Electronic copy available at: http://ssrn.com/abstract=981725Electronic copy available at: http://ssrn.com/abstract=981725

Financial Development and Asymmetric Information

Angelos A. Antzoulatos* Chris Tsoumas Dimitris Kyriazis

antzoul@unipi.gr ctsoum@unipi.gr dkyr@unipi.gr (210) 414-2185 (210) 414-2155 (210) 414-2464

Department of Banking & Financial Management University of Piraeus

80 Karaoli & Dimitriou street Piraeus 18534, Greece

Abstract. We test the hypothesis that the degree of asymmetric information should decrease as

financial systems develop, in a panel co-integration framework with annual data for 32

countries. To this end, we extend Barron et al.’s (1998) model to derive a measure of

analysts’ consensus that takes into account biases and herding in their forecasts. We

calculate this measure, which is negatively related to asymmetric information, at the

country level, with data from the I/B/E/S Global Aggregates database. In addition, we

proxy financial development with the extensive set of indices found in the Word Bank’s

Financial Development and Structure database. Consistent with expectations, and

despite the substantial differences across countries in terms of financial development

and the quality of the institutional framework, the measure of analysts’ consensus is

positively related to indices proxying for the development of the financial system.

JEL Classification Numbers: D82, G14, G20

Keywords: Asymmetric Information, Analysts’ Forecasts, Financial Development

___________ * Corresponding author. We thank Charles Calomiris, Martin Gruber, Gikas Hardouvelis, Gregory Koutmos, Roman Kräussl, Dimitris Malliaropoulos, Chris Pantzalis, Nikitas Pittis, George Skiadopoulos, Ekaterini Panopoulou and seminar participants at the 63rd International Atlantic Economic Conference, Madrid, March 2007, the 11th International Conference on Macroeconomic Analysis, Crete, Greece, May 2007, the 5th Infinity Conference on International Finance, Dublin, June 2007 and the University of Piraeus, for several insightful comments on an earlier version of this paper. The usual disclaimer applies.

Electronic copy available at: http://ssrn.com/abstract=981725Electronic copy available at: http://ssrn.com/abstract=981725

Financial Development and Asymmetric Information

Abstract.

We test the hypothesis that the degree of asymmetric information should decrease as

financial systems develop, in a panel co-integration framework with annual data for 32

countries. To this end, we extend Barron et al.’s (1998) model to derive a measure of

analysts’ consensus that takes into account biases and herding in their forecasts. We

calculate this measure, which is negatively related to asymmetric information, at the

country level, with data from the I/B/E/S Global Aggregates database. In addition, we

proxy financial development with the extensive set of indices found in the Word Bank’s

Financial Development and Structure database. Consistent with expectations, and

despite the substantial differences across countries in terms of financial development

and the quality of the institutional framework, the measure of analysts’ consensus is

positively related to indices proxying for the development of the financial system.

JEL Classification Numbers: D82, G14, G20

Keywords: Asymmetric Information, Analysts’ Forecasts, Financial Development

1. Introduction.

In this paper, we explore the well-developed but, to the best of our knowledge, not

tested so far hypothesis that the degree of asymmetric information should decrease as

financial systems develop and move towards the capital-market based norm –as

opposed to the bank-based one. In short, the need for public information, which is

crucial for the assessment of firms’ prospects and, hence, for giving them the external

funds they need in order to realize these prospects, is higher in capital-market based

financial systems than in bank-based ones. In the latter, the private information banks

acquire thanks to their close relationships with the borrowing firms, together with

banks’ higher bargaining leverage vis-à-vis these firms as compared to the leverage of

bond-holders and minority shareholders, attenuate this need. For more details, the

interested reader may refer to Beim and Calomiris (2001, pp. 150-192) and Mishkin

(2000, pp. 181-198). For a more robust theoretical framework, the reader may refer to

Diamond (1984).

Without doubt, the lack of relevant empirical work owes much to the difficulty

of quantifying the degree of asymmetric information and of financial development, both

of which are qualitative and multi-faceted. For example, to construct an index for the

degree of asymmetric information, it is not sufficient to take into account the

regulations concerning the quality, quantity and timeliness of information disclosed by

firms. Additionally, one has to take into account the incentives for compliance and the

quality of law enforcement. Indeed, not a simple task!

To overcome this difficulty, we combine two branches of the literature, ‘finance

and growth’ and ‘corporate finance’. The first proposes several proxies for financial

development, the second for asymmetric information. Levine (2004) provides an

excellent survey of the former, Clarke and Shastri (2001) a comprehensive list and a

2

neat taxonomy of the latter. Clarke and Shastri additionally discuss the theoretical

ambiguity concerning the relationship of most asymmetric information proxies with

asymmetric information, that is, whether it is positive or negative.

Compared to the existing literature, we use a more extensive set of financial

development indices, while our asymmetric information measure has more robust

theoretical foundations. Starting with the second, from the asymmetric information

proxies used so far, we work with the dispersion of analysts’ earnings forecasts,

perhaps, the most widely used proxy and with the strongest theoretical background.

Barry and Brown (1985) show that analysts’ beliefs about firms’ earnings tend to

converge as the amount of public information increases. Barry and Jennings (1992)

confirm this theoretical conclusion, even though they show that the effect of private

information on analysts’ diversity of opinion depends on the relative amounts of private

and public information in the market.

However, Barron et al. (1998) (henceforth BOSD) show that analysts’ consensus

is a better proxy of asymmetric information. This proxy, defined as the ratio of common

among analysts uncertainty to overall uncertainty, is negatively related with asymmetric

information. As BOSD show, it can be calculated from the observed statistics of their

forecasts. Botosan and Harris (2000), Barron et al. (2002) and Liang (2003) are among

those who have used this proxy.

To make a long story short, our testable hypothesis is that the consensus in

analysts’ forecasts should be positively related with indices that measure the

development of the financial system. Yet, in practice, analysts’ forecasts, and the

measure of consensus derived from them, is contaminated by several weaknesses that

potentially affect its relationship with the financial development proxies. These

weaknesses pertain to the conflicting incentives analysts face, rational biases, cognitive

3

biases and herding (see, for example, Antia and Pantzalis, 2006; Cooper et al., 2001;

Friesen and Weller, 2006; Lim, 2001).

However, by extending BOSD’s model, we show that the testable hypothesis

still holds. Briefly, we postulate that analysts produce their forecasts in two stages. In

the first, they perform a signal-extraction exercise that reflects the properties of their

information environment, as in BOSD. In the second, they adjust their first-stage

unbiased forecasts based on the aforementioned weaknesses. Each analyst knows his

first-stage forecasts, while the other analysts, as well as the investors, observe his

reported second-stage forecasts.

The identifying assumption is that the magnitude of the second-stage

adjustments decreases as asymmetric information decreases. So does their effect on the

consensus of analysts’ forecasts. Intuitively, more public information reduces analysts’

leeway and incentives to report biased forecasts. It also reduces the need for herding

and, perhaps, the potential for cognitive biases.

Graphically, the logical foundations of our work are illustrated in figure 1,

which portrays the structure of the financial system. In this figure, the line segments

AA, BB, CC, DD and EE mark the ‘points’ where asymmetric information exists. Our

focus is on the point marked by the AA-segment, which corresponds to the first-stage

unbiased forecasts. Yet, owing to the said weaknesses, we essentially measure

asymmetric information at the point marked by the BB-segment, which corresponds to

the reported forecasts. To say it differently, these weaknesses drive a wedge between

what analysts believe and what they report. They also drive a wedge between their

‘true’ and the observed consensus.

4

Figure 1. Financial System Structure and Asymmetric Information

Lenders/Savers• Households• Firms• Governments• Non-residents

Borrowers• Firms• Governments• Households• Non-residents

Financial Markets• Money Markets• Capital Markets

Financial Intermediaries• Banks• Other Financial Institutions• Other

Funds Funds

FundsFunds Funds

Direct Financing

Indirect Financing

Our FocusData ConstraintA

A

B

B

C

C

D D

E

E

To make a long story short, there are three influences on the consensus in

analysts’ forecasts: one from the ‘true’ asymmetric information, the second from

herding, and the third from analysts’ conflicting incentives and the rational biases. All

these influences are positively related to asymmetric information. But they affect

analysts’ consensus differently: The first and the third negatively, the second positively.

