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Equity Pricing in Islamic Banks: International Evidence

Jocelyn Grira* UAE University, College of Business & Economics

United Arab Emirates [email protected]

M. Kabir Hassan University of New Orleans, College of Business Administration

USA [email protected]

Chiraz Labidi UAE University, College of Business and Economics

United Arab Emirates [email protected]

Issouf Soumaré Laval University, Faculty of Business Administration

Canada [email protected]

* Corresponding author. Email: [email protected]

mailto:[email protected]





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Equity Pricing in Islamic Banks: International Evidence

Abstract

Using a large sample of publicly listed banks in 68 countries over the 1999-2012

period, we assess the ex-ante cost of equity financing of Islamic banks and

compare it to the ex-ante cost of equity capital of conventional banks. We show

that Islamic banks have, on average, higher equity financing cost than

conventional banks. The difference is economically significant, 258 bp, and is

persistent across the four cost of equity models: Claus and Thomas (2001),

Easton (2004), Gebhardt et al. (2001), and Ohlson and Juettner-Nauroth (2005).

Interestingly, the documented difference in the cost of equity among the two

banking systems largely varies across countries and can be partially explained

by institutional factors. We find the institutional quality to improve the cost of

equity for both Islamic banks and conventional banks, with the effect being

more pronounced for the Islamic banking system. Our findings are robust to

alternative assumptions and model specifications, disproportionate analyst

coverage relative to firm size, and other firm-specific and country-specific

factors.

Keywords: Islamic banks, Cost of Equity, Institutional environment

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1. Introduction

Islamic finance raised the interest of both investors and policymakers in the past years for

several reasons. The first is the rapid growth of Islamic finance. In fact, the yearly growth

rate of Islamic finance was estimated around 15 to 20 percent, with a market value of

Islamic assets in commercial banks exceeding $1.7 trillion in 2013 (Abedifar et al., 2016) and

$2.0 trillion in 2014 (Malaysia International Islamic Financial Centre, 2014). The second

reason is the recurrence of financial crises in the recent decades and the severity as well as

the global impact of the 2007-2009 financial crisis and how Islamic banks reacted compared

to their conventional peers. The third reason is discussed in Ibrahim (2015) and relates to

the penetration of Islamic finance to new markets in Europe and North America, far from

the traditional geographical location of its market shares, i.e. in Southeast Asia and the

MENA region. A fourth reason is the re-emergence of ethics as an important business

dimension as well as the growing pressure of the public opinion to move toward a more

corporate socially responsible financial system. A fifth reason is the duality of some

banking systems where both banking models coexist, which stresses the importance of

having a better understanding of the differences between both banking models. Gaining

more knowledge about these differences helps optimizing the current supervisory and

regulatory frameworks and contributes to the integration of both banking models into a

unique one that accounts the specificities of each of them. All put together, these reasons

contributed to shedding more light on the Islamic banking model as a new banking model

that differs, by construction, from the conventional banking model.

On the other side, scholars’ interest in Islamic finance increased in the last decade for other

related reasons, besides those mentioned above. In fact, previous literature shows that

differently shaped financial systems can affect economic outcomes (e.g. Beck and Levine,

2004; Berger et al., 2004; Levine et al., 2000; Merton and Bodie, 2005). Since Islamic finance

is a fundamentally different financial system compared to the conventional one, it offers an

interesting experimental setting that may be used to test socio-economic and financial

theories. Moreover, Islamic finance is necessarily linked to the economics of piety as

documented in Azzi and Ehrenberg’s (1975) seminal work. In fact, the traditional approach

in finance explains the investment and the saving behaviors by a decision to postpone

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today’s consumption to a future date. In the same vein, Azzi and Ehrenberg (1975)

introduce the idea of after-life utility where religious individuals directly gain utility from

adopting religious behavior consistently with their religious beliefs. Consumption of

Islamic financial services falls under the same theoretical umbrella. Accordingly, Islamic

banks represent the supply for Islamic banking services, and Muslim individuals represent

their targeted clientele, i.e. the demand for these services. As argued in Berg et al. (2016),

the difference between the two banking models translates into an opportunity cost of piety,

which implies higher financing cost at the end. More recent literature shows a positive

relationship between religiosity and an individual’s risk aversion (Abedifar et al., 2013;

Hilary and Hui, 2009; Osoba, 2003). Finally, previous literature documented mixed results

regarding the performance and efficiency of Islamic banks compared to their conventional

peers. In fact, some studies record few significant differences between the performance of

both models while others find that Islamic banks are less cost efficient than their

conventional peers (see Beck et al., 2013; Gheeraert and Weill, 2014; Grira et al., 2016; and

Johnes et al., 2014; among others).1 Consequently, the risk-return tradeoff in finance implies

that cost-inefficient banks would be less competitive than their rivals, would bear higher

financing costs both on the equity and the debt side, and would reward less their equity

owners.

Our work focuses on Islamic banks’ cost of equity financing and compares it to

conventional banks cost of equity financing for the following reasons. First, it provides an

economic assessment of Islamic banks’ equity pricing compared to their conventional peers.

In fact, from an investment perspective, investors need to integrate cost of equity

information in their valuation setting, hence reducing information asymmetry and

providing valuable quantitative input to make informed corporate decisions. Second, from a

risk management perspective, the differences in risk practices and exposures between

Islamic banks and their conventional peers as documented in previous studies (e.g. Beck et

al., 2013; Cihak and Hesse, 2010; Grira et al., 2016) stresses the particular importance of

accurately quantifying the cost of equity financing as it provides the market view of banks’

1 Refer to Abedifar et al. (2015) for a comprehensive literature review of the empirical findings related to Islamic banks performance and efficiency compared to conventional banks.

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funding risk from the equity side. Third, from a policy and regulatory perspective, it

highlights the differences between both models, which quantifies the cost of equity

financing barriers, provides guidance on the way to regulate both banking systems

specifically in the case of dual systems, and stresses the importance of the legal and

institutional environment as it partially explains the cross-country differences of Islamic

banks’ cost of equity. Finally, from an economic point of view, it shows how the cost of

equity of Islamic banks is impacted by economic and financial markets’ conditions

compared to the cost of equity of conventional banks.

We use a sample of 4,382 bank-year observations to assess the cost of equity capital of

Islamic banks over the 1999-2012 period and compare it to the cost of equity capital of

conventional banks. We find that Islamic banks have, on average, higher equity financing

cost than conventional banks. Consistent with Abdull-Majid et al. (2010) who find that the

relative performance of Islamic and conventional banks varies significantly across

countries, we show that cost of equity capital for both banking models varies across

countries. Moreover, we show that institutional factors partially explain the cross-country

differences in the cost of equity. Indeed, the quality of institutions improves the cost of

equity in both banking systems, with the effect being more pronounced in the Islamic

banking system. Our findings have policy and regulatory implications specifically for the

countries with dual banking systems.

Our work contributes to the body of knowledge on Islamic finance in several ways. First, it

is the first study that assesses the implied cost of equity capital for Islamic banks and

compares it to the implied cost of equity capital of conventional banks. Second, it highlights

the distinguishing features of Islamic banks in terms of equity valuation and financing cost.

Third, we add to the previous studies that explain the cross-country differences in the cost

of equity capital (Bhattacharya and Daouk (2002), Daouk et al. (2006) and Hail and Leuz

(2006)) as well as previous research on the impact of institutions on firm’s financing costs

(Eleswarapu and Venkataraman (2006), Qi et al. (2010) and Roe (2006)) by investigating the

relationship between the cost of equity capital and institutional factors in the specific

context of the banking sector, with the distinction between conventional and Islamic banks.

Finally, our study sheds more light on the potential benefits of standardized regulatory

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settings that apply to each banking system in order to harmonize the financing conditions

that both banking models face.

The remainder of the paper is structured as follows. Section 2 discusses the theoretical

background on our research question and presents the emerging hypotheses. Section 3

describes the sample, the cost of equity capital measures, and explains the empirical design.

Section 4 presents the statistical results and discussions on the empirical findings. Section 5

concludes.

2. Theoretical background and emerging hypotheses

2.1 Theoretical Background

As Abedifar et al. (2016) point out, the Islamic banking system provides financial products

and services that comply with Islamic principles2. It allows economic agents with religious

preferences to have access to financial solutions that satisfy their needs and to shift from an

informal to a formal banking system. Consequently, Islamic financial institutions can

reduce financial exclusion, hence promoting national efforts for poverty alleviation (Rajan,

2006). Even if Islamic and conventional banks compete on the same markets, their targeted

clientele have different religious beliefs, which may impact their financial behaviors. In fact,

Berg et al. (2016) argue that consumers of Islamic financial services have different utility

functions than the consumers of traditional financial services. Hence, their optimal choices

differ from their peers. Consequently, Islamic banks and conventional banks supply

distinct categories of clientele (i.e. supplying clienteles with distinct utility functions).

Supplying two pools of customers with different utility functions may imply different cost

structures that internalize the specificities of each group, namely in terms of financing cost

and pricing (Grira et al., 2016).

2 Briefly, receiving and paying interest rates charges (so-called “riba” in Islamic finance) such as investing in high yield bonds does not comply with the Islamic principles. Also, making risk-free profit (so-called “maysir” in Islamic finance) such as being involved in arbitrage strategies is not allowed. Making money from uncertainty (so-called “gharar” in Islamic finance) as is the case with the conventional insurance is not permitted. Finally, doing illicit investments or operations (so-called “haram” in Islamic finance) such as investing in tobacco or alcohol firms is not permitted.

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The cut between the two groups of clientele may not be as net as the duality between the

Islamic and conventional banking models may appear. In fact, Muhamad et al. (2012) argue

that the Islamic banking market is composed of three main clusters. The first cluster

primarily has economic incentives: his choices are economically rational and are not driven

by specific beliefs. A second group is composed of individuals who are cautious about

moral values without necessarily being observant of religion. The third cluster is mainly

motivated by religious beliefs. Consequently, the first group can easily switch from one

banking model to another depending on the economic incentives given by both Islamic and

conventional banks. The migration of pools of customers from a banking system to another

impacts banks’ competitiveness and profitability, hence affecting shareholders’ reward and

financing conditions. Assessing the cost of equity financing for both banking systems helps

disentangle the effects of these transfer phenomenon among both clienteles and sheds more

light on the main risk drivers for each banking model.

Previous studies extensively documented the specificities of Islamic financial institutions

compared to conventional financial institutions in terms of investment financing (Aggarwal

and Yousef, 2000; Hassan and Soumaré, 2015)), securitization (Jobst, 2007), mortgage loans

and lending behavior (Abdul Karim et al., 2014; Ebrahim, 2009), stability and business cycle

(Cihák and Hesse, 2010; Ibrahim, 2016, 2015), relationship banking (Ongena and Sendeniz-

Yüncü, 2011), business models (Beck et al.,2013), efficiency (Johnes et al., 2014; Saeed and

Izzeldin, 2014), credit quality (Baele et al.,2014; Farook et al., 2014; Narayan and

Bannigidadmath, 2015), mutual funds (Abdelsalam et al., 2014), and risk and valuation

(Abedifar et al., 2013; Elnahass et al., 2014).

However, none of these previous researches has dealt with the cost of equity capital for

both banking systems. To the best of our knowledge, our work is the first that assesses the

ex-ante cost of equity financing implied from analyst earnings forecasts and stock prices as

introduced by Hail and Leuz (2006, 2009) for Islamic versus conventional banks. As argued

in Chen et al. (2009), El Ghoul et al. (2011) and Hail and Leuz (2009), the implied cost of

capital approach is particularly useful because it makes an explicit attempt to isolate cost of

capital effects from growth and cash flow effects. Pastor et al. (2008) provide consistent

evidence, showing that the class of implied cost of capital models reasonably captures the

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time-variation in expected returns. Additionally, we identify the factors explaining the

recorded cross-country differences for each banking model, hence complementing the

financial literature on the impact of quality of institutions on economic outcomes (La Porta

et al., 1997, 1998, 1999, 2006; Lambert et al., 2007; Qi et al., 2010; among others).

2.2 Hypotheses

In this section, we provide theoretical arguments motivating our expectations that ceteris

paribus, (1) the cost of equity of Islamic banks is higher than the cost of equity of

conventional banks and (2) the cost of equity of Islamic banks is more sensitive to the

quality of institutional environment compared to the cost of equity financing of

conventional banks, ceteris paribus, and (3) hypotheses (1) and (2) remain valid when

restricting the comparison of the cost of equity of Islamic banks to the cost of equity of their

conventional peers in countries with dual banking systems.

2.2.1 Cost of equity financing

Previous literature documented the relationship between size and performance in the

banking sector. In fact, smaller banks tend to have a lower performance than larger banks

(Chapra, 2007). Since Islamic banks are typically smaller than conventional banks (Johnes et

al., 2014), we expect that the latter will perform better than their Islamic counterparts, hence

improving their competitiveness and access to external capital. Moreover, Islamic banks are

mainly domestically owned and there is evidence to support the contention that foreign-

owned banks perform better than their domestically owned peers (Sturm and Williams,

2004). This also implies that foreign-owned banks may be exposed to better financing

opportunities globally, hence reducing their equity financing cost. Finally, Islamic banks

have to comply to national regulatory rules and regulation in addition to their moral

and/or statutory obligation to comply to the Islamic principles. Therefore, compliance and

operational costs are more likely to be higher for Islamic banks and can be viewed as

additional investment burdens, hence reducing their profitability and financial flexibility.

Furthermore, the Islamic banking model gives rise to unique governance challenges

including protecting the interests of investment account holders and defining the

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governance mechanisms of Shariah compliance. According to Mejia et al. (2014), there

seems to be a need to enhance transparency regarding the mandate and accountability of

Shariah Supervisory Boards to help improve the Islamic banking system reputation and

reduce legal risks associated with Shariah compliance. More generally, and despite many

similarities shared with conventional banks, Islamic banks face some unique risks arising

directly from the specific characteristics of Islamic contracts, in particular the risk-sharing

feature. El-Hawary, Grais and Iqbal (2004) highlight, among other risks, Shariah

compliance risk that is specific to Islamic banks.3 Islamic banks also face risks similar to

those of conventional banks (credit risk, operational risk, liquidity risk, etc.). However, as

argued by Mejia et al. (2014), some of these risks could be higher in Islamic banks due to the

specific characteristics of Islamic financing and investment contracts. These additional risks

are likely to imply higher equity financing costs. Based on the aforementioned arguments,

we expect Islamic banks to exhibit higher cost of equity capital than their conventional

peers, ceteris paribus.

