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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
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]
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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
5
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
INSERT TABLE 2 ABOUT HERE
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.
INSERT TABLE 3 ABOUT HERE
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.
INSERT TABLE 4 ABOUT HERE
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.
INSERT TABLE 5 ABOUT HERE
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.
INSERT TABLE 6 ABOUT HERE
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.
INSERT TABLE 7 ABOUT HERE
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
INSERT TABLE 8 ABOUT HERE
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.
INSERT TABLE 9 ABOUT HERE
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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460–527.
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Organization, In Press, Available online 24 February 2014.
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Song, I., Oosthuizen, C., 2014. Islamic banking regulation and supervision: Survey results
and challenges. IMF Working Paper No. 14/220. Washington: International Monetary
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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
Conventional banks Islamic banks
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
Conventional banks Islamic banks
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
Conventional banks Islamic banks
Table 2: Cost of Equity Financing by Country and by Year - Islamic Banks & Conventional Banks (…)
Countries
Panel A: Cost of Equity Capital by Country (…)
Conventional banks Islamic banks
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)
Bureaucratic quality -0.021*
(-3.666)
Corruption -0.007**
(-3.416)
Government stability -0.001*
(-3.251)
Investment profile -0.001**
(-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)
Year effects Yes Yes Yes Yes Yes Yes
Country effects Yes Yes Yes Yes Yes Yes
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)
Bureaucratic quality -0.053* -0.027*
(-3.371) (-4.115)
Corruption -0.022** -0.027**
(-4.315) (-5.315)
Government stability -0.024* -0.021**
(-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)
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 0.074 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 (…)
VariablesIslamic banks Conventional banks
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)
Bureaucratic quality -0.047* -0.031*
(-4.171) (-4.264)
Corruption -0.027** -0.027**
(-3.049) (-3.051)
Government stability -0.024* -0.004*
(-2.719) (-5.921)
Investment profile -0.003 -0.002**
(-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)
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 0.079 0.087 0.092 0.097 0.084 0.081 0.289 0.288 0.284 0.349 0.293 0.290
Table 6: Robustness to alternative assumptions and model specifications (…)
VariablesIslamic banks Conventional banks
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)
Bureaucratic quality -0.047* -0.031*
(-3.171) (-3.295)
Corruption -0.027** -0.017*
(-4.046) (-3.051)
Government stability -0.014* -0.004*
(-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)
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 0.093 0.101 0.107 0.114 0.099 0.095 0.327 0.325 0.317 0.344 0.333 0.328
Table 6: Robustness to alternative assumptions and model specifications (…)
VariablesIslamic banks Conventional banks
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)
Government stability -0.025* -0.005*
(-5.734) (-6.632)
Investment profile -0.004 -0.003**
(-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)
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 0.127 0.139 0.147 0.156 0.135 0.130 0.311 0.309 0.302 0.327 0.317 0.312
Table 6: Robustness to alternative assumptions and model specifications (…)
VariablesIslamic banks Conventional banks
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)
Bureaucratic quality -0.036* -0.021*
(-4.161) (-4.183)
Corruption -0.016** -0.016**
(-3.035) (-4.041)
Government stability -0.013** -0.003*
(-6.712) (-3.511)
Investment profile -0.002 -0.001**
(-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)
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 0.122 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.
Table 6: Robustness to alternative assumptions and model specifications (…)
VariablesIslamic banks Conventional banks
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)
Law and Order -0.033**
(-3.765)
Bureaucratic quality -0.076*
(-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)
Year effects Yes Yes Yes Yes Yes Yes
Country effects Yes Yes Yes Yes Yes Yes
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
Panel A: Islamic banks sample
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)
Government stability -0.002*
(-3.252)
Investment profile -0.002**
(-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)
Year effects Yes Yes Yes Yes Yes Yes
Country effects Yes Yes Yes Yes Yes Yes
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 (…)
Panel B: Conventional banks sample
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)
Law and Order -0.051**
(-4.872)
Bureaucratic quality -0.080*
(-4.971)
Corruption -0.012*
(-5.053)
Government stability -0.006*
(-4.637)
Investment profile -0.001**
(-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)
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 0.663 0.675 0.663 0.668 0.664 0.663
Table 8: Robustness to tardiness of analyst reaction to information
Panel A: Islamic banks sample
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)
Bureaucratic quality -0.018*
(-4.666)
Corruption -0.009**
(-4.416)
Government stability -0.002*
(-3.251)
Investment profile -0.002**
(-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)
Year effects Yes Yes Yes Yes Yes Yes
Country effects Yes Yes Yes Yes Yes Yes
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 (…)
Panel B: Conventional banks sample
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)
Bureaucratic quality -0.087* -0.034*
(-3.972) (-3.410)
Corruption -0.017** -0.009*
(-4.055) (-3.316)
Government stability -0.004* -0.003**
(-3.637) (-3.371)
Investment profile -0.002 -0.001**
(-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)
Bureaucratic quality -0.053* -0.038*
(-3.371) (-3.119)
Corruption -0.032** -0.027**
(-4.602) (-3.316)
Government stability -0.024* -0.011**
(-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)
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 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
VariablesIslamic banks Conventional banks
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)
Bureaucratic quality -0.047* -0.031*
(-4.171) (-4.298)
Corruption -0.027** -0.017**
(-4.046) (-3.051)
Government stability -0.014* -0.004*
(-3.827) (-5.621)
Investment profile -0.003 -0.002**
(-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)
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 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
VariablesIslamic banks Conventional banks
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