The overall effect, although unknown a priori, is likely to be negative. Nevertheless, as

discussed in the concluding section, this ambiguity about the overall effect does not

diminish the importance of our results. In short, if the results are significant with the

noisy measure of consensus used here, they should be stronger with the ‘true’ one.

As for the financial development proxies, we use the extensive set of indices

found in the Word Bank’s Financial Development and Structure database (Beck et al.,

2000), in an effort to capture as many aspects of financial system structure and

development as possible. These indices measure the size, activity and efficiency of the

various segments of the financial system, i.e., of financial intermediaries, the insurance

5

industry and of the stock and bond markets, in a consistent across countries and across

time way. For the econometric analysis, we employ a panel co-integration framework,

with annual data for 32 countries, for the period 1990-2004. The sample is dictated by

data availability.

The econometric results are consistent with expectations. Briefly, analysts’

consensus is positively related with private credit by banks and other financial

intermediaries, as well as with the development of the life insurance industry and the

liquidity of the stock market. Higher values for these three indices are indicative of

more developed financial systems. In addition, the results are reinforced by several

robustness checks.

To the best of our knowledge, this is the first paper that attempts to empirically

examine the relationship between financial development and asymmetric information. A

related paper by Chang et al. (2000) has a more limited scope. It links asymmetric

information at the country level, proxied with the dispersion in analysts’ forecasts, and

in a cross-sectional setting, with observable country characteristics, such as, average

firm size, stock market capitalization over GDP, legal origin, and an index measuring

the quality of disclosure standards. In tune with the results of this paper, it finds that,

ceteris paribus, an Anglo-Saxon legal system, which presumably is more conducive to

the development of capital markets, is associated with lower dispersion.

Relative to Chang et al. (2000), the value-added of this paper stems from several

factors. To begin with, we use a more robust measure of asymmetric information. In

addition, some of the observable characteristics, such as, legal origin and quality of

disclosure standards, are not always good indicators of the level of financial

development. As several knowledgeable observers have noted, there may exist countries

with the same set of characteristics but different financial systems, for factors, such as,

6

politics and historical experience, have shaped their systems (Rajan and Zingales,

2003). In addition, using these characteristics, which do not vary very much over time,

in a cross-sectional setting, one cannot explore the joint evolution of the financial

systems and asymmetric information as is done here. Last but not least, the panel co-

integration technique employed here takes into account the influence of Chang et al.’s

largely time-invariable characteristics.

The remainder of the paper is organized as follows. Section 2 presents the

theoretical model that formalizes the testable hypothesis, while Section 3 discusses the

proxies for financial development. Section 4 presents the data and analyzes the

econometric issues related to unit-root testing and co-integration in a panel setting,

while Section 5 presents the empirical results. Section 6 concludes.

2. Analysts’ Forecasts and Asymmetric Information.

We extend BOSD’s model to relate the properties of analysts’ information environment

with the observed statistics of their forecasts, while taking into account the weaknesses

of these forecasts. As already noted, these weaknesses, i.e., the conflicting incentives

analysts face, their rational biases, cognitive biases and herding, drive a wedge between

what analysts believe –line segment AA in figure 1— and what they report – line

segment BB.

To facilitate the discussion, we begin with a brief outline of BOSD’s setting.

They consider a firm followed by N analysts who forecast firm’s earnings y. Each

analyst’s information set has two subsets, one containing common information available

to all, the other private information. The forecasts that are based on common

information have mean y and precision (inverse of variance) h, while the forecasts that

7

are based on analyst i's private information are denoted as zi = y + εi, where the

stochastic term εi is a signal observed only by analyst i. This term, which denotes that

private information differs across analysts, is independent of the other variables and

follows a normal distribution with zero mean and precision si. The higher the si, the

more accurate the analyst’s i forecast of firms’ earnings.

While y and h are in the public domain, εi and si are not. Investors and other

analysts know of the existence of the private signals but not their properties.

Nevertheless, an increase in the precision h of public information relative to total, that is

an increase in ish

h+

, denotes a decrease in the degree of asymmetric information

between firm managers and analysts. Each analyst’s forecast is his best estimate based

on available information, as shown in equation (1).

i

iiii sh

zsyhzyEµ

++

=≡ ]|[ (1)

BOSD define consensus, denoted as ρ, as the ratio of common uncertainty, denoted as

C, to overall uncertainty, denoted as V.

VC

≡ρ (2)

Common uncertainty is the average pair-wise covariance among analysts’ forecasts.

Overall uncertainty is the average across analysts expected variance of y, conditional on

their information set.

As they prove in their Appendix B, ρ can be expressed as

8

⎟⎠⎞

⎜⎝⎛

−−=

11

Nγαρ (3)

where

,11

hVN i

i =≡ ∑Ν

=

αα i

i shha+

= , 2

1

1 ∑=

⎟⎠⎞

⎜⎝⎛ −

≡N

i

i

VVV

Nγ and ( )[ ]

iii sh

yEV+

=−≡12µ .

BOSD note that ρ encompasses the effect of both the presence of asymmetric

information, measured through α, and the differential quality of private information,

measured through γ. Moreover, they show that consensus, ρ, and overall uncertainty, V,

are functions of the dispersion of analysts’ forecasts, D, the standard error in the mean

forecast, SE, and the number of analysts N.

To examine the relation between consensus and asymmetric information, we

transform equation (3) into

ασαρ

11−

−=N

(4)

where σ is the variance of αi’s around their mean, α (for this and all subsequent proofs

see the Appendix).

Differentiating equation (4) with respect to α, gives

aa

NN 22 11

111 ∂

∂

−−

−+=

∂∂

σ

ασ

αρ

(5)

9

Equation (5) provides the logical foundations of our work. Consensus ρ increases as α

increases, that is, as asymmetric information decreases. Algebraically,

0>∂∂αρ (6)

To see it, the first two terms in equation (5) are strictly positive. For the third term, there

are three cases: 1) it is zero when σ is independent of α, ∂σ/∂α = 0, 2) positive when σ is

negatively related to α, ∂σ/∂α < 0, and 3) negative when σ is positively related to α,

∂σ/∂α > 0. Only the third of the above cases may result to a decrease in ρ as α increases.

But it is logical to assume that as the degree of asymmetric information decreases, that

is, as α increases, the differential quality of private information across analysts,

expressed with σ, should not increase. All in all, most likely consensus ρ is an

increasing function of α, ,0>∂∂αρ and thus, a decreasing function of asymmetric

information.

Next, we extend BOSD’s model to take into account the effect of analysts’

conflicting incentives, rational biases and herding on the observed statistics of their

forecasts. To do so, we assume that each analyst does not report his best estimate of a

firm’s earnings, µi, but a biased one, µi*, given by equation (7).

iiii

iiii uµu

shzsyh

uzyE +=+++

=+≡ ]|[*iµ where ui ~ N(κi, )1

iλ (7)

To facilitate the comparison with BOSD’s results, we denote the variables of the

extended model with the same letter as in their paper plus an asterisk (*). Any cognitive

10

biases are subsumed into εi and we do not consider them any further. Yet, under the

assumption that the cognitive biases do not increase as asymmetric information

decreases, the conclusions are not affected.

The term ui in equation (7) accounts for the aforementioned weaknesses in

analyst i's forecasts. The weaknesses can vary across analysts. Yet, higher |κi| and 1/λi

denote bigger weaknesses and bigger bias in the forecasts. We further postulate that ui is

not correlated with the unbiased forecast of analyst i, that is Cov(ui, µi) = 0.