Hypothesis 1: Islamic banks have, on average, a higher cost of equity financing compared to

conventional banks, ceteris paribus.

2.2.2 Impact of the institutional framework

Previous studies documented how investors price the quality of institutions (Bhattacharya

and Daouk, 2002; Chen et al., 2009; Hail and Leuz, 2006). For instance, Qi et al. (2010)

highlight the importance of institutional factors in capital financing decisions. Lambert et

al. (2007) document that higher transparency is associated with a lower cost of equity

capital. Bushman et al. (2004) show that higher transparency is observed in countries with

high quality of institutions. Eleswarapu and Venkataraman (2006) further show that the

cost of equity capital is likely to be lower in countries with high quality of institutions.

Finally, Georgarakos and Pasini (2011) and Guiso et al. (2008) show that a strong

institutional environment reduces equity financing costs.

3 Shariah compliance risk can be defined as the risk of inadequate compliance with Shariah law that could result in weakening consumers protection and may lead to a loss of depositors’ confidence.

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Consistent with the previous hypothesis whereby Islamic banks would exhibit higher

equity financing cost, we argue that a higher financing cost reflects investors’ perception of

business risk, which translates in terms of equity pricing (Butler and Joaquin, 1998). We

argue that market perception of the risk of Islamic banks is amplified when these banks

operate in low quality institutional environment whereas it is deflated when they operate

in high quality institutional environment. Since conventional banks would show a lower

cost of equity capital, we argue that their risk sensitivity to the institutional factors is lower

compared to their Islamic counterparts. Based on the aforementioned arguments, we

enunciate our second hypothesis.

Hypothesis 2: The cost of equity of Islamic banks is more sensitive to the quality of

institutional environment compared to the cost of equity financing of conventional banks,

ceteris paribus.

2.2.3 Banking system duality

The dual banking system refers to the coexistence of Islamic banks and conventional banks

in the same country. Islamic banking is still considered in an early stage of development

compared to the mature conventional banking system and different countries, where

Islamic banking is conducted alongside conventional banking, have adopted different

approaches to accommodate the introduction and operations of Islamic banks (Song and

Oosthuizen, 2014). Many studies emphasized the challenges faced by regulators and

policymakers in dual banking systems in their attempts to harmonize the regulatory and

supervisory frameworks for both systems and to level the playing field between Islamic

banking and conventional banking. The documented lack of harmonization is likely to

result in a structural difference between both banking models. Consequently, we expect

that the cost of equity capital of Islamic banks and its sensitivity to the institutional

framework remain higher even when restricting the analysis to countries where both

systems coexist.

Hypothesis 3: Hypotheses 1 and 2 remain valid when restricting the comparison of the cost

of equity of Islamic banks to the cost of equity of their conventional peers in countries with

dual banking systems.

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3. Data and methodology

3.1. Sample Construction

To build our cost of equity financing measures, we first start by merging three databases:

Thomson Reuters Institutional Brokers Estimate System (I/B/E/S), which provides

analysts’ forecast data; Compustat Global, which provides industry affiliation and financial

data; and we collect information on stock returns from Datastream. We follow Dhaliwal et

al. (2006) and Gebhardt et al. (2001) and estimate the cost of equity in June of each year. To

do so, we extract from the I/B/E/S summary file forecast data recorded in June for all

firms that have positive 1- and 2-year-ahead consensus earnings forecasts and a positive

long-term growth forecast. For these firms, we further require that I/B/E/S database

provides a share price as of June, that Compustat reports a positive book value per share,

and that the firm belongs to one of the Fama and French (1997) 48 industry groups. We then

follow Dhaliwal et al. (2006) and Hail and Leuz (2006) and estimate the cost of equity

capital using four different models: the Claus and Thomas (2001) model, the Gebhardt et al.

(2001) model, the Ohlson and Juettner-Nauroth (2005) model, and the Easton (2004) model.

These models are discussed in the section below. We retain in our sample firms with

sufficient available data and with valid cost of equity estimates under all four models.

Second, we merge the resulting data with BankScope to add the global banking information

over the 1999-2012 period. BankScope presents an indicator that differentiates between

publicly listed banks and privately held ones. We use it to select publicly listed banks.

Similar to Beck et al. (2013), we double check the categorization of Islamic banks in

BankScope with information from Islamic Banking Associations and country-specific

sources.

Finally, we merge the resulting data with ICRG data that provides country-level

information about the quality of institutions, democratic tendencies, corruption, and

government action. The index is composed of 12 components, including External conflicts,

Internal conflicts, Ethnic tensions, Religious tensions, Military in politics, Government

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stability, Socioeconomic conditions, Investment profile, Bureaucracy quality, Corruption,

Law and order, and Democratic accountability.4

This procedure yields a final sample of 4,382 bank-year observations representing 68

countries over the period ranging from 1999 to 2012. Table 1 summarizes the sample

composition by country (Panel A) and by year (Panel B).

INSERT TABLE 1 ABOUT HERE

3.2. Cost of Equity Measures

Our interest in firms’ equity financing costs is motivated by the following considerations.

First, the cost of equity capital is the internal rate of return that the market applies to a

firm’s future cash flows to determine its current market value (El Ghoul et al., 2011). It is

the required rate of return given the market’s perception of a firm’s perceived risk. Second,

the cost of equity represents investors’ required rate of return on corporate investments and

thus is a key input in firms’ long-term investment decisions. Finally, consistent with Butler

and Joaquin (1998), the cost of capital is the channel through which capital markets price

risk, including financial risks in the banking sector.

Our choice to use the cost of equity implied in analysts’ forecasts and stock prices as a

proxy for firms’ market performance is motivated by previous work in accounting and

finance. In fact, Fama and French (1997) demonstrate that the traditional single-factor asset

pricing model as well as the Fama and French (1993) three-factor model offer poor proxies

for the cost of equity capital. Elton (1999) argues that conventional proxies for realized

returns fail in explaining the cross-sectional variations in these observed returns and

suggests finding alternative proxies for expected returns. Pàstor et al. (2008) provide

evidence that implied cost of capital models reasonably capture the time-variation in

4 The political risk rating is obtained by assigning risk scores to the 12 components with higher scores denoting lower risks. The components Government stability, Socio-economic conditions, Investment profile, External conflicts, and Internal conflicts have scores ranging from 0 to 12. The components Ethnic tensions, Religious tensions, Military in politics, Corruption, Law and order, and Democratic accountability have scores ranging from 0 to 6. The component Bureaucracy quality has a score ranging from 0 to 4.

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expected returns. Chen et al. (2009) and Hail and Leuz (2006, 2009) argue that the implied

cost of capital approach is particularly useful because it explicitly isolates the cost of capital

effects from the growth and cash flow effects.

Although prior research proposes various models to calculate firms’ implied cost of equity

capital, it provides little guidance on the relative performance of these models. We

therefore follow Chen et al. (2009) and Hail and Leuz (2006) and estimate the cost of equity

using four different models: the Claus and Thomas model (2001), the Gebhardt et al. model

(2001), the Ohlson and Juettner-Nauroth model (2005) and the Easton model (2004). The

four models, allowing for estimating the ex-ante cost of equity capital and presented in

details in the subsequent sub-sections, rely on the more general dividend discount model

where current stock price Pt equals the expected future dividends (𝐷𝑡+𝜏) discounted at the

cost of equity capital r:

𝑃𝑡 = ∑𝐷𝑡+𝜏

(1+𝑟)𝜏

∞

𝜏=1 (1)

Then, in line with Chen et al. (2011) and Dhaliwal et al. (2006), we subtract the 10-year US

Treasury bond yield from the estimated cost of equity of each model to get four implied

equity risk premiums: 𝑟𝐶𝑇, 𝑟𝐺𝐿𝑆, 𝑟𝑂𝐽𝑁, and 𝑟𝐸𝑎𝑠𝑡𝑜𝑛, respectively. To reduce the possibility of

spurious results associated with the use of a particular model (Dhaliwal et al., 2006), we

compute the average cost of equity premium based on the four models. This yields 𝑟𝐴𝑣𝑔,

which is the arithmetic average of 𝑟𝐶𝑇, 𝑟𝐺𝐿𝑆, 𝑟𝑂𝐽𝑁, and 𝑟𝐸𝑎𝑠𝑡𝑜𝑛, and represents the implied

equity risk premium that we use as dependent variable in our multivariate analysis.

3.2.1. Claus and Thomas (2001) model

This model assumes clean surplus accounting, allowing the current share price to be

expressed in terms of the cost of equity, the current book value, forecasted abnormal

earnings, and perpetual abnormal earnings growth. Forecasted abnormal earnings (𝑎𝑒) is

given by forecasted earnings per share (𝐹𝐸𝑃𝑆) minus a charge for the cost of equity. The

explicit forecast horizon is set to five years, beyond which forecasted residual earnings

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grow at a constant rate 𝑔𝑎𝑒 assumed to equal the expected inflation rate. The valuation

equation is given by:

𝑃𝑡 = 𝐵𝑡 + ∑𝑎𝑒𝑡+𝜏

(1+𝑟𝐶𝑇)𝜏

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𝜏=1+

𝑎𝑒𝑡+5(1+𝑔𝑎𝑒)

(𝑟𝐶𝑇−𝑔𝑎𝑒)(1+𝑟𝐶𝑇)5 , (2)

where 𝑃𝑡 is the stock price at time 𝑡, 𝐵𝑡 is the current book value per share (at the beginning

of year 𝑡), 𝑟𝐶𝑇 is the cost of equity capital, 𝑎𝑒𝑡+𝜏 = 𝐹𝐸𝑃𝑆𝑡+𝜏 − 𝑟CT ∙ 𝐵𝑡+𝜏−1, with 𝐵𝑡+𝜏, the

forecasted book value per share for year t + τ, measured using the clean surplus

relationship (i.e., 𝐵𝑡+𝜏 = 𝐵𝑡+𝜏−1 + 𝐹𝐸𝑃𝑆𝑡+𝜏(1 − 𝐷𝑃𝑅𝑡+𝜏), where 𝐷𝑃𝑅 is the dividend payout

ratio assumed to be equal to 50%). Knowing all the parameters, Eq. (2) is solved numerically

for 𝑟𝐶𝑇.

3.2.2. Gebhardt et al. (2001) model

This approach uses a discounted residual income model (RIM). It also assumes clean

surplus accounting, where the share price is expressed in terms of the cost of equity, the

current book value, and forecasted return on equity (𝑅𝑂𝐸) and book value. The explicit

forecast horizon is set to three years, beyond which forecasted 𝑅𝑂𝐸 decays to an industry-

specific target 𝑅𝑂𝐸 by the 12th year, and remains constant afterward. The model equation is

given by:

𝑃𝑡 = 𝐵𝑡 + ∑𝐹𝑅𝑂𝐸𝑡+𝜏−𝑟𝐺𝐿𝑆

(1+𝑟𝐺𝐿𝑆)𝜏

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𝜏=1𝐵𝑡+𝜏−1 +

𝐹𝑅𝑂𝐸𝑡+12−𝑟𝐺𝐿𝑆

𝑟𝐺𝐿𝑆(1+𝑟𝐺𝐿𝑆)11 𝐵𝑡+11; (3)

where Pt and Bt are defined as in the previous model, 𝐹𝑅𝑂𝐸𝑡+𝜏 is the forecasted 𝑅𝑂𝐸 for

year 𝑡 + 𝜏, and 𝑟𝐺𝐿𝑆 is the cost of equity capital. Knowing all the parameters, Eq. (3) is

solved numerically for 𝑟𝐺𝐿𝑆.

3.2.3. Ohlson and Juettner-Nauroth (2005) model

This model is an extension of the Gordon constant growth model. It expresses the share

price in terms of the cost of equity, the one-year-ahead earnings forecast, and near-term and

perpetual growth forecasts. The explicit forecast horizon is set to one year, after which

forecast earnings grow at a near-term rate that decays to a perpetual rate. Near-term

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earnings growth rate (𝑔2) is the average of: i) the growth rate of forecasted earnings per

share (FEPS) from year t + 1 to year t + 2, and ii) the I/B/E/S long-term growth forecast

(LTG). The perpetual growth rate (𝛾 − 1) is assumed to be equal to the expected inflation

rate. The valuation equation is given by:

𝑟𝑂𝐽𝑁 = 𝐴 + √𝐴2 +𝐹𝐸𝑃𝑆𝑡+1

𝑃𝑡(𝑔2 − (𝛾 − 1)) (4)

where Pt and FEPS are defined as in the previous models, 𝐴 ≡1

2((𝛾 − 1) +

𝐷𝑃𝑆𝑡+1

𝑃𝑡), and

𝐷𝑃𝑆𝑡+1 is equal to 𝐷𝑃𝑆0 the actual dividend per share in year 𝑡 − 1.5

Eq. (4) is solved analytically (i.e. the solution is a closed form expression for 𝑟𝑂𝐽𝑁). The

model requires that 𝐹𝐸𝑃𝑆𝑡+2 > 0 and 𝐹𝐸𝑃𝑆𝑡+1 > 0.

3.2.4. Easton (2004) model

This model is a generalization of the Price–Earnings–Growth (PEG) model. It expresses

current share price in terms of the cost of equity, the expected dividend payout, and one-

and two-year-ahead earnings forecasts. The explicit forecast horizon is set to two years,

after which forecasted abnormal earnings grow in perpetuity at a constant rate. The

expression of Easton’s (2004) valuation model is given by:

𝑃𝑡 =𝐹𝐸𝑃𝑆𝑡+2+𝑟𝐸𝑎𝑠𝑡𝑜𝑛𝐷𝑃𝑆𝑡+1−𝐹𝐸𝑃𝑆𝑡+1

𝑟𝐸𝑎𝑠𝑡𝑜𝑛2 , (5)

where Pt, FEPS and 𝐷𝑃𝑆𝑡+1 are defined as in the previous models.

Eq. (5) can be also rewritten as:

𝑟𝐸𝑎𝑠𝑡𝑜𝑛2 − 𝑟𝐸𝑎𝑠𝑡𝑜𝑛 𝐷𝑃𝑆𝑡+1 𝑃𝑡 − (𝐹𝐸𝑃𝑆𝑡+2 − 𝐹𝐸𝑃𝑆𝑡+1) 𝑃𝑡 = 0⁄⁄ (6)

𝑟𝐸𝑎𝑠𝑡𝑜𝑛 is obtained as the solution to this quadratic equation and the model requires that

𝐹𝐸𝑃𝑆𝑡+2 > 0 and 𝐹𝐸𝑃𝑆𝑡+1 > 0 so that Eq. (6) yields a positive root.

