Dissertation Final work

Efficiency of Banks in United Arab Emirates

Masters in Corporate and Financial Risk

Management

School of Mathematical and Physical Sciences

Sussex University - England

Mohieb AbuZant

2015

ACKNOWLEDGEMENT

The MSc in Corporate and Financial Risk Management program at the University of Sussex has been

one of the most beneficial experiences throughout my life. Without the support of the below

mentioned people and many other people that I have not mentioned, this dissertation may not have

materialized and may not have been possible.

First of all, I would like to thank my parents. Without them, I will not be able to write this dissertation

now. They encouraged and supported me a lot during the time that I have stayed in the Sussex

University. My sister and brother were also very supportive while studying. Without their

encouragement I don’t think I can finish my degree successfully.

I would like to express my gratitude to my Course Convener and official supervisor Dr. Qi Tang for

his guidance, caring, patience and providing me with a good atmosphere for doing the dissertation.

I profoundly thank my unconditional advisor Dr. Malgorzata Sulimierska for her supervision and

guidance. Thanks for your help whenever I needed you.

I would like also to deeply thank all my tutors and classmates in my course which is MSc Corporate

and Financial Risk Management. They helped and provided me with a lot of knowledge during this

year.

Finally, my gratitude also goes to the School of Mathematical and Physical Sciences.

Abstract

The present thesis pursues a research into the hypothetical differentials in the efficiency of

UAE banking as driven by use of either Islamic or conventional financial models. The

foremost difficulty rests in defining efficiency as opposed to productivity or profit

maximization, along with its characteristically Islamic counterparts, deliverables, or targets. It

has been detected that return on equity (ROE) essentially captures all of the critical facets of

efficiency, when it comes to striking a careful balance between financial and operating

leverage versus excessive risk to be avoided. With the aid of prior formalization, it has been

demonstrated how a dummy based OLS model could parsimoniously outperform the more

advanced regression designs such as the GLS or logit. The panel based model lends strong

support to its prior cross-sectional reduction, with both pointing to but limited linear

significance of the channel whereby dividend or reserves policies affect ROE efficiency. This

could either hint at nonlinear mechanisms that might in turn conceal the inherent real-world

complexity of decision making along these lines or otherwise warrant further tests of Granger

causality. Somewhat surprisingly, both tests, along with the posterior CHI-squared and t-

statistics on individual fixed effects, point to their being no material performance gaps

between the two models. In risk or variance adjusted terms, however, Islamic banking appears

to outmatch conventional models, which net-of-uncertainty type deliverables constitute the

core of Islamic efficiency in the first place.

Key words: Islamic banking, gharar, ROE, efficiency, OLS, GLS, panel data, dummy

variables.

TABLE OF CONTENTS

Acknowledgements

Abstract

Introduction

Chapter 1 : Literature Review

Chapter 2 : Methodology

2.1 Background

2.2 Data

2.3 Research Objectives

2.4 Research Questions

2.5 Economical Assumptions

2.6 Analysis of Financial Ratios

2.7 Hypothesis testing & OLS Modeling

2.8 Generalized ANOVA

2.9 Afterthoughts on Triangulation & Bootstrapping

Chapter 3 : Modeling & Testing

3.1 Qualitative Analysis

3.2 Cross-Sectional Regression

3.3 Heterokedasticity, Autocorrelation & GLS

Chapter 4 : Auxiliary Modeling

4.1 Caveats & Extensions

4.2 An Auxiliary Formalization

4.3 An Augmented Model

4.4 Caveats on Data

4.5 Panel Estimates

Chapter 5 : Empirical Results

5.1 Prior Estimation

5.2 Two-Stage Inference on Fixed Effects

Conclusion

Introduction

The UAE has 56 banks in total. 28 of them are foreign banks, 23 are local banks & the 5 rest

banks are investment banks (Central Bank official website). The huge number of commercial

banks, the high density of the branches, the technological change, and the increasingly

competition in the banking industry, which is one of the major characteristics of the banking

industry in the United Arab Emirates have placed tremendous pressure to improve

performance. Therefore, it is interesting to investigate the efficiency of the UAE banks where

the greater the efficacy the higher the effectiveness and vice versa. (Spathis et al., 2002).

Among the Gulf Cooperation Council (GCC) countries, the United Arab Emirates is historically and

still is one of the strongest economies. In 2013 it was ranked second among the GCC countries in

terms of GDP per capita with a GDP per capita at current prices of USD 43,048. This figure shows an

impressive growth over the decade as in 2002 this figure was only USD 18,903. The UAE GDP per

capita is also second to Qatar which had an impressive GDP in 2013 per capita of USD 93,714 and is

followed by Kingdom of Saudi Arabia with a GDP per capita of USD 25,961 and all other GCC

countries falling within the range of USD 22-25,000. (IMF, 2012)

The Conventional Bank theories assume that the main way for conventional banks to earn

profit is the difference between interest rates. Where the bank purchase transaction deposits

from depositors at low interest rates & resell those funds to borrowers at higher interest rates.

On the other hand, Islamic Banks based on the Islamic rules doesn’t allow interest rates, Islam

does allow for a number of financial mechanisms which do allow for the bank to profit. The

Islamic banking principle differs from the basic commercial banking theory that it doesn’t

allow interest rates so it replaces it with theories based on risk sharing which depends on

trading rather than risk transferring which is usually used in conventional banks. They use

different concepts such as Profit sharing (Modaraba), Joint venture (Mosharakah)

Safekeeping (Wadea), Cost plus (Morabaha) and leasing (Ejar).

Chapter 1: Literature Review

Existing studies in this area show different results and opinions about the efficiency of both

Islamic and Conventional Banks.

Taufiq Hassan et al. (2009) who studied the efficiency of the Islamic Banks VS

Conventional Banks under international basis collected data for each year from BankScope

database over the period 1990-2005. They evaluated in several countries data taken from the

financial statements of 80 Banks from 21 Islamic countries. They used the Data envelopment

analysis non-parametric approach to come out with the results for their work. The results

showed that there was no significant main difference in the revenues, costs and profit between

the Islamic and conventional banks. The second argument shows that the results may change

if there will be a comparison between two different groups. One taken from less developed

countries and the other taken from more developed countries due to the different economic

environment for both. Hence, proof on the consistency of the results in their study is offered

which reports insignificant differences in cost, revenue and profit mean scores between

conventional and Islamic banks. The authors here will affect my work by the measures of

efficiency of Islamic and Conventional banks. But here they are using a cross country

equation for more than a country, while I will be focusing on measuring the efficiency in

banks for the United Arab Emirates only.

Hussein A. Hassan Al-Tamimi & Husni Charif (2011) who assessed the performance

factors in the UAE commercial banks by using multiple approaches taking into consideration

the effect of the bank size. collected the data from the annual reports of Emirates Banks

Association and then used the methodology of analyzing ratios such as: (ROA) Return on

Assets, (ROE) Return on Equity, (MARG) the ratio of net interest margin to current assets,

(LODE) the ratio of loans to deposits, (CALO) the ratio of current assets to loans , (TAEQ)

the ratio of total assets to equity and (EQTA) the ratio of equity to total assets . The results

show that large banks perform better than small banks. It also provides support to the

proposition that in an environment where banks are highly fragmented, mostly small, and are

not performing well, there is a need to consolidate the operations of these banks.

This will be will affecting my dissertation by measures of performance in the same country

which is the United Arab Emirates, I will be using some of the same ratios but for different

period.

Thorsten Beck et al (2012) Have studied the Islamic vs. Conventional banks efficiency and

stability between the years 1995-2009. Then who collected data from Bank scope. Which

provides us with information about listed & non-listed banks. To construct and compare

indicators of efficiency, asset quality, business orientation and stability of both the Islamic

banks and conventional banks. The results show some difference in the business orientation

between conventional banks and Islamic banks. Islamic banks were less cost-effective, had

higher asset quality and were capitalized much better. They also found a large cross-country

variation between them, better stock performance of listed Islamic banks during financial

crisis is also due to their higher capitalized and better asset quality.

In this dissertation, they focus on the periods before the financial crisis and during the

financial crisis. While I will be using data from year 2006 till 2014, which will study the

efficiency and effectiveness of Islamic vs Conventional banks before, during and after the

Global Financial Crisis of 2008.

Fatima S. Al Shamsi et al (2007) measured and explained the efficiencies of the United Arab

Emirates banking system, They analyzed the data using the DEA ( Data envelop analysis)

parametric and non-parametric approaches to measure efficiency . The data was collected

from Bank Scope by collecting data from balance sheets and income statements from most of

the commercial banks, operating in the UAE for the last 5 years. The three major inputs

targeted by this study are labor, capital and deposits and the two outputs are loans and

investments.

This dissertation measures the efficiencies of the United Arab Emirates for only 5 years,

while I will be extending this period to be more accurate about the results by using a different

methodology.

Hussein A. Hassan Al-Tamimi and Faris Mohammed Al-Mazrooei (2007) in their paper

entitled “Banks’ risk management: a comparison study of UAE national and foreign banks”

The writers created a modified questionnaire, and then divided it into two parts, the first part

covered six aspects: understanding risk and risk management, risk assessment & analysis,

identification of risk, Monitoring risk, The authors developed a modified questionnaire.

Divided into two parts. The; risk monitoring; Practices of risk management and the analysis

of credit risk. This part includes 43 closed-ended questions based on an interval scale. The

second part consists of two closed-ended questions based on an ordinal scale dealing with two

topics: methods of risk identification, and risks facing the sample banks, in their article they

answered a lot of questions such as:

RQ1. Do the UAE banks’ staff understand risk and risk management?

RQ2. Have the UAE banks clearly identified the potential risks relating to each of their

declared aims and objectives?

RQ3. Do the UAE banks efficiently asses and analyze risk in general?

And many other questions using the methodology of evaluating the reliability of the scales

using Cronbach’s alpha, which measures the consistency with which respondents answer

questions within a scale, then regression analysis and one-way ANOVA were run to test the

research hypotheses.

Their results show that a lot of risks were facing the UAE commercial & and foreign banks,

the most important types of risk facing the country were:

1- Foreign exchange risk.

2- Credit risk.

3- Operating risk.

This dissertation is really helpful and will be affecting my work since is studies and compares

the differences between local and foreign banks which are 23 local banks and 28 foreign

banks in the county but using a different methodology.

Bashir M. et al (1999) results show that the Islamic banks’ efficiency is much better than the

conventional banks. In their study they used a set of data between the years 1990-2005 then

used the (DEA) Data Envelopment analysis to test the revenue, comparative cost & profit

efficiency for both Islamic & Conventional banks.

In this dissertation, they compare between Islamic and conventional banks efficiency during a

long period using a different methodology as well.

