0.27 0.10 0.07 10.46 0.06

Israel 0.59 0.22 3.53 2.63 21.35 2.48 9.86 2.68 0.42 0.23 0.18 0.19 10.30 0.09

Italy 0.51 0.23 4.82 2.29 20.72 2.31 10.50 2.60 0.42 0.23 0.25 0.14 10.52 0.02

Philippines 0.24 0.28 3.25 1.65 23.90 2.39 12.78 1.77 0.39 0.21 0.13 0.03 7.90 0.12

Poland 0.54 0.26 5.01 2.31 20.63 1.99 8.92 2.17 0.44 0.20 0.24 0.20 9.44 0.03

Qatar 0.20 0.23 3.82 1.47 23.46 1.49 10.35 1.57 0.38 0.19 0.42 0.27 11.19 0.10

South Africa 0.32 0.24 4.26 2.15 22.44 2.06 11.17 1.86 0.34 0.24 0.15 0.06 8.81 0.10

Turkey 0.18 0.25 3.13 2.26 21.12 1.86 12.83 1.53 0.41 0.16 0.19 0.13 9.22 0.07

United Arab Emirates

0.37 0.28 4.68 2.12 23.02 1.66 12.44 3.12 0.47 0.23 0.24 0.10 10.53 0.09

United Kingdom 0.32 0.22 2.97 1.79 18.46 2.56 10.19 2.58 0.53 0.38 0.44 1.91 10.65 0.05

Simple Average# 0.36 0.25 3.95 2.05 21.53 2.24 10.89 2.25 0.43 0.23 0.21 0.23 9.89 0.10

Simple Median# 0.35 0.26 3.74 2.09 21.48 2.30 11.04 2.11 0.42 0.23 0.19 0.12 10.38 0.09 # Simple averages/medians are country equally-weighted statistics.

33

Table 3: Descriptive Statistics This table presents the descriptive statistics of the dependent variable and independent variables under different institutional environment. SYNCH is the measure of annual stock return synchronicity calculated from equation (2) using the R2 obtained from the industry and market model regression of equation (1) from weekly observations. ANALYST is the natural logarithm of one plus the number of analysts. INVESTOR PROTECTION reflects the average score of transparency of related party transactions, director’s liability and ability to sue directors for misconduct. AUDITING STRENGTH reflects the auditing strength and quality of financial reporting of each country. INTANGIBLE is the total intangible assets relative to total assets of a firm. STRATEGIC HOLDINGS is the percentage of total strategic holdings relative to total number of shares. AGE is the number of years the firm has been listed on an exchange. DIVERSITY is a count of the number of SIC codes a firm’s operations are associated with. SIZE is the natural logarithm of company total assets. VOLUME is the natural logarithm of company yearly trading volume. COMPANY RISK is the annualised weekly stock return volatility. H INDEX is a revenue-based industry Herfindahl index. GNI is the logarithm of the gross national income of a country on a per capita basis.

INVESTOR PROTECTION AUDITING STRENGTH

High Low Different Strong Weak Different

Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median

SYNCH -1.3079 -1.2623 -1.1210 -1.1266 -0.1869*** -0.1357*** -1.3681 -1.3130 -1.0778 -1.0572 -0.2903*** -0.2558***

ANALYST 1.3858 1.0986 1.5056 1.3863 -0.1198*** -0.2877*** 1.3906 1.0986 1.4904 1.3863 -0.0998*** -0.2877***

INVESTOR PROTECTION

6.8825 8.0000 6.0636 5.7000 0.8190*** 2.3000***

AUDITING STRENGTH

5.6195 5.8669 5.7278 5.7911 -0.1083*** 0.0757

INTANGIBLES 0.0889 0.0220 0.0634 0.0168 0.0255*** 0.0053*** 0.0776 0.0155 0.0768 0.0236 0.0008 -0.0082***

STRATEGIC HOLDINGS

0.3895 0.3827 0.3805 0.3756 0.0091** 0.0071*** 0.3379 0.3117 0.4323 0.4700 -0.0944*** -0.1583***