5 Dividend per share is assumed to be constant.

16

3.3. Regression Model

To test our hypotheses on the impact of the quality of institutions on equity pricing, we

estimate the following model:

𝑟𝐴𝑣𝑔 = 𝛽0 + 𝛽1𝑆𝑖𝑧𝑒 + 𝛽2𝐵𝑇𝑀 + 𝛽3𝐿𝑒𝑣 + 𝛽4𝐹𝑏𝑖𝑎𝑠 + 𝛽5𝐷𝑖𝑠𝑝 + 𝛽6𝑅𝑣𝑎𝑟

+𝛽7𝑀𝑘𝑡 𝑇𝑢𝑟𝑛 + 𝛽8𝐼𝑛𝑓𝑙 + 𝛽9𝐺𝐷𝑃𝐶 + 𝛽10𝐼𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑎𝑙 𝑓𝑎𝑐𝑡𝑜𝑟𝑠, (7)

where

𝑟𝐴𝑣𝑔 is the average ex ante (implied) cost of equity capital based on the four models outlined

in section 3.2. Following prior studies (e.g., Dhaliwal et al., 2006; Hail and Leuz, 2006), we

include several determinants of the cost of equity capital in the above regression. As firm-

level controls, we include the natural logarithm of total assets in U.S. $ millions (Size), the

book-to-market value of equity (BTM), the ratio of long-term debt to total assets (Lev), the

forecast error defined as the difference between the one-year-ahead earnings forecast and

realized earnings deflated by beginning-of-period assets per share (Fbias), the dispersion in

analyst forecasts measured as the coefficient of variation of one-year-ahead analyst

forecasts of earnings per share (Disp), and the volatility of stock returns over the previous

12 months (Rvar). Moreover, we control for three economic factors: the market turnover

(Mkt Turn), the realized inflation rate over the next year (Infl), and the logarithm of GDP

per capita in U.S. $ (GDPC). We expect all the firm-level variables to be positively related to

the cost of equity financing, except the Size factor. Moreover, we expect inflation to be

positively related to the cost of equity financing. Finally, we expect GDPC, and market

turnover to be negatively related to the cost of equity capital.

We investigate five different institutional factors to assess the impact of the quality of

institutions on the implied cost of equity capital of Islamic versus conventional banks. First,

the “Law and Order” variable is the ICRG assessment of the law and order tradition in the

country. This variable ranges from 0 to 6. Higher scores indicate a better rule of law in the

country. Second, the “Corruption” variable is the ICRG assessment of a country's corruption

level. The variable ranges from 0 to 6, with higher scores indicating lower corruption level

in the country. Third, the “Bureaucratic quality“ variable measures institutional strength and

17

quality of the bureaucracy in a country. This variable ranges from 0 to 4. High scores are

given to countries where the bureaucracy has the strength and expertise to govern without

drastic changes in policy or interruptions in government services. Fourth, the “Government

stability” variable is an assessment both of the government’s ability to carry out its declared

program(s) and its ability to stay in office. This variable ranges from 0 to 12. Higher scores

indicate high government stability and vice versa. Finally, the variable “Investment profile”

is an assessment of factors affecting the risk to investment such as contract

viability/expropriation, profits repatriation, and payment delays. This variable ranges from

0 to 12. Higher scores indicate lower risk related to the listed risk factors. We expect a

negative relationship between the cost of equity capital and the five institutional factors,

meaning that the cost of equity decreases when the risk decreases (i.e. the score increases).

4. Empirical Results and discussions

4.1. Descriptive Statistics and univariate analysis

Table 1 presents the distribution of our two samples, Islamic and conventional banks, by

country (Panel A) and by year (Panel B). The statistics show that the Islamic banking sample

is distributed across 10 countries while the conventional banking sample is spread over a

higher number of countries around the globe. Around 75 percent of our sample of Islamic

banks is located in UAE, Malaysia, Indonesia, and Pakistan.

Table 2 presents the average cost of equity capital by country (Panel A) and by year (Panel

B) for conventional and Islamic banks. The first column for both samples presents the

average cost of capital computed using the outputs of the four pricing models given in the

next four columns: Claus and Thomas (2001) (column 2), Gebhardt et al. (2001) (column 3),

Ohlson and Juettner-Nauroth (2005) (column 4), and Easton (2004) (column 5). One

important result is the noticeable difference between the cost of equity capital across

countries (Panel A of Table 2), which may be driven by banks and industry level

characteristics, country level factors as well as institutional factors (our second hypothesis).

Moreover, as the yearly trend of equity cost shows (Panel B of Table 2), we record, to some

extent, a convergence of equity financing cost for Islamic banks toward the equity financing

cost for their conventional peers.

18


Table 3 presents the descriptive statistics and correlation coefficients for bank-level

variables on both samples: Islamic and conventional banks. In Panel A of Table 3, we

observe that the average cost of equity is lower in conventional banks than in Islamic banks.

Islamic banks have relatively smaller size and are less levered. The volatility of equity price

is higher in Islamic banks than in conventional banks. These findings are consistent with

previous results. Moreover, as the correlations matrix in Panel B of Table 3 shows, the

control variables have less correlation among themselves, hence the risk of multicollinearity

is negligible.


Finally, Table 4 presents the mean and median difference tests for the average costs of

equity capital and for each model estimates across subsamples of Islamic banks and

conventional banks. Regardless of the models used, Islamic banks exhibit higher equity

financing cost than their conventional counterparts. This difference is statistically significant

at the 1 percent significance level. This finding implies that investors price the risk of

investing in Islamic banks as higher than investing in conventional banks, hence require a

higher reward for their investment. In other words, Islamic banks pay a premium to finance

their moral oriented activities.


4.2. Regression Analysis

The results of the estimation of our main regression (Eq. (7)) given in Table 5 show that the

five institutional factors: “Law and Order”, “Corruption”, “Bureaucratic quality“, “Government

stability”, and “Investment profile” matter for both Islamic and conventional banks because

they have statistically significant coefficients to conventional levels. Moreover, we find that

Islamic banks are more sensitive to institutional factors than do their conventional peers

since the magnitude of the coefficient is higher on the Islamic banks sample (Table 5 Panel

A) than on the conventional banks sample (Table 5 Panel B). Our findings are important for

19

policymakers in countries where Islamic banks operate. In fact, we find that improving the

quality of institutions contributes to decrease the cost of equity financing for both Islamic

and conventional banks. However, the effect of the institutional quality on the cost of

equity is more pronounced for the sample of Islamic banks than for conventional banks.

Hence regulators should invest in enhancing the quality of institutions in order to improve

the financial conditions of their banking system, particularly the Islamic banking system.


The control variables have the expected signs. For instance, leverage (Lev), equity volatility

(Rvar) and book-to-market value of equity (BTM) have a positive impact on the cost of

equity capital, while the size of the bank has a negative impact on it. In addition, the

dispersion of analyst forecasts (Disp) as well as their forecast error (Fbias) have a positive

effect on equity premium. As regards to country-specific variables, inflation (Infl) has a

positive significant effect on equity premium in the sample of conventional banks, but is not

significant in the Islamic banks sample. GDP per capita (GDPC) and market turnover (Mkt

Turn) have non-significant coefficients in both samples.

4.3. Robustness Checks

In this section, we run a battery of sensitivity tests to examine whether our findings

reported in Table 5 are robust to: (1) alternative assumptions and model specifications; (2)

the duality of banking systems; (3) the potential noise in analyst forecasts originating

mainly from analyst optimism, as well as, (4) analyst use of information; and (5) using

different sampling periods.

4.3.1 Alternative assumptions and model specifications

The models whose results are reported in Table 5 use 𝑟𝐴𝑣𝑔, the arithmetic average of the

implied cost of equity capital from the four pricing models (𝑟𝐶𝑇,𝑟𝐺𝐿𝑆,

𝑟𝑂𝐽𝑁, and 𝑟𝐸𝑎𝑠𝑡𝑜𝑛) as the dependent variable. To test the robustness of our results, we run

the regressions on each of the individual pricing model. Panels A to D of Table 6 present

the estimation results for the Easton (2004) model, the Ohlson and Juettner-Nauroth (2005)

20

model, the Gebhardt et al. (2001) model, and the Claus and Thomas (2001) model,

respectively, on the same firm-level and country-level factors as in Table 5. Our regression

results reported in Table 6 corroborate the findings recorded in Table 5, i.e. institutional

factors have a significant impact on equity price with the effect being more pronounced in

the Islamic banks sample compared to the conventional banks sample.

Next, the Claus and Thomas (2001) and Ohlson and Juettner-Nauroth (2005) models

assume that the perpetual growth rate is equal to the expected inflation rate. Given the

sensitivity of the implied cost of equity estimates to the assumed long-term growth rates

beyond analysts' forecast horizons (e.g., Hail and Leuz, 2009), we apply two alternative

growth assumptions for the models of Claus and Thomas (2001) and Ohlson and Juettner-

Nauroth (2005): (i) a constant long-run growth rate of 3 percent6 and (ii) a perpetual growth

rate equal to the annual real GDP growth rate plus long-run inflation rate as a proxy for

nominal growth rate of dividends, respectively. The results reported in Panel E and F of

Table 6 using these alternative specifications support our main findings as recorded in

Table 5.


4.3.2 Duality of banking systems

Previous works emphasized the challenges faced by regulators and policymakers in dual

banking systems. The dual banking system refers to the case where both conventional and

Islamic banks coexist in the country. Our results are run on a sample of Islamic banks and

the comparison is done using a sample of conventional banks. However, some countries in

the sample of conventional banks are not concerned by the comparison against Islamic

banks because their banking system is composed exclusively by conventional banks.

Assessing the effect of institutional factors on the cost of equity in countries with dual

banking systems is powerful empirical proof showing that the same institutional factors

may impact differently Islamic and conventional banks, hence providing evidence for a

structural difference between both banking models after controlling for exposure to similar

institutional risk factors.

6 We choose a long-run growth rate of 3 percent following El-Ghoul et al. (2011).

21

Hence, we re-estimate the same set of regressions as in Table 5, while excluding the

countries with only one banking system. This reduces our sample to the eight following

countries only: Egypt, Indonesia, Malaysia, Morocco, Oman, Turkey, UAE and United

Kingdom.7 The results reported in Table 7 show that our findings hold, hence mitigating

the sample bias concerns related to the banking system duality argument.


4.3.3 Analyst forecast optimism

As documented in Kothari (2001), analysts tend to be over-optimistic, which biases the

estimations of the implied cost of equity upward. Following El Ghoul et al. (2011), we test

the robustness of our results against analyst optimism in two ways. First, we successively

exclude the top 5, 10, 25, and 50 percent of the firm-year observations in the

𝐹𝑏𝑖𝑎𝑠 distribution, i.e., highly optimistic earnings forecasts. Second, we address optimism

in long term forecasts by successively excluding the top 5, 10, 25, and 50 percent of the firm-

year observations in the long-term growth distribution LTG. In both cases, our results

(unreported) support our main findings.8

4.3.4 Tardiness of analyst reaction to information

Previous literature documented how analysts react relatively slowly or sluggishly to

publicly available information (Ali et al., 1992). To test the robustness of our results against

this concern, we follow Chen et al. (2009) and control for price momentum computed as the

compound stock returns over the past 3 months. Overall, our results reported in Table 8

support our main findings and mitigate the concern about the effect of analyst slowness in

treating information on our results. Unreported results using price momentum computed

as the compound stock returns over the past 6, and 12 months also corroborate our

findings.9

7 Our sample covers Islamic and conventional banks having coverage and earnings forecasts by financial analysts. Other countries with dual banking systems that are not subject to financial analysts’ forecasts (required input to compute the implied cost of equity) are not part of our sample. 8 The unreported results are available from the authors upon request. 9 Here also, the unreported results are available from the authors upon request.

22


4.3.5 Using alternative samples

Previous research in Islamic banking documented differences in the performance of Islamic

banks compared to their conventional counterparts during periods of financial distress

(Daher et al., 2015; Rizvi et al., 2015; El Alaoui et al., 2015; Gregoriou et al., 2016; Ibrahim,

2016; among others). The question whether Islamic banks did better than their conventional

peers during the global financial crisis because of structural embedded features or simply

because of their high capitalization remains to be answered. Above, we show that Islamic

banks have, on average, higher ex ante (implied) cost of capital than conventional banks. This

result relies on a sample covering the entire 1999-2012 period. In order to test the

robustness of our findings to the sampling time period, we re-estimate our regressions over

different sub-sample periods: a pre-crisis sample period covering the 1999-2006 period, a

crisis sample period covering the 2007-2009 period, and a post-crisis sample period

covering the 2010-2012 period. Our results reported in Table 9 show that our results hold

on the different subsample periods.


5. Conclusion

In this study, we use the cost of equity capital measure and show that conventional banks

have a competitive advantage on their Islamic counterparts in terms of the cost of equity

funding. The higher ex ante cost of equity capital for Islamic banks implies that markets

perceive the systematic risk of Islamic banks as higher than that of their conventional peers.

A higher risk pricing necessarily requires a higher reward for Islamic equity investors.

Our results show that the quality of institutions matters for both banking systems, and that

it matters more for Islamic banks than for conventional banks. Our findings hold even after

controlling for factors pertaining to equity pricing and following alternative model

specification and assumptions as robustness check. The results hold also when we restrict

our sample to only countries with dual banking system as well as over subsample periods.

23

Improving the quality of institutions represents a straightforward opportunity that

regulators and policymakers should work on in order to decrease the cost of equity

financing for Islamic banks, hence putting them at equal footing with their conventional

counterparts and providing them with more financial flexibility in a context where access to

capital is crucial for competitiveness and business expansion.

Islamic banking is also predicted to continue growing in the years ahead and its role is

likely to increase in promoting financial inclusion and supporting the real economy. In this

context, a sound institutional framework is certainly a key precondition for a safe

development of the Islamic banking system. Indeed, our analysis shows that during periods

of financial distress, institutional factors still matter. This further stresses the importance of

the institutional channel as part of the regulatory toolkit that may help mitigating risks

besides the traditional direct intervention tools.

24

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Saeed, M., Izzeldin, M., 2014. Examining the relationship between default risk and

efficiency in Islamic and conventional banks. Journal of Economic Behavior and

Organization, In Press, Available online 24 February 2014.

31

Song, I., Oosthuizen, C., 2014. Islamic banking regulation and supervision: Survey results

and challenges. IMF Working Paper No. 14/220. Washington: International Monetary

Fund.