S. KABLAN and O. YOUSFI (2011) who studied the efficiency of Islamic and Conventional

banking of 17 Islamic countries analyzed the Islamic banks efficiency between the years

2001-2008 and found that the banks were efficient at 78.9%. Their study displayed that Asia

had the highest score of efficiency with 84.64%. They found out also that the financial crisis

didn’t affect the Islamic banks as the results showed by the dummy variable. Also the

profitability and market power had a negative effect on the Islamic banking efficiency. In

their study they used the parametric approach and specify more the stochastic frontier analysis

(SFA). The objective function was the cost function. It allows to take into consideration banks

as financial companies and seek to evaluate their performance. Thereby minimize the costs

induced by the efficiency frontier. Then chose the Translog, as it best suits the multi-products

characteristic of banking technology, multiple inputs and outputs to be involved. When they

estimated the efficiency frontier they had to choose the inputs and outputs produced and used

by Islamic banks.

Chapter 2: Methodology

2.1 Background

The present study aims at comparing the efficiency-side performance of a set of banks

operating in the UAE jurisdiction as either Islamic or legacy-neutral entities. The two

alternate models could map into very different sets of implications likely to prove testable

along the regular econometric lines of ANOVA/ANCOVA inference versus ordinary-least

squares (OLS) modeling. The bulk of critical performance checks could be reduced to the

conventional investment ratios such as return on assets (ROA) or equity (ROE), which values

may swing around their historic averages while varying against the industry average or some

kind of alternative benchmark. Importantly, all of the relevant industry hurdles will be defined

from within the sample on hand, which is supposed to feature a reasonably complete set of

legacy trade-offs as shown in Table 1 below.

Table 1: Legacy Trade-Offs

Islamic Non-Islamic

UAE Non-UAE

It is important to know that some of the otherwise UAE headquartered banks could fall

under either legacy, Islamic or neutral, with the National Bank of Abu Dhabi being one

apparent case. Apart from the nearly corner outcomes (e.g. Abu Dhabi Islamic Bank=“UAE

and Islamic” or BNP Paribas=“non-UAE and non-Islamic”), of interest could be some interim

identities capturing players that are UAE yet non-Islamic or non-UAE and possibly outright

Western-headquartered yet characteristically Islamic-type.

In addition, one should distinguish between those faring as UAE nationals as opposed

to the operators falling under overseas jurisdictions yet working in UAE. For one thing, far

from all of the subsidiaries as headquartered in the UAE will have worked within their

homeland markets only. Conversely, many of the foreign banks will boast a strong or

emerging UAE presence amid this marketplace growing increasingly global. Although it

would be fully feasible technically as well as conceptually to add one extra dimension on to

the above identity matrix, visualizing the cubic or higher-dimensional construct might turn

out to be a rather involved enterprise. One way around the issue could be to view two

complementary matrices as stand-alone layers of analysis, with “UAE versus non-UAE” as

above referring to either the origin or the destination type jurisdiction. For now, this facet will

be assumed away for simplicity’s sake, even though in actuality, some further refinement will

be warranted to qualify the relevant scope as well as apparatus. The flipside or ‘silver lining’

is that the proposed set of identity trade-offs will be deployed on many a level for a variety of

specific tests and models.

When it comes to substantive underpinning of this taxonomy, any Islamic legacy

would involve a very distinct incentives structure likely to have affected the efficiency

outcomes in a systematic manner. Dwelling on these gaps could afford some prior analysis

beyond ad-hoc data mining with an eye toward rendering the candidate regressions or

ANOVA insights less of an atheoretic attempt.

Shari’a compliance, as long as it involves in-depth alignment rather than sporadic or

makeshift fatwa-based license, will target investment patterns that are truly productive as well

as equitably rewarding. Excessive risk is to be avoided, which also holds for ill-gotten

windfalls and unreasonable or speculative gains. In fact, this could formally be seen as

coming in line with generic optimization, in particular based on the Capital Asset Pricing

Model (CAPM). After all, even the risk seeking types are not supposed to buy into just any

risk or uncertainty extent, so long as less risky assets (or strategies as well as portfolios) are

conceivable as per the exact same payoff level. On the other hand, it is postulated under

strong-form market efficiency that there are limits to diversification or strategy building,

known as systemic or market risk, beyond which profitability will have to be compromised.

It is this grand trade-off between risk and expected return, or cost versus benefits, that

will be kept in mind throughout whenever judging on efficiency. In terms of Pareto

optimization or indeed CAPM, securing efficiency is about keeping the cost low as per any

given level of profitability. More specifically, a high ROE will implicitly refer to efficiency,

insofar as its embedded profitability versus leverage or gearing dimensions are distinct yet

inseparable. It will, inter alia, be demonstrated how efficiency implications may have to be

qualified for the Islamic representation, with excessive gearing at times referring to inherently

more cooperative securitization or scale sharing across banks or throughout the vertical value

chain.

2.2 Data

A total of 46 banks will constitute the cross-sectional sample drawing upon their

annual reports over the 2006 through 2014 time period. The respective number of

observations, suggesting a pooled or panel type dataset, affords 46*9=414 as per each metric

under study. In other words, regardless of the effective number of explanatory variables

acting to detract from the resultant degrees of freedom (DF), this value could be augmented

by multiplying the above number times that of the independent variables:

DF=46∗9∗N−N=413∗N

At that rate, any desired DF level can be ensured, albeit at the cost of mounting

complexity. Alternatively, as long as the structural shifts in the historical patterns, or fixed

and random effects, are less relevant, the full-blown scale could be collapsed to its cross-

sectional domain. For instance, average values could be obtained and plugged in place of the

time paths for each bank’s respective metric. Needless to say, though, the validity as well as

robustness of out-of-sample inference, be it in terms of posterior predictive power or prior

data quality with an eye on heteroscedasticity checks, cannot remain intact—which features

one other, meta-level efficiency trade-off. The good news is that a host of interim data tests,

such as serial or auto-correlation as well as endogeneity, can be waived. In any event, the

effective DF will be restricted subject to data availability, as the earlier reports or financial

statements might not be readily available on some of the banks.

Unless explicitly referred otherwise, the bulk of financial reports will be credited to a

standardized database implicitly restraining the sample to what amounts to a ‘population’

(UAE Central Bank 2015).

2.3 Research Objectives

The present thesis looks into whether banks that qualify as largely Islamic reveal

efficiency performance that is either superior quantitatively or downright distinct qualitatively

(substantively as well as structurally). In addition, approaches will be proposed to testing for

validity of Islamic self-identity beyond exogenous fatwas or claims to halal, adab, or mubah

compliance. Regardless of how far gharar is actually shunned or collective interests targeted

as well as maintained, a set of unbiased criteria could be invoked as part of an elegant yet

parsimonious optimization. Finally, one interim outcome would assess the feasibility of

mixed strategies or weak identities.

2.4 Research Questions

RQ1: Are efficiency gaps material between Islamic versus conventional banks that are

somehow associated with the UAE jurisdiction?

RQ2: Can these gaps be explained away in terms of the underlying legacy, with

qualitative analysis informing as well as qualifying the rigorous modeling?

2.5 Economical Assumptions

Among other things, the scope of efficiency checks can be reduced or contracted

bearing in mind the industry profile. For instance, gharar minimization could for banks boil

down to capital adequacy requirements as well as maturity gap management. However, the

former could be presumed as excessive in the Western legacy compared to Islamic setups,

given the nature of reserves or equity never acting as cushion against bank runs. It should

therefore come as little surprise if the respective equity proportions actually prove lower for

Islamic banks. For the same token, safe or minimalist gap management can be maintained for

Islamic banks inasmuch as they act largely as regular, non-financial type investment or

production networks—even if not engaged in the operations management directly.

Effectively, these areas of efficiency need not be tested in ways other than scale efficiency

assessment. This will in turn amount to the analysis of total as well as current asset

performance.

2.6 Analysis of Financial Ratios

The choice of ROA and ROE as representative of the relevant efficiency scope could

be motivated in a number of ways. Although these, first and foremost, capture the profitability

of assets versus capital, respectively, still they are so inherently intertwined that all of the

aforementioned facets of efficiency could be obtained as derivative parameters. Notably, both

ratios apply to real-sector and financial-type going concerns alike, thus amounting to a

transferable robustness bridge. Not least, the market stays largely unmoved as to whichever

asset types boast comparable performance that is balanced as well as sustainable—unless, of

course, the market is dominated by Islamic-profile investors exhibiting strong preferences for

assets showing minimum riba, gharar, or maysir.

On one level, the efficiency facet could be revealed via the Du Pont expansion of the

ROE structure building on the equity multiplier as the inverse of financial leverage (Brigham

& Ehrhardt 2011, pp.106-107):

ROE=Net IncomeEquity

=

Net IncomeTotal Assets

∗Total Assets

Equity=ROA∗EM

Remarkably, the equity multiplier reconciles asset versus capital performance while pointing

to genuine efficiency showing how the implied leverage, or the residual share of debt in the

capital structure, maps into superior profitability.

In fact, the exact same efficiency metric could further be unfolded by embarking on

one other Du Pont style representation, this time capturing asset performance in terms of scale

efficiency or the interplay of margins versus turnover:

ROE=

Net IncomeSales

∗Sales

Total Assets∗Total Assets

Equity=NM∗¿∗EM

Incidentally, the product of the net margin (NM), total asset turnover, and equity multiplier

captures it all. Whereas the former (drawing in the Islamic banking setup on salaf loans, ijara

leasing, and otherwise real-sector shirka stakes) captures the profitability upside, the turnover

metric points to scale efficiency as part of break-even point (BEP) analysis.

In light of the above, ROE based metrics could be thought of as the ultimate efficiency

check to be deployed as the dependent variable, while simultaneously capturing the ROA

core. Whichever instrument is chosen to represent it, one need not be log-separated, i.e.

modified via the logarithm of the underlying Du Pont product of ratios to appear as a linear

sum. For one thing, that would complicate the sign check, as any logarithm of values below

unity would yield a negative value thus modifying or blurring the estimates of the linear

coefficients. (Note that the turnover component of the log-product would be strongly positive

thus absorbing the other log’s negative contribution). Moreover, separating the dependent

variable rather than the independent or explanatory right-hand side would border on a vector

function suggesting very different estimation routines while ushering in unnecessary

computational complexity without matching it with an adequate increment in predictive

power.

2.7 Hypothesis Testing & OLS Modeling

The formal part will come in several sections, while at times presenting alternative or

mutually controlling devices in line with the triangulation agenda to be addressed at the end

of the chapter.

