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University of Nigeria Research Publications
TAKON ,Samuel Manyo
Aut
hor
PG/M.SC/03/37078
Title
Testing the Effectiveness of Camel Rating Model in Predicting Bank Distress in
Nigeria
Facu
lty
Business Administration
D
epar
tmen
t
Banking and Finance
Dat
e
November, 2006
Sign
atur
e
TESTING THE EFFECTIVENESS OF CAMEL RATING MODEL IN PREDICTING BANK DISTRESS IN NIGERIA
TAKON SAMUEL MANYO Reg. No. PGIM. SC103137078
DEPARTMENT OF BANKING AND FINANCE UNIVERSITY OF NIGERIA
ENUGU CAMPUS
NOVEMBER, 2006.
TITLE PAGE
TESTING THE EFFECTIVENESS OF CAMEL RATING MODEL IN PREDICTING BANK DISTRESS IN NIGERIA
TAKON SAMUEL MANY0 PGIM. SCl03137078
AN M.SC DISSERTATION SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTER OF
SCIENCE (M.SC) IN BANKING AND FINANCE
DEPARTMENT OF BANKING AND FINANCE UNIVERSITY OF NIGERIA
ENUGU CAMPUS
NOVEMBER, 2006.
CERTIFICATION
This is to certify that this M.Sc Dissertation by Takon, Samuel Manyo
(PGIM SCl03137078) presented to the Department of Banking and Finance,
University of Nigeria, Enugu Campus is original and has not been submitted for
award of any degree or diploma either in this or any other tertiary institution.
This is to certify that this M.Sc Dissertation by Takon, Samuel Manyo
(PGlM.SCl03l37078) presented to the Department of Banking and Finance,
University of Nigeria, Enugu Campus, meets the departments requirement and
has been submitted in partial fulfillment of the requirements for the award of M.Sc
Degree in Banking and Finance.
m4.* DR. B.E CHIKELEZE (Supervisor)
(+&- RS N J MODEBE Date 1
( ~ e d d L of Department)
i i i
DEDICATION
This work is dedicated to my family members, especially my wife Helen and
children Jamima and Enun Takon and all men of goodwill.
ACKNOLEDGEMEMTS
What can one do without first recourse to the MAKER. Life, knowledge, good
health, vision, blessings, protection and all come from Him. What He wills come to
pass. My deepest and profound appreciation goes to GOD ALMIGHTY for His
mercies and kindness towards me all the time.
My profound gratitude goes to my research supervisors, Dr. A.M.O. Anyafo
and Dr. B.E. Chikeleze for all the pains taken to read every bit of the work as well
as their frank criticisms
I am equally grateful to the entire members of the Department of Banking
and Finance, U.N.E.C. for their friendliness, encouragements, co-operation and
transparency in handling every case 1 presented before them. Special thanks to
my Head of Department, Banking and Finance, Mrs. N.J. Modebe, Prof. F.O.
Okafor, Prof. C.U Uche, Dr. (Mrs.) Ogamba and Mr. Egbenta of the Department of
Estate Management, U.N.E.C for their precious counsels.
Special thanks to my wife Helen, for her prayers, financial and material
support, and all inconveniencies she patiently endured throughout this
programme. I lack words to express my deep appreciation to one of my mates and
friend, Fidelis Atseye, for his wonderful contributions to the success of this
research work.
I remain immensely grateful to my brothers and in-laws, pastor
Roland Takon, William Takon, Mr. E.A. Ogbe and family Bassey Obaji for their
financial and moral support.
Others solidly behind me were Victor Egba of the NDIC, Mr. Ebuta, Senior
Manager UBA, Mr. Victor Agunwah, Mr. Sylvester Anyanor of the First Bank and
Mr. Sunday Nwite for their financial and material support. My sister Mbamba
Takon and my children, who were always rallying round me in order to encourage
me, are not left out. I equally thank Miss Precious lkpo of Helitze Consult and Miss
Modesty Udeaku of God Makes The Difference Biz Center for the nice typing work
done to this study.
Finally, I thank those who must have contributed in one way or the other to
the successful completion of this study. I say may the Almighty God reward them
abundantly.
TAKON,S.M.
DECEMBER, 2005
ABSTRACT
Distress in the Nigerian banking sector has a long history. Between 1950
and 1954 as a result of poor capitalization, incompetent management, poor
accounting records, weak deposits, amongst others, the banking industry
witnessed financial crises that led to the failure of 21 indigenous banks, The
enactment of 1952 and 1958 ordinances did not solve the problem.