Sturm, J.-E., Williams, B., 2004. Foreign bank entry, deregulation and banks efficiency:

lessons from the Australian experience. Journal of Banking and Finance, 28, 1775–1799.

32

N % N % N % N %

Argentina 35 0.82% 0 0.00% Pakistan 0 0.00% 13 10.92%

Australia 83 1.95% 0 0.00% Peru 12 0.28% 0 0.00%

Austria 33 0.77% 0 0.00% Pilippines 74 1.74% 0 0.00%

Belgium 36 0.84% 0 0.00% Poland 99 2.32% 0 0.00%

Bermuda 42 0.99% 0 0.00% Portugal 33 0.77% 0 0.00%

Botswana 6 0.14% 0 0.00% Romania 14 0.33% 0 0.00%

Brazil 61 1.43% 0 0.00% Russia 25 0.59% 0 0.00%

Bulgaria 12 0.28% 0 0.00% Singapore 72 1.69% 0 0.00%

Chile 32 0.75% 0 0.00% Slovenia 4 0.09% 0 0.00%

China 74 1.74% 0 0.00% South Africa 95 2.23% 0 0.00%

Colombia 5 0.12% 0 0.00% South Korea 64 1.50% 0 0.00%

Croatia 4 0.09% 0 0.00% Spain 93 2.18% 0 0.00%

Cyprus 18 0.42% 0 0.00% Sri Lanca 17 0.40% 0 0.00%

Czech Republic 16 0.38% 0 0.00% Sweden 41 0.96% 0 0.00%

Denmark 86 2.02% 0 0.00% Switzerland 168 3.94% 0 0.00%

Egypt 25 0.59% 7 5.88% Taiwan 64 1.50% 0 0.00%

Estonia 4 0.09% 0 0.00% Thailand 181 4.25% 0 0.00%

Finland 27 0.63% 0 0.00% Togo 1 0.02% 0 0.00%

France 118 2.77% 0 0.00% Turkey 111 2.60% 5 4.20%

Germany 143 3.35% 0 0.00% UAE 10 0.23% 38 31.93%

Ghana 8 0.19% 0 0.00% Ukraine 5 0.12% 0 0.00%

Greece 52 1.22% 0 0.00% United Kingdom 219 5.14% 3 2.52%

Hong Kong 127 2.98% 0 0.00% Venezuela 1 0.02% 0 0.00%

Hungary 22 0.52% 0 0.00% Zimbabwe 2 0.05% 0 0.00%

Iceland 5 0.12% 0 0.00% Total 4,263 100.00% 119 100.00%

India 257 6.03% 0 0.00%

Indonesia 108 2.53% 25 21.01%

Ireland 22 0.52% 0 0.00%

Israel 60 1.41% 0 0.00% N % N %

Italy 186 4.36% 0 0.00% 1999 113 2.65% 1 0.84%

Japan 691 16.21% 0 0.00% 2000 176 4.13% 1 0.84%

Jordan 4 0.09% 0 0.00% 2001 180 4.22% 2 1.68%

Kenya 17 0.40% 0 0.00% 2002 151 3.54% 3 2.52%

Lebanon 12 0.28% 0 0.00% 2003 149 3.50% 2 1.68%

Liechtenstein 23 0.54% 0 0.00% 2004 231 5.42% 2 1.68%

Lithuania 2 0.05% 0 0.00% 2005 324 7.60% 6 5.04%

Luxembourg 5 0.12% 0 0.00% 2006 368 8.63% 6 5.04%

Malaysia 62 1.45% 17 14.29% 2007 389 9.13% 9 7.56%

Mexico 46 1.08% 0 0.00% 2008 418 9.81% 16 13.45%

Morocco 8 0.19% 6 5.04% 2009 372 8.73% 14 11.76%

Netherlands 70 1.64% 0 0.00% 2010 476 11.17% 13 10.92%

Nigeria 30 0.70% 0 0.00% 2011 465 10.91% 20 16.81%

Norway 150 3.52% 0 0.00% 2012 451 10.58% 24 20.17%

Oman 31 0.73% 5 4.20% Total 4,263 100.00% 119 100.00%

This table presents the distribution of our sample by country and by year. Panel A presents the distribution of the bank-years observations for both conventional and Islamic banks,

by country. Panel B presents the distribution of the bank-years observations for both conventional and Islamic banks by year. Shaded areas correspond to dual banking system's

countries. i.e countries where Islamic banks and conventional banks coexist. The sample covers the period 1999-2012.

Panel B: Bank-year observations by year

Conventional banks Islamic banksYears

Countries

Table 1: Sample Distribution by Country and by Year - Islamic Banks & Conventional Banks

Countries

Panel A: Bank-years observations by country (…)

Conventional banks Islamic banks

Panel A: Bank-years observations by country


33

rAVG

Claus & Thomas

(2001)

Gebhardt et

al. (2001)

Ohlson and

Juettner-Nauroth

(2005)

Easton (2004) rAVG

Claus &

Thomas (2001)

Gebhardt et

al. (2001)

Ohlson and

Juettner-Nauroth

(2005)

Easton (2004)

Argentina 15.67% 15.58% 16.00% 9.28% 21.80% - - - - -

Australia 12.70% 12.12% 10.26% 14.34% 14.08% - - - - -

Austria 14.25% 14.62% 11.68% 15.50% 15.22% - - - - -

Belgium 13.58% 14.77% 12.83% 13.49% 13.22% - - - - -

Bermuda 15.26% 13.69% 16.84% - - - - -

Botswana 17.85% 18.23% 19.11% 17.02% 17.03% - - - - -

Brazil 22.48% 19.76% 22.12% 23.50% 24.54% - - - - -

Bulgaria 33.63% 30.88% 29.27% 41.60% 32.79% - - - - -

Chile 11.21% 11.46% 8.37% 12.62% 12.38% - - - - -

China 15.26% 16.40% 14.89% 15.61% 14.15% - - - - -

Colombia 17.30% 9.29% 23.88% 17.09% 18.95% - - - - -

Croatia 33.28% 60.02% 18.92% 20.89% - - - - -

Cyprus 19.82% 17.86% 16.03% 21.38% 24.03% - - - - -

Czech Republic 13.34% 12.13% 10.43% 14.71% 16.10% - - - - -

Denmark 14.39% 12.72% 11.46% 15.69% 17.71% - - - - -

Egypt 15.77% 17.47% 14.14% 13.13% 18.33% 15.81% 21.39% 13.17% 14.34% 14.34%

Estonia 17.64% 16.77% 20.12% 18.25% 15.43% - - - - -

Finland 11.59% 10.41% 10.98% 12.52% 12.47% - - - - -

France 14.20% 13.80% 12.24% 14.98% 15.77% - - - - -

Germany 13.29% 12.00% 10.26% 14.75% 16.13% - - - - -

Ghana 20.46% 18.15% 18.87% 21.53% 23.29% - - - - -

Greece 14.83% 13.73% 11.04% 17.34% 17.23% - - - - -

Hong Kong 12.69% 12.01% 9.74% 14.51% 14.50% - - - - -

Hungary 15.85% 15.20% 20.58% 14.20% 13.41% - - - - -

Iceland 14.47% 9.93% 28.06% 5.41% - - - - -

India 16.01% 17.15% 11.44% 18.32% 17.12% 19.99% 20.07% 17.07% 21.78% 21.04%

Indonesia 16.57% 17.04% 13.49% 18.55% 17.18% 17.20% 16.24% 18.81% 17.63% 16.13%

Ireland 14.54% 14.60% 18.03% 12.81% 12.71% - - - - -

Israel 17.51% 15.33% 14.13% 19.21% 21.38% - - - - -

Italy 13.97% 13.46% 10.03% 15.81% 16.59% - - - - -

Japan 9.98% 8.85% 6.67% 11.53% 12.85% - - - - -

Jordan 12.66% 12.26% 9.97% 15.49% 12.93% - - - - -

Kenya 19.86% 22.34% 17.39% 21.99% 17.72% - - - - -

Lebanon 16.24% 14.66% 17.84% 16.45% 16.01% - - - - -

Liechtenstein 12.19% 10.53% 13.85% - - - - -

Lithuania 15.94% 16.04% 15.83% - - - - -

Luxembourg 16.55% 15.20% 15.42% 16.46% 19.14% - - - - -

Malaysia 11.94% 11.75% 8.38% 13.45% 14.16% 11.36% 11.43% 7.97% 13.05% 12.98%

Mexico 14.84% 15.04% 13.08% 15.55% 15.68% - - - - -

Morocco 12.76% 13.99% 11.50% 12.78% 10.15% 9.19% 10.54% 10.75% 10.12%

Netherlands 15.26% 14.23% 12.45% 16.61% 17.77% - - - - -

Nigeria 24.76% 29.00% 21.39% 14.93% 33.70% - - - - -

Norway 14.09% 12.89% 16.05% 13.84% 13.58% - - - - -

Oman 15.01% 13.91% 11.28% 16.93% 17.92% 11.16% 12.02% 10.26% 13.17% 9.16%

Table 2: Cost of Equity Financing by Country and by Year - Islamic Banks & Conventional Banks

Countries

Panel A: Cost of Equity Capital by Country


34

rAVG

Claus & Thomas

(2001)

Gebhardt et

al. (2001)

Ohlson and

Juettner-Nauroth

(2005)

Easton (2004) rAVG

Claus &

Thomas (2001)

Gebhardt et

al. (2001)

Ohlson and

Juettner-Nauroth

(2005)

Easton (2004)

Pakistan - - - - - 20.02% 21.09% 16.80% 21.79% 20.39%

Peru 17.73% 18.31% 12.88% 20.26% 19.47% - - - - -

Pilippines 12.37% 12.89% 8.57% 14.47% 13.56% - - - - -

Poland 12.64% 12.43% 11.73% 13.93% 12.47% - - - - -

Portugal 12.71% 13.34% 11.70% 13.58% 12.22% - - - - -

Romania 15.33% 14.56% 13.11% 17.00% 16.65% - - - - -

Russia 19.47% 20.00% 18.03% 19.06% 20.81% - - - - -

Singapore 13.09% 13.22% 10.34% 13.96% 14.85% - - - - -

Slovenia 16.24% 15.07% 9.37% 17.25% 23.26% - - - - -

South Africa 17.18% 16.63% 16.83% 17.87% 17.39% - - - - -

South Korea 14.29% 14.04% 11.56% 15.42% 16.12% - - - - -

Spain 11.66% 11.18% 10.24% 12.52% 12.69% - - - - -

Sri Lanca 16.46% 16.13% 14.70% 17.39% 17.63% - - - - -

Sweden 12.86% 11.90% 12.15% 13.23% 14.14% - - - - -

Switzerland 11.12% 10.67% 9.40% 11.83% 12.59% - - - - -

Taiwan 10.75% 9.96% 8.41% 12.66% 11.98% - - - - -

Thailand 14.11% 12.21% 12.30% 15.10% 16.83% - - - - -

Togo 30.64% 28.60% 14.39% 34.64% 44.93% - - - - -

Turkey 18.05% 18.84% 16.98% 18.67% 17.70% 17.75% 17.61% 16.46% 18.33% 18.61%

UAE 15.73% 14.42% 14.48% 18.26% 15.74% 17.43% 17.91% 15.80% 18.63% 17.39%

Ukraine 41.16% 44.86% 43.47% 35.13% - - - - -

United Kingdom 12.99% 13.57% 9.29% 14.75% 14.34% 29.77% 30.77% 30.36% 28.00% 29.95%

Venezuela 28.74% 30.77% 24.95% 26.66% 32.59% - - - - -

Zimbabwe 26.91% 29.08% 23.44% 25.49% 29.61% - - - - -

Total 13.77% 13.11% 11.40% 14.94% 15.62% 16.35% 16.61% 15.01% 17.31% 16.47%

rAVG

Claus & Thomas

(2001)

Gebhardt et

al. (2001)

Ohlson and

Juettner-Nauroth

(2005)

Easton (2004) rAVG

Claus &

Thomas (2001)

Gebhardt et

al. (2001)

Ohlson and

Juettner-Nauroth

(2005)

Easton (2004)

1999 12.98% 12.26% 9.90% 13.96% 15.79% 17.03% 16.49% 18.82% 16.60% 16.20%

2000 12.85% 12.23% 10.24% 13.19% 15.72% 16.52% 11.91% 20.48% 15.62% 18.07%

2001 13.62% 11.64% 10.77% 14.59% 17.47% 23.73% 25.80% 34.25% 16.40% 18.47%

2002 14.27% 13.21% 9.82% 15.57% 18.47% 16.48% 9.93% 24.69% 12.52% 18.77%

2003 12.78% 12.07% 9.10% 14.07% 15.89% 20.82% 13.91% 23.81% 21.37% 24.20%

2004 11.64% 11.52% 8.80% 13.20% 13.06% 14.58% 9.84% 26.58% 13.04% 8.84%

2005 11.26% 10.99% 8.83% 13.07% 12.15% 11.72% 12.65% 8.80% 13.32% 12.11%

2006 11.15% 10.75% 9.41% 12.47% 11.95% 16.38% 14.01% 12.63% 17.44% 21.43%

2007 12.40% 12.46% 10.85% 12.96% 13.31% 13.72% 13.16% 12.63% 15.27% 13.82%

2008 17.23% 16.67% 16.38% 17.62% 18.26% 17.82% 18.25% 18.30% 17.76% 16.96%

2009 14.01% 12.78% 12.50% 15.27% 15.49% 15.19% 16.43% 14.41% 16.53% 13.40%

2010 14.38% 13.66% 11.28% 16.20% 16.39% 18.74% 18.69% 15.27% 20.39% 20.61%

2011 15.57% 14.78% 12.46% 16.93% 18.11% 17.63% 19.29% 14.04% 19.51% 17.69%

2012 14.86% 14.11% 12.59% 15.93% 16.83% 14.82% 16.00% 12.00% 16.31% 14.95%

Total 13.77% 13.11% 11.40% 14.94% 15.62% 16.35% 16.61% 15.01% 17.31% 16.47%

This table presents the averaged cost of equity capital by country (Panel A) and by year (Panel B) for conventional and Islamic banks. The first column for both samples presents the

average cost of capital computed using the outputs of the four cost of equity financing models: Claus and Thomas (2001) (column 2), Gebhardt et al. (2001) (column 3), Ohlson and

Juettner-Nauroth (2005) (column 4), and Easton (2004) (column 5).