Our identifying assumption is that the magnitude of these weaknesses is

positively associated with asymmetric information. To say it differently, as the precision

of public information h relative to total increases, that is, as i

i shha+

= increases, the

leeway of analysts to report biased forecasts because of the conflicting incentives they

face decreases. So do their leeway and incentive to report rationally biased forecasts, as

well as their need for herding. In terms of the statistical properties of ui, as the degree of

asymmetric information between firms’ managers and analysts decreases, |κi| and 1/λi

are expected to decrease as well.

Investors observe the mean across analysts forecast µ*

uush

zsyhΝΝ i

N

i i

iiN

ii +=+

++

=≡ ∑∑==

µµµ )(1111

** (8)

and the dispersion in forecasts 2*

1

** )-(1

1 µµ∑=−

≡N

iiN

d .

Since, by construction the unconditional expectation of d*, which

is the dispersion of the biased forecasts observed by investors, is

0, )( ** =− µµ iE

11

∑=

−−

=≡N

iiVar

NdED

1

*** )(1

1][ µµ (9)

The uncertainty of investors regarding firms’ earnings related to analyst i’s forecast ,

is the expected variance of y conditional on analyst’s i biased forecast

*iµ

i

ii λVyVarV 1 )( *

i* +=−≡ µ (10)

The overall level of uncertainty of investors is the average across analysts’ uncertainty

∑∑==

+=≡N

i i

N

i

*i

*

λNVV

NV

11

111 (11)

Comparing equations (10) and (11) with the corresponding equations in BOSD, the

incremental effect of the weaknesses is reflected on the terms iλ

1 and ∑=

N

i iN 1

11λ

, both of

which are expected to decrease as the degree of asymmetric information decreases. At

the limit, when there is no bias in the forecasts, 01=

iλ and V* of our extended model

will be equal to V of BOSD’s model.

The average pair-wise covariance among analysts’ biased forecasts, given in

equation (12),

uuCovN

1N

CCN

CN

1i

N

ijji

N

ii ∑ ∑∑

= ≠=⎥⎦

⎤⎢⎣

⎡−

+=≡ ),(1

111

** (12)

12

is equal to that in BOSD, C, plus the term .,uuCovN-N

N

i

N

ijji∑ ∑

= ≠ ⎥⎥⎦

⎤

⎢⎢⎣

⎡

1)(

111

The terms Cov(ui,uj) capture herding by analysts. Thus, they are positive. In

addition, according to our identifying assumption, they are expected to decrease as the

degree of asymmetric information decreases.

The standard error in the mean forecast, SE* , is

( )[ ](13) ),(111

12

2

12

2**

∑∑∑= ≠=

+++=

−=

N

i

N

ijji

N

i i

uuCovNN

SE

yESE

κλ

µ

where SE is the standard error of the unbiased forecasts.

Analogously to BOSD, we define consensus ρ* as

***

VC

=ρ .

Substituting equations (11) and (12) in this expression, it turns out that

B

AB ++

+=

11* ρρ (14)

where

0),(

111 N

1i>

⎥⎦

⎤⎢⎣

⎡−

=∑ ∑= ≠

V

uuCovNN

A

N

ijji

and 0

111 >=∑=

VN

B

N

i iλ .

13

A is related to herding, B to all weaknesses in analysts’ forecasts. According to our

identifying assumption, both A and B are positively related to asymmetric information,

0<∂∂

aA and 0<

∂∂

aB ,

and, hence, expected to decrease as financial systems develop. Also, in terms of figure

1, the line segment AA is associated with ρ, while the line segment BB with ρ*.

Differentiating equation (14) with respect to α gives

( )2

*

1

)(

)1()1( B

BA

B

A

B +∂∂

+−

+∂∂

++∂∂

=∂∂ α

ραα

ρ

αρ (15)

The three terms on the right hand side of equation (15) capture the three influences of

asymmetric information, measured through α, on the consensus of the biased forecasts,

ρ*. The first is negative – see equation (6) above. The second and the third are negative

and positive respectively, since 0<∂∂

aA and 0<

∂∂

aB .

All in all, even though the effect of an increase in asymmetric information on the

observed analysts’ consensus is not certain, it is likely negative.

0*

<∂∂αρ (16)

However, this ambiguity does not undermine the logical foundations of the

empirical work. Consider the case that the second term in equation (16) is relatively

large. This would obscure the effect of the first term, which is of our primary interest.

Yet, if the noisy consensus at the line segment BB turns out to be significantly and

14

positively related to the indices measuring financial development, then the latter’s true

effect at the line segment AA should be even stronger than that indicated by the

econometric results.

Lastly, as shown in the Appendix, ρ* and V* are functions of the SE*, D* and N.

**

**

*

)11( SEDN

NDSE

+−

−=ρ (17)

*** )11( SEDN

V +−= (18)

In the empirical analysis we use equations (17) and (18) to calculate consensus ρ* and

uncertainty V*, using the observable statistics of analysts’ biased forecasts, i.e.,

dispersion, D*, standard error, SE*, and the number of analysts, N.

3. Proxies for Financial Development.

Ideally, to measure financial development, one should quantify how well financial

systems accomplish their functions, i.e., the mobilization of savings, the easing of

exchange of goods and services, the ex ante production of information about

investments and the allocation of capital, the ex post monitoring and the exertion of

control of realized investments, the facilitation of trading, and the diversification and

management of risk (Levine, 2004). However, this is easier said than done, for the

majority of these functions are qualitative in nature. In addition, financial systems may

accomplish their functions equally efficiently under different structures. To overcome

this objective difficulty, several indices have been used in the literature. They fall into

15

two broad categories: Those attempting to measure financial development through the

observed outcomes, and those attempting to do so through characteristics of the

institutional environment.

Among the studies using indices falling into the first category, King and Levine

(1993) use liquid liabilities to GDP as a measure of the size of financial intermediaries,

credit to private enterprises to GDP as an activity measure, and the ratio of bank assets

to the sum of bank assets plus central bank assets. Demetriades and Hussein (1996)

measure financial development with the ratio of money to GDP. Alternatively, Neusser

and Kugler (1998) use the value-added of the financial system instead of simple

measures of its size. Rousseau and Wachtel (1998), and Levine et al. (2000) use

measures that include the assets of both banks and non-banks, such as private credit to

GDP from banks and non-deposit money banks. Levine and Zervos (1998), and Arestis

et al. (2001) add measures of stock market size and liquidity to bank development

measures. Last but not least, Beck et al. (2001) include measures of life insurance and

private pension fund assets.

The studies using indices falling into the second category follow the seminal

work of La Porta et al. (1997). Specifically, La Porta et al. show that a country’s legal

tradition and quality of law enforcement affect financial development and structure, for

financial decisions are based on contracts and laws. In addition, La Porta et al. (2002)

use the degree of public ownership of banks around the world as a proxy for financial

development. The intuition behind this measure is that publicly owned banks are less

efficient than private ones.

We work with indices falling into the first category, for the other indices are

largely time-invariant and, hence, cannot be readily used to explore the time evolution

of the degree of asymmetric information. Nevertheless, our estimation technique, panel

16

estimation with country and time dummies, captures the effect of the time-invariant

characteristics of the institutional environment.

Yet, Benhabib and Spiegel (2000) raise a serious concern. Most of the studies in

the first category use a limited set of indices that are related to specific segments of the

financial system and, therefore, are unlikely to capture all aspects of its structure and

development. Moreover, noting that these indices are correlated with the largely

unobservable country characteristics, like those identified in the studies in the second

category, they point out that the interpretation of their econometric results suffers from

an omitted variables bias.

With the aim to address Benhabib and Spiegel’s concern to the extent possible,

we use the extensive set of indices in the World Bank’s Financial Development and

Structure database (Beck et al., 2000). They measure, in a consistent across countries

and time way, the development of the main sectors of the financial system, namely,

banks and other financial intermediaries, the insurance industry, the stock market, the

private and public bond markets.

Table 1 summarizes the twelve indices used. The first column reports the

symbol used, the second provides a short description and the third further details. The

indices are organized in four groups, each group corresponding to a major segment of

the financial system: five indices, denoted as FIi (i=1,5), measuring the size and activity

of banks and other financial intermediaries, as well as the efficiency and structure of the

banking sector; two indices, denoted as INSi (i=1,2), measuring the development of the

insurance industry; three indices, denoted as SMi (i=1,3), measuring the size, liquidity

and depth of the stock market; and two indices, denoted as BMi (i=1,2), measuring the

size of the private and public bond markets.

