Assessing probabilities offinancial distress of banks in UAE

Ehab ZakiCollege of Business Administration, University of Dubai, Dubai,

United Arab Emirates

Rahim BahGrenoble Ecole de Management, Grenoble, Paris, France, and

Ananth RaoCollege of Business Administration, University of Dubai, Dubai,

United Arab Emirates

Abstract

Purpose – Commercial and Islamic banks are important players in the UAE financial market.However, little is known about their financial distress because these financial institutions usuallyresolve financial distress within their own organisations, which means that outsiders cannot explicitlyobserve distress. The purpose of the research is therefore to identify the main drivers of financialinstitutions’ financial distress.

Design/methodology/approach – The paper estimates a probability distress prediction modelusing the BankScope Database and the annual reports of UAE financial institutions submitted to UAESecurity Exchange Authority. The paper also analyses the impact of macroeconomic information forforecasting financial institutions’ financial distress.

Findings – The fundamentals of financial institutions in terms of cost income ratio, equity to totalassets, total asset growth and ratio of loan loss reserve to gross loans (all these variables with a lag ofone year) positively impacted the probability of financial distress in the next year. Recent findings foremerging economies have cast some doubt on the usefulness of macroeconomic information forfinancial institutions’ risk assessment. Similar results are found for UAE financial institutions inpredicting the probability of financial distress.

Originality/value – This is the first study to provide empirical evidence on the drivers of financialdistress of commercial and Islamic banks in UAE during 2000-2008, and to examine the extent of thefinancial distress that can be can be attributed to internal bank-specific fundamental factors andexternal factors in the economy.

Keywords Financial institutions, Distress, Financial distress probability,Panel binary response analysis, Financial risk, United Arab Emirates

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1743-9132.htm

Ehab Zaki acknowledges Professor Rahim Bah and Professor Ananth Rao: the completion of thissecond research paper would not have been possible without the support of these supervisors.Ehab Zaki would also like to thank Professor Bah for his support and valuable advice on thetopic’s methodology, and is indebted to Professor Rao whose help, stimulating suggestions andencouragement were a constant help during the writing of this research paper. Ehab Zaki wouldalso like to acknowledge the clarifying series of reviewers’ comments.

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Received 23 November 2010Accepted 23 January 2011

International Journal of ManagerialFinanceVol. 7 No. 3, 2011pp. 304-320q Emerald Group Publishing Limited1743-9132DOI 10.1108/17439131111144487

1. IntroductionFinancial distress refers to a period when a borrower (either individual or institutional)is unable to meet a payment obligation to lenders and other creditors. This distressmay be due to borrower specific factors like reputation, leverage, volatility of earnings,collateral or may be due to market specific factors like the economic condition and levelof interest rates. So far, these factors were used by banks to explain the probability ofloan default of borrowers. More generally, it is recommended that “5 C’s”[1] of creditshould be included in such analysis and the decision makers may need to weight thesefactors in a more objective or quantitative manner (Saunders and Cornett, 2008, p. 315),rather than letting such factors enter into the decision process in a purely subjectivefashion. In this dissertation, we analyse the financial distress of banks by using the5 C’s framework, and quantify these 5 C’s so that meaningful and consistent decisionscan be made regarding the creditworthiness of banks in UAE.

The period 2006-2008 in UAE[2] is considered one of the major financial distressperiods due to the recent global economic crisis. During this period borrowers haddifficulty in meeting their obligations, as a result lenders suffered losses. Theimportance of this paper is derived from the fact that distress of financial institutionsin UAE has become the most serious issue facing the regulatory authorities andstakeholders because of its potentially destabilising effect on the financial systemthrough contagion and because a healthy financial system is a pivotal point forfinancial stability.

From a microeconomic perspective also, it is desirable to learn more about thedrivers of financial distress of banks in UAE. For example, the modern risk controltechniques that are permitted under the Revised Capital Framework of the BaselCommittee on Banking Supervision (Basel II) require that creditors be able to estimatetheir debtors’ probabilities of distress. For private customers and non-financial firms,there are many well-established methods for the estimation of probabilities of distressand a sizeable body of evidence about the main risk drivers. However, there is almostno empirical evidence for the extent of distress of financial institutions in UAE inparticular and Middle East in general.

The main contribution of this paper is therefore to present empirical evidence offinancial distress probabilities in UAE using the data set for 2000-2008 financialstatements and notes information from 12 commercial banks and four Islamic banks allincorporated in UAE[3]. We estimate the probability of financial distress for thesecommercial and Islamic banks in UAE using a distress prediction model that allowsthe identification of the main risk drivers. The main questions posed in this paper are:

. What were the drivers of financial distress of commercial and Islamic banks inUAE during 2000-2008?

. How much of this financial distress can be attributed to internal bank-specificfundamental factors and how much can be attributed to external factors(macroeconomic developments)?

These issues have growing importance, since deregulation and increased competitionseem to have tightened the link between the riskiness of financial institutions andmacroeconomic developments. In fact, the recent economic downturn, with numerousinsolvencies and the collapse of the stock market, have probably had a greater impact

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on their resilience than ever before in the post-war period (see, for example,International Monetary Fund, 2003, 2009, 2010a, b).