Years

Panel B: Cost of Equity Capital by Year


Table 2: Cost of Equity Financing by Country and by Year - Islamic Banks & Conventional Banks (…)

Countries

Panel A: Cost of Equity Capital by Country (…)


35

Variable N Mean Std Q1 Median Q3 Variable N Mean Std Q1 Median Q3

rAVG 119 0.162 0.064 0.117 0.153 0.194 rAVG 4263 0.139 0.066 0.099 0.124 0.163

Size 119 9.069 1.299 8.045 9.317 10.081 Size 4263 9.596 2.048 8.299 9.629 10.887

Leverage 119 0.066 0.076 0.000 0.054 0.098 Leverage 4263 0.165 0.213 0.000 0.068 0.270

RVAR 119 0.105 0.050 0.062 0.100 0.143 RVAR 4263 0.098 0.053 0.061 0.086 0.122

BTM 119 1.627 12.486 0.492 0.812 1.207 BTM 4263 1.144 3.736 0.492 0.739 1.163

Disp 119 0.142 0.192 0.038 0.082 0.167 Disp 4263 0.164 0.230 0.052 0.095 0.179

Fbias 119 -0.777 6.107 -0.193 0.000 0.104 Fbias 4263 8.483 62.028 -0.420 0.000 0.490

Variable rAVG Size Leverage RVAR BTM Disp Variable rAVG Size Leverage RVAR BTM Disp

Size -0.131 Size -0.115

Leverage -0.031 0.080 Leverage 0.030 -0.044

RVAR 0.207 -0.247 -0.001 RVAR 0.294 -0.110 0.118

BTM 0.303 -0.013 -0.263 0.101 BTM 0.159 -0.003 0.011 0.064

Disp 0.154 -0.154 0.411 0.257 -0.322 Disp 0.278 -0.025 0.037 0.309 0.051

Fbias 0.063 0.013 0.062 0.079 -0.191 -0.034 Fbias 0.050 0.027 0.067 0.110 -0.005 0.131

This table presents descriptive statistics (Panel A) and Pearson correlation coefficients (Panel B) for bank-level variables. The sample of Islamic banks comprises

119 banks–year observations and the sample of conventional banks comprises 4,263 banks–year observations over the period 1999–2012. rAVG, our dependent

variable, is the average cost of equity obtained from four models developed by Ohlson and Juettner-Nauroth (2005), Easton (2004), Claus and Thomas (2001),

and Gebhardt et al. (2001).

Islamic banks sample Conventional banks sample

Panel A: Descriptive statistics

Table 3: Descriptive statistics and correlation matrix

Panel B: Correlation matrix

Islamic banks sample Conventional banks sample

36

Islamic

banks

Conventional

banks(2) - (1)

Islamic

banks

Conventional

banks(4) - (3)

(1) (2) [T-stat] (3) (4) [Z-stat]

rAVG 16.348% 13.771% -2.577%*** 15.307% 12.413% -2.894%***

[-10.458] [-9.295]

rEaston 16.471% 15.622% -0.849%*** 15.422% 14.081% -1.341%***

[-7.269] [-10.496]

rOJN 17.307% 14.943% -2.363%*** 16.205% 13.470% -2.735%***

[-9.845] [-7.121]

rGLS 15.007% 11.404% -3.602%*** 14.051% 10.280% -3.771%***

[-10.821] [-6.296]

rCT 16.607% 13.114% -3.492%*** 15.550% 11.821% -3.728%***

[-9.402] [-9.698]

Table 4: Univariate tests

This table presents the mean and median difference tests for the average and individual costs of equity capital estimates across

subsamples of Islamic banks and conventional banks. The sample of Islamic banks comprises 119 banks–year observations and

the sample of conventional banks comprises 4,263 banks–year observations over the period 1999–2012. rAVG, our dependent

variable, is the average cost of equity obtained from four models developed by Ohlson and Juettner-Nauroth (2005), Easton

(2004), Claus and Thomas (2001), and Gebhardt et al. (2001). The superscript asterisk *** denotes statistical significance at the

1% level.

Means Medians

37

Variables (1) (2) (3) (4) (5) (6)

Size -0.017* -0.023* -0.018* -0.019* -0.016** -0.017*

(-3.270) (-3.749) (-3.270) (-3.417) (-3.198) (-3.216)

Leverage 0.043*** 0.046* 0.043** 0.054* 0.054* 0.047**

(3.556) (3.558) (3.556) (3.682) (3.636) (3.572)

RVAR 0.324** 0.341** 0.324** 0.342** 0.324** 0.318*

(4.162) (4.141) (4.162) (4.158) (4.168) (3.898)

BTM 0.003 0.002* 0.003 0.003** 0.002* 0.002

(1.679) (1.963) (1.679) (2.083) (1.767) (1.704)

Disp 0.053* 0.051* 0.053* 0.052* 0.053* 0.053*

(3.157) (3.116) (3.157) (3.140) (3.141) (3.150)

Fbias 0.001** 0.002** 0.001** 0.003** 0.001** 0.001**

(3.801) (3.652) (3.801) (1.034) (3.889) (3.763)

Inflation 0.009 0.007 0.008 0.009 0.009 0.010

(0.143) (0.308) (0.143) (0.0393) (0.191) (0.185)

GDP per capita -0.002 -0.002* -0.002 -0.003 -0.002 -0.002

(-1.311) (-1.880) (-1.311) (-1.644) (-1.424) (-1.254)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000

(0.651) (0.569) (0.742) (0.947) (0.664) (0.773)

Law and Order -0.055**

(-4.874)

Bureaucratic quality -0.087*

(-3.972)

Corruption -0.017*

(-2.955)

Government stability -0.004*

(-3.637)

Investment profile -0.002**

(-3.163)

Constant 0.324* 0.621* 0.089** 0.431** 0.330* 0.341*

(4.769) (4.903) (3.411) (3.123) (3.654) (3.696)

Year effects Yes Yes Yes Yes Yes Yes

Country effects Yes Yes Yes Yes Yes Yes

Observations 119 119 119 119 119 119

R-squared 0.647 0.659 0.647 0.652 0.648 0.647

Panel A: Islamic banks sample

Table 5: Cost of Equity Capital and Institutional Factors - Islamic vs. Conventional Banks

38

Variables (1) (2) (3) (4) (5) (6)

Size -0.001** -0.001** -0.001** -0.001** -0.001** -0.001**

(-3.473) (-3.497) (-3.421) (-3.475) (-3.473) (-3.464)

Leverage 0.003* 0.003* 0.003* 0.002* 0.003* 0.003*

(0.588) (0.604) (0.622) (0.477) (0.549) (0.582)

RVAR 0.189*** 0.189*** 0.187*** 0.190*** 0.188*** 0.187***

(5.751) (5.752) (5.729) (5.789) (5.684) (5.686)

BTM 0.014*** 0.014*** 0.014*** 0.014*** 0.014*** 0.014***

(7.240) (7.138) (7.196) (7.315) (7.245) (7.225)

Disp 0.051*** 0.052*** 0.051*** 0.050*** 0.052*** 0.051***

(6.938) (6.950) (6.964) (6.749) (6.936) (6.821)

Fbias 0.021** 0.023** 0.026** 0.019** 0.011** 0.020**

(3.998) (3.983) (4.055) (3.911) (4.001) (4.005)

Inflation 0.011*** 0.011*** 0.013*** 0.012*** 0.011*** 0.013***

(3.174) (3.180) (3.203) (3.189) (3.174) (3.200)

GDP per capita -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

(-1.386) (-1.485) (-1.232) (-0.963) (-1.442) (-1.317)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000

(0.651) (0.569) (0.742) (0.947) (0.664) (0.773)

Law and Order -0.002***

(-3.444)


(-3.666)

Corruption -0.007**

(-3.416)


(-3.251)


(-2.516)

Constant 0.171*** 0.181*** 0.234*** 0.197*** 0.169*** 0.182***

(11.940) (6.471) (5.784) (11.470) (10.160) (7.001)



Observations 4,263 4,263 4,263 4,263 4,263 4,263

R-squared 0.411 0.411 0.411 0.413 0.411 0.411

This table presents the estimation results from regressing the implied cost of equity capital (rAVG) on different bank-level and country-level factors. rAVG, our dependent variable, is the average

cost of equity obtained from four models developed by Ohlson and Juettner-Nauroth (2005), and Easton (2004), Claus and Thomas (2001), and Gebhardt et al. (2001). The explanatory factors are

the following: Size is the natural logarithm of total assets. Leverage is defined as the ratio of long-term debt to total assets. RVAR is the volatility of stock returns over the previous 12 months. BTM

is the book value to market value of equity. Disp is the dispersion of analyst forecasts, defined as the coefficient of variation of one-year-ahead analyst forecasts of earnings per share. Fbias is the

signed forecast error, defined as the difference between the one-year-ahead consensus earnings forecast and realized earnings deflated by beginning-of-period assets per share. Inflation is the

realized inflation rate over the next year. GDP per capita is the natural logarithm of the country's GDP per capita. Law and Order is the ICRG assessment of the law and order tradition in the

country. This variable ranges from 0 to 6. Higher scores indicate a higher rule of law in the country. Corruption is the ICRG assessment of a country's corruption rescaled. The original variable

ranges from 0 to 6. After rescaling, higher scores indicate lower corruption in the country. Bureaucratic quality measures institutional strength and quality of the bureaucracy in a country. High

points are given to countries where the bureaucracy has the strength and expertise to govern without drastic changes in policy or interruptions in government services. This variable ranges from 0

to 4. Government stability is an assessment both of the government’s ability to carry out its declared program(s) and its ability to stay in office. This variable ranges from 0 to 12. Higher scores

indicates high government stability and vice versa. Investment profile is an assessment of factors affecting the risk to investment such as contract viability/expropriation, profits repatriation, and

payment delays. This variable ranges from 0 to 12. Higher scores indicates lower risk related to the listed risk factors. Beneath each coefficient estimate is reported the t-statistic based on

Newey–West correction for heteroscedasticity and serial correlation. The superscript asterisks ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 5: Cost of Equity Capital and Institutional Factors - Islamic vs. Conventional Banks (…)

Panel B: Conventional banks sample

39

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

Size -0.035 -0.035* -0.032* -0.032* -0.037 -0.037* -0.002** -0.003* -0.003** -0.002** -0.003** -0.002**

(-3.270) (-3.745) (-3.270) (-3.413) (-3.174) (-3.215) (-3.322) (-3.173) (-3.123) (-3.272) (-3.312) (-3.252)

Leverage 0.043 0.045 0.043 0.054 0.054 0.047* 0.002* 0.003* 0.001* 0.002* 0.002* 0.003*

(2.555) (2.554) (2.555) (2.562) (2.535) (2.572) (2.424) (2.534) (2.472) (2.473) (2.444) (2.322)

RVAR 0.324 0.341 0.324 0.342 0.324 0.316 0.152*** 0.171** 0.137*** 0.172** 0.156*** 0.147***

(2.152) (2.141) (2.152) (2.154) (2.154) (1.674) (3.731) (5.153) (4.325) (2.725) (3.534) (3.265)

BTM 0.003 0.003 0.003 0.004 0.003 0.002 0.021** 0.025*** 0.027*** 0.022** 0.027*** 0.025***

(1.575) (1.753) (1.575) (2.063) (1.753) (1.704) (2.242) (2.132) (3.275) (3.512) (4.042) (2.222)

Disp 0.053* 0.051* 0.047 0.052* 0.053 0.053* 0.037** 0.043*** 0.055* 0.050*** 0.046*** 0.037***

(1.153) (1.115) (1.153) (1.140) (1.141) (1.150) (4.434) (4.250) (3.714) (3.245) (2.435) (2.645)

Fbias 0.001 0.002 0.002** 0.003** 0.002 0.002** 0.025** 0.023** 0.022** 0.022** 0.032** 0.025**

(0.602) (0.552) (0.602) (2.034) (0.665) (0.753) (2.474) (2.353) (2.674) (2.322) (2.322) (2.222)

Inflation 0.007 0.007 0.006 0.007 0.007 0.020 0.023* 0.023*** 0.025* 0.022*** 0.022* 0.023***

(0.243) (0.304) (0.243) (0.0373) (0.272) (0.262) (3.222) (3.265) (3.242) (3.263) (3.272) (3.224)

GDP per capita -0.002 -0.002 -0.002 -0.003 -0.002 -0.002 -0.022 -0.024 -0.022 -0.023 -0.025 -0.002

(-2.322) (-2.660) (-2.322) (-2.544) (-2.424) (-2.254) (-0.765) (-2.372) (-0.533) (-2.042) (-2.272) (-2.224)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.552) (0.555) (0.742) (0.743) (0.554) (0.773) (0.022) (0.005) (0.022) (0.033) (0.034) (0.033)

Law and Order -0.055 -0.022***

(-0.674) (-3.554)

Bureaucratic quality -0.067* -0.034*

(-3.772) (-3.420)

Corruption -0.027** -0.007*

(-5.052) (-4.325)

Government stability -0.004* -0.003**

(-4.533) (-5.372)

Investment profile -0.002 -0.002**

(-3.253) (-6.262)

Constant 0.324** 0.522*** 0.067*** 0.432*** 0.330** 0.342* 0.272*** 0.262*** 0.234*** 0.277*** 0.257*** 0.262***

(2.755) (2.703) (3.422) (2.223) (2.554) (2.575) (22.74) (5.472) (5.764) (22.47) (20.25) (7.002)

Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 119 119 119 119 119 119 4,263 4,263 4,263 4,263 4,263 4,263

R-squared 0.066 0.060 0.062 0.065 0.060 0.064 0.411 0.392 0.385 0.406 0.397 0.401

Table 6: Robustness to alternative assumptions and model specifications

VariablesIslamic banks Conventional banks

Panel A: Cost of equity capital using Easton (2004) model

40

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

Size -0.025 -0.026* -0.029** -0.021** -0.028* -0.025* -0.004*** -0.002* -0.002* -0.001* -0.005** -0.001**

(-3.050) (-3.041) (-3.136) (-3.173) (-3.143) (-3.424) (-3.020) (-3.326) (-3.020) (-3.213) (-3.104) (-3.041)

Leverage 0.053 0.056 0.027 0.026 0.027 0.052 0.009* 0.007* 0.021* 0.002* 0.003* 0.005*

(3.474) (3.333) (3.656) (3.463) (3.266) (3.343) (3.801) (3.653) (3.801) (2.034) (3.876) (3.863)