17

Insert Table 1 here

To save space, more details will be discussed in the empirical section for the indices

that are statistically significant.

4. Empirical Issues.

4.1 Consensus Measures.

We use data from the I/B/E/S Global Aggregates database to construct the consensus

and uncertainty measures, ρ* (equation (17)) and V* (equation (18)). This database

provides the weighted average standard deviation of analysts’ earnings per share

(henceforth EPS) forecasts, the mean earnings per share forecasts, the realized EPS, the

total number of estimates and the number of companies with a mean EPS forecast for

stock-market indexes both at the country and industry levels. For a country index, these

variables are the weighted averages of the relevant variables for the index’s constituent

companies. They are measured using the country’s national currency (for details, see

I/B/E/S Global Aggregates Reference Guide 2). For each available index, I/B/E/S

Global Aggregates reports the relevant data for fiscal years 1 and 2, where fiscal year 1

(henceforth FY1) corresponds to forecasts for the current calendar year and fiscal year 2

(henceforth FY2) for the next.

As in Botosan and Harris (2000), Barron et al. (2002) and Liang (2003), the

dispersion in analysts’ forecasts D* is calculated as the square of the weighted average

standard deviation of analysts’ EPS forecasts (see equation (9)), while the standard error

SE* is calculated using equation (13). In these calculations, y and µ* are the weighed

18

average realized EPS and mean forecast respectively. Finally, the number of analysts N

for each fiscal year is proxied by the ratio of the number of forecasts for each index

divided by the number of companies.

Thus, we construct consensus and uncertainty measures for the calendarized FYi

period i (i=1,2), for the FTSE (denoted ρ*iFTSE and V*iFTSE respectively) and MSCI

(denoted ρ*iMSCI and V*iMSCI respectively) indexes for each country. The I/B/E/S data are

available from 1987 for many countries. However, due to data availability constraints,

the sample period is restricted to 1990-2004. The data frequency is annual, dictated by

the financial development data availability. The consensus and uncertainty measures are

calculated on a monthly basis and then are averaged over the 12-month forecast period

which begins in March and ends next February. Using the 12-month averages reduces

the level of high-frequency noise and, additionally, overcomes the difficulty of which

month of a forecast year to choose.

The FTSE index includes 22 countries, most of which are OECD members:

Australia, Austria, Belgium, Brazil, Canada, Denmark, Finland, France, Germany,

Ireland, Italy, Japan, Mexico, Netherlands, New Zealand, Norway, South Africa, Spain,

Sweden, Switzerland, U.K. and U.S.A. The MSCI index includes 32 countries: the

above, excluding Brazil, plus Chile, Greece, India, Indonesia, Korea, Pakistan, Peru,

Philippines, Poland, Portugal and Turkey.

4.2 Descriptive Statistics.

Table 2 provides summary statistics for the dependent variables, i.e., the four consensus

and the four uncertainty measures. The columns correspond to the variables, the rows to

the sample countries. Each cell reports the sample mean (over time). The last two

19

couples of rows report the average and standard deviation across countries of the

country means, the first for the whole sample and the second without the three outliers

for the uncertainty measures, Brazil, Mexico and Turkey.

As Table 2 indicates, the consensus measures exhibit cross-country variation

that is slightly larger for the FTSE than the MSCI index for both FY1 and FY2.

Specifically, the ratio of the standard deviation to the average for FY1 is 0.17

(=0.072/0.425) for the FTSE and 0.22 (=0.088/0.401) for the MSCI index, while the

relevant figures for FY2 are 0.19 and 0.21 respectively. As for the uncertainty measures,

the respective ratio is in the order of four or higher (first of the aforementioned two

couples of rows). Removing the three large outliers brings this ratio down to a little over

one for the FTSE index and over two for the MSCI index. Note, however, that the

results of the empirical analysis are virtually the same with or without these countries.

Insert Table 2 here

4.3 Econometric Issues.

We test a joint hypothesis: First, the degree of asymmetric information is negatively

related to financial development. Second, analysts’ leeway and incentives to report

biased forecasts, as well as the need for herding, decrease as asymmetric information

decreases. To test this hypothesis, we estimate equation (21) using panel co-integration

techniques.

20

(21) kttk

jjktj

j j j jjktjjktjjktjjktjkt

uCONTROL

IPFθBMζSMεFIδY

++++

+++++=

∑∑ ∑ ∑ ∑

−

= = = =

ξνλ

β

1

5

1

3

1

2

1

2

1

FIjkt denotes the financial intermediaries index j, for country k, at year t. Similarly, the

SMjkt, BMjkt and IPFjkt denote the indices for the stock market, the bond markt and the

insurance industry.

This is a fixed effects model with country intercepts, νk, and time dummies, ξt.

The country intercepts capture country-specific factors that are time-invariant, such as,

factors pertaining to the institutional environment. The time dummies capture shocks

that are common to all countries.

To control for macroeconomic uncertainty and market risk, we include in

equation (21) as control variables, denoted as CONTROL, the real GDP growth rate,

inflation –measured with the CPI, and the within-the-year standard deviation of

inflation and of the monthly returns of the total market return index (dividends

included). In terms of the theoretical model, the control variables are associated with the

lower precision of common information h, and herding behavior. The control variables

are measured at t-1 because, as previously mentioned, the consensus and the uncertainty

measures are averaged over the period from March of each year to February of the

subsequent year. However, the results are virtually the same when the control variables

are dated at t. Lastly, to overcome scaling problems of the four uncertainty measures,

owing mainly to that the I/B/E/S forecasts are in national currencies, we take the

logarithms of the V* measures.

All variables in the above equation were tested for common unit roots in a panel

framework, using the Breitung’s t-stat and Hadri’s z-stat. The former employs a null

21

hypothesis of a unit root, and is preferred relative to the Levin, Lin and Chu test, as

having substantially higher power (Baltagi, 2005, p. 243). The later uses a null of no

unit root, being analogous to the Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) test

in the time series framework. A variable is characterized as I(1) when at least one test

gives this indication. With the exception of the real GDP growth rate, all variables were

found to be I(1). To save space, the results of the panel unit root tests are not reported

here but are available upon request.

To estimate the long-run (co-integrating) relationship between the I(1) variables

in equation (21), we use the panel Dynamic OLS (DOLS) estimator. As is well known,

DOLS uses a parametric approach to deal with serial correlation, and is more

appropriate than the Fully Modified OLS (Baltagi, 2005, p.258). Using the Schwarz

criterion, we choose the appropriate number of leads and lags for the I(1) independent

variables. Additionally, we use the cross-section SUR (Panel Corrected Standard Errors

- PCSE) standard errors and covariance method, corrected for degrees of freedom, to

deal with possible cross-sectional heteroskedasticity and cross-sectional

contemporaneous correlation. Finally, the residuals of equation (1) were tested for unit

roots, using the aforementioned panel unit root tests, and were found stationary,

indicating the presence of panel co-integration among the statistically significant I(1)

variables.

Additionally, we estimate the relevant panel error-correction models for both

analyses, where the differences of only the statistically significant I(1) variables are

used, together with the error-correction term of the relevant panel co-integrating

equations.

22

5. Results.

Tables 3 and 4 summarize the empirical results of the panel co-integrating equations.

The first presents the results for the consensus and the second for uncertainty, for the

FTSE index in Panel A and the MSCI index in Panel B. The two tables have the same

structure: The first row shows the dependent variable, while the other rows report the

estimated coefficients (t-statistics in parentheses) of the statistically significant

regressors, the adjusted R2 and the Durbin Watson statistic.

In summary, the results are consistent with expectations. There is a long-run

relationship between the consensus (Table 3) and uncertainty measures (Table 4) with

indices that measure the activity of financial intermediaries, the efficiency of the

banking segment, as well as the development of the insurance industry and the stock

and bond markets.