The paper is organised as follows. Section 2 briefly reviews the financial distressliterature and describes the conceptual framework of financial distress more precisely.Section 3 discusses the methods used for estimation and states the set of hypotheses.Section 4 discusses the results. Section 5 concludes the study findings with statementof limitations and direction of future research.

2. Literature review and conceptual frameworkAltman (1968) and Beaver (1968) were the first to use discriminant analysis to predict theprobability of default. They were followed by Sinkey (1975) and Martin (1977), who useddiscriminant analysis to predict the probability of the default of bank borrowers. Thedrawback of discriminant analysis is that the probability of default could fall outside the0-1 range (Saunders and Cornett, 2008, p. 317). As an alternative, logit and probitmethods, which use maximum-likelihood methods, have been used more frequently(Martin, 1977; Lennox, 1999). Logit and probit procedures are advantageous not only forstatistical reasons but also because they are non-linear and estimate the probability ofdefault (PDs) directly. More recently, Porath (2006) estimated the probabilities of defaultfor German savings banks and cooperative banks using logit, probit and clog logspecifications. Porath (2006) concluded that relevant factors for the estimation of a bank’sprobability of default comprised the general macroeconomic environment and the bank’sreturn, credit risk, market risk, and – most importantly for determining the default risk– the capitalisation. This study also concluded that savings banks and cooperativebanks are affected by the same risk drivers, but that savings banks are morerisk-sensitive, and that rating tools that rely solely on financial ratios may not be suitablefor capturing the risk level of a bank. At the same time, adding macroeconomicinformation to the model greatly improves the forecasting performance.

Porath (2006) used a hazard technique for the distress data, which were available onan annual basis. In our case since distress data are available only annually (after auditby regulatory or supervisory authorities to assess the quality of financial institutions’assets), we apply logit, probit and variants of these techniques to the annual data ofbanks, which has a panel structure (i.e. both time series and cross-sectional data). Forpanel data, hazard models are equivalent to panel binary response models, which arealso a regression method, but the variable Y is a binary random variable that takes ononly the value 0 (meaning that there was no financial distress in the financialinstitution during the year t) and 1 (meaning that there was financial distress in thefinancial institution in the year t). The econometric problem is to estimate theconditional probability Y ¼ 1, i.e. the banks that are in financial distress as a functionof the explanatory variables.

3. Conceptual frameworkThe following is the conceptual framework:

Yit ¼ FðS itÞwith Sit ¼ b0 þj

XbjX it21 þ

jþ1

Xgjþ1Z it21 þ 1it

ði ¼ 1; . . . ; m andJ þ 1 ¼ 1; . . . ; nÞ;

ð1Þ

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where Yit gives the probability of distress (PD) of financial institution i at time t, Sit isthe score that constitutes an order of the ith financial institution according to theirriskiness in time t, F is the link function that transforms the score into the PD, X it21

(m £ 1) is the vector of covariates (i.e. financial institution specific variables such as the5 C’s), Zit21 (n £ 1) is the vector of covariates (i.e.,macroeconomic factors that areconstant for all i ), b and g ((1 £m and 1 £ n) are vectors of coefficients, and 1it is anerror terms with various assumptions of distributions stated in equations (2)-(4).

The rationale for the one-year lag is that the information as to whether or not bank iexperiences financial distress in time t £ 1 becomes available only during the audit ofthe bank in time t. For financially distressed banks i ¼ 1; . . . ; n in time t, Yit is asequence of 1 for that particular year t, and is a sequence of 0 for banks notexperiencing financial distress in year t. When it includes such a restriction, the binarypanel model is also called a “time-discrete hazard model” (Porath, 2004, 2006). Someresearchers use logit (for its closed functional form), while some researchers use probitor its variant (for its open functional form). In what follows, we estimate the model withthe following specifications:Logit:

FðSitÞ ¼exp Sit

1 þ exp Sit

: ð2Þ

Probit:

FðSitÞ ¼ ð1=ffiffiffiffiffiffi2p

pÞ

Z Sit

21

e 2 0:5x 2 dx: ð3Þ

Complementary log-logistic (clog log) link function:

Sit ¼ ln½2lnð1 2 litÞ�: ð4Þ

Equation (2) is the logit specification and is computationally simple. Equation (3) is theprobit specification and has the popularity of the normal distribution. Both equation (2)and equation (3) are symmetric and give similar values, although the tails of thelogistic distribution are fatter than that of the normal distribution. Equation (4) is thediscrete-time version of the proportional Cox model and is asymmetric (for moretechnical details, see Kalbfleisch and Prentice, 1980). All three specifications containsimilar information on the marginal effects of independent variables with probabilityof financial distress. The criteria for choosing the best model are:

. lowest log-likelihood ratio;

. lowest Akaike information criterion (AIC);

. lowest Bayesian information criterion (BIC); and

. lowest Hannan Quinn information criterion (HQIC) (for more technical details ofthese criteria, see SAS Institute, 2008).

4. Methodology and dataEconomic theory suggests a rough guideline for specifying a financial distress model.The usual procedure is to define categories of variables that are supposed to impact onthe future financial distress. Examples are the categories of 5 C’s, capital adequacy,

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asset quality, management quality, earnings, liquidity, and sensitivity to market risk,called (CAMELS), and Moody’s RiskCalc model, with the categories capital, assetquality, concentration, liquidity, profitability, and growth (see Kocagil et al., 2002). Inthis paper we use the categories of 5 C’s, i.e. capacity, capital, collateral and condition(both internal and external to financial institution). Table I presents the categories withthe set of variables considered.

