Assessing probabilities of financial distress of banks in UAE
Assessing probabilities of
financial distress of banks in
CAGB6101- Accounting for Business Decision Making
Semester one 11/24/2011
1-Introduction & Objectives
2-Methodology( specifying a financial distress model)
3- Preliminary Statistics
4-Marginal effects of Variables
5-Conclusion; Results & Discussion
6- Q & A
Financial distress :
Refers to a period when a borrower (either individual or institutional) is
unable to meet a payment obligation to lenders and other creditors.
The period 2000-2008 in UAE is chosen.
Using a distress prediction model for 12 commercial banks and 4 Islamic
banks in UAE
1-What were the drivers of financial distress of commercial and Islamic banks
in UAE during 2000-2008?
2-How much of this financial distress can be attributed to internal bank-
specific fundamental factors and how much can be attributed to external
factors (macroeconomic developments)?
specifying a financial distress model
define variables that are supposed to impact on the future
The paper used the categories of 5 C’s (except the Character)
5 C’s Probability of
Cash Flow(CF) -
Profitability (ratio of costs to total revenue) +
Liquidity(ratio of current assets to current liabilities) -
Capital () +/- (depends)
Collateral (security represented by total asset growth) +
Credit Risk (non-performing loans to gross loans) +
Market Risk (price to earnings ratio) -
Market Risk (market-to-book value) +/- (dep. PE)
Business Cycle indicators -
Macroeconomic prices -
Marginal effects of Variables cont.
Macro economic variables did not influence the
probability of financial distress of the financial
institutions in UAE
Conclusion; Results & Discussion
Previous tables present the marginal effects of each of the
independent variable in the model specifications on the probability of
The Probit model specification has the lowest log-likelihood ration
likewise (AIC, BIC, HQIC) models which are all desirable in estimating
the probability of financial distress.
Fixed Effect Model: Represents the observed quantities in terms of
explanatory variables that are treated as if the quantities were non-
random. This is in contrast to random effects models in which some
of the explanatory variables are treated as if they arise from the
Discussion of fixed effect
Capacity: measured by cost to Income Ratio Variable (CIR), was +ve &
statistically significant. 1% increase in time t is expected to increase
the probability of financial distress by 0.02% in time t+1.
Capital: measured by Equity to Total Assets (ETA), high ETA of a
financial institutions in time t should lead to reduction in probability of
financial distress in time t+1.
Collateral: measured Total Asset Growth(TAG),variable increased
assets lead to increase loan default.
Condition (internal): measured by credit risk variable, higher credit
risk in previous year result will increase probability of financial
distress in current year.
Limitation and Future Direction
UAE is relatively small economy in MENA region.
There were no available data that would made the analysis more
No data loan default experience of banks
Panel B of Table V two macro economic variables i.e. real GDP (Gross
domestic product refers to market value of all final goods & services
produced within a country in a given period) and oil prices didn’t
effect the probability of financial distress because there is no open-
market operation in UAE.
MENA: Is the greatest Middle East