5.1 Consensus.

Specifically, consensus is positively related with the index measuring the development

of private credit by banks and other financial intermediaries in all cases, i.e., for the

FTSE and the MSCI index and forecast horizons FY1 and FY2. This index is similar to

that in Rousseau and Wachtel (1998) and Levine et al. (2000). The positive relationship

is consistent with the hypothesis that financial development is associated with lower

asymmetric information, as well as with Diamond’s (1984) theoretical argument that

well functioning financial intermediaries minimize informational asymmetries between

suppliers and users of funds. Indeed, the long-run relationship between this variable and

asymmetric information proxy indicates that as banks and other financial intermediaries

23

expand private credit, valuable public information diffuses in the economy about firms’

prospects and profitability and thus, asymmetric information decreases. In addition,

lower asymmetric information facilitates the expansion of private credit.

Insert Table 3 here

In greater detail, as Panel A in Table 3 documents, the consensus for the FTSE index

and fiscal year 1, ρ*1FTSE, forms a panel co-integrating vector with the private credit by

banks and other financial intermediaries, the stock market liquidity and public bond

market size (indices FI2, SM2 and BM2 respectively) for FY1. The consensus for the

same index for fiscal year 2 forms a panel co-integrating vector with FY2 only.

The signs of the estimated coefficients are reasonable and interesting.

Specifically, the positive coefficient of the FI2 index in the equations for ρ*1FTSE and

ρ*2FTSE (coefficients/t-statistics: 0.21/1.85 and 0.36/4.13 respectively) indicate that as

private credit by banks and other financial intermediaries increases, the consensus in

analysts’ EPS forecasts for both forecast horizons also increases, and, hence,

asymmetric information decreases. The positive coefficient of the SM2 index

(coefficient/t-statistic: 0.15/3.03) shows that the liquidity of the stock market is positive

related to consensus, and thus, negatively to asymmetric information. The negative

coefficient of the BM2 index (coefficient/t-statistic: -0.62/-2.89) indicates that the

development of the public bond market is positively associated with asymmetric

information. Leaving aside institutional barriers for the development of private bond

markets in repressed and less-developed financial systems, this indicates that in markets

with high informational asymmetry and uncertainty investors prefer government bonds

over private bonds.

24

Panel B in Table 3 reports the relevant results for the MSCI index. ρ*1MSCI and

ρ*2MSCI are positively associated with private credit (coefficients/t-statistics 0.22/3.05

and 0.32/3.67) In addition, ρ*1MSCI is positively associated with the development of life

insurance (coefficients/t-statistics 3.38/2.20), while ρ*2MSCI with banks’ overhead costs

(index FI3), and inflation (INF) (coefficients/t-statistics 4.37/2.05 and 0.01/1.86). INS1’s

positive sign is related to the fact that life insurance companies are themselves financial

intermediaries, which are engaged on relationship lending and, thus, complement banks

as informational agents. The sign of FI3 cannot be readily explained. The positive sign

of INF probably indicates that the EPS have an inflationary component. Intuitively,

firms’ earnings consist of two parts, one related to inflation and the other to firms’ real

activity—deflated earnings. High inflation, which is easier to forecast than deflated

earnings, may lead to increased precision of public information h.

5.2 Uncertainty.

As table 4 documents, uncertainty is positively related with the index measuring banks’

overhead costs. Large overhead costs reflect cost inefficiency (Levine et al., 2007).

They are also associated with small banks that do not have substantial income from fee-

based activities and/or operate in a restrictive environment (Demirguc-Kunt et al.,

2004). Thus, the results indicate that a less developed and/or less efficient banking

segment is related to higher uncertainty. Reasonable, indeed!

Insert Table 4 here

25

In greater detail, banks’ overhead costs (index FI3) are significant in almost all cases,

that is for both FTSE and MSCI indexes and for the two forecast horizons, FY1 and FY2.

This result indicates that the (in)efficiency of the banking sector is associated with

lower precision of information and/or higher biases.

Other financial development indices, such as INS1, INS2, SM2 and SM3 enter the

co-integrating vectors of the uncertainty measures, however in a non-systematic way.

From these, only SM2 has a negative sign, indicating that high stock market liquidity is

related to lower asymmetric information. Finally, market risk (index MKTRISK) is

significant for both, FTSE and MSCI indexes for the longer forecast horizon FY2,

indicating that higher market volatility is associated with greater analyst uncertainty.

Lastly, in the relevant error-correction models for the asymmetric information

and the uncertainty measures, the error-correction term is significant at the 1% level in

all cases, with negative sign –as expected. To save space, the results are not reported but

are available upon request.

Several robustness checks provide further support to the paper’s thesis, for their

results were essentially the same with the above. Specifically, we repeated the analysis

without the countries that experienced crises during the period under examination, i.e.,

Brazil, Indonesia, Japan, Mexico, Philippines and Turkey, for crises may have exerted

singular influence on the explanatory variables. Additionally, we lagged once the

financial development indices in the right-hand-side of equation (19), we also used their

lead values and performed the econometric analysis for various sub-samples of

countries. Lastly, we experimented with another asymmetric information proxy that has

been used in the literature and which is readily available at the country level, namely,

the open interest of the futures contracts on the FTSE and MSCI indexes for each

country, as percentage of total contracts traded.

26

6. Concluding Remarks.

By combining two branches of the literature, i.e., ‘corporate finance’ and ‘finance and

growth’, this paper makes a significant –we believe— contribution towards empirically

examining the relationship between financial development and asymmetric information.

The results provide strong evidence that the degree of asymmetric information

decreases as financial systems develop. Perhaps, what is astonishing is the significance

of the results, despite the substantial differences in the structure and development of the

financial systems of the sample countries, and the use of noisy proxies for asymmetric

information. Worth also noticing is that the results, taken literally, suggest that the

development and the efficient operation of financial intermediaries seem to have been

among the key determinants of the reduction in asymmetric information in the sample

countries over the 15-year period under examination.

A few remarks are in order. We use a measure of analysts’ consensus which is

negatively related to asymmetric information. This measure, in addition to uncertainty

about the prospects of firms owing both to asymmetric information and business risk,

encompasses several weaknesses of the forecasts themselves, namely, rational biases,

cognitive biases, herding, and analysts’ conflicting incentives. The data does not allow

us to evaluate how financial development has affected genuine asymmetric information

and the various biases in the sample countries over the sample period. Nevertheless, the

results unambiguously suggest that as financial systems develop the consensus in

analysts’ forecasts increases, which, in turn, provides strong evidence that asymmetric

information decreases.

27

And this is good for investors. As financial systems develop, investors can put

more faith in analysts’ forecasts. This, however, does not imply that they should not

make their own research. Moral hazard is ever present. That is, as financial systems

develop, investors may perceive that the value of information gathering and analysis

decrease and, as a result, the free-rider problem may get exacerbated.

Closing, the results suggest two lines for further research. First, a re-examination

of the evidence from cross-sectional analysis that use analysts’ forecasts. For example,

Diecther et al. (2002) find evidence that stocks with higher dispersion earn lower future

returns than otherwise similar stocks. Lim (2001) documents the existence of rational

forecast bias that is larger for companies for which the problem of asymmetric

information is bigger and for analysts who rely more on access to the firms management

for information (such access presumably attenuates the asymmetric information

problem). Halov (2006), who uses the dispersion of analysts’ forecasts at different

horizons as a proxy for current and future asymmetric information, finds evidence

consistent with the hypothesis that firms try to minimize inter-temporally the adverse

selection cost, which is positively related with asymmetric information, when issuing

securities. Under the assumption that financial systems develop as time goes by, we

expect that the effect of asymmetric information will progressively decrease.

Finally, the results call for a re-thinking of policy issues related to financial

regulation. Perhaps, common rules, while appealing for their cross-country conformity,

may not be optimal. Basle II, for example, may prove to be too much of a straight-

jacket. If, as comes out from this paper, the degree of asymmetric information differs –

and likely changes at different speed— across countries, its Pillar III may not be equally

effective in all of them. What are its implications needs to be explored.