Thus all 5 C’s except the character (good management) of the financial institutions arecaptured in the above set of variables. We argue that if the financial institution managesall the four C’s stated above well, then implicitly the fifth C is also captured, since goodmanagement implies good control of the financial institution. This rationale also reflectsthe active management of financial institutions who are keen that their capacity, capital,collateral and condition are quite safe. Specifically, variables under 1 to 3 in Table Ipertain to the first three C’s (i.e. capacity, capital and collateral), specific to financialinstitutions. Variables under 4A relate to the fourth C, i.e. condition (vulnerability).Variables under 4B relate to macroeconomic variables to explain the impact of real GDPgrowth and the oil price on the financial institution’s financial distress (if any).

Following the practice of rating agencies (e.g. Falkenstein et al., 2000; Kocagil et al.,2002), we calculate many variables for the empirical analysis. We choose the mostadequate variables based on the 5 C’s rating system used by regulators, mainly for on-siteexamination or audit. Like Kocagil et al. (2002), we have accommodated growth throughtotal asset (TA) growth (total assets growth is change in assets in between current andpast year divided by total asset of past year, i.e. [ðTAt 2 TAt21Þ=TAt21]. Additionally,concentration and asset quality are subsumed into credit risk and there is a separatecategory for market risk (which covers equity price risk through PE and MB). Thesemarket risk variables are readily available publicly for all financial institutions over thestudy period and they are well known in the academic and professional community.

4.1 Hypotheses4.1.1 Net cash flow (NCF). Net cash flow depicts the general health of a financialinstitution. This is computed as sum of the net cash flow from operations, the net cash

5 C’s Examples Variable code

1. Capacity1A. Cash flow (CF) Net cash flow (CF from operations þ CF from

financing þ CF from investing) (NCF)NCF

1B. Profitability Cost income ratio CIR1C. Liquidity Current assets/current liability CR

2. Capital (wealth) Equity capital to total assets ETA3. Collateral (security) Total asset growth TAG

4A. Condition (financial institution – vulnerability)4A1. Credit risk Non-performing loans to total loans LLRGL4A2. Market risk Price to earnings (PE) ratio PE

Market value to book value (MB) ratio MB

4B. Condition (economy)4B1. Business cycle indicators Real GDP rate (per cent) RGD4B.2 Macroeconomic prices Oil price ($/barrel) OIL

Table I.Variables characterisingthe financial distress ofUAE financialinstitutions

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flow from investments and financing. In a way NCF (without explicit opening and closingcash balances) represents the free cash available to the financial institution afteraccounting for uses (i.e. capital expenditure and financing activities). This NCF is criticalbecause the new owners typically want to use free cash flow as funds available for retiringoutstanding debts. NCF also represents total cash available to the financial institution fordistribution to owners and creditors after funding all worthwhile investment activities. Apositive NCF over time suggests the financial institution is healthy and can substitutedebt component in its financial structure through its NCF, and thus reduce leverage. Tothat extent the financial institution will not experience financial distress. Thus, wehypothesise a negative relationship between NCF and financial distress.

4.1.2 Cost income ratio (CIR). CIR is computed as a ratio of costs to total revenue.Costs include overheads (i.e. general þ administrative expenses). Total revenueincludes net interest revenue plus other operating income. CIR is an indirect measure ofprofitability of financial institution. Cost includes interest costs, overhead, general andadministrative costs. A declining CIR over time signifies the prudent management ofthe financial institution through cost minimisation, ensuring efficient operations. Tothat extent, profitability improves and financial distress declines. Thus, wehypothesise a positive relationship between CIR and financial distress.

4.1.3 Liquidity (current ratio; CR). Current ratio is measured as a ratio of currentassets to current liabilities. It measures the liquidity status of the financial institution.With a higher current ratio over time, the financial institution will be able to meet itscurrent obligations and experience less financial distress. Hence, a negativerelationship is hypothesised between CR and financial distress.

4.1.4 Capital (wealth; ETA). We expect that a rising equity ratio will, ceteris paribus,lead to a lower PD on the average of all financial institutions, because higher equityrepresents lower debt in the capital structure of the firm, with the resultant lowerfinancial risk. However, there are some exceptions, the most important being variablesthat are affected by volatility, which indicate increased riskiness. The growth of theequity ratio, for example, typically manifests a negative monotone relationship to PDfor low and moderate ETA values. However, due to volatility, very high ETA valuesmay be associated with growing PDs. Hence, we hypothesise that the relationshipbetween ETA and financial distress is mixed (i.e. both þ and 2 ) depending on themagnitude of volatility of ETA.