It would be straightforward to present the above rationale in terms of a simple OLS

model seeking to capture ROE performance in terms of size, or the book value of the total as

well as current assets. On the one hand, that would indirectly point to scale efficiency. On the

other hand, interpreting scale in Islamic versus conventional contexts would stumble into

multi-layered ambiguity or structural uncertainty. For one, Islamic agents may be perceived as

largely productivity and profit driven rather than efficiency concerned—which would suggest

scale efficiency is the last thing to be kept in mind at the outset. However, that would be a

very superficial conjecture, as partnership and vertical as well as horizontal sharing along the

musharaka lines could be consistent with either small or large assets, insofar as the former

can be reduced to the latter being aptly redesigned and participated in. The generic model

could look as follows:

ROE¿=α0+α 1∗TA¿+ε¿

This test spans a set of 46 banks’ ROE values over the time horizon of 2006-2014—and the

same goes for their total assets (TA). The applicable null hypothesis would posit

insignificance as the cut-off or weak criterion:

H 0 :α 0=α 1=0

The alternative hypothesis would constructively posit these values as non-zero at

whatever levels of significance chosen, with the free term or intercept plausibly referring to

either a minimum or the industry-average ROE, and the slope distributed much like the Du

Pont differential residual:

α 1∂ ROE∂ TA

= EM∗∂ ROA∂ TA

=−EM∗¿TA2 =−EM∗ROA

TA=−ROE

TA

Given the negative sign as well as the quadratic term in the denominator, the slope might well

turn out to be marginally non-positive, if at all significant based on the t-statistic. Put simply,

scale might act to detract from ROE based efficiency, as long as the intercept captures the

industry leader’s ROE as a benchmark. That said, the effect is second-order or decelerating,

with ROE itself implicitly acting as a first-order catalyst. On second thought, the differential

expansion could refer to the locally compensatory effect, whereas the ‘global’ or ‘levels’

counterpart is regular, or trivially positive.

Of course, a model such as this one can in no manner be deemed as either complete or

conclusive. Adding on extra explanatory variables might alter or offset this particular slope’s

sign, while rendering the potential inefficiency of the otherwise under-identified specification

less of an issue. In order to pin down the rest of the structural ambivalence, a different

modeling framework could be attempted building on dummy type instruments. The lefthand-

side dependent variable DROE will be represented by a status indicator or dummy variable

taking on a value of 1 if the bank’s ROE exceeds the industry or sample average, and zero

otherwise. The righthand-side or explanatory variables will all be dummy type as well. Better

yet, all of the identity dimensions will be separated and studied as either standalone channels

or as interactive patterns. The former version amounts to individual dummies, whereas the

latter to a product of these. For instance, the size dummy will take on a value of 1 in case the

asset size exceeds the sample average—and the same holds for the rest of individual dummies

capturing UAE identity, Islamic legacy, and their match as a product.

D¿ROE=β0+β1∗D¿

UAE+β2∗D¿ISL+β3∗( D¿¿¿UAE∗D¿

ISL)+ϵ ¿¿

In a complete setup like this, the residual should prove smaller, even though serial

correlations will have to be checked against, regardless. In addition, encoding the values as

indicator type variables should curb the bulk of multi-collinearity as well as spurious

covariance. That said, some of the channels, notably pertaining to couples such as,(β1 , β3),

and (β2 , β3) evidently featuring shared component factors, might be prone to formal multi-

collinearity. The flipside is that the alternative hypothesis could suggest a well-defined

structural inter-linkage across these propagation channels. By contrast, the null hypothesis

will again presume naïve prior insignificance:

H 0 : β0=β1=β2=β3=0

Outside the F-test revealing the model’s overall relevance, it is likely that only some of the

channels will prove [in]significant, thus reconciling the polar hypotheses.

2.8 Generalized ANOVA

Regardless of whether enough data is made available or technically fit to enable

BLUE (best linear unbiased estimate) efficiency in the OLS regression modeling, an

alternative test could be run irrespective of DF constraints. On the one hand, it could draw

upon the same ROE criterion as a matter of control test. Alternatively, the study could tap the

residual or supplementary layers of analysis. The generic approach could be based on the

CHI-square test in its Pearson form (Hayter 2012, pp. 469-470):

CHI 2 ∑i=1

46

∑t=2006

2014 ( x¿−e¿)2

e¿

The observations are checked against their respective expectation hurdles. The

subsequent section will dwell on how these could be defined in proportion terms as mutually

exclusive and collectively exhaustive series of identity matches. For now, suffice it to point to

the remarkable interim result reconciling the various and remote layers of analysis. Whereas

the entire set of tests could be done in regular ANOVA table terms, it happens that the

dummy variable approach as above and the CHI-squared inference amount to versions of

ANOVA or ANCOVA, too.

2.9 Afterthoughts on Triangulation & Bootstrapping

All of the attempted methods and approaches have been sought to allow for an

effective as well as efficient analytical framework. Bootstrapping could in this setup be

viewed as aligning a handful of straightforward approaches to whatever data available without

collapsing the analysis to sheer or theoretically unaided data mining. Triangulation has

targeted reliability and robustness in ways that posit all of the methods being proposed as

vehicles of mutual control and reinforcement. The dual methodological trade-off has to do

with how some of the methods have qualified or augmented others while revealing strains or

excesses of their own. Prospective research might look into how the more rewarding of these

could either be improved upon any further as standalone tools or re-arranged as systemic

frameworks building on well-defined concepts and criteria defining identity and performance.

Chapter 3: Modeling & Testing

3.1 Qualitative Analysis

The effective residual sample features some 27 banks out of the 46 as in the Central

Bank’s database. This implies a response or turnout rate of about 27/46=59%. In net or

adjusted terms, bearing in mind an 11% share of values missing in the panel of 27*9=243

observations, the effective response is secured at (27*9-270/(46*9)=59%*(1-0.11)=52%,

which amounts to the sample size based on the aggregate population. Although it would be

desirable to draw upon a larger time series, e.g. spanning a horizon of 1994 through 2014,

primary or open-source data constraints do affect the design being proposed, along with the

randomization issues. On the other hand, the ex-ante estimate of the share of missing values

could set the potential for improvement in the predictive power of whichever specifications

attempted. This issue will be addressed later in text, and for now suffice it to zoom in on the

qualitative shifts, if any, within as well as between the identity-based sub-samples along the

lines of the Table 1 taxonomy as suggested from the outset.

A total of 6 subsamples could be addressed based on the alternate as well as

complementary identities with an eye toward any possible performance gaps and subject to

varying sub-sample sizes. As Table 2 indicates, the number of non-Islamic banks dominates

as 20 versus 7. Out of these, it appears that all Islamic banks are associated with the UAE (7),

even though the latter pure or elemental identity is broader (18). A similar match is manifest

between banks that are non-UAE versus those neither UAE nor Islamic (9). In terms of

relative weights, these categories claim as follows: Islamic and UAE Islamic above one-

quarter each, non-UAE at large as well as non-Islamic outside of the UAE one-third, UAE

overall two-thirds, and non-Islamic on the whole just under three-quarters.

The analysis can be rendered even more meaningful based on the sub-sample or intra-

group average performance subject to the distributions. For instance, non-Islamic and non-

UAE entities have exhibited a slightly superior performance, with excess ROE ranging in

between 1.09% and 1.11% (13.16% and 13.6% for the respective categories as compared to

12.05% and 12.51% for the Islamic versus UAE benchmarks). However, the data for the

benchmarks have shown to be more homogeneous and possibly homoscedastic, as depicted in

the applicable standard deviations. The resultant ratios of average (in-sample or retrospective

expected) to SD, which is akin to the Sharp ratio based performance net of uncertainty,

demonstrates a systematically reversed pattern, with Islamic banking marked as strong leaders

(4.23 versus 2.43). Although the UAE identity is still marginally outmatched (2.57 versus

2.71), overall the Islamic UAE corner has outperformed its conjugate of those neither Islamic

nor UAE (4.23 versus 2.71).

The latter might be a first indication of the Islamic identity dominating as a contributor

to the explanatory power. This finding will be seconded in a naïve, cross-sectional regression

test drawing upon time averages or historical trends and equilibria rather than regular time

series. The reduced or collapsed version of the panel is warranted as a first estimate for lack

of temporal variability in the core explanatory variables such as Islamic, UAE, and combined

identity. An expanded panel version will then follow.

In terms of ROA based efficiency, the findings are even stronger and far less mixed, as

the ratio proves higher across all the elemental and interactive categories. For one, the bare-

bones ROA appears slightly higher for non-Islamic banks (1.73% versus 1.69%), which gap

reverses itself to a favorable .6% excess for UAE entities (1.92% versus 1.32%) and an

overall upside divide between the UAE Islamic banks versus those non-Islamic outside the

UAE (1.69% versus 1.32%). Evidently, the Islamic constituency again dominates amongst the

key explanatory factors, with the interactive coefficients largely mimicking the dominant

elementary ones.

3.2 Cross-Sectional Regression

This section will embark on some possibly non-manipulative data mining with an eye

on the constraints that more full-fledged panel testing is likely to confront at later stages.

Among other things, historical averages as point estimates will be used as a proxy for either

trend equilibria or actual time series. Whereas it might appear as a natural choice pertaining

to dummy identities which do not show enough variability anyway, imposing a similar

heuristic on ROE and ROA instruments, let alone actual values, should be taken with a grain

of salt. For the same token, although the intercept might capture the bulk of the higher-order

average, providing for one as part of the specification might usher in some awkward trade-

offs.

For one thing, even the sub-sample averages may have appeared largely convergent or

compressed around the gross (industry or sample) average. At the same time, assuming that

an intercept makes a difference would act to detract from the rest of the variables’ explanatory

or predictive power. The respective slopes or partial coefficients will be smaller, thus denying

any more active role to these propagation channels:

y=β0+∑i=1

m

β i∗xi+ε=0+∑i=1

m

β ' i∗x i+ε '

Not only will the latter specification effectively curb the naïve or exogenous component

amidst prior modeling incompleteness, the more degrees of freedom will amount to lower F

and t hurdles, thus going one further step toward higher modeling efficiency. The underlying

efficiency trade-off can rigorously be qualified by seeing whether and how the introduction of

extra variables measures up against a limited number of observations:

DF=N∗m− (m+1 )+1−∆ m=( N−1 )∗m−∆ m

Essentially, an increment in the predictive power should be weighed in against that in

the efficiency hurdle:

∆ R2

∆ F=

∂ R2

∂ m

∂∂ m

[( R2

1−R2 )∗N−m

m−1]

It can be shown that the ultimate sign of the trade-off, or local effect, will be determined by

that of the denominator, whose zero value turns the setup into an explosively inconclusive one

albeit well-defined structurally:

∂ R2

∂ m= N−1

( N−m )(m−1)∗R2∗(1−R2)

Given the oscillatory nature of the right-hand side with respect to the initial condition, or the

ongoing level of R-squared, along with the mixed impact arising from m in the denominator,

the knife-edge pattern should come as little surprise. In fact, the above relationship could be

used as a cut-off criterion. Based on a given N=27 and m=5 (as well as a delta step for m of

1), an increment of predictive power at the current level of, say, .25, should be at least

∆ R2= 1∗27−1(27−5 )(5−1)

∗.25∗(1−.25 )=.0554

In other words, phasing in another explanatory variable only pays if a resultant increment in

the explanatory power totals at least 5.54%, now hitting in excess of 30%. Conversely, if the

initial model featured m=6 independent variables, none of these could be omitted unless the

resultant loss of explanation or predictive power stops short of reaching 4.64%. Importantly, a

linear rule cannot apply, as the R-squared hurdle will have to be updated at each subsequent

stage. For instance, following the initial introduction of a variable given an R-squared of 25%,

its subsequent opportunity cost would be about 6.27% instead of 5.54%, and so on the setup

unwinds. With an initial m=4, the stake is even higher at 8%, which suggests how exacting

could an initially parsimonious model turn out to be. In contrast, it will be demonstrated later

on how mere omission of an intercept triggers an overwhelming increment in predictive

power far outweighing any complexity savings attainable otherwise.