28

Appendix.

Proof of equation (4).

( ) ⎥⎦

⎤⎢⎣

⎡−=

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛+

−+

=

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛+

−+

=

⎟⎠⎞

⎜⎝⎛ −

≡

∑

∑ ∑

∑ ∑

∑

=

= =

= =

=

N

ii2

N

i

N

i ii

N

i

N

i ii

N

i

i

N1

shh

Nshh

N1

Vh

shNshN1

V

VVV

N

1

2

1

2

122

1

2

12

1

2

1

11

1111

1

ααα

γ

So,

29

( )

( )

( )

1)-Ν(Ν1

1-NN

1)-Ν(Ν1

NNN

N

NNN

NN1)-Ν(Ν1

1)-Ν(Ν1

1)-Ν(Ν1

N1

N

ii

N

ii

N

i

N

iii

N

iii

N

ii

N

ii2

∑

∑

∑ ∑

∑

∑

∑

=

=

= =

=

=

=

−=

−−

−−

+=

−−

−+−=

+−−=

−−=

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

−Ν

⎥⎦

⎤⎢⎣

⎡−

−=

1

2

1

2

1 1

2

1

22

1

2

1

2

)1()1(2

)1()1(2

2

1

1

1

αα

α

αα

ααα

αααα

α

ααααα

α

ααα

α

ααα

αρ

Let kii += αα

Then and ∑=

=N

iik

10 σ=∑

=

N

iik

N 1

21 , where σ is variance of αi’s.

Thus ∑∑ ∑ ∑ ∑==

+=+=++=N

ii

N

i

N

i

N

i

N

iiii NNkNkk

1

222

1

222 2 σααααα

Finally, substituting this last expression into ρ,

ασα

αααρ

11

)1(1

11

1 1

2

−−=

−−

−−

−= ∑

=

N

kNNNN

N N

ii

30

Proof of equation (10).

Let µi* = µi + ui, where ui ~ N(κi, )1

iλ, Cov(y-µi, ui) = 0.

Then,

[ ]

[ ]

[ ]

[ ]

[ ] [ ] [ ]

ii

iiii

iii

iiiiiiiiii

iiiiiiiii

iii

iiii

iiii

*i

*i

*i

*i

λV

uEuEuVarV

uEuEV

uEuEµyΕuEµyE,uµyCovuEuEV

uEuEuEµyΕuµyΕuEuEµyE

uE-uµyE

uEµyE-uµyE

uµyEuµyE

y--EyE

yVarV

1

))(())(()(

))(()(

))((2)()(2)()(2)(2))(()(

)(2)()(2)(2))(()()(

))()((

))()()((

))()((

))()((

)(

22

22

222

222

2

2

2

2

+=

−++=

−+=

−−+−−−−++=

−−+−−++−=

+−=

+−−−=

−−−−−=

−=

−≡

µµ

µ

31

Proof of equation (12).

Assuming that Cov(y-µi, uj) = 0 i, j, ∀

),(1-N

11C

),(1-N

11))((1

1N1

),(1-N

11)(,

)(1

1N1

)(),()(),([1

11

)y,(1

11

)y,(1

11

1

N

1i

N

1i1

N

1i1

1

1

**

1

1

**

∑ ∑

∑ ∑∑ ∑

∑ ∑∑ ∑

∑∑

∑∑

∑∑

∑

= ≠

= ≠= ≠

= ≠= ≠

≠=

≠=

≠=

=

⎥⎥⎦

⎤

⎢⎢⎣

⎡+=

⎥⎥⎦

⎤

⎢⎢⎣

⎡+

⎥⎥⎦

⎤

⎢⎢⎣

⎡

++−=

⎥⎥⎦

⎤

⎢⎢⎣

⎡+

⎥⎥⎦

⎤

⎢⎢⎣

⎡

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

+

−−

+−−

−=

⎥⎥⎦

⎤

⎢⎢⎣

⎡+−−−−−−

−=

⎥⎥⎦

⎤

⎢⎢⎣

⎡−−−−

−=

⎥⎥⎦

⎤

⎢⎢⎣

⎡−−

−=

≡

N

ijji

N

ijji

N

i

N

ij ji

N

ijji

N

i

N

ij j

jj

i

ii

N

ijjijijiji

N

i

N

ijjjii

N

i

N

ijji

N

i

N

ii

uuCovN

uuCovNshsh

hN

uuCovNsh

εsyyhsh

εsyyhCov

N

,uuCovuyCov,yuCovyyCovNN

uuyCovNN

yCovNN

CN

C

µµµµ

µµ

µµ

32

Proof of equation (13).

Let ∑=

=N

iiu

Nu

1

1 and ∑=

=N

iiN 1

1 κκ .

It is

∑ ∑∑∑= = ==

===N

i

N

i

N

jjii

N

ii uuCov

NuVar

Nu

NVaruVar

1 1 1,22

1

)(1)(1)1()(

Thus,

( )[ ]

( ) ( )[ ]( )[ ] ( )[ ]

( )

∑∑∑

∑∑∑

∑∑

∑

= ≠=

= ≠=

= =

=

+++=

+++=

++=

++−−=

+−−−=

+−−−=

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎠

⎞⎜⎝

⎛−−=

−=

N

i

N

ijji

N

i i2

N

i

N

ijji

N

ii2

N

i

N

jji

N

ii

uuCovNN

1SE

uuCovN

uVarN1SE

uuCovN

SE

uEuVaryEuESE

uEuyEyΕ

uuyyE

uN

yE

yESE

12

2

1

2

112

1 1

22

2

22

22

2

1

2**

),(11

),(1)(

),(1

)()()()(2

)(2

2

1

κλ

κ

κ

µ

µµ

µµ

µ

µ

33

Proof of equations (17) and (18).

Let . Niµye ii ,,1for ** K=−≡

Then,

)1(V

),(1

11)1(1)1(N

1-N

)],()[()1(

1)1(1

)1()1(

)],()}2()1(2{)1()1[()1(

1

)],(),()1(2)1[()1(

1

))((11

)(11

)1(1

1

**

**

11

*

1

*

1 1

**2

1

*2

*2

2

1

****22

1 ,

******22

1

**2

1

*

1

*2

1

*

1

**

ρ−=

=

−−+=

−−

+−

−+

−

−=

−+−−+−+−−

=

+−−−−

=

−=

−=

−−

=

∑∑∑∑

∑ ∑ ∑∑

∑ ∑

∑ ∑ ∑ ∑ ∑

∑ ∑

∑∑

∑∑

≠===

= ==

= ≠

= ≠ ≠ ≠ ≠

= ≠

==

==

**

N

ijji

N

i

N

ii

N

ii

N

i

N

i

N

ijji

N

iii

N

i

N

ijjiii

N

i

N

ij

N

ij

N

ij

N

jikjijiji

N

i

N

jiji

N

jj

N

ii

N

jj

N

ii

-CV

eeCovNN

VNN

VN

eeCovNNN

VNN

NVNN

N

eeCovNNVNVNNN

eeCoveeCovNVVNNN

eeVar)N(N-

eNeVar)N(N-

eN

eVarN

D

≠

34

In turn, let and ** µ−= ye **ii ye µ−=

Then,

NDVρ

NDC

NCNCV

CN

NVN1

eeCovVN1

yVarN1

yN1Var

eN

VarSE

*

*

N

i

N

i

N

ijjii2

N

ii2

N

ii

N

ii

**

**

**

**

1 1

**

1

*

1

*

1

**

1

),(

)(

)(

1

+=

+=

−+=

−+=

⎥⎦

⎤⎢⎣

⎡+=

⎥⎦

⎤⎢⎣

⎡−=

⎥⎦

⎤⎢⎣

⎡−=

⎥⎦

⎤⎢⎣

⎡=

∑ ∑∑

∑

∑

∑

= = ≠

=

=

=

µ

µ

Solving the system of the D* and SE* equations, gives equations (17) and (18) in the

main text.

35

References.