4.1.5 Collateral (security represented by total asset growth; TAG). TAG is a growthmeasure reflecting the notion that a growing firm over time acquires more assets.Assets of UAE banks comprise more personal and real estate loans relative toinvestments. Thus, growth in these loans results in growth in default risks. Thusbanks with higher TAG are more disposed to financial stress. Rapid growth can putconsiderable strain on a bank’s resources, and unless management is aware of thiseffect and takes active steps to control it, rapid growth can lead to financial distressand eventually bankruptcy. On the other side of the spectrum, banks that fail toappreciate the financial implications of slow growth, they will become potentialcandidates for takeover by more perceptive raiders. Thus banks should be able tosustain growth (g ¼ b*ROE, where g is the sustainable growth rate, b is the retentionrate or 1 2 dividend payout, and ROE is a return on equity; Higgins, 2001).

Hence, we hypothesise a positive relationship between TAG and the probability offinancial distress. For example, Emirates NBD, a major UAE bank, experienced a loan

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impairment of 10 per cent during 2008-2009; its non-performing loan ratio is expectedto rise 4.5 per cent despite an increase in asset base over the years. Similarly, anothermajor bank (NBAD) saw its loan provision jumped to 12 per cent. This trend is alsoquite common in other UAE banks.

4.1.6 Condition (vulnerability/credit risk, non-performing loans to gross loans;LLRGL). Non-performing loans are those loan amounts that are overdue (includinginterest). Such debts are considered by financial institution as bad, and a loan lossreserve is created to charge off such bad debts. Higher loan loss reserves relative tototal loans signify that the loan quality of the financial institution is weak, and hencemore provisions are made for charging off bad debts. The higher the provisions thatare made in the books of financial institutions, the higher the probability of financialdistress, as higher provisions reflect declines in the quality of assets. Hence wehypothesise a positive relation between the higher loan loss reserve ratio and theprobability of financial distress.

4.1.7 Market risk (price to earnings ratio; PE). A financial institution’s PE ratiodepends principally on two things:

(1) its future earnings prospects; and

(2) the risk associated with those earnings.

In a constant dividend growth model PE ratio is determined by Pi=E1 ¼½ðD1=E1Þ=ðk2 gÞ� where D1 is the expected dividend payment to stockholders in thenext year, E1 is the expected earnings in the next year, k is the required rate of return tostock holders and g is the growth rate of the financial institution. The spread betweenk2 g is the main determinant of the size of the PE ratio. This spread reflects theriskiness of the financial institution: if the risk is high, k is higher relative to g, thek2 g spread is high and the PE ratio falls. Similarly, the numerator (D1/E1) reflects thedividend payout ratio. The PE ratio rises with improved earnings prospects and higherdividend payout ratio, and it falls with decreased earnings prospects and increasedearnings, risks signalling financial distress. Thus, we hypothesise a negative relationbetween PE and the probability of financial distress.

The market risk (market-to-book value; MB) ratio (also known as the PB ratio)depends on expected levels of future profitability, while the PE ratio depends onexpected changes in future profitability (Fairfield, 1994). PB is less variable than PEs.The market value (MV) of the equity of a financial institution is subject to fluctuationsin the required market interest rates. The degree to which the book value (BV) of afinancial institution’s capital deviates from its true economic MV depends on a numberof factors, especially:

. interest rate volatility (the higher the interest rate volatility, the higher thediscrepancy); and

. examination and enforcement from financial institution regulators (the morefrequent the on-site and off-site audit examinations and the stiffer theregulator/examiner standards regarding loan charge-offs of problem loans, thesmaller the discrepancy).

Rising interest rates reduce the MV of banks’ long-term fixed income securities andloans while floating-rate instruments, if instantaneously re-priced, find their MVlargely unaffected. On the other hand, BVs of assets are acquired at historical costs and

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are not affected by interest rates. In the BV accounting world, when all assets andliabilities reflect their original cost of purchase, the rise in interest rate has no affect onthe value of assets, liability or the BV of equity. In other words, BV of equity ¼ parvalue þ retained earnings þ loan loss reserves.

With regard to the second factor, the ratio MV/BV (or simply MB ratio, also knownas the PB ratio, i.e. the price-to-book ratio) becomes important for a financial institutionto answer the concerns of investors and stock owners of the financial institution andthe soundness of the financial institution as perceived by the regulators. The MB ratioshows the discrepancy between the MV of a bank’s equity capital as perceived byinvestors in the capital market and the BV of capital on its balance sheet. The lowerthis ratio, the more the BV of capital overstates the true equity of banks as perceivedby investors in the capital market.

The MB ratio is also a function of future profitability relative to book value andgrowth in book value, while the PE ratio is a function of future profitability relative tothe current level of earnings. Financial institutions with a high MB and high PE ratioare the highest performing (high-growth) companies. Thus, if financial institutions arefacing serious difficulties as their existing investments are not expected to earn areturn in excess of the cost of capital, then profitability is expected to decline fromcurrent levels. In such a scenario, we hypothesise a negative relation between MB andthe probability of financial distress.

On the other hand, financial institutions with a high MB and low PE are expected toreport positive residual profits but falling earnings. These financial institutions are stillhaving positive NPV investments, but are in a state of decline. These are financialinstitutions that are not improving their operations, and hence are candidates forfinancial distress. In such scenarios, we hypothesise a positive relation between MBand the probability of financial distress.