After the deregulation of the banking industry in the early 1990's the
banking crises re-surfaced which led to the liquidation of 26 distressed banks by
the CBN in 1998. This created widespread financial panic arid loss of public
confidence.
Considering the fact that bank distress has a multiplier effect on the
economy, there is need for a robust predicting model that will serve as an early
warning signal as to reduce the occurrence of distress. This study attempted a
test of the effectiveness of the CAMEL model in predicting bank distress in
Nigeria.
Data for this study were collected from published annual reports and
accounts of 12 banks between
distressed and non-distresses
technique was utilized.
the period 200
groups of six
i - 2004. The banks were under the
banks each. A random sampling
, In order to achieve objectives of the study, two statistical tools were
employed in the analysis of data: 'Multiple Discriminant Analysis (MDA) and
Multiple Regression Analysis using the SPSS.
MDA Model was initially used by Altman in 1968 to predict corporate
bankruptcy. This theoretical framework has come to be known as Altman 2-score.
In the second analysis, the CAMEL was regressed against the Z-score in order to
determine their impact on the prediction of distress
vii
The results show that all of the observed CAMEL ratios exhibit a
deteriorating trend as distress approaches. The regressed estimates show that all
components of the CAMEL Rating have a significant relationship with the variable
(Z-score). Apart from earnings strength, all the other components of CAMEL rating
model negatively correlated with the predicting variable.
viii
TABLE OF CONTENTS
Title Page
Certification
Dedication
Acknowledgements -
Abstract - -
Table of Content - -
List of Tables- -
List of Appendixes -
CHAPTER ONE: INTRODUCTION
Background of the Problem
Statement of the Problem -
Research Objectives
Research Questions -
Research Hypotheses -
Scope of the Study - -
Significance of the Study -
Definition of Terms - -
References
CHAPTER TWO: REVIEW OF RELATED LITERATURE
2.1 Prediction of Bank Distress: Theoretical Framework -
iv
- vi
viii
xi
xii
Symptoms and Causes of Bank Distress- -
Symptoms of Bank Distress - - -
Causes of Bank Distress - - - -
Failure Resolution Measure Adopted In Nigeria -
Camel Assessment in Relation to Bank Distress
Capital Adequacy - - - - -
Measurement of Capital Adequacy - -
Fixed Minimum Capital Requirement - - Limitation of Lending Limits - - -
Weighted RiskIAsset Ratio- - - -
Implication of Bank Distress - - -
Studies and Models in Predicting Bank Distress
The Early Warning Models - - - -
Critical Variables Considered in Previous Studies
References
CHAPTER THREE: RESEARCH METHODOLOGY
Research Design - - - - - - - 3 9
Sources of Primary and Secondary Data- - - - 3 9
Methods of Data Collection - - - - - 3 9
$opulation and Sample Selection - - - - - 40
Theoretical Model and Data Analysis - - - - 40
Rationale for Using Multiple Discrminnant Analysis (MDA)
in this study - - - - - - - - 4 1
X
Model Specification - - - - - - - 42
Variable Definition - - - - - - - 43
Techniques of Data Analysis - - - - - 44
Statement of Null and Alternate Hypotheses - - - 44
Hypotheses Test Statistics - - - - - - 45
References
CHAPTER FOUR: DATA PRESENTATION AND ANALYSIS
Introduction - - - - - - - -
Presentation of Data - - - - - - -
Data Analysis and Presentation - - - - -
Computing "Critical Value" To Determine Distress and
Non-Distress Group - - - - - - -
Presentation of Regression Results - - - -
Discussion of Findings - - - - - - -
Test of Hypotheses - - - - - -
Summary of Findings - - - - - -
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 Summary - - - - - - - 63
5.2 Recommendations - - - - - - 64 a
5.3 Conclusion - - - - - - 64
5.4 Suggested Area for further Study - - - - - 65
BIBLOGRAPHY
LIST OF TABLES
Table 1: Classification of Banks Based On the Proposed
Rating System - - - - -
Table 2: Proposed Factor/Component Weights - -
Table 3: Official Weights Allocated to Camel Variables -
Table 4.1 .O: Variables Means - - - - - -
Table 4.1 .I : Value of Camel Variables - - - -
Table 4.2a: Average Ratio for the Distressed Group for the
Study Periods - - - - -
Table 4.2b: Average Ratio for the Non-Distressed Group for
the Study Periods . - - - - -
Table 4.3a - - - - - - - -
Table 4.3b - - - - - - - -
Table 4.4: The Regression Results - - - -
Table 4.5: Coefficients - - - - - -
Table 4.6: Descriptive Statistics for al Banks - - -
xii
~ppendix 1 - - Appendix 2 - - Appendix 3 - - Appendix 4 - -
LIST OF APPENDICES
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE PROBLEM
Modern Banking operations started in Nigeria in 1892 when the
British Colonial authorities established the African Banking Corporation
(ABC) for the purpose of distributing currency notes of the Bank of England
(NDIC, 1997).