AGE 17.5425 10.3069 12.8430 10.5507 4.6996*** -0.2438*** 16.7871 9.8534 14.0017 10.9425 2.7855*** -1.0890***

DIVERSITY 3.6838 3.0000 3.5162 3.0000 0.1676*** 0.0000*** 3.3487 3.0000 3.8625 3.0000 -0.5138*** 0.0000***

SIZE 19.5473 19.4670 20.1205 19.7474 -0.5732*** -0.2804*** 19.4055 19.1386 20.2107 19.9929 -0.8052*** -0.8543***

VOLUME 10.2544 10.3244 8.9895 8.7909 1.2649*** 1.5334*** 9.9603 10.2614 9.3910 9.3529 0.5694*** 0.9085***

COMPANY RISK 0.4879 0.4079 0.4967 0.4235 -0.0088* -0.0156*** 0.5681 0.4955 0.4165 0.3608 0.1516*** 0.1347***

H INDEX 0.3907 0.1499 0.1729 0.1560 0.2178*** -0.0061*** 0.4140 0.1456 0.1794 0.1560 0.2346*** -0.0104***

GNI 10.3103 10.5989 10.4839 10.6899 -0.1736*** -0.0910*** 10.4616 10.6808 10.3190 10.5989 0.1427*** 0.0819***

*, ** and *** denote significance at the 10%, 5% and 1% level, respectively.

34

Table 4: Correlation Matrix This table presents the correlation matrix for the dependent and independent variables. SYNCH is the measure of annual stock return synchronicity. ANALYST is the natural logarithm of one plus the number of analysts. INVESTOR PROTECTION reflects the average score of transparency of related party transactions, director’s liability and ability to sue directors for misconduct. AUDITING STRENGTH reflects the auditing strength and quality of financial reporting of each country. INTANGIBLE is the total intangible assets relative to total assets of a firm. STRATEGIC HOLDINGS is the percentage of total strategic holdings relative to total number of shares. AGE is the number of years the firm has been listed on an exchange. DIVERSITY is a count of the number of SIC codes a firm’s operations are associated with. SIZE is the natural logarithm of company total assets. VOLUME is the natural logarithm of company yearly trading volume. COMPANY RISK is the annualised weekly stock return volatility. H INDEX is a revenue-based industry Herfindahl index. GNI is the logarithm of the gross national income of a country on a per capita basis.

SYNCH ANALYST INVESTOR

PROTECTION AUDITING

STRENGTH INTANGIBLES

STRATEGIC HOLDINGS

AGE DIVERSITY SIZE VOLUME COMPANY

RISK H

INDEX GNI

SYNCH 1.0000

ANALYST 0.3578 1.0000

INVESTOR PROTECTION

-0.1721 -0.0430 1.0000

AUDITING STRENGTH

-0.1388 -0.0150 0.3629 1.0000

INTANGIBLES -0.0287 0.0349 0.0929 0.0283 1.0000

STRATEGIC HOLDINGS

-0.0837 -0.0594 -0.0734 -0.1800 -0.0145 1.0000

AGE 0.0334 0.2258 0.1737 0.1396 -0.0487 -0.0518 1.0000

DIVERSITY 0.1560 0.2359 -0.1030 -0.1790 -0.0508 0.0909 0.1637 1.0000

SIZE 0.3846 0.5909 -0.1991 -0.2331 -0.1508 0.0160 0.1141 0.3597 1.0000

VOLUME 0.2709 0.3880 0.1528 -0.0743 0.0356 -0.2811 0.0596 0.0553 0.3981 1.0000

COMPANY RISK -0.0219 -0.2407 0.0379 0.1467 0.0674 -0.1202 -0.1279 -0.1625 -0.3255 0.1125 1.0000

H INDEX 0.0198 -0.0045 0.0838 0.0828 -0.0264 -0.0143 -0.0045 -0.0310 -0.0048 -0.0048 0.0271 1.0000

GNI -0.0298 0.0277 0.0306 0.3848 0.0854 -0.0505 0.1137 -0.1361 -0.3647 -0.2230 0.0902 0.0513 1.0000

35

Table 5: Dependent variable: SYNCH

Reg 5.1 Reg 5.2 Reg 5.3 Reg 5.4 Reg 5.5 Reg 5.6 Reg 5.7

FULL

SAMPLE Strong Auditing Strength & Quality of

Financial Reporting Weak Auditing Strength & Quality of

Financial Reporting

ANALYST 0.2617 0.2203 0.2379 0.1981 0.2777 0.279 0.2462

[10.8084]*** [13.4887]*** [12.2969]*** [9.4594]*** [7.5028]*** [7.1161]*** [5.5040]***