RVAR 0.134 0.371 0.214 0.221 0.419 0.216 0.152*** 0.171** 0.177*** 0.161* 0.179*** 0.193***

(0.863) (0.841) (0.974) (0.843) (0.844) (0.893) (4.256) (4.473) (4.176) (4.020) (4.363) (4.004)

BTM 0.003 0.002 0.003 0.003 0.002 0.004 0.027*** 0.027** 0.024*** 0.024** 0.026*** 0.029**

(0.846) (0.893) (0.961) (0.873) (0.894) (0.893) (5.163) (5.141) (5.163) (5.154) (5.164) (5.894)

Disp 0.055 0.056* 0.027** 0.055** 0.027* 0.021* 0.059*** 0.052*** 0.037*** 0.056*** 0.050*** 0.051***

(5.123) (5.021) (5.091) (5.124) (5.201) (5.021) (5.574) (5.604) (5.623) (5.473) (5.546) (5.573)

Fbias 0.002 0.001* 0.002** 0.001** 0.001** 0.001** 0.021** 0.023** 0.026** 0.029** 0.021** 0.023**

(4.051) (4.056) (4.053) (4.053) (4.073) (4.066) (4.444) (4.956) (4.173) (4.541) (4.001) (4.013)

Inflation 0.007 0.007 0.007 0.007 0.020 0.009 0.021*** 0.022*** 0.023* 0.026** 0.024* 0.026*

(0.064) (0.036) (0.056) (0.026) (0.073) (0.053) (3.436) (3.274) (3.556) (3.553) (3.525) (3.473)

GDP per capita -0.001 -0.002 -0.001 -0.002 -0.003 -0.002 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

(-1.051) (-1.125) (-1.021) (-0.894) (-1.004) (-1.054) (-1.545) (-1.111) (-1.431) (-1.453) (-1.311) (-1.113)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.023) (0.025) (0.001) (0.023) (0.021) (0.021) (0.543) (0.511) (0.511) (0.524) (0.534) (0.553)

Law and Order -0.022 -0.005**

(-5.311) (-3.324)


(-3.371) (-4.115)

Corruption -0.022** -0.027**

(-4.315) (-5.315)


(-4.413) (-3.113)

Investment profile -0.023 -0.007*

(-3.355) (-5.115)

Constant 0.374*** 0.375*** 0.175*** 0.371*** 0.417*** 0.141*** 0.134*** 0.171*** 0.115*** 0.145*** 0.153*** 0.171***

(3.455) (3.141) (4.413) (3.175) (3.145) (3.141) (7.915) (5.171) (5.553) (5.471) (7.144) (7.133)



Observations 119 119 119 119 119 119 4,263 4,263 4,263 4,263 4,263 4,263

R-squared 0.074 0.085 0.087 0.103 0.078 0.076 0.367 0.404 0.372 0.392 0.415 0.377

Table 6: Robustness to alternative assumptions and model specifications (…)


Panel B: Cost of equity capital using Ohlson and Juettner-Nauroth (2002) model

41

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

Size -0.022 -0.019** -0.021*** -0.026** -0.026** -0.021** -0.003** -0.003** -0.002** -0.001** -0.002** -0.001**

(-3.020) (-3.326) (-3.020) (-3.213) (-3.104) (-3.041) (-3.174) (-3.179) (-3.313) (-3.392) (-3.013) (-3.084)

Leverage 0.033 0.039 0.033 0.034 0.034 0.037 0.005* 0.004* 0.003* 0.004* 0.005* 0.003*

(4.439) (4.274) (4.459) (4.492) (4.426) (4.472) (4.431) (4.974) (4.931) (4.454) (4.420) (4.411)

RVAR 0.194 0.191 0.314 0.321 0.321 0.318 0.191*** 0.243** 0.274** 0.251*** 0.262*** 0.227***

(1.182) (1.741) (1.143) (1.182) (1.704) (1.494) (3.418) (3.472) (3.471) (3.119) (3.514) (3.112)

BTM 0.002 0.002 0.002 0.002 0.002 0.002 0.021* 0.024** 0.022*** 0.019* 0.027*** 0.021***

(1.259) (1.473) (1.179) (1.020) (1.393) (1.004) (4.491) (4.951) (4.159) (4.341) (5.042) (4.712)

Disp 0.019 0.021* 0.019** 0.026** 0.027* 0.027** 0.039** 0.049* 0.051*** 0.042* 0.042*** 0.047*

(3.453) (3.318) (3.413) (3.144) (3.001) (3.152) (2.474) (3.002) (2.912) (2.459) (2.502) (3.126)

Fbias 0.001 0.001** 0.001*** 0.001* 0.001** 0.001* 0.021* 0.027** 0.026* 0.026** 0.021* 0.027**

(4.019) (4.052) (4.031) (4.034) (4.208) (4.079) (4.494) (4.193) (4.722) (4.374) (4.949) (4.419)

Inflation 0.008 0.007 0.007 0.008 0.008 0.02 0.008* 0.020*** 0.021** 0.022*** 0.007* 0.020***

(1.043) (1.031) (1.043) (1.039) (1.081) (1.033) (3.279) (3.261) (3.264) (3.271) (3.273) (3.263)

GDP per capita -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

(-1.011) (-1.070) (-0.421) (-0.944) (-1.124) (-0.754) (-0.742) (-1.004) (-0.942) (-0.941) (-0.942) (-0.313)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.021) (0.008) (0.022) (0.033) (0.024) (0.033) (0.444) (0.422) (0.432) (0.924) (0.921) (0.414)

Law and Order -0.024* -0.008*

(-3.274) (-3.264)


(-4.171) (-4.264)

Corruption -0.027** -0.027**

(-3.049) (-3.051)

Government stability -0.024* -0.004*

(-2.719) (-5.921)


(-4.199) (-4.218)

Constant 0.194*** 0.251*** 0.279*** 0.271*** 0.327*** 0.261** 0.271*** 0.271*** 0.194*** 0.267*** 0.269*** 0.272***

(3.772) (3.742) (0.211) (3.919) (3.264) (3.379) (3.544) (3.261) (3.174) (3.454) (4.071) (3.241)



Observations 119 119 119 119 119 119 4,263 4,263 4,263 4,263 4,263 4,263

R-squared 0.079 0.087 0.092 0.097 0.084 0.081 0.289 0.288 0.284 0.349 0.293 0.290



Panel C: Cost of equity capital using Gebhardt et al. (2001) model

42

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

Size -0.013 -0.013** -0.014*** -0.016** -0.016** -0.014** -0.003** -0.003** -0.002** -0.001** -0.002** -0.001**

(-3.126) (-3.326) (-3.450) (-3.268) (-3.175) (-3.253) (-3.185) (-3.186) (-3.313) (-3.363) (-3.013) (-3.095)

Leverage 0.033 0.036 0.033 0.034 0.034 0.037 0.005* 0.004* 0.003* 0.004* 0.005* 0.003*

(0.436) (0.285) (0.556) (0.563) (0.526) (0.473) (3.531) (3.685) (3.631) (3.455) (3.52) (3.514)

RVAR 0.234 0.231 0.314 0.321 0.321 0.316 0.131*** 0.143** 0.184** 0.151*** 0.162*** 0.138***

(1.163) (1.841) (1.143) (1.163) (1.805) (1.495) (3.416) (3.483) (3.481) (3.133) (3.515) (3.143)

BTM 0.002 0.002 0.002 0.002 0.002 0.002 0.014* 0.014** 0.013*** 0.013* 0.017*** 0.014***

(1.256) (1.473) (1.176) (1.02) (1.363) (1.005) (4.46) (4.651) (4.156) (4.341) (5.043) (4.713)

Disp 0.023 0.021* 0.023** 0.019** 0.017* 0.018** 0.039** 0.046* 0.051*** 0.042* 0.042*** 0.048*

(3.453) (3.316) (3.413) (3.14) (3.001) (3.15) (2.475) (3.003) (2.913) (2.456) (2.503) (3.136)

Fbias 0.001 0.001** 0.001*** 0.001* 0.001** 0.001* 0.014* 0.017** 0.016* 0.019** 0.014* 0.018**

(3.023) (3.053) (3.031) (3.035) (3.106) (3.076) (3.465) (3.233) (3.723) (3.375) (3.646) (3.423)

Inflation 0.009 0.007 0.008 0.009 0.009 0.012 0.009* 0.010*** 0.014** 0.013*** 0.008* 0.010***

(0.043) (0.03) (0.043) (0.0393) (0.091) (0.033) (3.176) (3.191) (3.165) (3.181) (3.183) (3.163)

GDP per capita -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

(-1.014) (-1.080) (-0.421) (-0.645) (-1.135) (-0.755) (-0.843) (-1.005) (-0.943) (-0.941) (-0.943) (-0.313)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.021) (0.006) (0.013) (0.033) (0.015) (0.033) (0.445) (0.523) (0.433) (0.625) (0.621) (0.415)

Law and Order -0.014 -0.009**

(-3.275) (-4.265)


(-3.171) (-3.295)

Corruption -0.027** -0.017*

(-4.046) (-3.051)


(-4.823) (-5.621)

Investment profile -0.003* -0.002**

(-3.236) (-3.216)

Constant 0.234*** 0.251*** 0.179*** 0.281*** 0.327*** 0.191** 0.171*** 0.181*** 0.234*** 0.197*** 0.169*** 0.182***

(3.773) (3.743) (3.214) (3.623) (3.295) (3.386) (3.545) (3.261) (3.185) (3.455) (4.081) (3.241)



Observations 119 119 119 119 119 119 4,263 4,263 4,263 4,263 4,263 4,263

R-squared 0.093 0.101 0.107 0.114 0.099 0.095 0.327 0.325 0.317 0.344 0.333 0.328



Panel D: Cost of equity capital using Claus and Thomas (2001) model

43

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

Size -0.023 -0.024** -0.022*** -0.026** -0.026** -0.022** -0.004** -0.004** -0.003** -0.002** -0.003** -0.002**

(-3.030) (-3.436) (-3.030) (-3.324) (-3.205) (-3.052) (-3.275) (-3.276) (-3.424) (-3.463) (-3.024) (-3.085)

Leverage 0.044 0.046 0.044 0.045 0.045 0.046 0.005* 0.005* 0.004* 0.005* 0.005* 0.004*

(0.546) (0.375) (0.556) (0.563) (0.536) (0.563) (3.542) (3.675) (3.642) (3.555) (3.530) (3.522)

RVAR 0.345 0.342 0.425 0.432 0.432 0.426 0.242*** 0.254** 0.275** 0.252*** 0.263*** 0.237***

(2.263) (2.752) (2.254) (2.263) (2.705) (2.585) (4.526) (4.573) (4.572) (4.234) (4.525) (4.223)

BTM 0.003 0.003 0.003 0.003 0.003 0.003 0.022* 0.025** 0.023*** 0.024* 0.026*** 0.022***

(2.356) (2.564) (2.266) (2.03) (2.464) (2.005) (5.56) (5.652) (5.256) (5.452) (5.053) (5.623)

Disp 0.034 0.032* 0.034** 0.028** 0.026* 0.027** 0.048** 0.056* 0.052*** 0.053* 0.053*** 0.057*

(3.554) (3.426) (3.524) (3.25) (3.002) (3.25) (3.565) (4.003) (3.823) (3.556) (3.503) (4.236)

Fbias 0.002 0.002** 0.002*** 0.002* 0.002** 0.002* 0.022* 0.026** 0.026* 0.028** 0.022* 0.027**

(3.034) (3.053) (3.042) (3.045) (3.206) (3.066) (4.565) (4.344) (4.633) (4.465) (4.656) (4.534)

Inflation 0.008 0.006 0.007 0.008 0.008 0.021 0.008* 0.020*** 0.022** 0.023*** 0.007* 0.020***

(0.054) (0.04) (0.054) (0.0484) (0.082) (0.044) (3.266) (3.282) (3.265) (3.272) (3.274) (3.264)

GDP per capita -0.003 -0.003 -0.003 -0.003 -0.003 -0.003 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002

(-2.022) (-2.070) (-0.532) (-0.655) (-2.235) (-0.655) (-0.753) (-2.005) (-0.853) (-0.852) (-0.853) (-0.424)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.032) (0.006) (0.023) (0.044) (0.025) (0.044) (0.555) (0.533) (0.543) (0.635) (0.632) (0.525)

Law and Order -0.025 -0.008*

(-3.365) (-4.365)

Bureaucratic quality -0.056* -0.042**

(-3.262) (-3.385)

Corruption -0.036* -0.026***

(-4.056) (-4.052)


(-5.734) (-6.632)


(-5.346) (-5.326)

Constant 0.345*** 0.352*** 0.268*** 0.372*** 0.436*** 0.282** 0.262*** 0.272*** 0.345*** 0.286*** 0.268*** 0.273***

(3.663) (3.653) (0.322) (3.634) (3.385) (3.476) (4.555) (4.362) (4.275) (4.555) (5.072) (4.352)



Observations 119 119 119 119 119 119 4,263 4,263 4,263 4,263 4,263 4,263

R-squared 0.127 0.139 0.147 0.156 0.135 0.130 0.311 0.309 0.302 0.327 0.317 0.312



Panel E: Cost of equity capital using a constant long-run growth rate of 3%

44

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

Size -0.011 -0.012** -0.011*** -0.015** -0.015** -0.011** -0.002** -0.002** -0.001** -0.001** -0.001** -0.001**

(-3.010) (-3.215) (-3.010) (-3.112) (-3.103) (-3.031) (-3.173) (-3.175) (-3.212) (-3.251) (-3.012) (-3.083)

Leverage 0.022 0.025 0.022 0.023 0.023 0.026 0.004* 0.003* 0.002* 0.003* 0.004* 0.002*

(0.325) (0.173) (0.445) (0.451) (0.415) (0.361) (4.421) (4.573) (4.521) (4.343) (4.41) (4.411)

RVAR 0.123 0.121 0.213 0.211 0.211 0.215 0.121*** 0.132** 0.173** 0.141*** 0.151*** 0.117***

(1.151) (1.731) (1.132) (1.151) (1.703) (1.383) (3.315) (3.371) (3.371) (3.112) (3.413) (3.111)

BTM 0.001 0.001 0.001 0.001 0.001 0.001 0.011* 0.013** 0.011*** 0.012* 0.016*** 0.011***

(1.145) (1.362) (1.165) (1.01) (1.252) (1.003) (3.35) (3.541) (3.145) (3.231) (4.031) (3.611)

Disp 0.012 0.011* 0.012** 0.018** 0.016* 0.017** 0.028** 0.035* 0.041*** 0.031* 0.031*** 0.037*