The problem of distress in the banking sector in particular and the
financial system in general also has a long history. It dates back to the
establishment of the Central Bank of Nigeria in 1959.
During the colonial days, expatriate banks then served the interest of
the foreigners only. To redress this, Nigerian entrepreneurs went into
banking between 1920 and 1950 as to meet the financial needs of
Nigerians. See for example, Uche (1996) and (2000).
Due to the problem of inadequate capital and unfair competition from
foreign banks, 21 out of 25 banks established up to 1954 became
distressed and failed. However, the intensity, scope and dept of the current
distress condition since 1989, has been more serious in many ways
unprecedented. Within the late 1990s a large number of operators in the
country's banking system were in severe distress, a situation that has
caused regulatory authorities to put several measures in place as to arrest
the deteriorating trend.
Doguwa (1996) observed that the increased risks assumed by the
commercial and merchant banks, poor quality of loans and a'dvances.
mismanagement, fraud and local national economic trends, amongst other
things, were responsible for the increase in the number of problem banks in
, Nigeria. Between 1989 and 1997, the operating environment for
commercial and merchant banks became generally unstable due to civil
disturbances, industrial unrest, high inflation, negative real interest rates
and low public confidence, mainly as a result of distress in banks. In 1998,
the CBN liquidated 26 distressed banks made up of 13 commercial and 13
merchant banks after realizing that these banks were irredeemable and
terminally distressed. Before then, five other banks had been liquidated in
1994 and 1995. The distressed episode eroded public confidence in the
Nigerian Banking system, thereby encouraging a lot more people to
patronize the informal sector.
Despite the effort by the regulatory authorities to arrest this ugly
situation, the outcome has not been completely satisfactory as the problem
still persisted.
Bank distress therefore serves as a signal, hence an early warning
to the regulatory authorities and the general public.
According to Doguwa (1996), a number of factors make an effective
statistical early warning system desirable, especially in aiding regulatory
authorities respond efficiently to initial signs of distress. First, significant
changes in a bank management policies and financial condition can occur
between examinations. Second, an on-site examination is a lengthy and
expensive process and not always the most cost effective method of
tracking small, but important changes in bank's financial condition. Third,
although examiners generally are sensitive to the developing trends that
indicate potential future management or financial problems and normally
comment on such problems or matters in their reports, they must
necessarily emphasize their findings concerning the actual condition of the
bank rather than the estimated impact of potential problem. ~ourth, an
examiner's findings are part of the official record and could provide the
basis for the enforcement or other supervisory actions. In practice,
statistical early warning measure can be informal; affording the opportunity
for experiments with techniques to uncover financial weakness at its
earliest stages. Moreover, an efficient early warning system can be a useful
tool of analysis in the appraisal of a bank's financial condition.
1.2 STATEMENT OF THE PROBLEM
Conceptually, two major forms of problems could confront a bank
like many other profit-seeking organizations, the world over. These are
problems of illiquidity and insolvency. Both are sources of worry for owners
and management of banks as well as monetary authorities.
The increasing problem of bank distress in Nigeria especially in the
1990's has refocused attention on efforts to identify problem banks and to
predict distress/failures with sufficient lead time of regulators and
management as to institute remedial action to prevent them from going into
insolvency. Apart from the regulators and management, those who share
the concern in this issue include the depositors, bankers, shareholders and
the general public. The recent increase in the capitalization requirement for
commercial banks by the Central Bank of Nigeria is a pointer to the
aforesaid.
Accordingly, Nigerian Banks are required to have a minimum capital
of N25 billion (Twenty five billion naira) with effect from January 2006.
However, capital adequacy is not the only index for assessing the strength
or weakness of commercial banks. Other indices (variables) used in
identifying distressed banks are captured by the "CAMEL Assessment".
What constitutes a problem in this research report is the
effectiveness of CAMEL rating model in predicting bank distress in Nigeria.
RESEARCH OBJECTIVES.
In the light of the above problems identified in the preceding section,
the major objectives of the research was to determine the effectiveness of
CAMEL Rating Model in predicting bank distress in Nigeria.
Specifically, the research was designed to achieve the following
objectives:
To determine the impact of capital adequacy in predicting bank distress in
Nigeria.
To determine the impact of asset quality in predicting distress in the
Nigerian banking industry.
To determine the impact of Management competency in predicting bank
distress in Nigeria.