DD_ AUDITING STRENGTH 0.0535

[1.5954]

DD_INVESTOR PROTECTION

-1.2282 -2.6099 -2.6084 -2.6157 -2.1068 -2.1071 -2.1494

[-2.3565]** [-1.8039]* [-1.8005]* [-1.8093]* [-3.5241]*** [-3.5235]*** [-3.6211]***

ANALYST x DD_ AUDITING STRENGTH

-0.0497

[-3.1344]***

DD_INTANGIBLES -0.1325 -0.0778 -0.1324 0.0807 0.0844 0.0783

[-6.3749]*** [-2.1720]** [-6.3655]*** [2.1009]** [1.8093] [2.0248]**

DD_AGE -0.0027 -0.0021 -0.0586 0.0537 0.0537 -0.0254

[-0.1221] [-0.0941] [-1.5573] [1.6348] [1.6359] [-0.4791]

ANALYST x DD_INTANGIBLES

-0.0384

-0.0025

[-1.9579]*

[-0.1062]

ANALYST x DD_AGE 0.0416

0.0558

[1.9769]**

[2.0708]**

DIVERSITY 0.0062 0.0086 0.0092 0.008 0.0038 0.0039 0.0035

[1.3441] [1.5315] [1.6392] [1.4341] [0.5273] [0.5320] [0.4803]

SIZE 0.0839 0.0729 0.0726 0.0727 0.073 0.073 0.0719

[13.1874]*** [10.5922]*** [10.5431]*** [10.5790]*** [5.9534]*** [5.9500]*** [5.9124]***

VOLUME 0.0751 0.0813 0.0813 0.0799 0.0686 0.0686 0.0672

[7.8273]*** [13.6845]*** [13.7050]*** [13.4190]*** [3.6182]*** [3.6213]*** [3.5588]***

COMPANY RISK 0.4518 0.3839 0.3846 0.3812 0.5249 0.5248 0.5207

[12.8416]*** [9.6373]*** [9.6766]*** [9.5832]*** [7.4192]*** [7.4181]*** [7.3167]***

H INDEX 0.0248 0.0279 0.0277 0.0276 0.604 0.6033 0.6082

[4.0035]*** [4.6835]*** [4.6584]*** [4.6486]*** [3.7602]*** [3.7378]*** [3.7969]***

GNI 0.726 0.2417 0.2404 0.2495 -0.4796 -0.4795 -0.4951

[4.0353]*** [1.2862] [1.2795] [1.3293] [-1.7586]* [-1.7592]* [-1.8118]*

Constant and fixed effects YES YES YES YES YES YES YES

Observations: 16367 8141 8141 8141 8226 8226 8226

R-squared: 0.2815 0.2838 0.2841 0.2842 0.2712 0.2712 0.2716

F-statistic: 291.05*** 201.22*** 189.67*** 189.69*** 145.41*** 138.78*** 139.04***