4.1.8 Condition (economy; business cycle indicators – i.e real GDP rate – andmacroeconomic prices – i.e. oil price, $/barrel). It is intuitive that if the economy isgrowing, real GDP growth rate increases, and financial distress declines due to positivebusiness sentiments. Similarly, if the oil price increases, liquidity increases for oilexporting countries and positively impacts business sentiments. In the UAE, the oilprice is a key macro variable that impacts the sentiments since UAE is one of the OPECcountries. Thus, we hypothesise a negative relationship between these increasingtrends of macro variables and probability of financial distress. The last column inTable II summarises the hypothesised relationship between the set of variables and theprobability of financial distress. As discussed in the Methodology section, to beconsistent with expectation framework, all independent variables are lagged1 2 period. These sets of variables have been used in earlier studies (e.g. Porath, 2006).

4.2 Preliminary descriptive statistics on status of financial distress in UAE banksDue to a lack of long time series, rating systems (e.g. Falkenstein et al., 2000; Kocagilet al., 2002; Lane et al., 1986), often incorporate annual changes of ratios. Following thispractice of rating agencies, we compute percentage changes in annual equity[4], annualreturn on average equity (ROAE)[5] and annual net interest margin (NIM)[6] for eachyear from 2000 to 2008 for UAE banks. We take the median (instead of mean) values ofthese variables for the UAE banks (Table III) for each year since the data was skewed,as can be seen from Figures 1-3. UAE banks experienced largest decline in their NIM

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Table II.Variables characterisingthe financial distress ofUAE financialinstitutions

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and ROAE in the year 2006 compared to 2007 and 2008, which were the years whenUAE Central Bank and the UAE government intervened in the market by supportingthe distressed banks, which experienced declining profitability and declining equity.

Column 2 in Table III and Figure 1 show that the median equity change of UAEbanks was 9.54 per cent in 2000 and 171.14 per cent in 2005 due to an increase in oilprices during the period. As oil prices started to decline, the equity change alsodeclined to a peak of 56.569 per cent in 2006. An excessive negative change signifiesfinancial distress. The median equity change during 2000-2008 was 65.66 per cent.

Column 3 of Table III and Figure 2 show that the median ROAE change in UAE waserratic during 2000-2008 with a median ROAE change of 103.48 per cent. Themaximum decline was 39.86 per cent in 2006.

Similarly, column 4 of Table III and Figure 3 show that the median NIM change ofUAE banks was erratic during 2000-2008, ranging from 224.838 per cent in 2006 to22.544 per cent in 2005. The median equity change during 2000-2008 was 1.11 per cent.

Therefore, we categorised those UAE banks as experiencing financial distress(Y ¼ 1) in a year if their annual equity change was less than or equal to 65.66 per cent,the change in NIM was less than or equal to 1.11 per cent and the change in ROAE wasless than or equal to 130.48 per cent. We develop these categorisation criteria offinancial distress from a supervisory and audit perspective. The purpose of prudentialsupervision is to prevent financial distress and insolvencies, so the definition covers all

YearMedian equity change

(per cent)Median ROAE change

(per cent)Median NIM change

(per cent)

2000 9.54 5.5689 3.44532001 14.304 20.0828 24.8962002 12.137 29.189 24.8962003 64.392 225.876 22.6552004 65.998 210.16 21.6062005 171.14 87.046 22.5442006 56.569 239.86 224.8382007 161.59 972.8 14.8992008 35.252 226.2 215.254Median 2000-2008 65.66 130.48 1.11

Table III.Status of the financial

distress of UAE banks asmeasured by annual

changes in ROAE, NIMand equity during

2000-2008

Figure 1.Median equity change

(per cent)

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events indicating that the banks is in danger of ceasing to exist as a going-concernwithout outside intervention. Based on these categorisation criteria, Table IV showsthe financial institutions in UAE that were identified as those experiencing financialdistress during 2001 to 2008.

The data set for the explanatory variables combines financial information aboutindividual financial institution with market information and macroeconomic data forUAE[7]. To add the financial distress information to the data set, a dummy variable Yit

is created that takes a value of 1 if distress is experienced by the financial institutionduring the year t based on the above definition, and 0 otherwise. This categorisation isconsistent with binary response model stated above.

Figure 3.Median NIM change (percent)

Year Commercial financial institution Islamic financial institution Total financial institution

2001 7 4 112002 7 3 102003 7 3 102004 8 1 92005 5 – 52006 8 3 112007 3 1 42008 5 1 6

Table IV.Number of financialinstitutions in financialdistress in UAE

Figure 2.Median ROAE change(per cent)

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The total number of financial institutions experiencing financial distress is too small todevelop two separate models, i.e. one for commercial banks and the second for Islamicbanks. It is to be noted that commercial financial institutions and Islamic financialinstitutions are similar in their regional focus and organisation, but differ only from theperspective of the Islamic values of doing business. Recently, many commercialfinancial institutions have also resorted to Islamic principles through offering Islamicfinancial products in their portfolio.