Antia, M., Pantzalis, C., 2006. Security analyst incentives and quality of analyst

generated information. The Journal of Investing, Spring.

Arestis, P., Demetriades, P.O., Luintel, K.B., 2001. Financial development and

economic growth: The role of stock markets. Journal of Money, Credit and

Banking 33, 16-41.

Baltagi, B.H., 2005. Econometric Analysis of Panel Data. John Wiley & Sons, Ltd, 3rd

Edition.

Barron, O.E., Oliver, K., Steve, C.L., Douglas, E.S., 1998. Using analysts’ forecasts to

measure properties of analysts’ information environment. The Accounting Review

73, 421-433.

Barron, O.E., Byard, D., Kile, C., Riedl, E., 2002. High technology intangibles and

analysts’ forecasts. Journal of Accounting Research 40, 289-312.

Barry, C.B., Brown, S.J., 1985. Differential information and security market

equilibrium. Journal of Financial and Quantitative Analysis 20, 407-422.

Barry, C., Jennings, R., 1992. Information and diversity of analyst opinion. Journal of

Financial and Quantitative Analysis 27, 169-183.

Beck, T., Demirgüç-Kunt, A., Levine, R., 2001. The financial structure database, In:

Demirgüç-Kunt, A., Levine, R. (Eds.), Financial Structure and Economic Growth:

A Cross-Country Comparison of Banks, Markets, and Development. MIT Press,

Cambridge, MA, pp. 17-80.

Beck, T., Demirgüç-Kunt, A., Levine, R., 2000. A new database on financial

development and structure. World Bank Economic Review 14, 597-605.

Benhabib, J., Spiegel, M.M., 2000. The role of financial development in growth and

investment. Journal of Economic Growth 5, 341-360.

36

Beim, D.O., Calomiris, C.W., 2001. Emerging Financial Markets. Mc-Graw-Hill –

Irwin, New York.

Botosan, C.A., Harris, M.S., 2000. Motivations for a change in disclosure frequency and

its consequences: An examination of voluntary quarterly segment disclosures.

Journal of Accounting Research 38, 329-353.

Chang, J. J., Khanna, T., Palepu, K., 2000. Analyst activity around the world.

Unpublished working paper. Harvard Business School.

Clarke, J., Shastri, K., 2001. On information asymmetry metrics. Unpublished working

paper, University of Pittsburgh.

Cooper, R. A., Day, T.E., Lewis, C.M., 2001. Following the leader: A study of

individual analysts’ earnings forecasts. Journal of Financial Economics 61, 383-

416.

Demetriades, P., Hussein, K., 1996. Does financial development cause economic

growth? Time series evidence from 16 countries. Journal of Development

Economics 51, 387-411.

Demirguc-Kunt, A, Laeven, L., Levine, R., 2004. Regulations, market structure,

institutions and the cost of financial intermediation. Journal of Money, Credit and

Banking 36, 593-622.

Diamond, D.W., 1984. Financial intermediation and delegated monitoring. Review of

Economic Studies 51, 393-414.

Friesen, G., Weller, P.A., 2006. Quantifying cognitive biases in analyst earnings

forecasts. Journal of Financial Markets 9, 333-365.

Halov, N., 2006. Dynamics of Asymmetric Information and Capital Structure.

Unpublished working paper. New York University.

I/B/E/S Global Aggregates Reference Guide, Issue 2. Thomson Financial Limited.

37

King, R. G., Levine, R., 1993. Finance and growth: Schumpeter might be right.

Quarterly Journal of Economics 108, 717-738.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R., 1997. Legal determinants of

external finance. Journal of Finance 72, 1131-1150.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 2002. Government ownership of

commercial banks. Journal of Finance 57, 265-301.

Levine, R, 2004. Finance and growth: Theory and evidence. NBER working paper No.

10766.

Levine, R., Barth, J., Caprio, G., 2007. The microeconomic effects of different

approaches to bank supervision. In Haber, S., North, D., Weingast B. (Eds.), The

Politics of Financial Development. Stanford University Press.

Levine, R., Loayza, N., Beck, T., 2000. Financial intermediation and growth: Causality

and causes. Journal of Monetary Economics 46, 31-77.

Levine, R., Zervos, S., 1998. Stock markets, banks, and economic growth. American

Economic Review 88, 537-558.

Liang, L. 2003. Post-earnings announcement drift and market participants’ information

processing biases. Review of Accounting Studies 8, 321-345.

Lim, T., 2001. Rationality and analysts’ forecast bias. Journal of Finance 56, 369-385.

Mishkin, F. S., 2000. The Economics of Money, Banking and Financial Markets.

Addison, Wesley, Longman, New York.

Neusser, K., Kugler, M., 1998. Manufacturing growth and financial development:

Evidence from OECD countries. Review of Economics and Statistics 80, 636-646.

Rajan, R., Zingales, L., 2003. The great reversals: The politics of financial development

in the twentieth century. Journal of Financial Economics 69, 5-50.

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Rousseau, P.L., Wachtel, P., 1998. Financial intermediation and economic performance:

Historical evidence from five industrial countries. Journal of Money, Credit and

Banking 30, 657-678.

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Table 1. Financial Development Indices

Index Definition

FI1 Deposit money bank assets to GDP Claims on domestic real non-financial sector by deposit money banks as a share of GDP

FI2 Private credit by deposit money banks and other financial institutions to GDP

Private credit by deposit money banks and other financial institutions to GDP

FI3 Banks’ overhead costs Accounting value of a bank's overhead costs as a share of its total assets.

FI4 Banks’ net interest margin Accounting value of bank's net interest revenue as a share of its interest-bearing (total earning) assets.

FI5 Banks’ concentration Assets of three largest banks as a share of assets of all commercial banks in the system

INS1 Life insurance penetration Life insurance premium volume as a share of GDP

INS2 Non-life insurance penetration Non-life insurance premium volume as a share of GDP

SM1 Stock market capitalization to GDP Value of listed shares to GDP

SM2 Stock market total value traded to GDP

Total shares traded on the stock market exchange to GDP.

SM3 Stock market turnover ratio Ratio of the value of total shares traded and average real market capitalization

BM1 Private bond market capitalization to GDP

Private domestic debt securities issued by financial institutions and corporations as a share of GDP

BM2 Public bond market capitalization to GDP

Public domestic debt securities issued by government as a share of GDP

Source: Financial Development and Structure database, World Bank.

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Table 2. Descriptive Statistics – Sample Means of the Dependent Variables