4. ,To determine Earnings Strength in predicting bank distress in Nigeria.
5. To determine the impact of Liquidity in predicting bank distress in Nigeria.
1.4 RESEARCH QUESTIONS
1. What is the impact of Capital Adequacy in predicting bank distress?
2. What is the impact of Asset quality in predicting bank distress?
3. What is the impact of management competency in predicting bank
distress?
4. What is the impact of Earnings strength in predicting bank distress?
5. What is the impact of liquidity strength in predicting bank distress.
RESEARCHHYPOTHESES
There is no significant relationship between Capital Adequacy and the
prediction of bank distress.
There is no significant relationship between Asset Quality and the
prediction of bank distress.
There is no significant relationship between Management Competency and
predicting of bank distress.
There is no significant relationship between Earnings Strength and
predicting of bank distress.
SCOPE OF THE STUDY
This study is restricted to commercial banks in Nigeria.
Currently there are 89 commercial banks in the Nigerian banking industry.
As a result of the researcher's inability to lay hands on their Annual reports
and Accounts, some banks that have already been identified with distress
symptoms are excluded. They include Fortune Bank Plc, Savannah Bank
Plc, etc. Therefore, 83 commercial banks wme targeted for the study.
The study period spanned 2001 - 2004. This period is significant
because it marks the beginning of a new decade. Moreso, previous studies
were carried out prior to this period.
SIGNIFICANCE OF THE STUDY
The significance of this study is borne out of the vantage position the b
banking sector occupies in an economy in terms of financial leverage,
capital formation, provision of an efficient payment system and facilitating
the implementation of monetary policies. Banks are catalysts in the
economic system and virtually all economic activities revolve round the
bank, therefore, its collapse would be so contagious to the extent that other
sectors of the economy may be paralyzed.
In the light of the above, any case of mass bank failureldistress
especially in a developing economy like ours could be seen as a macro-
economic issue which should be properly handled to sustain public
confidence. As at December, 1995, the market share of 60 distressed
banks in deposit mobilization was N70 billion, constituting 33.5 per cent of
total industry deposit of N211 billion, and loans and advances of N66.5
billion constituting 38 per cent of the industry's total loans and advances of
N175.9 billion. These facts and figures therefore lend confidence to the
significance of the study in order to avert a collapse of the financial system
and ensure that the banking industry remains safe and sound. Beside their
uses by supervisory authorities, problem banks identification models could
be of great value to the management of any bank. For instance,
identification models that provide quantitative estimates of weights of key
financial variables in the determination of the probability of distresslfailure
could be used by them to determine its probability of failure or distress.
Lastly, for fact that the issue of bank distress is no respecter of the
level of economic development, developed or developing, failureldistress
resolution options in the industry should be taken with all seriousness by
the regulatory authorities. Without doubt, this study should be able to make
some useful recommendations of how distress syndromes in the Nigerian
banking system could be resolved amicably.
1.8 DEFINITION OF TERMS
Some of the terms used in the study are explained hereunder so that
the study could be easily comprehended by any non-professional. .
1. DISTRESS
A bank is said to be distressed when it is unable to meet its , b
obligations to the customers, as well as the owners and the economy. Such
inability, often results from weakness in its financial, operational and
managerial conditions which would have rendered it either illiquid andlor
insolvent (CBNINDIC 1995).
2, INFORMAL FINANCIAL SECTOR
This describes entities and other financial transactions not directly
open to control by fundamental monetary and financial policy instruments
that are not regulated. It consists of borrowing and lending amongst
individuals and firms that are not registered with government as financial
intermediaries and are not subject to government supervision.
3. REGULATION
This means a body of specific rules of agreed behaviour, either
imposed by some government or other external agency or self-imposed by
explicit or implicit agreement within the industry that limits the activities and
business operation or financial institutions (NDIC, 1997).
4. REGULATORY AUTHORITIES
Within the context of this study, the Central Bank of Nigeria (CBN)
and the Nigerian Deposit Insurance Corporation (NDIC) constitute the
nation's regulatory and supervisory authorities for the nation's banking '
sector. They therefore act as the representative of the federal government.
5. INSOLVENCY AND ILLIQUIDITY
According to Uche (1996), Insolvency refers to a condition in which
the sum of assets of a firm is less than the sum of its liabilities (Jimoh,
1993).
6. BANK FAILURE
For the purpose of this study, bank failure is the inability of a bank to
meet its obligation to its customers, owners and to the economy
, occasioned by a fault of weakness in its operation, which had rendered it
either illiquid or insolvent.