4.3 Results and discussionTables V and VI present the marginal effects of each of the independent variable in themodel specifications on the probability of financial distress. The results are organizedinto two panels (A and B) for ease of discussion of the models without and withmacroeconomic variables. The probit specification has the lowest log-likelihood ratio,the lowest Akaike information criterion (AIC), the lowest Bayesian information

ModelsLogit Probit Clog log

Panel A: Fixed effectsNCFLAG 0.0004 0.0004 0.0004CIRLAG 0.0233 * * * 0.0231 * * * 0.0245CRXLAG 20.2797 20.2746 20.252ETALAG 0.0515 * * * 0.0505 * * * 0.0514TAGLAG 0.0108 * * * 0.0104 * * * 0.01LLRGLLAG 0.0749 * * * 0.0718 * * * 0.0728PELAG 20.0061 * 20.006 * 20.0063MBLAG 0.0722 * * * 0.0712 * * 0.0764RGDPGLAG – – –OILLAG – – –LOGLIKELHOOD 268.87 268.62 268.71AIC 1.4511 1.4472 1.4486BIC (Fixed) 1.9859 1.9819 1.9834HQIC 1.6684 1.6644 1.6659

Panel B: Fixed effects (with macro effect)NCFLAG 0.0004 0.0004 0.0004CIRLAG 0.0194 * 0.0194 * * 0.0218CRXLAG 20.2408 20.2349 20.2016ETALAG 0.0449 * * * 0.0441 * * * 0.0446TAGLAG 0.0134 * * * 0.0129 * * * 0.0128LLRGLLAG 0.057 * * 0.0555 * * 0.0546PELAG 20.0047 20.0047 20.0054MBLAG 0.0688 * * 0.0681 0.0734RGDPGLAG 20.0175 20.0165 20.0149OILLAG 20.0048 20.0047 20.0049LOGLIKELHOOD 268.00 267.74 267.75AIC 1.4688 1.4647 1.4648BIC (Fixed) 2.0481 2.044 2.044HQIC 1.7042 1.7000 1.700

Notes: *Significant at 6-10 per cent; * *significant at 5 per cent; * * *significant at 1 per cent

Table V.Results of fixed effects of

binary choice models

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criteron (BIC), and the lowest Hannan Quinn information criterion (HQIC), which are alldesirable in estimating the probability of financial distress. Hence we restrict ourdiscussion to the probit model specification.

4.3.1 Discussion of fixed effect model results. The results in panel A of Table Vindicate that of the 5 C’s (discussed in Table III), the first C, i.e. capacity, as measuredby the cost to income ratio variable (CIR), was positive and statistically significant.

ModelsLogit Probit Clog log

Panel A: Random effectNCFLAG 0.0001 0.00003 0.00005CIRLAG 20.0003 20.0003 20.00033CRXLAG 20.2829 20.06149 20.10705ETALAG 0.01 0.00221 0.00348TAGLAG 0.0022 0.00046 0.00057LLRGLLAG 0.0244 0.00538 0.00723PELAG 0.001 0.00025 0.00045MBLAG 0.0057 0.00117 0.00146RGDPGLAG – – –OILLAG 279.21 2283.06 2100.06LOGLIKELHOOD – 276.89 288.66AIC 1.3783 4.548 1.6885BIC 1.5788 0.3412 0.3412SIC – 4.7263 1.8667HQIC 1.4598 0.222 0.222Hosmer-Lemeshow x 2 (NS) 12.54 7.34 –Correct classification percentage: distress 65.15 65.15 59.09Correct classification percentage: non-distress 51.61 51.61 51.61

Panel B: Random effects (with macro effect)NCFLAG 0.0004 0.00006 0.0001CIRLAG 20.0002 20.0001 20.00007CRXLAG 20.0878 20.04922 20.0709ETALAG 0.0091 0.00518 0.0062TAGLAG 0.0039 0.00204 0.002LLRGLLAG 0.0183 0.00962 0.0088PELAG 0.0014 0.00081 0.0011MBLAG 0.0129 0.00721 0.0078RGDPGLAG 20.0193 20.01041 20.0124OILLAG 20.0031 20.00171 20.0022LOGLIKELHOOD 276.89 288.66 288.66AIC 1.3734 4.54 1.6848BIC 1.6185 0.41697 0.4169SIC – 4.77 1.9076HQIC 1.4729 0.2715 0.2715Hosmer-Lemeshow x 2 (NS) 10.45 9.53 –Correct classification percentage: distress 0.697 0.6969 68.18Correct classification percentage: non-distress 0.5645 0.5484 59.68

Notes: *Significant at 6-10 per cent; * *significant at 5 per cent; * * *significant at 1 per cent

Table VI.Results of random effectsof binary choice models

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Consistent with our hypothesis, a 1 per cent increase in CIR of financial institutionsin time t is expected to increase the probability of financial distress by 0.02 per cent intime t þ 1.

As regards the second C, capital, as measured by equity to total assets (ETA), ahigher ETA of financial institutions in time t should lead to a reduction in theprobability of financial distress in time t þ 1. However, the results show a positive andsignificant relation between the ETA and the probability of financial distress. Whilethis may at first glance seem counter-intuitive, the results are plausible (as explained insection 3.1.4) because the volatility of the ETA increased from 5.4 per cent in 2003 to10.34 per cent in 2006, reflecting the uncertainty in capital adequacy of the banksduring the study period. This is one of the reasons for the promoted intervention of theUAE regulatory authority to infuse capital to support ailing banks.