All regression results are from Panel Least Squares regressions with heteroskedasticity and contemporaneous correlation corrected standard errors. T-statistics are in brackets. SYNCH is the measure of annual stock price synchronicity. ANALYST is the natural logarithm of one plus the number of analysts. DD_INVESTOR PROTECTION is a dummy variable that equals one if INVESTOR PROTECTION is greater than the median value across the sample of countries and zero otherwise. INVESTOR PROTECTION reflects the average score of transparency of related party transactions, director’s liability and ability to sue directors for misconduct. DD_AUDITING STRENGTH is a dummy variable that equals one if AUDITING STRENGTH is greater than the median value across our sample of countries and zero otherwise. AUDITING STRENGTH reflects the auditing strength and quality of financial reporting of each country. DD_INTANGIBLES is a dummy variable that equals one if INTANGIBLES is greater than the median value across our sample of countries and zero otherwise. INTANGIBLES is the total intangible assets relative to total assets of a firm. DD_AGE is a dummy variable that equals one if AGE is greater than the median value across our sample of countries and zero otherwise. AGE is the number of years the firm has been listed on an exchange. DIVERSITY is a count of the number of SIC codes a firm’s operations are associated with. SIZE is the natural logarithm of company total assets. VOLUME is the natural logarithm of company yearly trading volume. COMPANY RISK is the annualised weekly stock return volatility. H INDEX is a revenue-based industry Herfindahl index. GNI is the logarithm of the gross national income of a country on a per capita basis. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.

36

Table 6: Dependent variable: SYNCH

Reg 6.1 Reg 6.2 Reg 6.3 Reg 6.4 Reg 6.5 Reg 6.6 Reg 6.7

FULL

SAMPLE High Investor Protection Low Investor Protection

ANALYST 0.151 0.1806 0.2068 0.1189 0.3282 0.3278 0.3249

[7.4011]*** [12.1400]*** [11.7521]*** [6.5307]*** [6.0788]*** [6.8424]*** [6.7986]***

DD_ AUDITING STRENGTH -0.3025 -0.1227 -0.1224 -0.1237 0.1847 0.1847 0.1846

[-15.4051]*** [-4.5099]*** [-4.5008]*** [-4.5541]*** [0.5407] [0.5405] [0.5404]

DD_INVESTOR PROTECTION

-0.0415

[-1.4268]

ANALYST x DD_INVESTOR PROTECTION

-0.0278

[-1.7864]*

DD_INTANGIBLES -0.0678 0.0013 -0.0712 0.059 0.0576 0.059

[-3.4437]*** [0.0383] [-3.6239]*** [1.0854] [0.9765] [1.0860]

DD_AGE 0.0509 0.0526 -0.0987 -0.0145 -0.0145 -0.0229

[2.4182]** [2.4983]** [-2.8910]*** [-0.2985] [-0.2979] [-0.2688]

ANALYST x DD_INTANGIBLES

-0.05

0.0009

[-2.7964]***

[0.0291]

ANALYST x DD_AGE 0.1124

0.0058

[5.8515]***

[0.1329]

DIVERSITY 0.0236 0.0166 0.0172 0.0164 -0.0037 -0.0038 -0.0039

[5.0189]*** [3.2336]*** [3.3428]*** [3.2048]*** [-0.3397] [-0.3445] [-0.3665]

SIZE 0.1574 0.106 0.1052 0.1045 0.0306 0.0306 0.0306

[19.5520]*** [15.5577]*** [15.3865]*** [15.2988]*** [1.8033]* [1.7984]* [1.7937]

VOLUME 0.0502 0.0814 0.0809 0.0786 0.0664 0.0664 0.0662

[8.4428]*** [14.6184]*** [14.5768]*** [14.0686]*** [3.2847]*** [3.2836]*** [3.2491]***

COMPANY RISK 0.4649 0.3952 0.3929 0.3904 0.509 0.509 0.5081

[12.4987]*** [10.0887]*** [10.0744]*** [10.0753]*** [3.1749]*** [3.1836]*** [3.1971]***

H INDEX 0.0238 0.0268 0.0265 0.0261 1.1723 1.172 1.1717

[3.7349]*** [4.3439]*** [4.2971]*** [4.2419]*** [3.6217]*** [3.6444]*** [3.6285]***

GNI 0.2807 0.1513 0.1511 0.184 -0.5732 -0.5733 -0.5753

[13.0686]*** [0.8421] [0.8409] [1.0256] [-1.3772] [-1.3773] [-1.3737]

Constant and fixed effects YES YES YES YES YES YES YES

Observations: 16367 8859 8859 8859 7508 7508 7508

R-squared: 0.2227 0.3616 0.3622 0.364 0.2341 0.2341 0.2341

F-statistic: 360.47*** 313.04*** 295.29*** 297.69*** 127.17*** 120.46*** 120.46***