7. PROBLEM BANK
For the purpose of this study, problem bank is used for the likely
distressed bank. They are therefore used interchangeably.
According to Sinkey (1975) a problem bank is one that, in the eyes of the
Federal banking agencies, has violated a law or regulation or engaged in
an "unsafe or unsound" banking practice to such an extent that the present
or future solvency of the bank is in question.
8. CAMEL
Camel is a measure applied to assess the quality of bank
management and interpreted as follows:
C = Capital Adequacy,
A = Asset quality,
M = Management Quality,
E = Earnings strength and
L = Liquidity.
It is a measure for judging .how well managed a bank is or the degree of the
bank's mismanagement (moral hazard behaviour) and establishes the level of
capital adequacy of a bank. (ONOH 2002)
REFERENCES
CBN INDIC (1 995), "A study of Distress in the Nigerian financial Services
Industry".
Doguwa, S.1 (1996), "Early Warning Models for the ldentification of Problem
Banks in Nigeria1', CBN Economic and financial Review, Vol. 34 (NO.l),
462- 487
Jimoh, A. (1993), "The Role of Early Warning Models in the ldentification of
Problem Banks; Evidence from Nigeria", Nigeria Financial Review, Vo1.6
(NO. 1),29-40
NDlC (1997), "Safe and Sound Banking Practices in Nigeria, Selected Essays; by
John U. Ebhodahe",
Onoh, J.K. (2002), "Dynamics of Money, Banking and Finance in Nigeria -An
Emerging Market" P 220.
Sinkey, J.F (1975), "A Multivariate Statistical Analysis of The Characteristics of
Problem banks", The journal of Finance NO.l, 21-36.
The Research Department, Nigerian Deposit Insurance Corporation (NDIC, 1997),
"A Study of Distress in the Nigeria Financial Services Industry" by Peter
Umoh (Ed.) F S R C L NDlC Publication.
Uche, C.U (1996) , "The Nigerian failed Bank Dec~ee; A Critique, "Journal of
Inten~ational Banking Law", Vol. 11 (issue 10). 436-441.
6
Uche , C.U (2000), "Banking regulation in an era of structural Adjustment. The
case
of Nigeria", Journal of Financial Regulation and compliance Vol. 8-(NO .2)
157-165.
CHAPTER TWO
REVIEW OF RELATED LITERATURE
PREDICTION OF BANK DISTRESS: THEORETICAL FRAMEWORK
Literally, the term "Distress" is used to describe a bank or a financial
institution that is facing financial difficulties. It is a state of "inability" or
"weakness" which prevents the achievement of set goals and operations.
Bentson (1986), associates distress with a cessation of independent
operations or continuance only by virtue of financial assistance from the
banking system's safety net such as the supervisory/regulatory agency or a
deposit insurer. Ologun (1994) describes a financial institution as unhealthy
if it exhibits several financial operational and managerial weaknesses.
Predicting corporate failure or bankruptcy may be credited to two
distinguished scholars for their pioneering works: Secrit (1938) and Altman
(1968), Secrit's theoretical model was designed to identify symptoms of
failure and non-failure of banks. In his Autopsy and Diagnosis, he examined
seven hundred and forty one (741) national banks that failed between 1920
and 1930 and one hundred and eleven (1 11) that did not fail prior to 1933 as
to "source indication of likely survival or of deathV.This
comparative analysis utilized discriminatory theoretical framework.
Altman (1 968) in his work on financial ratios and discriminant analysis
employed the Multiple Discriminant Analysis (MDA) framework to predict
corporate bankruptcy.
Another modeling approach that classified banks into problem and
non-problem group is the Logit Model. This has been the conceptual
framework adopted by Jimoh (1993), Ako (1999), Doguwa (1996), West
(1985) and Sinkey (1975).
From the foregoing, early warning models are anchored on the above
theoretical frameworks-Multiple Discriminant Analysis and Logit Models. See
also Nyong (1994) and Martin (1977).
SYMPTOMS AND CAUSES OF BANK DISTRESS
SYMPTOMS
The symptoms of bank distress are varied in nature but the most
common observable ones as indicated by Oguneleye (1993), include the
following:
late submission of returns to the regulatory authorities;
falsification of returns;
rapid staff turnover;
frequent top management changes;
inability to meet obligation as at when due;
use of political influence;
petition/anonymous letters;
persistent adverse clearing position;
borrowing at desperate rates;
persistent contravention of laid down rules;
weak deposit base;
inadequate capitalization; and
persistent overdrawn current account position at the CBN.