As regards the third C, i.e. collateral, as measured by total asset growth (TAG),variable increased assets also lead to increased loan default, as explained earlier.Consistent with the hypothesis, a growing financial institution that is experienceshigher growth in total in time t requires more cash in time t þ 1 to sustain growth evenwhen it is profitable. Institutions can meet this need for some time by increasingleverage, but eventually they will reach their debt capacity, thus increasing theirleverage risk, and hence the probability of financial distress is expected to increase.The statistically significant result indicates that a 1 per cent growth in total assets ofthe financial institution in the last year resulted in a 0.0104 per cent increase in theprobability of financial distress of financial institution in the current year. Theimplication from this result is that, rapidly growing, marginally profitable financialinstitutions show cash deficits in their operations, probably leading to an increasedprobability of financial distress. While slowly expanding, profitable financialinstitutions imply cash surpluses and probably do not experience financial distress.

As regards the fourth C, i.e. condition, as measured by the credit risk variable, ashypothesised the marginal impact on the probability of financial distress was highestand statistically significant. A 1 per cent increase in the loan loss reserve to gross loanin the last year resulted in a 0.0708 per cent increase in the probability of financialdistress for the financial institution in the current year. This result implies that ahigher perceived credit risk in the previous year at a financial institution will result inan increased probability of financial distress in the current year.

In panel B of Table V, two macro economic variables – i.e. real GDP growth rateand oil prices ($/barrel) – are added to the set of variables discussed above to examinethe second research issue of whether macro economic variables impacted financialdistress of the financial institution. The test statistics AIC, BIC and HQIC increasedmarginally when macro economic variables were added to the model. While themarginal effects of real GDP growth rate and oil prices on the probability of financialdistress were negative as hypothesised, they were not statistically significant. Hencewe conclude that macro economic variables did not influence the probability offinancial distress of the financial institutions in UAE. It is basically the fundamentalsof the financial institution that triggered financial distress in UAE financialinstitutions, as explained by the five C variables. Because there are no open-marketoperations in UAE, unlike other well developed economies, the equity market is in itsnascent stages of growth, the bond market is yet to catch on, and competition is very

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intense since UAE is over-banked, and hence banks are struggling to maintainprofitability through the management of bank fundamentals.

All the other variables (in Table V) had the right signs on their marginal effectcoefficients but were statistically less significant.

4.4 Random effect model resultsThe results and test statistics in panel A and B in Table V reveal that a random effectsmodel[8] was not a good specification to analyse the research issue of the financialdistress of a financial institution.

With the selection criteria, viz. AIC, BIC, HQIC test statistics increased, the majorityof the variables had signs opposite to those hypothesised and none of the marginaleffects were statistically significant. Therefore, we conclude that the fixed effectsmodel described the probability of the financial distress of financial institutions inUAE during 2000-2008 well. These results are also consistent with other researchfindings such as Nuxoll (2003), who demonstrated that macroeconomic informationdoes not improve the forecasts of bank defaults.

5. ConclusionsThis paper presents an evaluation of the fundamentals underlying the analysis of thefinancial distress of UAE financial institutions during 2000-2008 as a sequel to theglobal financial crisis. The paper also attempts to identify the factors that drive theprobability of financial distress. The data set comprised 16 financial institutions inUAE (no foreign financial institution were considered to avoid any complexities), ofwhich 12 were commercial banks and four were Islamic banks. We used panel discretechoice models. The probit panel model was found to be the best model based onlog-likelihood ratio, AIC, BIC and HQIC criteria.

We conclude that the relevant factors for the estimation of a financial institution’sprobability of financial distress are the following:

. capacity, expressed as the cost to income ratio (CIR);

. capital, expressed as equity to total assets (ETA);

. collateral, expressed as total asset growth; and

. condition (internal), expressed as the credit risk (represented by thenon-performing loan to total loan ratio; LLRGL) all with one period lag.

We also conclude that macroeconomic information did not significantly impact theprobability of financial distress of financial institution in UAE. These findings areconsistent with similar studies done on German banks by Nuxoll (2003).

5.1 Limitations and future direction of researchThe analysis focused exclusively on the UAE, which is a relatively small economy inthe MENA region. Relevant data for other financial institutions in the MENA regionwere not available; their availability would have made the analysis moreregion-specific. This is the first limitation. Future research could evaluate thefinancial distress of banks by including data on the loan default experience of banks inaddition to the three criteria used in this paper for comparing the robustness ofprobability distress studied in this paper.

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Notes

1. These 5 C’s are: character (good citizen in case of individual borrowers and corporategovernance in case of institutional borrowers), capacity (cash flow), collateral (security incase of individual and quality of assets in case of institutions), capital (wealth in case ofindividuals and capital adequacy in case of institutions), and condition (economic, especiallydownside vulnerability).

2. This working paper exclusively focuses on an analysis of UAE financial institutions.Although initially we proposed a comparative analysis of UAE with European Unionfinancial institutions, we later found that the size of the UAE economy is quite a smallfraction of the size of the EU, which could be a potential future research area on its own.

3. No foreign banks are included in the study to avoid any spillover effect from foreignoperations.

4. Percentage change in annual equity ¼ ½ðequityt*equityt21Þ=equityt�*100.

5. Percentage change in return on average annual equity (ROAE) ¼ [(ROAEt 2 ROAEt21)/ROAEt]*100.

6. Percentage change in net interest margin ðNIMÞ ¼ ½ðNIMt 2 NIMt21Þ=NIMt�*100.

7. Due to the confidential nature of these banks, individual financial institution names aresuppressed through the use of codes.