This latter layer of data mining can be ensured empirically by trying out from amongst

a variety of alternate sub-specifications. Table 3 showcases standardized output as per

alternate specifications to be run on dummy counts as opposed to averages, and a zero-

intercept as distinguished from full-blown estimation. Notably, it is presumed that the squared

t-statistics do not converge to their F counterparts unless the sample is very large, in which

event both tend to the normal distribution. Otherwise, the t hurdle is taken for N-m=27-6=21

degrees of freedom in the intercept-active version at a weak alpha significance of .10 and 27-

5=22 at alpha=.01 as per the zero-intercept setup. For the F hurdle, the degrees of freedom

falling under nu1 and nu2 obtain as (5, 22) and (5, 23) respectively. These DF conventions are

in themselves divergent, which is reasonable for a multivariate setup. The null hypothesis

maintains insignificance, or statistical zero, for each channel being tested.

As the comparison of Tables 3A-B versus C-D suggests, only the no-intercept

specification suggests a significant model along with a high explanatory power. Both the

counts and averages based functional forms in the full-blown cases yield an R-squared of

26.38% amidst model insignificance, as depicted by a low F=1.97 which does not exceed the

2.13 hurdle even at a weak 10% significance level. By contrast, the zero-intercept scenario

fetches an enviable R-squared of 78.17% while marking the model as significant even at 1%,

with an F=20.59 clearly beating the 3.94 hurdle.

That said, overall significance need not translate into that of the individual

coefficients. As it happens, only the DROA dummy proves strongly significant, with its t=3.995

exceeding the 2.508 hurdle. As far as the rest are concerned, judgment shifts in between the

counts based versus averages denominated layouts, in that significance switches between the

Islamic versus interactive (UAE Islamic) dummy instruments. Interestingly, the prohibitively

large t or infinitesimal p-values appear in sharp contrast with the virtually zero coefficient

estimates, which implies that these channels’ mere presence is enough to make an explanatory

difference without necessarily affecting the predictive power or the actual forecast. Of course,

micronumerosity could be one other concern when it comes to testing for fine and intricate

mixed effects.

Peculiarly, the same holds for the nonzero-intercept cases, with the intercept versus

DROA strongly surpassing the t hurdle at 4.39 and 2.55, respectively, which may sustain at

more demanding significance levels in light of the low p-values. Whereas the low R-squared

in the intercept-laden specifications might lead one to presume that the model easily needs

major rethinking given the low overall and standalone significance, this is not the case bearing

in mind the alternate success stories. The latter building on a zero intercept, it may well be

that it was multicollinearity that accounted for the bulk of individually inefficient variables.

As was shown previously, this conjecture gains support in the prior qualitative

analysis, as the Islamic and UAE Islamic sub-samples showed a perfect match, which

convergence must have implied linear dependence between their underlying variable vectors.

Although the UAE identity is wider, the Islamic sub-domain proves dominant—as was shown

in the qualitative counts and the quantitative regression analysis alike. Somehow, the Islamic

identity shines through the provisionally intermittent or non-robust significance of the Islamic

and UAE Islamic instruments alike, with the overlap being apparent. Therefore, a natural

choice would be to omit either variable given their perfect collinearity, while lending some

extra significance to the rest. However, given that the UAE neither detracts from nor

reinforces the Islamic domain, it would be about as wise to go with the UAE Islamic

interactive coefficient only, while leaving the standalone or elemental identities out. The DROA

instrument should stay as well, as it bridges some important predictive gaps in reducing the

residual without showing any trivial correspondence with DROE.

When it comes to the (c+r) delta, its mediocre performance has not fallen short of

expectations. In fact, it will be shown that it is the lagged values of this differential, perhaps

by as long as two periods, that should have a material impact on ROE performance—be it an

Islamic setup or otherwise. For that matter, this value exhibited enough inter-temporal

variability for it to avoid being collapsed to a historic average. Therefore, this could be an

important layer of explanatory power yet to be addressed in a panel design. While at it,

regardless of how complete one might happen to be, the aforementioned 11% response or data

error slack could be invoked to update the ongoing R-squared of 78% to about a 89%

potential.

The more economical cross-sectional reduction could be re-specified as follows:

DiROE=β1∗Di

ISL∗DiUAE+β2∗D i

ROA+ϵ i

As Table 3E points out, the explanatory power remained nearly the same at 77.38%, whilst

the overall significance has soared better than twofold, as is evident in an F value of 42.76

rising above a hurdle of 5.57. However, the individual significance is now more mixed than

that. Whereas the DROE instrument has gained even more merit with a t=8.165 refuting the

null threshold of 2.485 at any alpha level, the prohibitively high p-value and low t for the

interactive dummy suggest that it is statistically zero for all practical purposes.

That said, it could still be used for explanatory purposes yet not as a matter of forecasting.

One way it might come in handy is by indicating that banks that are both UAE and Islamic

are only likely to under-perform should their ROA fall below average. The estimated equation

would look as follows:

E ( DROE )=.898∗DROA−.018∗D ISL∗DUAE

To illustrate the tentative predictive power, suppose the bank under study is UAE

Islamic. As long as its ROA exceeds the industry average, the odds are

about .898*1-.018*1*1=.88, or 88% that so too does its ROE. Now, of course the 12%

discount is unlikely to have been accounted by UAE Islamic identity—in fact, it should count

toward a 2% loss at the very worst, whereas a material residual was from the outset

potentially ascribed to data errors due to an 11% (or even 1-27/46=41%) non-response.

Moreover, banks that are either Islamic or UAE or both should boast more reliable

performance, which nets out as higher Sharpe-like efficiency. However, the present model

only addresses gross performance indicators—worse yet, as mapped into dummy instruments

which, by nature and design, cannot possibly claim anywhere near perfect predictive power.

Suppose, for ease of intuitive comparison, that the bank in question is either non-

Islamic or non-UAE, or neither. In the event its ROA is above average, odds are on the order

of 90% that so does its ROE. Either scenario illustrates consistency across alternate yet related

efficiency criteria. Now, presuming its ROA fell short, one can claim with near certainty that

ROE must have concurred. In fact, this suggests why the interactive coefficient might be

insignificant by the very nature of dummy normalization or additivity: After all, if it were

UAE Islamic, the expected status could prove slightly negative, which does not make much

economic sense other than seconding the same strong judgment. In other words, ROA

performance remains the core of analysis in this reduced, cross-sectional setup. As it happens,

the panel will not differ very critically.

3.3 Heteroskedasticity, Autocorrelation & GLS

Apart from the micronumerosity issue making room for outcomes as polar as excess

multi-collinearity versus spurious correlation, non-homoskedastic residuals may well render

the entire modeling as prone to structural shortcomings denying its OLS coefficients the

BLUE prerequisites. To begin with, not only do the two groups of banks in question—Islamic

versus regular entities—reveal some variance gap in the actual dependent variable and

possibly residuals. In fact, that would be an important piece of information in its own right,

provided it is statistically significant beyond divergent Sharp ratios. What is more,

performance may have shown some structural shift post-2008 in the aftermath of the crisis

back then.

On top of that, the very nature of the dummy based model being proposed, with both

the explanatory and the independent variables normalized as probability-like indicators, is

such that the resultant residuals are distributed binomially rather than showing a random

normality. Finally, the time series dimension of the panel yet to be addressed might exhibit

autocorrelation in the residuals, in which event some systemic pattern still remains untapped

while dimming the estimates’ efficiency. Among other things, heteroskedasticity is a cross-

sectional or static counterpart of temporal autocorrelation, insofar as various static error terms

might reveal non-zero covariances. On the other hand, the two alternative cases feature off-

diagonal covariances as either all zeros or otherwise, with diagonal variances either varying or

converging.

In fact, it is the dummy based design rather than particular specifications per se that

may either have smoothed the less significant gaps or exacerbated these. Interestingly, the

binomial or oscillatory distribution of the error terms may imply a seemingly chaotic pattern

in the first differences or derivatives looking as if the residuals were uncorrelated or more

generally independent in a difference based model such as the one under study.

One way of getting rid of non-constant variance, without having to look into the in-

sample nature of the added issues, could be to deploy Generalized Least Squares (GLS), with

OLS estimates being covariance-adjusted, which might waive the bulk of the above issues.

That said, it might be an option to stick with the OLS estimates for explanatory purposes, if it

were not for the regular and irreduced pattern in the error terms denying the model its

unbiased status. On the other hand, GLS lends or recoups the ‘best’ feature of BLUE, so that

interim or marginal improvements are not at stake anyway, when it comes to aligning

predictive and explanatory powers.

It is important to appreciate that the grand design or particular specifications need not

change, and nor does the GLS modification necessarily have to be re-run from scratch—as

applying a modifying matrix to whatever OLS coefficients should be enough. On the one

hand, based on the F-test, the cross-sectional model already proves a success, which should

carry over into the full-blown panel setup, so it is largely about the individual t-tests. On the

other hand, boosting the degrees of freedom or sample size by so many times would only

improve the resultant t-statistic by about the square root of that.

For instance, if the cross-section of banks were to be restored to the complete

population of 46 (which is not an option as the rest are either no longer out there or fail to

report their financials on a regular basis)—with observations being defined over twice as long

a horizon starting from 1994—the resultant increment in the t-statistic or contraction in the p-

value would at best be on the order of some 100%, or about 40% in case of either being

feasible. That could hardly remedy a .89 p-value for the non-lagged (c+r) differential or UAE

Islamic identity. Worse yet, since neither expansion is an option (with the actual response

being so objective as to posit the sample as the best proxy for the population), such data issues

should be assumed away with an eye on the binding data constraints. Not least, post-2008

structural shifts and learning effects may have turned out to be permanent yet less than

relevant in themselves as per the scope on hand, in which light delving into too distant a

history might never lend any major explanatory power to the model while ushering in an extra

data error of systemic, qualitative sort.