CAUSES OF BANK DISTRESS
The broad causes of bank distress particularly in Nigeria include, but
are not limited, to the following:
Policy and Regulatory Environment:
Prior to the adoption of comprehensive economic reform programme
under the Structural Adjustment Programme (SAP), the Nigerian banking
system could simply be described as highly regulated. Some of these
regulations had sometimes been counter-productive and had contribuled to
the strains in our banking system. Banks were subjected to some restrictions
thereby limiting their ability to adapt to changing market conditions. The
government had generally in the past made the control over banks an
important tool of the country's economic development strategies. Some of
the policies manifested in the form of direct control and the establishment of
interest rate ceiling as well as the restrictions on entry into the banking
industry. The adoption of such measures, though sometimes imperative,
exposed many weak banks and threatened them with illiquidity and
insolvency.
(b) Capital Inadequacy:
A function of capital in a bank is to serve as a means by which losses
can be absolved. It provides a cushion to withstand abnormal losses not
covered by current earnings, enabling banks to regain equilibrium and to re-
establish a normal earnings pattern. The problem of inadequate capital has
been further accentuated by the huge amount of non-performing loans which
has eroded some banks' capital base.
(c) Economic Downturn:
The adverse economic condition in Nigeria since mid 1981 had been
characterized by high inflation, depreciating value of the Naira, large fiscal
deficits, heavy external and internal debt, overhang and slow growth. Arising
from this stress in the economy, many borrowers were unable to service
their loans thereby making many banks to come to severe crises.
(d) ' Borrowing and Lending Culture:
This problem of economic downturn has been exacerbated by the
altitude of some borrowers who are unwilling to repay even when they are
known to have the means to service their debts. There are also some
"professional" borrowers who through connivance with some banks staff take
bank loans with no intention to repay them, These problems greatly impaired
the quality of the banks' assets as non-performing loans and advances
become unbearable and turn out to be a high burden on many of them. b
(e) Asymmetric information:
This is described as a situation whereby a borrower taking,out a loan
has superior information about the potential returns and the risk associated
with the investment project than the bank lending the money. This problem
of asymmetric information is often rampant in an unstable economy as loans
are likely to extend for risky projects and borrowers may have incentives to
misallocate funds for personal use or invest them in unprofitable projects.
Some loans are being diverted for personal use and for projects not meant
for such loans.
(f) Poor Corporate GovernmentIManagement:
It has become a worldwide dictum that the quality of corporate
governance or management makes an important difference between sound
and unsound banks. Just as it is in other parts of the world, so it is
established in Nigeria that mismanagement is the main culprit causing
banking crises. A very significant characteristic of mismanagement is in the
negative attitude and behavior of bank managers which is difficult to reverse
by the application of external policies and measures.
The common types of mismanagement which include technical
mismanagement, cosmetic mismanagement, desperate mismanagement
and fraud are often identified to be common in banks, and are prominent in
banking industry and they often undermine the health of our banks.
(g) Aftermath of Competition:
The deregulation of the economy has brought about, increased
competition and innovation in the market place. The increasing competitive
environment, allocative and operational efficiency require that inefficient and
marginal firms be crowded out and allowed to go under. In a way, it is a
paradox that while competition enhanced the menu of bank products
available to customers such competition has indirectly caused the insolvency
and failure of some banks in Nigeria through increased cost schedules.
(Adeyemi, 1993).
2.3 FALIURE RESOLUTION MEASURE ADOPTED IN NIGERIA
Depending on the severity and peculiarity of the situation, the
Regulatory Authorities in Nigeria have over the years, successfully adopted
the following measures among others to address bank distress.
(i) Accommodation facilities were granted to ten (10) banks which had liquidity
crises to the tune of N2.3 billion in 1989 following withdrawal'of pirblic
sector funds from commercial and merchant banks and the transfer of
same to CBN during that year.
(ii) Take-over of management and control of twenty-four (24) distressed banks
by the Regulatory Authorities to safeguard their assets.
(iii) Acquisition, restructuring and sale of seven (7) distressed banks to new
investors.
(iv) Closure of 26 terminally distressed banks that failed to respond to various
regulatory / supervisory initiatives. While the liquidation of Savannah Bank
of Nigeria and Peak Merchant Bank were suspended due to court action, it
is noteworthy that all the banks were closed with minimal disruption to the
banking system.
(v) . The promulgation and implementation of the failed Banks (Recovery of
Debts) and financial Malpractices Decree No.18 of the 1994 was to ensure
speedy dispensation of justice. The main thrust of the Decree was to assist
the recovery of debts owed to failed banks and to punish individuals
involved in the monumental incidence of financial malpractices in the
distressed banks. The initiative was indeed a complimentary measure.