8. Random effect models (REM) allow the possibility that parameter coefficients may varysystematically or randomly by introducing interaction terms between independentvariables. In this case, each of the coefficient terms will have a time subscript (bit), whichimplies that for each bank observation, all the coefficients may change. The purpose of thisalternative specification is to make the regression function flexible enough to accommodatethe true data generation process. For more technical details, see Judge et al. (1988, pp. 437-9).

References

Altman, E.I. (1968), “Financial ratios, discriminant analysis and the prediction of corporatebankruptcy”, The Journal of Finance, Vol. 23, pp. 589-609.

Beaver, W.H. (1968), “Alternative accounting measures as predictors of failure”, The AccountingReview, Vol. 43, pp. 113-22.

Fairfield, P.M. (1994), “P/E, PB and the present value of future dividends”, Financial AnalystsJournal, Vol. 50 No. 4, pp. 23-31.

Falkenstein, E., Boral, A. and Carty, L. (2000), “RiscCalc private model: Moody’s distress modelfor private firms”, Moody’s Investors Service, Boston, MA.

Higgins, R.C. (2001), Analysis for Financial Management, 6th ed., Irwin McGraw-Hill, New York,NY, pp. 114-21, chapter 4, pp. 114-21.

International Monetary Fund (2003), “Germany: financial system stability assessment”, availableat: www.imf.org

International Monetary Fund (2009), “Financial stability frameworks and the role of centralbanks: lessons from the crisis”, Working Papers WP/09/70, available at: www.imf.org

International Monetary Fund (2010a), “Are financial crises alike?”, Working Papers WP/10/14,available at: www.imf.org

International Monetary Fund (2010b), “The spillover effects of the global crisis on economicactivity in MENA emerging market countries – an analysis using financial stress index”,Working Papers WP/10/8, available at: www.imf.org

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Judge, G.G., Carter Hill, R., Griffiths, W.E., Lutekpohl, H. and Lee, T.-C. (1988), Introduction to theTheory and Practice of Econometrics, 2nd ed., Wiley, New York, NY.

Kalbfleisch, J.D. and Prentice, R.L. (1980), The Statistical Analysis of Failure Time Data, Wiley,New York, NY.

Kocagil, A.E., Reyngold, A., Stein, R.M. and Ibarra, E. (2002), “Moody’s RiskCalc model forprivately-held US banks”, Moody’s Investors Service, Boston, MA.

Lane, W.R., Looney, S.W. and Wansley, J.W. (1986), “An application of the Cox proportionalhazards model to bank failure”, Journal of Banking & Finance, Vol. 10, pp. 511-31.

Lennox, C. (1999), “Identifying failing companies: a reevaluation of the logit, probit and DAapproaches”, Journal of Economics and Business, Vol. 51, pp. 347-64.

Martin, D. (1977), “Early warning of bank failure”, Journal of Banking & Finance, Vol. 1,pp. 249-76.

Nuxoll, D.A. (2003), “The contribution of economic data to bank-failure models”, Working Paper,Federal Deposit Insurance Corporation, Washington, DC.

Porath, D. (2004), “Estimating probabilities of distress for German savings banks and creditcooperatives”, Deutsche Bundesbank Discussion Paper, Series 2, 06/2004, DeutscheBundesbank, Frankfurt am Main.

Porath, D. (2006), “Estimating probabilities of distress for German savings banks and creditcooperatives”, Schmalenbach Business Review, Vol. 58, pp. 214-33.

SAS Institute (2008), SAS/Stat 9.2 User’s Guide: The Glimmix Procedure, SAS Institute, Cary,NC, p. 2098.

Saunders, A. and Cornett, M.M. (2008), Financial Institutions Management – A Risk ManagementApproach, 6th ed., McGraw-Hill International, New York, NY, pp. 315-7.

Sinkey, J. (1975), “A multivariate statistical analysis of the characteristics of problem banks”,Journal of Finance, Vol. 30, pp. 21-36.

Further reading

Cole, R.A. and Gunther, J.W. (1995), “Separating the likelihood and timing of bank failure”,Journal of Banking & Finance, Vol. 19, pp. 1073-89.

Estrella, A., Park, S. and Peristiani, S. (2000), “Capital ratios as predictors of bank failure”,Federal Reserve Bank of New York: Economic Policy Review, July, pp. 33-52.

Hamerle, A., Liebig, T. and Scheule, H. (2004), “Forecasting credit portfolio risk”, DeutscheBundesbank Discussion Paper, Series 2, No. 01/2004, Deutsche Bundesbank, Frankfurt amMain.

Molina, C.A. (2002), “Predicting bank failures using a hazard model: the Venezuelan bankingcrisis”, Emerging Markets Review, Vol. 2, pp. 31-50.

Moody’s Investors Service (2003), “Banking system outlook”, available at: www.moodys.com

Pregibon, D. (1981), “Logistic regression diagnostics”, Annals of Statistics, Vol. 9, pp. 705-24.

Corresponding authorAnanth Rao can be contacted at: arao@ud.ac.ae

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