All regression results are from Panel Least Squares regressions with heteroskedasticity and contemporaneous correlation corrected standard errors. T-statistics are in brackets. SYNCH is the measure of annual stock price synchronicity. ANALYST is the natural logarithm of one plus the number of analysts. DD_INVESTOR PROTECTION is a dummy variable that equals one if INVESTOR PROTECTION is greater than the median value across the sample of countries and zero otherwise. INVESTOR PROTECTION reflects the average score of transparency of related party transactions, director’s liability and ability to sue directors for misconduct. DD_AUDITING STRENGTH is a dummy variable that equals one if AUDITING STRENGTH is greater than the median value across our sample of countries and zero otherwise. AUDITING STRENGTH reflects the auditing strength and quality of financial reporting of each country. DD_INTANGIBLES is a dummy variable that equals one if INTANGIBLES is greater than the median value across our sample of countries and zero otherwise. INTANGIBLES is the total intangible assets relative to total assets of a firm. DD_AGE is a dummy variable that equals one if AGE is greater than the median value across our sample of countries and zero otherwise. AGE is the number of years the firm has been listed on an exchange. DIVERSITY is a count of the number of SIC codes a firm’s operations are associated with. SIZE is the natural logarithm of company total assets. VOLUME is the natural logarithm of company yearly trading volume. COMPANY RISK is the annualised weekly stock return volatility. H INDEX is a revenue-based industry Herfindahl index. GNI is the logarithm of the gross national income of a country on a per capita basis. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.

37

Table 7: Dependent variable: SYNCH

Reg 7.1 Reg 7.2 Reg 7.3 Reg 7.4

Strong Auditing Strength & Quality of

Financial Reporting Weak Auditing Strength & Quality of Financial

Reporting

ANALYST 0.2144 0.1803 0.2743 0.2775

[12.3309]*** [10.9507]*** [7.2711]*** [6.7466]***

DD_AGE 0.002 0.0512 0.0542 0.0521

[0.0896] [2.4362]** [1.6334] [1.5761]

DD_INVESTOR PROTECTION -2.6266 -3.3021 -2.0717 -2.0735

[-1.8034]* [-2.2830]** [-3.4374]*** [-3.4527]***

DD_SH BOTTOM25 0.0943

0.0562

[2.2868]**

[0.7909]

DD_SH TOP25 -0.1532

-0.0789

[-3.2702]***

[-1.6021]

ANALYST*DD_SH BOTTOM25 -0.0227

0.0522

[-1.1296]

[1.6814]*

ANALYST*DD_SH TOP25 0.0352

0.0244

[1.3224]

[0.8302]

Constant and fixed effects YES YES YES YES

Control variables YES YES YES YES

Observations: 8141 8141 8226 8226

R-squared: 0.2812 0.3417 0.2723 0.2708

F-statistic: 186.96*** 191.55*** 139.49*** 138.50***

All regression results are from Panel Least Squares regressions with heteroskedasticity and contemporaneous correlation corrected standard errors. T-statistics are in brackets. SYNCH is the measure of annual stock price synchronicity. ANALYST is the natural logarithm of one plus the number of analysts. DD_AGE is a dummy variable that equals one if AGE is greater than the median value across our sample of countries and zero otherwise. AGE is the number of years the firm has been listed on an exchange. DD_INVESTOR PROTECTION is a dummy variable that equals one if INVESTOR PROTECTION is greater than the median value across the sample of countries and zero otherwise. INVESTOR PROTECTION reflects the average score of transparency of related party transactions, director’s liability and ability to sue directors for misconduct. DD_SH BOTTOM25 is a dummy variable that equals one if STRATEGIC HOLDINGS is in the lowest quartile across our sample of countries and zero otherwise. DD_SH TOP25 is a dummy variable that equals one if STRATEGIC HOLDINGS is in the highest quartile across our sample of countries and zero otherwise. STRATEGIC HOLDINGS is the percentage of total strategic holdings relative to total number of shares. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.