The highly acclaimed implementation of the Failed Bank' Decree
which was facilitated by the Regulatory Authorities was indeed a major
plank in the resolution to contain distress and promote the soundness of
the Nigerian banking system. Following the implementation of the Decree a
reasonable amount of recoveries had been made.
As part of failure resolution measures the Nigeria Deposit Insurance
(NDIC) continues to serve as the liquidator to 34 closed banks. The
-corporation's activities in this regard include the following:
(a) Payment of Liquidation Dividend to Depositors:
In addition to the payment of insured depositors of the closed banks,
depositors with credit balances in excess of the insured limit were paid
liquidation dividends based on the volume of proceeds of the closed banks
assets realized by the corporation.
Payment of Liquidation Dividend to General Creditors:
Liquidation dividend has also been paid to some general creditors of
some of the banks.
The combined effect of these measures was a significant'reduction
in the level of distress in the banking system as well as enhancement of the
public confidence in the system.
CAMEL ASSESSMENT IN RELATION TO BANK DISTRESS.
The majority of prior research for prediction of bank distress focused
on capturing information representative of capital adequacy (c) Asset
quality (A),management quality (M), Earnings (E), and Liquidity (L) which
are designated as CAMEL rating or Model. The choice of these factors
occurs based on the theory that each is representative of a majorcelement
in a bank's financial statement. Earlier studies such as Sinkey (1975),
Altman (1977), Martin (197'7), Hanweck (1984) and Barth et al (1985)
analysed the financial characteristics of banks and of saving and
associations.
Accordingly, these studies adopt more or less the same variables,
based on the five categories of CAMEL. A weakness in any of these
variables may present a threat to the bank's continuing existence. One of
these threats represented in CAMEL exists in covering loan repayment
defaults and off-set the threat of losses or large withdrawals that might
occur. A measure of capital adequacy (C) represents past income
with its cushion to absorb future losses and that of Earnings (E) describes
present income. Both can assist in covering threats of losses. The
management (M) factor opens or closes t k door to risk, as management
takes action with assets and makes decisions related to capital and
earnings.
CAMEL was originally developed by the FDIC for the purpose of
determining when to schedule an on-site examination of a bank (Thomson,
1991). This related to the likelihood that bank distress may result when any
of the five factors is inadequate. Although researchers have a common
adherence to the broad guide-lines available in the CAMEL criteria,
previous studies for prediction of bank distresslfailure contained no
consistent set of CAMEL measures.
In Nigeria, an institution that has not met the minimum capital
adequacy and liquidity ratios may have manifested some symptoms of
distress without providing a good measure of the extent of distress.
In order to derive an efficient measure of distress through the
establishment of thresholds, attempts have been made at developing a
composite measure based on these CAMEL parameters for supervisors to
determine on a uniform platform, the extent of distress in each of the banks
as and by extension in the financial system as a whole. Such measure can
then be calibrated into a rating category like sound, weak, distressed,
terminally distressed, etc (NDIC, 1995). The usefulness of the CAMEL
criteria for prediction of distress exists in establishment of a foundation for
understanding a bank's health. As the FDIC does not divulge the details of
the representative measures for CAMEL, the ensuring complication for
researchers arises in determining which measures best correspond to the
five CAMEL factors. Typically, in the past, researchers collected a variety of
measures relating to each of the factors with variable selection often
occurring as a function of a statistical method, such as step wise regression
or factor analysis.
Lane et al (1986), included 21 ratios representative of factors in
CAMEL, Martin's (1977) and Tam and Kiang (1992), employed 19 variables
covering 4 CAMEL categories, Looney et al (1989) and Lane et al (1986)
used a step-wise approach to narrow their set of variables from 21 to 4.
Majority of the studies examined only four of the CAMEL factors and
excluded representative measure for management, as it proves to be the
most difficult to capture.
A composite measure could equally be used. This simply is a
weighted average of the CAMEL parameters summed up to unity (100%).
The monetary authorities in Nigeria recently embarked on the
development of a rating system for banks. This was initiated jointly by the
CBN and NDIC and the drafting rating system provides for classifying
banks as shown below, given their composite scores.
TABLE l
CLASSIFICATION OF BANKS BASED ON THE PROPOSED RATING SYSTEM
CLASS COMPOSITE SCOPE (%)
86 - 100
71 - 85
56 - 70
41 - 55
0 - 4 0
Source: NDiC Quarterly Voi. 7 SeptIDec. 1997
RATING -1 Very sound 1 Sound
Satisfactory
Marginal
Unsound
Details of weights and financial ratios used for assessing each CAMEL
factors are also shown in Table 11.