Again, there is no need to assume independence between the error terms and the

explanatory variables—much less with respect to Islamic or UAE identity, as the latter is,

among other things, what could come of interest given the scope of the present study. For that

matter, it should come as little surprise that the residuals’ distributions will vary depending on

the alternate designs or specifications. They may or may not show linear independence or

zero correlation with the explanatory variables, yet in any event the weaker prerequisite is

that the generally conditional or design-sensitive error expectation be zero ad hoc. The dual

outcome to be shunned is such that the errors supposedly independent by design ex ante turn

out to reveal heteroskedasticity ex post as a sample-specific feature at odds with either the

scope or the desired state.

The natural way to proceed in coming up with a GLS adjustment would be to presume

a knowledge of how exactly the variances differ from the constant OLS counterpart, and

adjust the variables accordingly. As it was proposed, the basic OLS estimates (slopes) would

not change, even though the intercept may end up affected:

VAR ( ε|X )=σ2∗G →~y= yG

=β0

G+∑

i

m ~β i∗xi

G+

ε i

G

~β i=∑~x∗~y

∑~x2

=

1G2∗∑ x∗y

1G2∗∑ x2

=β i

VAR (~βi )=VAR (~ε )1

G2∗∑k

N

x ik2=

1G2∗σ 2

1G2∗∑

k

N

xik2=VAR (β i

OLS)

In other words, a generic GLS adjustment does indeed yield a BLUE estimate whose variance

corresponds to that warranted by a qualified OLS procedure. In contrast, a naïve OLS design

overlooking the inefficiency issue would build on coefficients whose variances total a squared

factor of G times the regular OLS counterparts. Notably, in the particular setup being

attempted, the intercept will not be affected as it was put zero from the outset.

Now, of course, this is just the mnemonic scheme, whereas the actual G factor will

likely have to be estimated as a matrix in line with feasible GLS (or FGLS) rather than

assumed to have been known as a quasi-scalar function. Moreover, since the estimated G

operator will likely be nonlinear in the explanatory variables at times thus resulting in

nonlinear coefficient estimates, it has been observed that FGLS may not outcompete the OLS

in small samples despite asymptotic convergence, even though routinely it does (Verbeek

2000, p. 78).

To illustrate this in an alternate as well as generalized fashion, suppose there are only

2 core groups to define heterogeneity over: Islamic versus non-Islamic. Their implied error

variance-based GLS weights (omega) may well differ from either symmetry (50% each) or

the respective naïve OLS counterparts (alpha) that can be inferred based on an overall

coefficient (beta) versus standalone, sub-sample bA and bB. It can be shown that the GLS

versus OLS estimates would differ as follows:

βFGLS−βOLS= (ω−α )∗[bA−bB]

Since neither the weights nor the dummy based coefficients ever exceed 1, their

differences should be small—more so the product of these. In other words, the two alternate

coefficient estimates should prove very close unless heteroskedasticity is very pronounced. In

fact, this is not evident from the prior qualitative analysis. In the proposed model with 27

banks and 2 explanatory variables on hand, it can be demonstrated that even in the case of

perfectly equal group variances (as opposed to sample error variances drawing upon the

degrees of freedom), it is the divergent subsample or group sizes that might make the whole

difference. The resultant variance based weight could be about 58% while having little to do

with heteroskedasticity per se and thus blurring the inference rather than aiding it. By

contrast, heteroskedasticity induced weights may not map into a material gap between the

GLS versus OLS estimates. In addition, small sample and hence sub-sample sizes may render

the group estimates too inefficient ex ante, with posterior efficiency improvement thus largely

appearing to be a self-induced problem.

On the other hand, a two-group GLS might neither pay nor suffice, as the effective

number of would-be groups totals 8, i.e. 4 cross-sectional along the lines of the original

identity matrix and 2 temporal with respect to 2008 marking a potential structural or learning

landmark. One way of desolating the contingent component over and above the regular BLUE

variance as a meta-intercept would be to regress the squared OLS residuals on the exact same

model that was deployed as the OLS core yet to be refined as GLS:

log( y i−E( y iOLS))2≡ log ei

2=log σ2+β '1∗DiISL+β '2∗Di

UAE+…≡ β ' 0+E ( yiOLS )+ei '

However, the parsimony criterion serves as an embedded mechanism driving the

refinement choice. On the one hand, an OLS regression of the kind has already been run, and

is unlikely to prove more of a success in the residuals once again than it did in levels—much

less with an intercept being allowed for. Somewhat paradoxically, this test is bound to show

insignificance in all the variables except the BLUE variance intercept, which is why one could

safely presume no heteroskedasticity.

Based on all of the above, there is no need to run any GLS per se, as it is unlikely to

beat the OLS on hand. Extra complexity involved is hence not justified from the standpoint of

parsimony. It will be shown later on how a similar economical approach maintains a

minimalist design as robust, in that few if any extra variables appear to be warranted in a full-

fledged panel setting.

Chapter 4: Auxiliary Modeling

4.1 Caveats & Extensions

It has been proposed that any major structural gaps or shifts over time in performance

could largely be accounted for by either a differential impact of Islamic entrepreneurship and

management or UAE regulations and otherwise relevant institutions, or both—as implicitly

captured in the respective dummy type instruments. Notably, though, these can reasonably be

presumed to hold identically over time—which would deny the critical variability in the right-

hand side it takes for a dummy mediated model to explain that in the dependent variable. At

the very least, that could question the very panel’s relevance in the first place, even though

getting rid of one would deny an important layer of explanatory power. Alternatively, it might

be feasible technically to consider less stable, or non-stationary ‘effective’ identities, e.g.

based on a varying proportion of Islamic solutions in the total asset composition or capital

structure—or indeed define the dummies in terms of deviation relative to an industry average

which does change inter-temporally. However, that would be a rather artificial approach to

measuring Shari’a compliance as a contributor to ROE denominated performance.

Had it been for some institutional pillars that evolve with the passage of time without

varying across the entities, the cross-sectional stability of the sort could be depicted in the

fixed effects under a panel design. In actuality, the setup is reversed with material identities

only varying cross-sectionally yet not inter-temporally. Although it might be an option to just

reverse the dimensionality in the computation, still the SPSS design and any other software

would likely have been fine-tuned to a particular convention when it comes to specific

standardized tests building on a precise order or sequentiality of declaring the dimensions.

One way around the issue could be about supplying some extra explanatory variables

that show enough temporal variability to make it up in the initial specification. That said, a

few trade-offs will have to be considered. To begin with, one should stay wary of a

specification error or bias which might render the overall model more effective while denying

efficiency to the individual explanatory variables, or linear coefficients. That could be made

manifest in a higher R-squared along with a stronger F-test amidst weaker t-values. On the

other hand, these exact same metrics would in any event have to be traded off against a likely

increment in complexity in light of a more involved specification—as compared to the status

quo or incumbent model. At the end of the day, however, it would be rather strange to have

anticipated that any combination of Islamic and UAE identity, as well as their interaction or

downright denial, could possibly explain the entire short-run ROE difference, if any.

Consequently, a formal model will be proposed to further rationalize would-be ROE

performance while bridging some of the aforementioned testing gaps.

4.2 An Auxiliary Formalization

The premises of Islamic banking have little to do with the conventional ends as well as

means pertaining to effective monetary expansion as based on the issuing of loans and subject

to reserve constraints. For one thing, Islamic banking entities do sell a variety of lease- or

loan-like instruments that are more of an equity nature while building on non-interest payoffs

by and large. Legal reserves may well be there, yet the underlying rationale is not about bank

runs, or non-performing and ‘toxic’ assets per se. Some of the reserves may be intended as

cushion or hedging tools to offset partial losses, and in any event the discretionary layer far

outweighs the arbitrary reserves or fund requirements being imposed exogenously. Apart

from the structural gap extending beyond capital adequacy or weak Basel II compliance, the

very share of reserves could either be lower or higher depending on the business model or

asset structure of the Islamic bank, or possibly in line with the stage of the economy cycle, yet

definitely not as a mere constraint on loan issuance.

In a sense, Islamic banks are more characteristically contingent on the savings rate, or

the marginal propensity to save at the macro level, as well as by the retention ratio as its

loose, micro-level counterpart. In effective terms, their performance as well as expansion is

surprisingly akin to the Keynesian type investment multiplier building on the marginal

propensity to consume C as opposed to save (1-c), rather than a conventional money

multiplier as in Mishkin (2010, pp. 358-359), in terms of the banking system’s capacity to

create money or account for its velocity as in MV=PQ.

In fact, it should be straightforward to incorporate both, with the peripheral

mechanism of conventional banking reflected in terms of an exogenous parameter N, or bank

network size. What affects ROE is net income along with the ongoing equity. The latter may

largely be made up of retained or cumulative earnings as well as assets—or, at any rate, the

actual structure may either be unknown or arbitrarily reshuffled for reporting purposes.

However, the one pillar of relevance that has to do with ROE as the profitability of equity

pertains to growth in equity. By contrast, growth in assets is hardly the ultimate criterion of

profitability, or capital gain, because it can be secured in ways that deny intrinsic earning

power or organic expansion. Some of this phenomenon is evident in just how abruptly the

total assets may change when reporting on a holding as opposed to an entire group, or indeed

following ‘non-organic’ M&A expansion. It is fortunate that none of that has shown to alter

the core identity in the data on hand. More generally, ROE appears to be a rather

dimensionless metric whose scale-invariance qualifies it for meaningful inter-bank

comparison within as well as across any sub-samples in question.

4.3 An Augmented Model

The aforementioned formalization could serve as a critical add-on acting to augment

the core specification by possibly reducing the residual while lending some extra variability to

the explanatory part. The differential in ROE, while keeping this dependent variable

commensurate with the one in the core model, could be interpreted in a variety of ways. In

addition to generic sensitivity, it could refer to any gap as compared to the industry average.

For that matter, it could capture the gap in between the Islamic versus conventional business

models, insofar as it can be inferred from the sample structure. For instance, suppose the

effective sample (based on the actual response or turnout rate) features about 2/3 of the banks

qualifying as UAE (which makes the analysis largely UAE centered) and ¼ of them as

Islamic. This suggests that the industry average or sample mean would exhibit a bias toward

the non-Islamic and UAE legacy being incumbent. That could affect the actual (c+r)

difference as well as its composition. Although the generic response function would

structurally remain invariant, its actual values will likewise vary depending on the model.

Suppose an Islamic model shows, in theory, utter disregard for basic efficiency as

opposed to ultimate ROE efficacy, which might imply a low value of K ≡1−c−r. However,

it was pointed out that its zero asymptote might leads to a very high ROE (Figures 1A-1B).

Yet this oversimplification enables one to appreciate just how close the ROE gap comes to a

(c+r) differential anywhere around that level. In other words, any decrease in the basic

efficiency will compromise ROE efficacy by about the same percentage. Incidentally, the

response function amounts to an elasticity and not just a derivative. Therefore, depending on

the actual bank’s model or identity, the percentage response could either show overreaction to

or compression of whatever gap being determined by the sample composition (Figures 1A-

2B).