(3.342) (3.215) (3.312) (3.13) (3.001) (3.14) (3.363) (4.001) (3.811) (3.345) (3.401) (4.115)

Fbias 0.001 0.001** 0.001*** 0.001* 0.001** 0.001* 0.011* 0.016** 0.015* 0.018** 0.011* 0.017**

(3.012) (3.041) (3.021) (3.023) (3.105) (3.065) (3.353) (3.122) (3.611) (3.263) (3.535) (3.312)

Inflation 0.008 0.006 0.007 0.008 0.008 0.011 0.008* 0.010*** 0.011** 0.011*** 0.007* 0.010***

(0.032) (0.02) (0.032) (0.0282) (0.081) (0.022) (5.165) (5.181) (5.153) (5.171) (5.172) (5.152)

GDP per capita -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

(-1.011) (-1.070) (-0.311) (-0.533) (-1.113) (-0.643) (-0.731) (-1.003) (-0.831) (-0.831) (-0.831) (-0.212)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.011) (0.005) (0.011) (0.022) (0.013) (0.022) (0.333) (0.411) (0.321) (0.513) (0.511) (0.313)

Law and Order -0.013 -0.008*

(-3.163) (-3.153)


(-4.161) (-4.183)

Corruption -0.016** -0.016**

(-3.035) (-4.041)

Government stability -0.013** -0.003*

(-6.712) (-3.511)


(-5.125) (-6.115)

Constant 0.123*** 0.141*** 0.168*** 0.171*** 0.216*** 0.181** 0.161*** 0.171*** 0.123*** 0.186*** 0.158*** 0.171***

(3.661) (3.631) (3.111) (3.512) (3.183) (3.275) (4.433) (4.151) (4.173) (4.343) (3.071) (4.131)



Observations 119 119 119 119 119 119 4,263 4,263 4,263 4,263 4,263 4,263

R-squared 0.122 0.134 0.141 0.150 0.130 0.125 0.277 0.275 0.269 0.292 0.282 0.278

This table presents the estimation results from regressing the implied cost of equity capital obtained from the four models: Easton (2004) (Panel A), Ohlson and Juettner-Nauroth (2005) (Panel B), Gebhardt et al. (2001) (Panel C),

and Claus and Thomas (2001) (Panel D). Panel E presents the regression results of the cost of equity capital using a constant long-run growth rate of 3%. Panel F presents regression results of the cost of equity capital using a

perpetual growth rate = Annual real GDP growth rate + long-run inflation rate. The explanatory factors are the following: Size is the natural logarithm of total assets. Leverage is defined as the ratio of long-term debt to total assets.

RVAR is the volatility of stock returns over the previous 12 months. BTM is the book value to market value of equity. Disp is the dispersion of analyst forecasts, defined as the coefficient of variation of one-year-ahead analyst

forecasts of earnings per share. Fbias is the signed forecast error, defined as the difference between the one-year-ahead consensus earnings forecast and realized earnings deflated by beginning-of-period assets per share. Inflation is

the realized inflation rate over the next year. GDP per capita is the natural logarithm of the country's GDP per capita. Law and Order is the ICRG assessment of the law and order tradition in the country. This variable ranges from

0 to 6. Higher scores indicate a higher rule of law in the country. Corruption is the ICRG assessment of a country's corruption rescaled. The original variable ranges from 0 to 6. After rescaling, higher scores indicate lower

corruption in the country. Bureaucratic quality measures institutional strength and quality of the bureaucracy in a country. High points are given to countries where the bureaucracy has the strength and expertise to govern

without drastic changes in policy or interruptions in government services. This variable ranges from 0 to 4. Government stability is an assessment both of the government’s ability to carry out its declared program(s) and its ability to

stay in office. This variable ranges from 0 to 12. Higher scores indicates high government stability and vice versa. Investment profile is an assessment of factors affecting the risk to investment such as contract

viability/expropriation, profits repatriation, and payment delays. This variable ranges from 0 to 12. Higher scores indicates lower risk related to the listed risk factors. Beneath each coefficient estimate is reported the t-statistic based

on Newey–West correction for heteroscedasticity and serial correlation. The superscript asterisks ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.



Panel F: Cost of equity capital using a perpetual growth rate = Annual real GDP growth rate + long-run inflation rate

45

Variables (1) (2) (3) (4) (5) (6)

Size -0.016** -0.013* -0.017** -0.016* -0.016** -0.016*

(-3.160) (-3.656) (-3.160) (-3.516) (-3.167) (-3.116)

Leverage 0.053** 0.056* 0.053** 0.034* 0.035** 0.056**

(4.336) (4.337) (4.336) (4.671) (4.636) (4.361)

RVAR 0.315** 0.361** 0.314** 0.351** 0.315** 0.317*

(3.161) (3.151) (3.161) (3.137) (3.167) (3.767)

BTM 0.003 0.001* 0.003 0.003** 0.001* 0.001

(1.666) (3.663) (1.666) (3.073) (3.666) (1.605)

Disp 0.033* 0.031* 0.034* 0.031* 0.033* 0.033*

(4.136) (4.116) (4.136) (4.150) (4.151) (4.130)

Fbias 0.001** 0.001** 0.001** 0.003* 0.001** 0.001**

(5.701) (5.631) (5.701) (1.035) (5.776) (5.663)

Inflation 0.006 0.004 0.007 0.006 0.006 0.014

(0.153) (0.307) (0.153) (0.036) (0.161) (0.173)

GDP per capita -0.001 -0.001* -0.001 -0.003 -0.001 -0.001

(-1.311) (-2.770) (-1.311) (-1.655) (-1.415) (-1.135)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000

(0.631) (0.366) (0.651) (0.656) (0.645) (0.663)


(-3.765)


(-3.661)

Corruption -0.016**

(-3.033)

Government stability -0.005**

(-4.636)

Investment profile -0.001*

(-5.163)

Constant 0.315* 0.611* 0.076** 0.531** 0.330* 0.351*

(3.666) (3.603) (3.511) (3.113) (3.635) (3.666)



Observations 106 106 106 106 106 106

R-squared 0.397 0.389 0.397 0.382 0.398 0.397

Table 7: Countries with dual banking systems


46

Variables (1) (2) (3) (4) (5) (6)

Size -0.002** -0.003* -0.002** -0.004* -0.002** -0.002**

(-3.423) (-3.492) (-3.422) (-3.425) (-3.423) (-3.444)

Leverage 0.003 0.003* 0.003* 0.002** 0.003* 0.003*

(3.566) (3.404) (3.422) (3.422) (3.549) (3.562)

RVAR 0.269*** 0.269*** 0.262*** 0.290*** 0.246*** 0.262***

(5.252) (5.252) (5.229) (5.269) (5.464) (5.464)

BTM 0.024*** 0.025*** 0.024*** 0.023*** 0.024*** 0.024***

(4.243) (4.236) (4.294) (4.325) (4.245) (4.225)

Disp 0.052*** 0.054*** 0.052*** 0.050*** 0.053*** 0.052***

(4.936) (4.95) (4.944) (4.249) (4.934) (4.622)

Fbias 0.022** 0.023** 0.024** 0.029** 0.022** 0.020**

(2.996) (2.963) (3.055) (2.922) (3.002) (3.005)

Inflation 0.022*** 0.022* 0.023*** 0.022* 0.022*** 0.023***

(4.224) (4.263) (4.203) (4.269) (4.224) (4.211)

GDP per capita -0.002 -0.003 -0.002 -0.003 -0.002 -0.004

(-2.364) (-2.465) (-2.232) (-0.443) (-2.442) (-2.322)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000

(0.052) (0.049) (0.142) (0.942) (0.444) (0.223)

Law and Order -0.002***

(-3.444)

Bureaucratic quality -0.032**

(-4.964)

Corruption -0.016*

(-5.424)


(-3.252)


(-6.524)

Constant 0.222*** 0.262*** 0.234*** 0.292*** 0.249*** 0.262***

(8.943) (4.422) (5.264) (3.425) (9.241) (3.002)



Observations 574 574 574 574 574 574

R-squared 0.381 0.384 0.381 0.383 0.381 0.382

Table 7: Countries with dual banking systems (…)


This table presents the estimation results from regressing the implied cost of equity capital (rAVG) on different bank-level and country-level

factors using a sample where dual banking systems exist. rAVG, our dependent variable, is the average cost of equity obtained from four models

developed by Ohlson and Juettner-Nauroth (2005), and Easton (2004), Claus and Thomas (2001), and Gebhardt et al. (2001). The explanatory

factors are the following: Size is the natural logarithm of total assets. Leverage is defined as the ratio of long-term debt to total assets. RVAR is

the volatility of stock returns over the previous 12 months. BTM is the book value to market value of equity. Disp is the dispersion of analyst

forecasts, defined as the coefficient of variation of one-year-ahead analyst forecasts of earnings per share. Fbias is the signed forecast error,

defined as the difference between the one-year-ahead consensus earnings forecast and realized earnings deflated by beginning-of-period assets

per share. Inflation is the realized inflation rate over the next year. GDP per capita is the natural logarithm of the country's GDP per capita. Law

and Order is the ICRG assessment of the law and order tradition in the country. This variable ranges from 0 to 6. Higher scores indicate a higher

rule of law in the country. Corruption is the ICRG assessment of a country's corruption rescaled. The original variable ranges from 0 to 6. After

rescaling, higher scores indicate lower corruption in the country. Bureaucratic quality measures institutional strength and quality of the

bureaucracy in a country. High points are given to countries where the bureaucracy has the strength and expertise to govern without drastic

changes in policy or interruptions in government services. This variable ranges from 0 to 4. Government stability is an assessment both of the

government’s ability to carry out its declared program(s) and its ability to stay in office. This variable ranges from 0 to 12. Higher scores indicates

high government stability and vice versa. Investment profile is an assessment of factors affecting the risk to investment such as contract

viability/expropriation, profits repatriation, and payment delays. This variable ranges from 0 to 12. Higher scores indicates lower risk related to

the listed risk factors. Beneath each coefficient estimate is reported the t-statistic based on Newey–West correction for heteroscedasticity and

serial correlation. The superscript asterisks ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

47

Variables (1) (2) (3) (4) (5) (6)

Size -0.017* -0.023* -0.017* -0.019* -0.016** -0.018*

(-3.270) (-3.748) (-3.270) (-3.417) (-3.197) (-3.215)

Leverage 0.041*** 0.044* 0.041** 0.052* 0.051* 0.049**

(4.555) (4.557) (4.555) (4.681) (4.635) (4.571)

RVAR 0.322** 0.341** 0.321** 0.311** 0.312** 0.315*

(4.161) (4.141) (4.161) (4.157) (4.167) (3.897)

BTM 0.003 0.002* 0.004 0.004** 0.002* 0.002

(3.678) (3.963) (3.678) (3.083) (3.767) (2.702)

Disp 0.051* 0.052* 0.054* 0.053* 0.053* 0.055*

(3.157) (3.115) (3.157) (3.145) (3.141) (3.153)

Fbias 0.001** 0.002** 0.001** 0.003** 0.001** 0.001**

(3.801) (3.651) (3.801) (4.032) (3.888) (3.763)

Momentum - 3 months -2.145** -1.856*** -1.227* -0.981*** -2.145* -1.491*

(-9.312) (-6.878) (-7.327) (-5.627) (-4.451) (-11.227)

Inflation 0.009 0.007 0.008 0.009 0.009 0.01

(0.143) (0.307) (0.143) (0.0393) (0.191) (0.183)

GDP per capita -0.002 -0.002* -0.002 -0.003 -0.002 -0.002

(-1.311) (-2.880) (-1.311) (-1.642) (-1.422) (-1.252)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000

(0.651) (0.568) (0.741) (0.947) (0.662) (0.773)


(-4.872)


(-4.971)

Corruption -0.012*

(-5.053)


(-4.637)


(-4.163)

Constant 0.317* 0.622* 0.082** 0.405** 0.338* 0.323*

(3.768) (3.903) (4.411) (4.123) (3.652) (3.695)



Observations 119 119 119 119 119 119

R-squared 0.663 0.675 0.663 0.668 0.664 0.663

Table 8: Robustness to tardiness of analyst reaction to information


48

Variables (1) (2) (3) (4) (5) (6)

Size -0.002** -0.001** -0.002** -0.002** -0.001** -0.001**

(-3.472) (-3.495) (-3.421) (-3.474) (-3.472) (-3.463)

Leverage 0.004* 0.003* 0.003* 0.001* 0.005* 0.004*

(3.586) (3.603) (3.621) (3.475) (3.547) (3.581)

RVAR 0.181*** 0.182*** 0.187*** 0.190*** 0.184*** 0.186***

(5.751) (5.751) (5.727) (5.787) (5.683) (5.686)

BTM 0.014*** 0.014*** 0.014*** 0.014*** 0.014*** 0.014***

(7.243) (7.136) (7.196) (7.314) (7.244) (7.224)

Disp 0.050*** 0.054*** 0.053*** 0.055*** 0.052*** 0.053***

(6.936) (6.953) (6.963) (6.747) (6.936) (6.821)

Fbias 0.027** 0.021** 0.022** 0.016** 0.014** 0.018**

(3.996) (3.982) (4.054) (3.911) (4.001) (4.004)

Momentum - 3 months -1.849** -1.237*** -2.491* -1.121*** -1.129* -2.139*

(-6.353) (-5.876) (-3.336) (-9.621) (-7.491) (-9.251)

Inflation 0.011*** 0.011*** 0.013*** 0.012*** 0.011*** 0.013***

(3.173) (3.181) (3.202) (3.187) (3.173) (3.211)

GDP per capita -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

(-1.386) (-1.484) (-1.231) (-0.962) (-1.441) (-1.315)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000

(0.651) (0.567) (0.741) (0.945) (0.663) (0.772)

Law and Order -0.003*

(-3.443)


(-4.666)

Corruption -0.009**

(-4.416)


(-3.251)


(-3.516)

Constant 0.192*** 0.194*** 0.205*** 0.254*** 0.174*** 0.182***

(11.942) (6.471) (5.783) (11.472) (10.161) (7.001)



Observations 4,263 4,263 4,263 4,263 4,263 4,263

R-squared 0.421 0.421 0.421 0.423 0.421 0.421

Table 8: Robustness to Tardiness of analyst reaction to information (…)


This table presents the estimation results from regressing the implied cost of equity capital (rAVG) on different bank-level and country-

level factors. rAVG, our dependent variable, is the average cost of equity obtained from four models developed by Ohlson and Juettner-

Nauroth (2005), and Easton (2004), Claus and Thomas (2001), and Gebhardt et al. (2001). The explanatory factors are the following: Size

is the natural logarithm of total assets. Leverage is defined as the ratio of long-term debt to total assets. RVAR is the volatility of stock

returns over the previous 12 months. BTM is the book value to market value of equity. Disp is the dispersion of analyst forecasts, defined

as the coefficient of variation of one-year-ahead analyst forecasts of earnings per share. Fbias is the signed forecast error, defined as the

difference between the one-year-ahead consensus earnings forecast and realized earnings deflated by beginning-of-period assets per share.