38

Table 8: Dependent variable: SYNCH

Reg 8.1 Reg 8.2 Reg 8.3 Reg 8.4

High Investor Protection Low Investor Protection

ANALYST 0.1571 0.186 0.3456 0.2988

[5.5754]*** [7.3159]*** [6.0696]*** [5.4234]***

DD_AGE 0.0519 0.0510 -0.0103 -0.0035

[1.2428] [1.2040] [-0.2052] [-0.0681]

DD_STRENGTH_AUDITING -0.1229 -0.1229 0.1825 0.1844

[-0.6244] [-0.6235] [0.5352] [0.5401]

DD_SH_BOTTOM25 0.0118 0.1188

[0.2027] [1.7225]*

DD_SH_TOP25 0.0357

-0.3098

[0.8141]

[-3.7942]***

ANALYST*DD_SH_BOTTOM25 0.0589 -0.0329

[2.1297]** [-1.2951]

ANALYST*DD_SH_TOP25 -0.0576

0.1226

[-3.2513]***

[3.9761]***

Constant and fixed effects YES YES YES YES

Control variables YES YES YES YES

Observations: 8859 8859 7508 7508

R-squared: 0.3628 0.3613 0.2343 0.2361

F-statistic: 296.10*** 294.19*** 120.58*** 121.82***

All regression results are from Panel Least Squares regressions with heteroskedasticity and contemporaneous correlation corrected standard errors. T-statistics are in brackets. SYNCH is the measure of annual stock price synchronicity. ANALYST is the natural logarithm of one plus the number of analysts. DD_AGE is a dummy variable that equals one if AGE is greater than the median value across our sample of countries and zero otherwise. AGE is the number of years the firm has been listed on an exchange. DD_INVESTOR PROTECTION is a dummy variable that equals one if INVESTOR PROTECTION is greater than the median value across the sample of countries and zero otherwise. INVESTOR PROTECTION reflects the average score of transparency of related party transactions, director’s liability and ability to sue directors for misconduct. DD_SH BOTTOM25 is a dummy variable that equals one if STRATEGIC HOLDINGS is in the lowest quartile across our sample of countries and zero otherwise. DD_SH TOP25 is a dummy variable that equals one if STRATEGIC HOLDINGS is in the highest quartile across our sample of countries and zero otherwise. STRATEGIC HOLDINGS is the percentage of total strategic holdings relative to total number of shares. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.

39

Table 9: Placebo Test: Dependent variable: SYNCH

coef Reg 5.1 Reg 6.1

ANALYST negative 0 0

positive 100 100

DD_INVESTOR PROTECTION negative 3 12

positive 8 29

DD_AUDITING STRENGTH negative 0 0

positive 76 24

ANALYST x DD_AUDITING STRENGTH negative 49

positive 0

ANALYST x DD_INVESTOR PROTECTION negative 13

positive 4

Constant and fixed effects

YES YES

Control variables

YES YES

All regression results are from Panel Least Squares regressions with heteroskedasticity and contemporaneous correlation corrected standard errors. SYNCH is the measure of annual stock price synchronicity. ANALYST is the natural logarithm of one plus the number of analysts. DD_INVESTOR PROTECTION is a dummy variable that equals one if INVESTOR PROTECTION is greater than the median value across the sample of countries and zero otherwise. INVESTOR PROTECTION reflects the average score of transparency of related party transactions, director’s liability and ability to sue directors for misconduct. DD_AUDITING STRENGTH is a dummy variable that equals one if AUDITING STRENGTH is greater than the median value across our sample of countries and zero otherwise. AUDITING STRENGTH reflects the auditing strength and quality of financial reporting of each country.