ρ*1FTSE ρ*2FTSE ρ*1MSCI ρ*2MSCI V*1FTSE V*2FTSE V*1MSCI V*2MSCI

Australia 0.349 0.547 0.342 0.565 2.84 8.60 16.77 54.27

Austria 0.428 0.533 0.433 0.510 8.68 33.08 66.99 187.78

Belgium 0.373 0.478 0.332 0.503 7.20 20.45 86.54 256.29

Brazil 0.394 0.471 - - 4,713.24 5,825.69 - -

Canada 0.436 0.624 0.417 0.568 5.33 18.90 63.61 222.19

Chile - - 0.307 0.442 - - 1,412.20 3,033.94

Denmark 0.477 0.484 0.425 0.497 53.65 84.93 770.85 1,356.85

Finland 0.480 0.562 0.484 0.598 72.86 128.05 67.67 121.44

France 0.494 0.683 0.535 0.713 13.49 36.07 239.21 642.89

Germany 0.370 0.526 0.317 0.510 6.94 20.63 38.98 112.87

Greece - - 0.321 0.348 - - 186.98 538.82

India - - 0.458 0.425 - - 5,942.61 6,640.20

Indonesia - - 0.299 0.584 - - 3.98 11.12

Ireland 0.518 0.758 0.538 0.640 25.20 60.96 27.87 70.06

Italy 0.393 0.546 0.393 0.467 5.21 11.71 184.89 407.94

Japan 0.573 0.684 0.542 0.699 2.09 4.34 159.49 353.19

Korea - - 0.328 0.578 - - 29.15 84.13

Mexico 0.351 0.489 0.406 0.423 231,174.30 507,400.60 17,678.44 43,007.12

Netherlands 0.493 0.695 0.511 0.681 17.25 49.75 122.26 325.81

New Zealand 0.515 0.665 0.496 0.669 1.87 3.54 5.57 10.76

Norway 0.457 0.615 0.434 0.618 95.65 171.19 2,060.95 2,988.11

Pakistan - - 0.138 0.253 - - 21.25 40.00

Peru - - 0.343 0.393 - - 59.19 121.29

Phillipines - - 0.472 0.635 - - 71.63 221.90

Poland - - 0.421 0.633 - - 1,154.64 3,008.13

Portugal - - 0.358 0.411 - - 83.41 11.12

South Africa 0.275 0.298 0.354 0.459 28.98 73.32 13.76 32.44

Spain 0.322 0.428 0.307 0.436 49.99 86.18 66.13 198.61

Sweden 0.403 0.678 0.407 0.615 62.00 239.18 4,365.41 15,795.06

Switzerland 0.414 0.492 0.452 0.544 14.37 30.61 78.66 190.15

Turkey - - 0.355 0.396 - - 130,000,000.00 182,000,000.00

U.K. 0.411 0.600 0.439 0.612 9.28 27.85 267.70 733.44

U.S.A. 0.422 0.617 0.454 0.622 2.06 13.30 10.73 66.55

Average 0.425 0.567 0.401 0.533 10,744.20 23,379.50 4,063,604.92 5,690,026.39

St.Deviation 0.072 0.107 0.088 0.112 49,243.88 108,114.63 22,980,769.00 32,172,898.51

Average* - - - - 24.25 56.13 589.30 1,261.24

St.Deviation* - - - - 27.55 61.48 1,344.36 3,089.78

Notes: 1. Time averages for each variable for each country.

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2. Sample period: 1990-2004, except for: FTSE indexes: • ρ*iFSTE and V*iFSTE: Beginning year 1993 for Mexico, 1995 for Brazil

MSCI indexes: • ρ*iMSCI and V* iMSCI : Beginning year 1992 for Indonesia and Portugal, 1993 for Mexico,

1994 for South Africa, 1995 for Chile, Greece, India, Pakistan, Peru and Turkey, 1996 for Poland

3. Variable definitions and Raw Data sources: • ρ*iFSTE and ρ*iMSCI (i=1,2): Analysts’ consensus about the EPS forecast for the calendarized FYi

fiscal period for the respective index for each country. Source: I/B/E/S Global Aggregates and authors calculations.

• V*iFSTE and V* iMSCI (i=1,2): Analysts’ uncertainty about the EPS for the calendarized FYi fiscal period for the respective index for each country. Source: I/B/E/S Global Aggregates and authors calculations.

4. The last two couples rows of the Table report the average and standard deviation of each variable across countries. The first couple includes all countries, the second couple excludes the big outliers Brazil, Mexico and Turkey.

5. Sources: I/B/E/S Global Aggregates and authors’ calculations.

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Table 3. Panel Co-integrating Equation – Analysts’ Consensus

kttkj

jktjj j j j

jktjjktjjktjjktjkt uControlIPFθBMζSMεFIδ ++++++++= ∑∑ ∑ ∑ ∑ −= = = =

ξνλρ 1

7

1

3

1

2

1

2

1

β*

Panel A. FTSE Index Panel B. MSCI Index

ρ*1FTSE ρ*2FTSE ρ*1MSCI ρ* 2MSCI

Constant 0.40 (2.11)**

0.21 (2.54)***

0.09 (1.02)

0.07 (0.64)

FI2 0.21

(1.85)** 0.36

(4.13)*** 0.22

(3.05)*** 0.32

(3.67)***

FI3 4.37 (2.05)**

INS1 3.38 (2.20)**

SM2 0.15

(3.03)***

BM2 -0.62

(-2.89)***

INF 0.01 (1.86)**

Adj. R2 0.35 0.25 0.21 0.32

D.W. 2.15 1.69 1.85 1.65

Notes: 1. Estimation technique: Panel Dynamic OLS. 2. Sample period: 1990-2004. 3. Sample countries:

• FTSE index (22 countries): Australia, Austria, Belgium, Brazil, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Mexico, Netherlands, New Zealand, Norway, South Africa, Spain, Sweden, Switzerland, UK, US.

• MSCI index (32 countries): All the above excluding Brazil, plus Chile, Greece, India, Indonesia, Korea, Pakistan, Peru, Philippines, Poland, Portugal and Turkey.

4. The Table reports the statistically significant I(1) variables (t-statistics in parentheses) in the DOLS equation. In all cases the cross-section SUR (PCSE) standard errors and covariance (d.f. corrected) method was used.

5. Variable definitions: • ρ*iFSTE and ρ*iMSCI (i=1,2): Analysts’ consensus for the EPS forecast for the calendarized

FYi fiscal period for the respective index for each country • FI2: Private credit by deposit money banks and other financial institutions to GDP • FI3: Banks’ overhead costs • INS1: Life insurance premium volume as a share of GDP • SM2: Stock market total value traded to GDP • BM2: Public bond market capitalization to GDP • INF: Change in CPI, end of period

6. One (*), (**) and three (***) asterisks denote significance at respectively the 10%, 5% and 1% level.

7. Sources: I/B/E/S Global Aggregates, World Banks’ Financial Development and Structure Database, Datastream and authors’ calculations.

43

Table 4. Panel Co-integrating Equation – Analysts’ Uncertainty

kttkj

jktjj j j j

jktjjktjjktjjktjkt uControlIPFθBMζSMεFIδV ++++++++= ∑∑ ∑ ∑ ∑ −= = = =

ξνλ 1

7

1

3

1

2

1

2

1

β*

Panel A. FTSE Index Panel B. MSCI Index

V1FTSE V2FTSE V1MSCI V2MSCI

Constant 5.50 (4.07)***

3.38 (2.53)***

8.22 (7.73)***

11.83 (7.78)***

FI3 0.60 (2.19)**

0.98 (3.27)***

0.99 (3.29)***

INS1 1.37 (4.41)***

INS2 0.95

(2.62)***

SM2 -0.46

(-1.77)*

SM3 0.54

(1.87)** 0.29 (2.02)**

STDINF 0.68 (3.60)***

0.64 (2.59)***

MKTRISK 1.35 (3.23)*** 1.18

(3.50)***

Adj. R2 0.87 0.91 0.89 0.88

D.W. 1.65 1.58 1.49 1.60

Notes: 1. Estimation technique: Panel Dynamic OLS. 2. Sample period: 1990-2004. 3. Sample countries:

• FTSE index (22 countries): Australia, Austria, Belgium, Brazil, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Mexico, Netherlands, New Zealand, Norway, South Africa, Spain, Sweden, Switzerland, UK, US.

• MSCI index (32 countries): All the above excluding Brazil, plus Chile, Greece, India, Indonesia, Korea, Pakistan, Peru, Philippines, Poland, Portugal and Turkey.

4. The Table reports the statistically significant I(1) variables (t-statistics in parentheses) in the DOLS equation. In all cases the cross-section SUR (PCSE) standard errors and covariance (d.f. corrected) method was used. All variables are in logarithms.

5. Variable definitions: • V*iFSTE and V*iMSCI (i=1,2): Analysts’ uncertainty for the EPS for the calendarized FYi

fiscal period for the respective index for each country • FI3: Banks’ overhead costs • INS1: Life insurance premium volume as a share of GDP • INS2: Non-life insurance premium volume as a share of GDP • SM2: Stock market total value traded to GDP • SM3: Stock market turnover ratio • STDINF: Standard deviation of change in CPI, rolling 12-month periods • MKTRISK: Standard deviation of returns of the total market return index (dividends

included) for each country, rolling 12-month periods

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6. One (*), (**) and three (***) asterisks denote significance at respectively the 10%, 5% and 1% level.

7. Sources: I/B/E/S Global Aggregates, World Banks’ Financial Development and Structure Database, Datastream and authors’ calculations.

45