A wealth of efficiency implications could be drawn from the above conjecture. The

study of the basic efficiency differential could alone usher in a plethora of scenarios. For

instance, the individual impacts of legal versus discretionary reserves could be

compensatorily inverse or negatively related in case their combined ratio is constrained. For

the same token, they might net out to a rather mixed differential when it comes to industry

benchmarking. Whereas the idle or legal reserves could prove far lower in the Islamic setup,

the discretionary or productive counterpart might offset that.

For now, the whole of the ROE-augmented model to be addressed later in text could

be added to the core model as an interactive variable. Only in this case can linearity in the

coefficient be ensured, with invariance hardly attainable for any K. In other words, the

response function does vary, so that measuring it as part of the added coefficient of K might

not make a lot of sense in an OLS or dummy instrument LS (DILS) setup. The augmented

specification could look as follows:

D¿ROE=M +γ∗X ¿+u¿

In this specification, M captures the entire core, dummy-based model, and X refers to the

variable expansion yet to be covered. Although it might be an option to define the (c+r)

differential in dummy terms as well, the mixed structure within the same instrumental

variable could hardly prove productive beyond mechanical reduction in the residual. On the

other hand, paradoxically, some collinearity between this differential and Islamic identity

would be sought rather than shunned, which lends extra rationale behind keeping at least

some of the supposedly related variables as non-dummies so as to minimize the formal multi-

collinearity excess.

On second thought, it should be straightforward to motivate the entire setup in plain

and natural terms. To begin with, an add-on regression for delta ROE can indeed be run on a

(c+r) differential as an OLS regression. For one thing, if ever, the applicable coefficient

might prove linear in weakly differential or local rather than strong or levels denominated

terms:

∆ ROE=f (c , r )∗∆ (c+r )=

∂ ROE∂ X (c , r )

∗∂ X

∂(c+r )∗∆ (c+r )=β∗∆(c+r )

One may wonder if the implied and unknown function underlying the linear estimated

coefficient would likely be so well-behaved as to enable a pattern that is smooth over (c+r).

Remarkably, it is both ensured and irrelevant in the way of its actual representation. This is

easiest to show by noting that the above differential likely amounts to a Taylor expansion

around some initial conditions or benchmark like the industry average:

∆ ROE=ROE (c+r )−ROE (c0+r0 )≅ RO E ' ( c0+r 0 )∗[ (c+r )−( c0+r 0 )]

The implied mapping, be it a derivative or a coefficient, is inherently constant or fixed thus

rendering the transform linear, as it is defined at one point, e.g. industry average, baseline, or

initial conditions. In fact, it is for this reason that both the explanatory and the dependent add-

ons can be transformed arbitrarily around the linear stretching. As one possibility, both can be

represented in dummy terms, thus showing even more consistency with the core model:

At this point, however, one should come to realize the recursive endogeneity as

implied in a two-way and inherently auto-regressive interaction between (c+r) and ROE. On

the one hand, the present reserves and dividend policies should affect ROE performance in the

next period, e.g. as a compensatory cushion responding to an earnings drop. On the other

hand, ROE may in turn have an impact on these policies in the subsequent time period in

proportionate rather than compensatory terms, as a response to good rather than poor

earnings. The two-way setup suggests a double lag, i.e. a period over which the extra

variables significance is most pronounced.

However, including the second term would usher in linear dependence on the first one,

or multi-collinearity, by the very definition of a linear inter-linkage between the two as

conjectured in the lefthand-side scheme.

In fact, any attempts at further improving upon the model could prove about as

detailed as they too are superfluous. To begin with, the residual could further be reduced by

embarking on higher-order Taylor differentials based on the powers of (c+r) differences.

However, this series converges very quickly, because even if the sum is anomalously large

above 1 in levels, it is still regular and below unity in differences—and these die off

instantaneously starting with second-order terms. Therefore, this channel is unlikely to come

in handy, either.

4.4 Caveats on Data

The annual reports have not shown to be very accurate or reliable in a variety of ways.

For starters, the same terminology has tended to be used to denote very different layers of

income statement reporting, e.g. net operating revenues have at times been stated as operating

profit, as it were net of the operating costs. On the other hand, the Du Pont representation as

attempted has enabled meaningful control over the relevant interim ratios and the implied

base figures, given that the net margin can be discerned from ROE net of the equity

multiplier.

For that matter, accounting for the reserves has appeared rather manipulative. Not only

has the total value been at times adjusted in retrospect, it was not always possible to

distinguish between the regulatory or legal versus discretionary layers due to arbitrary

reshuffling. A similar challenge would confront anyone trying to infer the actual dividend

payout, which is why it had to be reconstructed based on a residual of net income net of

changes in reserves and retained earnings.

Whereas the systemic and likely cross-sectional component of discrepancy may have

had to do with the gap between reporting legacies (e.g. country-tailored GAAP as opposed to

uniform IFRS), idiosyncratic and possibly time-series type shifts largely stemmed from

reversals on impairments and provisions. Whilst the former captures minor counterparts of

bank runs, the latter could take on forms as diverse as, cushion against non-performing loans,

hedging vehicles, and translation or reporting-specific disparities—let alone currency risk

mitigation in a cross-border setup. Pension and insurance type commitments could be one

other layer of importance showing how provisions may pertain to gap management and fixed-

income portfolio immunization beyond derivatives hedging per se.

The re-allocation setup becomes even more entwined in light of provisions such as

‘dividend reserves’ (ABN Amro 2014, p. 184), which lend some extra rationale behind a

proposed setup in which retention and reserve ratio enter ROE symmetrically—even if totally

unrelated ex ante or are defined against very different bases, e.g. net income versus

cumulative or retained earnings which are trivially related in terms of flows versus stock and

hence compatible anyway. On the other hand, this dividend reserve may have been tied in to a

dividend payout target (ABN Amro 2011, p. 113), which essentially amounts to a C input in

ROE being posited as inseparable from the r impact. In passing note an important distinction

between the two corner dividend policies. For instance, whereas the non-Islamic, non-UAE

legacy may fare on a 40% share of net income (ABN Amro 2011), its Islamic UAE

counterparts declare an intended 40% on any paid-in capital (ADCB 2014, p. 9).

Moreover, although dividend payout versus reservation rate could be seen as related to

working capital versus fixed assets potentially, their complementarity can neither be

overlooked nor fully appreciated as technology-accommodative c/r ratio. At the end of the

day, processes should not vary materially across banks within the same legacy, so that it is the

latter dimension that makes the difference time and time again.

Importantly, the bulk of these may first have been accounted for as special income

statement items, followed by posterior transfer into either the retained earnings or offsetting

reserves falling under the respective balance sheet categories. In any event, the single most

important challenge has been about the meta-translation or reconciliation uncertainty as

affecting the data quality in ways that can hardly outmatch bank-specific translation and

allocation efforts.

The more serious divide rests with the very gap in conventions whereby sukuk, as an

effective supplement or extension of regular dividends or at any rate difficult formally to

distinguish from preferred payouts, is routinely reported as a tax deductible expense item in

the income statement, i.e. counting toward pre-tax allocation rather than after-tax distribution

(ADIB 2014).

Although manipulable items cannot possibly provide a careful account of the ultimate

variability in the key performance indicators (KPI), what might be possible to detect, among

other things, is the group-systemic or legacy-specific patterns of deviation or reporting errors

that could prove statistically significant in their own right. This is another way of saying that,

say, Islamic banks might tend to under-report their reserves in somewhat distinct ways that

are still of relevance to ROE. In a sense, this higher-order dimension of efficiency pertains to

a layer of transaction or deliberation costs, which exhibits an overlap or interaction between

macro-level institutional characteristics versus bank-specific discretion, rather than confining

performance to micro-level operations or internal environment alone.

It remains to be seen whether institutional convergence, or indeed reporting

harmonization, can either be expedited by hedging against the implied translation mismatch,

or whether that could be a major facet of market efficiency accounting for some of the

potential ROE slack yet to be seized. Put simply, it is unclear whether harmonization can

implicitly be fostered ex post by making hedging or reconciliatory choices, or if these should

be made ex ante as a matter of hedging against a smaller expected ROE or its higher standard

deviation. One way or the other, the attempted study could shed some light on whether the

two alternate legacies have been more or less apt at addressing the aforementioned efficiency

trade-offs.

4.5 Panel Estimates

The typical choice facing any panel setup is between fixed versus random effect

approaches. In a sense, the latter could be treated as an extension of the former, with both the

regular intercept and the error terms rethought in major ways. Whereas in the fixed-effects

case, the intercept pertains to a fixed part that varies cross-sectionally, in the random effects a

stable core is added on along with residuals that could thus be treated as largely the temporal

shocks or short-run propagations.

Although it should be reasonable to discard random effects at the outset due to the

questionable explanatory impact of a fixed-core intercept as before, a fixed effects version

could well be afforded as well as warranted, in that a higher R-squared might be salvaged

along with the now-more significant and efficient estimates. The latter is secured with an eye

on the added time dimension as a natural extension of the cross-sectional sample, whose

increase by a factor of T would bring in a significance boost of about the square root of that.

In other words, considering 9 time periods might secure a triple increase as opposed to an

adequate reduction in the t-statistics and p-values respectively.

Although the presence of fixed effects might not be desirable to define and measure

conditional probabilities such as those building on a dummy setup, it could later on be

possible to see whether the averaged group intercepts have shown to vary. On second thought,

that might put into question the deployment of identity dummies in the explanatory vector

which do not vary temporally anyway. Should there be a good reason to expect that these

variables remain insignificant the regular way, it might be worthwhile to include them

implicitly in the fixed-effect free terms so as to measure any of the 4 cross-sectional identity

impacts later on. At that rate, the model should prove even more economical as well as panel

fit—more so given that the other 2 temporal dimensions or transition shifts are not

immediately relevant to the attempted scope anyway.

The parsimony trade-off might not be that straightforward, bearing in mind that even if

all but one or (m-1) explanatory variables are removed, another N fixed-effect host is

introduced, which results in updated degrees of freedom applying to t-tests.

The candidate specification could look as follows:

D¿ROE=α i+ β1∗D¿

ROA+β2∗D i, t−1c+r +ϵ t

H 0 : β1=0=β2

In fact, there is no need to explicitly restrict the individual effects to zeros as well. After all,

they ‘explain’ very little by themselves, should the rest of the coefficients turn out

insignificant.

It is remarkable that a model represented in deviations from the temporal means,

which is the equivalent keeping the fixed effect terms irrelevant, resembles the cross-section

counterpart building on differentials with respect to industry averages. In fact, this lends even

more support to the original decision of assuming a zero intercept in that cross-sectional

reduction.

Since temporal shifts are less relevant to the intended scope, and the cross-sectional

heteroskedasticity has been discussed previously, an OLS panel estimation procedure can be

attempted. For that matter, no endogeneity issues are envisaged, as these have been eliminated

along with the would-be extra multicollinearity between the candidate lagged regressors.