40

Table 10: Robustness Tests Panel A: New Control Variables

Dependent variable: SYNCH Reg 10.1 Reg 10.2 Reg 10.3 Reg 10.4

Auditing Strength & Quality of Financial

Reporting Investor Protection

Strong Weak High Low

ANALYST 0.2688*** 0.3346*** 0.2478*** 0.3732*** DD_INTANGIBLES -0.0646* 0.1334*** 0.0512 0.0588 DD_AGE -0.0136 0.0591* 0.0625*** -0.0100 ANALYST x DD_INTANGIBLES -0.0516*** -0.0380* -0.0982*** 0.0012 DD_INVESTOR PROTECTION -2.284 -0.7431

DD_ AUDITING STRENGTH

-0.1521*** -0.167

Constant and fixed effects YES YES YES YES Control variables YES YES YES YES Observations: 8072 8273 8811 7534 R-squared: 0.3069 0.3002 0.372 0.2704 F-statistic: 209.74*** 160.84*** 306.38*** 146.55***

Panel B: Excluding Large Firms

Dependent variable: SYNCH Reg 10.5 Reg 10.6 Reg 10.7 Reg 10.8

Auditing Strength & Quality of Financial Reporting

Investor Protection

Strong Weak High Low

ANALYST 0.2699*** 0.2054*** 0.2356*** 0.2283*** DD_INTANGIBLES -0.0624* 0.0356 0.0269 -0.0688 DD_AGE 0.006 0.0711*** 0.067*** -0.0148 ANALYST x DD_INTANGIBLES -0.0507*** -0.0328* -0.0626*** -0.0154 DD_INVESTOR PROTECTION -0.029 -0.6681***

DD_AUDITING STRENGTH -0.1743*** 0.1963

Constant and fixed effects YES YES YES YES Control variables YES YES YES YES Observations: 7872 7560 8398 7034 R-squared: 0.2961 0.3643 0.3689 0.2967 F-statistic: 236.04*** 288.22*** 350.01*** 211.53***

Panel C: Excluding Small Countries

Dependent variable: SYNCH Reg 10.9 Reg 10.10 Reg 10.11 Reg 10.12

Auditing Strength & Quality of Financial Reporting

Investor Protection

Dep. Var: Strong Weak High Low

ANALYST 0.2049*** 0.1761*** 0.1871*** 0.3531*** DD_INTANGIBLES -0.0714* 0.0105 0.009 0.0436 DD_AGE -0.0256 -0.0524 0.0519** 0.0197 ANALYST x DD_INTANGIBLES -0.0498** -0.02 -0.0577*** -0.0267 DD_INVESTOR PROTECTION -0.0056 -0.0785

DD_ AUDITING STRENGTH -0.1147*** 0.2521

Constant and fixed effects YES YES YES YES Control variables YES YES YES YES Observations: 8029 7947 8692 7284 R-squared: 0.26 0.2318 0.3442 0.2237 F-statistic: 201.16*** 159.58*** 325.26*** 149.58***

All regression results are from Panel Least Squares regressions with heteroskedasticity and contemporaneous correlation corrected standard errors. SYNCH is the measure of annual stock price synchronicity ANALYST is the natural logarithm of one plus the number of analysts. DD_INVESTOR PROTECTION is a dummy variable that equals one if INVESTOR PROTECTION is greater than the median value across the sample of countries and zero otherwise. INVESTOR PROTECTION reflects the average score of transparency of related party transactions, director’s liability and ability to sue directors for misconduct. DD_AUDITING STRENGTH is a dummy variable that equals one if AUDITING STRENGTH is greater than the median value across our sample of countries and zero otherwise. AUDITING STRENGTH reflects the auditing strength and quality of financial reporting of each country. DD_INTANGIBLES is a dummy variable that equals one if INTANGIBLES is greater than the median value across our sample of countries

41

and zero otherwise. INTANGIBLES is the total intangible assets relative to total assets of a firm. DD_AGE is a dummy variable that equals one if AGE is greater than the median value across our sample of countries and zero otherwise. AGE is the number of years a firm has been listed in an exchange. The new set of controls used in Panel A are the log of the market capitalization of the firm, stock market volatility, a dummy variable equal to one if the firm has business operations in more than one SIC, and zero otherwise, trading volume by total outstanding shares, and the logarithm of a country’s GDP per capita. In Panel B we remove from the sample the five largest firms in each country. In Panel C we delete countries with less than 100 firms. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.