Momentum 3 months is the compound stock returns over the past 3 months. Inflation is the realized inflation rate over the next year. GDP

per capita is the natural logarithm of the country's GDP per capita. Law and Order is the ICRG assessment of the law and order tradition

in the country. This variable ranges from 0 to 6. Higher scores indicate a higher rule of law in the country. Corruption is the ICRG

assessment of a country's corruption rescaled. The original variable ranges from 0 to 6. After rescaling, higher scores indicate lower

corruption in the country. Bureaucratic quality measures institutional strength and quality of the bureaucracy in a country. High points

are given to countries where the bureaucracy has the strength and expertise to govern without drastic changes in policy or interruptions

in government services. This variable ranges from 0 to 4. Government stability is an assessment both of the government’s ability to carry

out its declared program(s) and its ability to stay in office. This variable ranges from 0 to 12. Higher scores indicates high government

stability and vice versa. Investment profile is an assessment of factors affecting the risk to investment such as contract

viability/expropriation, profits repatriation, and payment delays. This variable ranges from 0 to 12. Higher scores indicates lower risk

related to the listed risk factors. Beneath each coefficient estimate is reported the t-statistic based on Newey–West correction for

heteroscedasticity and serial correlation. The superscript asterisks ***, **, and * denote statistical significance at the 1%, 5%, and 10%

levels, respectively.

49

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

Size -0.017 -0.023* -0.018* -0.019* -0.016 -0.017* -0.002** -0.001* -0.002** -0.000** -0.001** -0.002**

(-3.270) (-3.749) (-3.270) (-3.417) (-3.198) (-3.216) (-3.325) (-3.197) (-3.123) (-3.295) (-3.315) (-3.262)

Leverage 0.043 0.046 0.043 0.054 0.054 0.047* 0.002* 0.003* 0.001* 0.002* 0.002* 0.003*

(0.556) (0.558) (0.556) (0.682) (0.636) (4.572) (4.428) (4.534) (4.472) (4.477) (4.444) (4.322)

RVAR 0.324 0.341 0.324 0.342 0.324 0.318 0.162*** 0.171** 0.139*** 0.192** 0.168*** 0.147***

(2.162) (2.141) (2.162) (2.158) (2.168) (1.898) (3.731) (5.153) (4.329) (2.729) (3.634) (3.286)

BTM 0.003 0.003 0.003 0.004 0.003 0.002 0.021** 0.016*** 0.017*** 0.022** 0.019*** 0.026***

(1.679) (1.963) (1.679) (2.083) (1.767) (1.704) (3.245) (3.132) (3.296) (3.615) (4.045) (3.225)

Disp 0.053* 0.051* 0.049 0.052* 0.053 0.053* 0.037** 0.043*** 0.056* 0.050*** 0.048*** 0.037***

(3.157) (3.116) (3.157) (3.140) (2.141) (3.150) (4.438) (4.250) (3.914) (3.249) (3.436) (3.849)

Fbias 0.001 0.002 0.001** 0.003** 0.001 0.001** 0.015** 0.023** 0.021** 0.022** 0.031** 0.026**

(0.801) (0.652) (2.801) (3.034) (0.889) (3.763) (3.498) (3.367) (3.894) (3.311) (3.321) (3.125)

Inflation 0.009 0.007 0.008 0.009 0.009 0.010 0.013* 0.013*** 0.015* 0.012*** 0.012* 0.013***

(0.143) (0.308) (0.143) (0.0393) (0.191) (0.185) (4.211) (4.189) (4.242) (4.287) (4.175) (4.218)

GDP per capita -0.002 -0.002 -0.002 -0.003 -0.002 -0.002 -0.012 -0.014 -0.021 -0.023 -0.016 -0.002

(-1.311) (-1.880) (-1.311) (-1.644) (-1.424) (-1.254) (-0.986) (-1.391) (-0.637) (-1.041) (-1.291) (-1.218)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.651) (0.569) (0.742) (0.947) (0.664) (0.773) (0.021) (0.009) (0.012) (0.037) (0.014) (0.033)

Law and Order -0.055 -0.012***

(-3.874) (-4.654)


(-3.972) (-3.410)

Corruption -0.017** -0.009*

(-4.055) (-3.316)


(-3.637) (-3.371)


(-1.163) (-4.281)

Constant 0.324** 0.621*** 0.089*** 0.431*** 0.330** 0.341* 0.171*** 0.181*** 0.234*** 0.197*** 0.169*** 0.182***

(4.769) (4.903) (4.411) (3.123) (4.654) (4.696) (11.940) (6.471) (5.784) (11.470) (10.160) (7.001)

Year effects No No No No No No No No No No No No

Country effects No No No No No No No No No No No No

Observations 23 23 23 23 23 23 1,692 1,692 1,692 1,692 1,692 1,692

R-squared 0.065 0.068 0.070 0.064 0.062 0.065 0.211 0.192 0.185 0.206 0.197 0.201

Panel A: Pre-Crisis Sample

Table 9: Robustness to different periods/subsamples

Conventional banksVariables

Islamic banks

50

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

Size -0.015 -0.018* -0.019** -0.021** -0.019* -0.017* -0.004*** -0.002* -0.002* -0.001* -0.005** -0.001**

(-3.050) (-3.041) (-3.139) (-3.185) (-3.142) (-3.428) (-3.020) (-3.329) (-3.020) (-3.217) (-3.108) (-3.041)

Leverage 0.043 0.056 0.037 0.036 0.038 0.042 0.009* 0.007* 0.011* 0.002* 0.003* 0.005*

(0.474) (0.332) (0.656) (0.462) (0.266) (0.345) (3.801) (3.652) (3.801) (4.034) (3.889) (3.763)

RVAR 0.134 0.381 0.214 0.221 0.419 0.216 0.152*** 0.171** 0.187*** 0.161* 0.189*** 0.193***

(0.862) (0.841) (0.974) (0.845) (0.844) (0.892) (4.259) (4.473) (4.179) (4.020) (4.367) (4.004)

BTM 0.003 0.002 0.003 0.003 0.002 0.004 0.017*** 0.018** 0.014*** 0.014** 0.016*** 0.019**

(0.846) (0.895) (0.961) (0.872) (0.898) (0.892) (3.162) (3.141) (3.162) (3.158) (3.168) (3.898)

Disp 0.045 0.046* 0.038** 0.045** 0.027* 0.011* 0.049*** 0.052*** 0.038*** 0.046*** 0.050*** 0.051***

(4.127) (4.021) (4.091) (4.128) (4.201) (4.021) (4.588) (4.604) (4.622) (4.477) (4.549) (4.582)

Fbias 0.002 0.001* 0.002** 0.001** 0.001** 0.001** 0.011** 0.013** 0.016** 0.019** 0.011** 0.023**

(0.041) (3.056) (3.042) (3.043) (3.085) (3.069) (3.448) (3.956) (3.175) (3.541) (3.001) (3.015)

Inflation 0.008 0.008 0.007 0.008 0.010 0.009 0.011*** 0.012*** 0.013* 0.016** 0.014* 0.016*

(0.064) (0.036) (0.049) (0.029) (0.072) (0.057) (5.436) (5.288) (5.556) (5.562) (5.526) (5.472)

GDP per capita -0.001 -0.002 -0.001 -0.002 -0.003 -0.002 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

(-1.051) (-1.129) (-1.021) (-0.794) (-1.004) (-1.054) (-1.546) (-1.112) (-1.432) (-1.463) (-1.312) (-1.117)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.027) (0.019) (0.002) (0.027) (0.012) (0.031) (0.542) (0.511) (0.611) (0.624) (0.638) (0.665)

Law and Order -0.022 -0.005**

(-3.311) (-3.328)


(-3.371) (-3.119)

Corruption -0.032** -0.027**

(-4.602) (-3.316)


(-5.444) (-4.227)

Investment profile -0.013 -0.008*

(-0.369) (-4.216)

Constant 0.384*** 0.385*** 0.285*** 0.371*** 0.427*** 0.241*** 0.134*** 0.172*** 0.216*** 0.146*** 0.163*** 0.181***

(4.459) (4.245) (4.427) (4.186) (4.149) (4.241) (7.926) (6.285) (5.567) (6.475) (7.148) (7.237)



Observations 39 39 39 39 39 39 1,179 1,179 1,179 1,179 1,179 1,179

R-squared 0.084 0.095 0.097 0.103 0.089 0.086 0.168 0.184 0.182 0.192 0.188 0.178

Panel B: Crisis Sample


Table 9: Robustness to different periods subsamples (…)

51

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

Size -0.012 -0.013** -0.011*** -0.016** -0.016** -0.011** -0.003** -0.003** -0.002** -0.001** -0.002** -0.001**

(-3.020) (-3.329) (-3.020) (-3.217) (-3.108) (-3.041) (-3.184) (-3.189) (-3.317) (-3.362) (-3.017) (-3.094)

Leverage 0.033 0.036 0.033 0.034 0.034 0.037 0.005* 0.004* 0.003* 0.004* 0.005* 0.003*

(0.436) (0.288) (0.556) (0.562) (0.526) (0.472) (3.531) (3.684) (3.631) (3.454) (3.520) (3.511)

RVAR 0.234 0.231 0.314 0.321 0.321 0.316 0.131*** 0.143** 0.184** 0.151*** 0.162*** 0.128***

(1.162) (1.841) (1.143) (1.162) (1.804) (1.498) (3.419) (3.482) (3.481) (3.127) (3.514) (3.112)

BTM 0.002 0.002 0.002 0.002 0.002 0.002 0.011* 0.014** 0.012*** 0.013* 0.017*** 0.011***

(1.259) (1.473) (1.179) (1.020) (1.367) (1.004) (4.460) (4.651) (4.156) (4.341) (5.045) (4.712)

Disp 0.023 0.021* 0.023** 0.019** 0.017* 0.018** 0.039** 0.046* 0.051*** 0.042* 0.042*** 0.048*

(1.457) (3.316) (3.417) (3.140) (3.001) (3.150) (4.478) (3.005) (4.912) (4.459) (4.502) (3.126)

Fbias 0.001 0.001** 0.001*** 0.001* 0.001** 0.001* 0.011* 0.017** 0.016* 0.019** 0.011* 0.018**

(2.023) (3.052) (3.031) (3.034) (3.109) (3.076) (4.468) (4.233) (4.725) (4.374) (4.649) (4.427)

Inflation 0.009 0.007 0.008 0.009 0.009 0.010 0.009* 0.010*** 0.011** 0.012*** 0.008* 0.010***

(0.043) (0.030) (0.043) (0.0393) (0.091) (0.037) (3.179) (3.191) (3.164) (3.181) (3.187) (3.167)

GDP per capita -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001

(-1.011) (-1.080) (-0.421) (-0.644) (-1.124) (-0.754) (-0.845) (-1.004) (-0.942) (-0.941) (-0.942) (-0.317)

Market turnover 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.021) (0.009) (0.012) (0.037) (0.014) (0.033) (0.448) (0.522) (0.432) (0.624) (0.621) (0.418)

Law and Order -0.014 -0.009*

(-3.274) (-3.264)


(-4.171) (-4.298)

Corruption -0.027** -0.017**

(-4.046) (-3.051)


(-3.827) (-5.621)


(-4.239) (-4.216)

Constant 0.234*** 0.251*** 0.179*** 0.281*** 0.327*** 0.191** 0.171*** 0.181*** 0.234*** 0.197*** 0.169*** 0.182***

(4.775) (4.745) (0.211) (4.623) (4.294) (4.386) (3.548) (3.261) (3.184) (3.458) (4.081) (3.241)



Observations 57 57 57 57 57 57 1,392 1,392 1,392 1,392 1,392 1,392

R-squared 0.114 0.124 0.131 0.139 0.120 0.116 0.190 0.188 0.184 0.199 0.193 0.190


This table presents the estimation results from regressing the implied cost of equity capital (rAVG) on different bank-level and country-level factors using the pre-crisis sample, i.e 1999-2006 (Panel A), the crisis sample, i.e

2007-2009 (Panel B), and the post-crisis sample (Panel C). rAVG, our dependent variable, is the average cost of equity obtained from four models developed by Ohlson and Juettner-Nauroth (2005), and Easton (2004), Claus

and Thomas (2001), and Gebhardt et al. (2001). The explanatory factors are the following: Size is the natural logarithm of total assets. Leverage is defined as the ratio of long-term debt to total assets. RVAR is the volatility of

stock returns over the previous 12 months. BTM is the book value to market value of equity. Disp is the dispersion of analyst forecasts, defined as the coefficient of variation of one-year-ahead analyst forecasts of earnings per

share. Fbias is the signed forecast error, defined as the difference between the one-year-ahead consensus earnings forecast and realized earnings deflated by beginning-of-period assets per share. Inflation is the realized

inflation rate over the next year. GDP per capita is the natural logarithm of the country's GDP per capita. Law and Order is the ICRG assessment of the law and order tradition in the country. This variable ranges from 0 to

6. Higher scores indicate a higher rule of law in the country. Corruption is the ICRG assessment of a country's corruption rescaled. The original variable ranges from 0 to 6. After rescaling, higher scores indicate lower

corruption in the country. Bureaucratic quality measures institutional strength and quality of the bureaucracy in a country. High points are given to countries where the bureaucracy has the strength and expertise to govern

without drastic changes in policy or interruptions in government services. This variable ranges from 0 to 4. Government stability is an assessment both of the government’s ability to carry out its declared program(s) and its

ability to stay in office. This variable ranges from 0 to 12. Higher scores indicates high government stability and vice versa. Investment profile is an assessment of factors affecting the risk to investment such as contract

viability/expropriation, profits repatriation, and payment delays. This variable ranges from 0 to 12. Higher scores indicates lower risk related to the listed risk factors. Beneath each coefficient estimate is reported the t-

statistic based on Newey–West correction for heteroscedasticity and serial correlation. The superscript asterisks ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 9: Robustness to different periods subsamples (…)

Panel C: Post-Crisis Sample

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