Finally, employing a fixed effects model is all the more reasonable given that the dummy

identities as implied in the alpha individual effects are plausibly correlated with the rest of the

explanatory variables—or so the underlying theory predicts.

As per the time series dimension of the panel design, the dummy based specification

secures ‘stationarity’ by the very nature of binary or limited variables that represent

transforms of first differences or gaps around the static means. On second thought, ‘co-

integration’ issues are less relevant, in that auto-regression has been avoided as a matter of

sterilizing multicollinearity while boosting the explanatory rather than predictive power

around the proposed regressors.

Interestingly, the fixed effects design appears to fit into the micronumerosity issue

thus making it a forte rather than an efficiency constraint. As proposed before, the rationale is

that a total of N extra individual-effect variables are introduced as per a cross-sectional

sample size of N, which suggests it is conceivable for small samples only—unless the T

dimension features very many periods and not just a few extra years. Incidentally, the

individual effects cannot be estimated consistently in any event, so it is no wonder if the issue

of less efficient dummies carries over into the panel setup. The silver lining, though, could be

that the cross-sectional reduction was a reasonably good and representative starting point

along many lines.

A conditional maximum likelihood (CML) as one way around the issue could be seen

as an analogue of the aforementioned conditional variance decomposition, which both are

likely to depend on exactly the dummy variables being approximated by individual effects yet

to be bypassed in turn—and possibly with as meager a chance. Much like in that case

showing effective GLS irrelevance, the basic linear setup need not be altered this time around

either. For the same token, the alternative designs such as logit, tobit, or probit will not be

contemplated, as less relevant theoretically or less fit for comparative purposes against the

cross-sectional reduction or at odds with fixed effects. Worse yet, the ‘initial conditions’ issue

pertains to the inherent difficulty to argue that the initial probabilistic value is fully exogenous

rather than path-dependent or arbitrarily chosen in an otherwise cross-sectionally determined

setup.

Finally, the panel can be presumed balanced for all practical purposes, as there was a

near complete overlap between the banks available and those reporting their financials

throughout.

Chapter 5: Panel Findings

5.1 Prior Estimation

The panel data test has been run on SPSS/PASW 17.0, with ‘mixed modeling’

corresponding to a panel, ‘subject’ to cross-sectional dimension for the purpose of fixed

effects estimation, and the entire setup regressed under the restricted estimates maximum

likelihood (REML) mode. The common or random effects intercept has been waived, whereas

the individual fixed effects have been estimated for a total of 27 banks. The missing values

have totaled 32 for DROE and DROA and 52 for D(c+r) as lagged by one period. The estimated

coefficients along with their significance parameters have been provided in the output below.

Table 4: Panel or Mixed Modeling Output (SPSS/PASW 17.0)

Coefficient Estimate t-stat p-value Correlation DISL DUAE

(1) DROA=1 .5908 5.165 .0000 (1,4)=-.651,

(1,5)=-.623,

(1,6)=-.633,

(1,7)=-.681,

(1,8)=-.669,

(1,9)=-.766,

(1,10)=-.611

(2) Bank=AAIB .6062 3.734 .0000 (2,4)=.420,

(2,5)=.418,

(2,6)=.411

0 0

(3) Bank=ABK .4806 3.209 .0020 (3,4)=.438,

(3,5)=.437,

(3,6)=.442,

(3,7)=.429,

(3,9)=.443=(3,10)

0 1

(4) Bank=ABN .4054 3.055 .0030 (4,5)=.512,

(4,6)=.510,

(4,7)=.505,

(4,9)=.508

0 0

(5) Bank=ADCB .4788 3.643 .0000 (5, 4)=.512,

(5,6)=.507

1 1

(6) Bank=ADIB .4781 3.642 .0000 (6, 4)=.510,

(6,5)=.507,

1 1

(6,10)=.502

(7) Bank=Baroda .3780 2.704 .0080 (7,4)=.505,

(7,9)=.528

0 0

(8) Bank=BLOM .5279 3.348 .0010 (8,9)=.515 0 0

(9) Bank=BNP .4826 3.361 .0010 (9,4)=.508,

(9,7)=.528,

(9,8)=.515

0 0

(10) Bank=FGB .4258 3.259 .0010 (10,6)=.502 0 1

Pseudo R2 .87

Average bank

FE

2/9 4/9

The expanded model shows a clear improvement on the cross-sectional reduction,

even though the core of it has shown to be transferable and robust. For instance, based on the

ROA dummy as an invariably dominant factor now enjoying an F at 83.47, that appears to

measure up against the expected significance boost as an outcome of temporal sample

expansion, which effectively totaled [27*9-(32+32+52)]/27=4.7—exactly by how much the F

statistic has increased, with the t-counterpart going up by about the square root of that, i.e.

5.17 up from 2.49 for DROA. The pseudo-R2 can now be estimated at about 87%, which is a

clear improvement on the original 78% albeit again in line with the 11% expectation on data

error slack. Notably, all of the individual bank fixed effects that proved highly significant at

most levels have seen strongly convergent estimates anywhere in between .40 and .60 (with

mode around .47) and t-statistics (3.055 to 3.73 with a mode at 3.64) alike. Interestingly, all of

the Akaike related criteria have likewise been compressed around 84 to 88 values.

Although for the most part these banks are non-Islamic, the proportion of UAE players

is about twice as large as that of Islamic legacy. Spectacularly, this smaller sample showing

utmost significance is representative of the overall structure. The lagged reserves and

dividend differential proved insignificant again, yet for the most part its inverse relationship

with the ROE and ROA is strongly apparent throughout, with missing values not denying the

regularity. That said, this pattern is clearly far from linear, which renders GLS and related

estimates nearly pointless as per this regressor.

The estimates have turned out to be remarkably similar at about .48 for the few Islamic

as well as these and UAE banks for which they proved very significant, even though for UAE

banks as such these are more disparate (.48 versus .43). For that matter, the correlations across

the banks’ individual effects have been rather compressed around .4 to .5, even though DROA

induces some excessive correlations with these varying anywhere within the -.6 to -.8 range.

It should come as little surprise that there is a compensatory trade-off between the explanatory

terms, if only insofar as these have to be restricted to 1.

The non-Islamic banks are far more scattered when it comes to the unconditional

expectation component of enjoying an excessive ROE regardless of ROA gap—even though

the average for the two groups is about the same at nearly 50%. In fact, that seconds the cross-

sectional qualitative reduction showing superior Sharpe based, or risk adjusted performance

as boasted by Islamic banks. The Islamic bank, and one of UAE Islamic legacy for that

matter, will enjoy the 48% sure bet on seeing an excess ROE, with the contingent component

adding up to near certainty in the event of there being an excess ROA.

In fact, the bulk of these macro-outcomes could have been discerned from visual

inspection of the data distributions. Figure C1 showcases all of the core inter-relationships

within as well as across datasets. Apparently, ROE and ROA gap dummies are distributed

much alike, with sparse areas or zero values dominant (as is evident from Figure C2).

Although the (c+r) gap dummy appears more of a normally distributed set, one is led to

accede to the conjecture explaining why it lacks the linear efficiency. Whereas ROA and ROE

dummies appear monotonously convergent, that other dummy is either dually or orthogonally

co-distributed with them. Put simply, evident is sheer lack of linear correspondence, even

though that is not to deny the conjectured inverse relationship as predicted theoretically ex

ante.

5.2 Two-Stage Inference on Fixed Effects

It should now be readily apparent how the fixed effects could be utilized in tracing

through the revealed yet latent identity impacts. The identity matrix as in Table 1 can now be

filled in with respect to each sub-domain, to arrive at the 4 group averages to run the CHI-

squared and t-tests on.

Table 5A: Identity Matrix Made Operational

ISLAMIC NON-ISLAMIC

UA

E

ADCB

ADIB

ABK

FGB

Non

-UA

E n/a

AAIB

ABN

Baroda

BLOM

BNP

Table 5B: Fixed Effects Averages

ISL*UA

E

ISL*nUA

E nISL*UAE

nISL*nUA

E ISL nISL UAE nUAE

.47845 0 .4532 .48002 .47845 .47236 .46583 .48002

The above table lends ultimate support to the original qualitative finding whereby

there is a perfect match between Islamic and UAE Islamic models, and very little difference

in unadjusted terms between Islamic versus non-Islamic performances. In fact, it may appear

ironic that the two polar extremes, ‘UAE Islamic’ versus ‘non-UAE non-Islamic’ can hardly

be distinguished between. It is unlikely that a naïve CHI-squared test against the common

average ever shows material inter-group gaps. Althought it may appear that the non-UAE

Islamic is an outlier in that it is not represented, still that would suggest a zero gap between

each [missing] individual parameter and the [non-existent] subgroup average by definition. In

order to avoid the missing value or selection bias, a CHI-squared will be run based on sub-

sample averages rather than a general sample average.

It is for this reason that one may want to run a t-test making use of asymmetry such as

drastically differential sub-sample sizes which might usher in statistically significant chasms

even amidst the otherwise indistinguishable sub-sample averages.

The CHI-squared across the 4 identity subgroups barely totals a 1.075, with the bulk of

it stemming from the non-UAE Islamic outlier that alone brings in as much as 1. Even so, it is

impossible to reject the null hypothesis of there being no material differences with respect to

any fine or aggregated identity gaps.

By contrast, a t-test will be run for Islamic versus non-Islamic and UAE versus non-

UAE or weakly dual identities. Notably, for two subsamples of unequal size, the resultant t

statistic could be inferred as (Hayter 2012, p. 402),

t=μ ISL−μnonISL

√ σ ISL2

nISL+

σ nonISL2

N−nISL

Largely due to the size asymmetries for N=9 degrees of freedom, the tests for Islamic

versus UAE identity gaps yields 1.716 and 2.02 respectively, which barely suffices to reject

the null hypothesis of similarity on margin at about 6% and 2% significance levels,

respectively. In other words, resorting to rigorous tools and tests did not secure any major

qualifying concerns on the initial, naïve analysis. In fact, the latter has been supported

consistently.

Conclusion

The present study aims at looking into the key and intertwined drivers of efficiency for

UAE banks, in particular what pertains to approaches and mechanisms distinguishing between

Islamic versus conventional finance. It has been shown that, for all practical as well as formal

purposes, efficiency as risk-adjusted versus leveraged slack picking could be represented in

terms of ROE and its underlying Du Pont decomposition touching on areas as diverse as

profitability margin, financial leverage, and scale efficiency or operating leverage.

A set of related modeling tools has been proposed to arrive at similar findings as

stemming from very distant sets of assumptions. In particular, the inter-relationship between

reserves and dividend policies versus ROE performance has been treated from a variety of

standpoints. Lagged responses have been modeled in ways that are economical as well as

autocorrelation sparing. For that matter, the very design of the grand approach and particular

models has economized on extra dimensions without compromising explanatory rigor and

predictive validity.

Remarkably, the bulk of the selection analysis was done based on design-driving

theoretical considerations as well as study-accommodating specifications that delineate

structure as a robust choice beyond sample-specific tests or manipulative data mining. The

empirical part is closely related to a formal modeling rationale in ways that secure parsimony

and bootstrapping with an eye toward hard data constraints.

The less formal qualitative analyses as well as technical scrutiny unequivocally point

to very minor distinctions between the alternate models in unadjusted terms. On second

thought, in terms of Sharpe-like performance net of excessive risks, which is particularly in

line with the Islamic discipline, Islamic and UAE identity has lent itself with superior

performance. Whereas dividends and reserves have posited a controversial agenda in its own

right, as evident in the initial theoretical discussion, still the reportedly insignificant linear

impact amidst an apparently inverse inter-linkage with respect to ROE and ROA alike may

hint at non-linear response patterns yet to be addressed in future research. On second thought,

it remains to be seen whether the perceived non-linearity hides the utter complexity of

underpinning decisions that cannot otherwise be reduced to any elegant or generalized

mechanisms or rationales. In fact, this agenda reaches far beyond residual minimization or

predictive pragmatics.

One further direction could be attempted, with the Islamic identity dummy acting as a

dependent variable running on the rest as explanatory regressors rather than the other way

around. In a sense, this dual setup would appear to be complementary with respect to the one

undertaken in this study, and largely descriptive or positive in determining what an Islamic

identity is about rather than normative in judging on what an optimum identity should be.

References

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Hussein A. Hassan Al-Tamimi Husni Charif, (2011),"Multiple approaches in performance

assessment of UAE commercial banks", International Journal of Islamic and Middle Eastern

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27 (433-447)

Fatima S. Al Shamsi , Hassan Y. Aly & Mohamed Y. El-Bassiouni (2009) Measuring and

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Appendix

Appendix A

Table 2A: ROE Based Qualitative Cross-Sectional Comparison (By Author 2015)

ROE

average D^ISL

D^non-

ISL D^UAE

D^non-

UAE D^ISL&UAE

D^non-ISL &

non-UAE

AAIB 15.25% 0 1 0 1 0 1

ABK 9.08% 0 1 1 0 0 0

ABN 17.15% 0 1 0 1 0 1

ADCB 11.46% 1 0 1 0 1 0

ADIB 12.45% 1 0 1 0 1 0

ARBIFT 4.10% 0 1 1 0 0 0

Baroda 16.46% 0 1 0 1 0 1

BLOM 15.25% 0 1 0 1 0 1

BNP 8.35% 0 1 0 1 0 1

CBD 15.09% 1 0 1 0 1 0

CBI 7.78% 0 1 1 0 0 0

DIB 14.45% 1 0 1 0 1 0

FGB 15.90% 0 1 1 0 0 0

HBL 18.17% 0 1 1 0 0 0

HSBC ME 17.68% 0 1 0 1 0 1

Janata 2.40% 0 1 0 1 0 1

Mashreq 12.96% 1 0 1 0 1 0

NBAD 14.41% 0 1 1 0 0 0

NBB 15.90% 0 1 0 1 0 1

NBF 9.59% 0 1 1 0 0 0

NBO 13.97% 0 1 0 1 0 1

NBQ 10.62% 0 1 1 0 0 0

NoorI 11.56% 1 0 1 0 1 0

RAK 24.63% 0 1 1 0 0 0

Sharjah 8.70% 0 1 1 0 0 0

SharjahI 6.39% 1 0 1 0 1 0

UAB 17.85% 0 1 1 0 0 0

count (sub- 7 20 18 9 7 9

sample size)

weight .259 .741 .667 .333 .259 .333

average 12.05% 13.16% 12.51% 13.60% 12.05% 13.60%

SD .0285 .0542 0.0488 .0502 .0285 .0502

ratio   4.231 2.429 2.565 2.711 4.231 2.711

Table 2B: ROA Based Qualitative Cross-Sectional Comparison (By Author 2015)

ROA

average D^ISL

D^non

-ISL D^UAE

D^non-

UAE D^ISL&UAE

D^non-ISL &

non-UAE

AAIB 1.63% 0 1 0 1 0 1

ABK 1.35% 0 1 1 0 0 0

ABN .57% 0 1 0 1 0 1

ADCB 1.41% 1 0 1 0 1 0

ADIB 1.49% 1 0 1 0 1 0

ARBIFT 1.07% 0 1 1 0 0 0

Baroda .95% 0 1 0 1 0 1

BLOM 2.45% 0 1 0 1 0 1

BNP .34% 0 1 0 1 0 1

CBD 2.43% 1 0 1 0 1 0

CBI 1.14% 0 1 1 0 0 0

DIB 1.78% 1 0 1 0 1 0

FGB 2.64% 0 1 1 0 0 0

HBL 1.65% 0 1 1 0 0 0

HSBC ME 1.52% 0 1 0 1 0 1

Janata .55% 0 1 0 1 0 1

Mashreq 1.91% 1 0 1 0 1 0

NBAD 1.59% 0 1 1 0 0 0

NBB 1.95% 0 1 0 1 0 1

NBF 1.32% 0 1 1 0 0 0

NBO 1.91% 0 1 0 1 0 1

NBQ 2.63% 0 1 1 0 0 0

NoorI 1.29% 1 0 1 0 1 0

RAK 4.33% 0 1 1 0 0 0

Sharjah 1.84% 0 1 1 0 0 0

SharjahI 1.50% 1 0 1 0 1 0

UAB 3.19% 0 1 1 0 0 0

count (sub-

sample size) 7 20 18 9 7 9

weight .259 .741 .667 .333 .259 .333

average 1.69% 1.73% 1.92% 1.32% 1.69% 1.32%

SD .0039 .0096 .0084 .0074 .0039 .0074

ratio   4.293 1.798 2.287 1.776 4.293 1.776

Table 3A: Cross-Sectional, Counts Based Regression

Regression output

R-squared .263796018

Modified R-squared .084486203

Standard error

2.19517418

4

Observations 27

  df SS MS F

F

hurdle

Regression 5 37.98662662 7.597325323

1.97075557

3 2.13

Residual 22 106.0133734 4.818789699

Total 27 144      

  Coefficients SE t-stat p-value t hurdle

Intercept

4.79104355

5 1.090346987 4.394054013 .000230413 1.323

D^ISL -.404150999 1.069688266 -.377821289 .709184079

D^UAE -1.35090712 1.098274581 -1.230026756 .231678664

D*D 0 0 65535

D^(c+r) -.175414787 .158482535 -1.106839857 .280319295

D^ROA .452262114 .177582207 2.546776068 .018382884  

Table 3B: Cross-Sectional, Averages Based Regression

Regression output

R-square .263796018

Modified R-

square .084486203

Standard error .243908243

Observations 27

  df SS MS F

F

hurdle

Regression 5 .468970699 .09379414 1.970755573 2.13

Residual 22 1.308807079 .059491231

Total 27 1.777777778      

  Coefficients SE t-stat p-value t hurdle

Intercept .532338173 .121149665 4.394054013 .000230413 1.323

D^ISL 0 0 65535

D^UAE -.150100791 .122030509 -1.230026756 .231678664

D*D -.044905667 .118854252 -.377821289 .709184079

D^(c+r) -.175414787 .158482535 -1.106839857 .280319295

D^ROA .452262114 .177582207 2.546776068 .018382884  

Table 3C: Cross-Sectional, Counts Based Regression—No Intercept

Regression output

R-squared .781739944

Modified R-squared .66631472

Standard error

2.94185072

4

Observations 27

  df SS MS F F

hurdle

Regression 5 712.9468293 142.5893659

20.5947197

7 3.94

Residual 23 199.0531707 8.654485681

Total 28 912      

  Coefficients SE t-stat p-value t hurdle

Intercept 0 n/a n/a n/a 2.508

D^ISL 0 0 65535

D^UAE -.423604612 1.444419067 -.293269884 .77194571

D*D -.109862719 1.430724316 -.076788182 .939456127

D^(c+r) .166112689 .185093086 .897454857 .378774885

D^ROA .831178367 .20804276 3.995228517 .000569247  

Table 3D: Cross-Sectional, Averages Based Regression—No Intercept

Regression output

R-squared .781739944

Modified R-squared .66631472

Standard error .326872303

Observations 27

  df SS MS F

F

hurdle

Regression 5 8.801812708 1.760362542

20.5947197

7 3.94

Residual 23 2.457446551 .106845502

Total 28 11.25925926      

  Coefficients SE t-stat p-value t hurdle

Intercept 0 n/a n/a n/a 2.508

D^ISL

-.01220696

9 .158969368 -.076788182 .939456127

D^UAE

-.04706717

9 .160491007 -.293269884 .77194571

D*D 0 0 65535

D^(c+r) .166112689 .185093086 .897454857 .378774885

D^ROA .831178367 .20804276 3.995228517 .000569247  

Table 3E: Cross-Sectional Reduced, Averages Based Regression—No Intercept

Regression output

R-squared .773815129

Modified R-squared .724767734

Standard error .319166045

Observations 27

Analysis of

variance

  df SS MS F

F

hurdle

Regression 2 8.712585153 4.356292576

42.7645273

2 5.57

Residual 25 2.546674107 .101866964

Total 27 11.25925926      

  Coefficients SE t-stat p-value t hurdle

Intercept 0 n/a n/a n/a 2.485

D*D -.017646649 .137657237 -.128192669 .899022167

D^ROA .897677338 .109935941 8.165458305 1.61456E-08  

Appendix B: Simulated ROE Distribution

Figure 1A: A Plot for ROE(c) (By Author 2015, from MS Excels)

5% 10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

0%5%

10%15%20%25%30%35%40%45%

ROE

ROE

Figure 1B: A Plot for ∂ ROE

∂ c (By Author 2015, from MS Excels)

c 5% 10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

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65%

70%

75%

80%

85%

90%

00.020.040.060.080.1

0.120.140.160.180.2

ROE'

ROE'

Figure 2A: A Plot for ROE (c, r)=ROE(c+r) (By Author 2015)

5%10%15%20%25%30%35%40%45%50%55%60%65%70%75%80%85%90%

-200%

-150%

-100%

-50%

0%

50%

100%

5%30% 55%80%

Figure 2B: A Plot for ∂ ROE

∂ r=∂ ROE

∂ c (By Author 2015)

5%10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

-15

-10

-5

0

5

10

15

5%20%35%50%65%80%

Appendix C: Graphic Output on Panel

Figure C1: Visualized Joint Distributions in the Data (By Author 2015, from SPSS PASW

17.0)

Figure C2: Zooming in on ROE Distribution (By Author 2015, from SPSS PASW 17.0)