TABLE ll
PROPOSED FACTORICOMPONENT WEIGHTS
-
FACTOR
CAPITAL
-- - - - - . - ASSET QUALITY
MANAGEMENT
LIQUIDITY
COMPONENT
a. Capital to Risk Assets Ratio.
b. Adjusted Capital Ratio
c. Capital Growth rate
a. Non-performing Risk Asset to total Risk Assets
b. Reserve for losses to non-performing Risk Assets
c. Non-performing Risk Assets to Capital and Reserve
a. CAELl85 b. Compliance with
Laws & Regulation a. Profit before Tax to
Total Assets. b. Total Expenses to
Total Income c. Net lnterest lncome
to total Earning Assets
d. lnterest Expenses to Total Earnings Assets
a. Liquidity Ratio b. Net loan and
Advances to Total Deposits.
TOTAL
Source,: NDIC QUARTERLY Vo1.6 (Nos 3 x 4)
SeptlDec. 1996.
2.5 CAPITAL ADEQUACY
COMPONENT WEIGHT (%)
15
The capital of a firm can be defined as the money that has to be
raised as to purchase real assets. Hence, it can be taken to be the net
worth (assets less liabilities) of a firm. What then constitutes an adequate
measure of capital in any organization? For incisive and thoughtful
explanations, we shall limit adequacy of capital concept to commercial
banks' fund. What constitutes a commercial banks' fund or capital is its own
fund consisting mainly of: share holders' funds, long term debt, deposit and
other short term liabilities.
The share holders' funds normally is a composition of paid-up
capital, share premium, statutory reserves, profits for distribution and other
reserves. For some regulatory reasons, the monetary authorities recognize
only paid-up capital and statutory reserves as constituents of banks fund in
the measurement of capital adequacy. Some questions that may be asked
at this point are: why do banks require adequate capital? Is it not possible
that a bank without adequate capital can yet function properly and
efficiently? In the context of bank capital and its adequacy, the above
questions are very important because they tend to address the critical
issues upon which adequate capital is recommended for banks.
In an attempt to answer the first question, we can say that banks
require adequate capital to serve as a fall-back and of course, shock
absorber in the event of losses resulting from fund placement by banks. It
is necessary to measure capital to determine whether a bank's capital is
adequate to cushion possible losses resulting from loan losses and
disappointing interest margins. Such funds, should therefore, not be
subjected to fixed interest or fixed redemptions as these impose their own
limitations on usage of the funds.
In answer to the second question as to whether it is not possible that
a bank with adequate capital can function promptly and efficiently. There is
no bank that can function either promptly or efficiently without adequate ,
capital. The capital fund is an amalgam of equity funds and long-term debt
without which no business organization can function, how much less a b
bank. That is the more reason which capital adequacy becomes one of the
five key elements (basket of elements) considered in the assessment of
whether a bank is in distress or otherwise.
These five elements usually considered are: capital adequacy, asset
liquidity, managetilerit efficiency, earning performance and liquidity
position. These elements, which serve as index for assessment, are called
uniform inter- agency bank rating system represented with the acronym "
CAMEL". It therefore follows that for a bank to perform efficiently and
considered healthy, the provision of adequate capital cover must be
ensured.
Apart from the above, ensuring capital adequacy is an act of fund
management, which is a pre- requisite for any organization's survival.
Capital management includes the capital of a bank. It should be noted that
sound capital management in banking requires the maintenance of
adequate base of capital funds supplemented by long term debt.
However, it is the policy of banks generally to maintain sound capital
growth by balancing earnings allocation between dividend pay out and
profit retention in order to enhance future assets and earnings and by
issuing this point, we have been able to state the need to maintain
adequate capital in an organization especially bank and more impartially,
the management of such capital.
2.5.1 MEASUREMENT OF CAPITAL ADEQUACY
Somehow one may be tempted to think that the measurement of
adequate capital is not only a relative concept, a question of fact, but also a
subjective index whose common denominator is "what constitutes a safe
heaven for stock holders" interest. Hence the questions-how can capital
adequacy be determined? Whose duty is it to measure capital adequacy
and under what circumstances? The concept of capital adequacy has no
straight forward approach or definition. This is because banks vary in
sizes, location, magnitude of activities and level of risks arising from their
operations (Onyia, 1998).
At any rate, we can say that capital adequacy can be measured in
,many organizations especially banks, ratio analysis is used as a set
standard to measure the adequacy of capital funds even though that some
scholars warn that in accepting this criteriori, it should not be taken as an
end itself. The reason they argue is that the emphasis of ratio analysis of
common ratios and therefore sheds no light neither on the particular bank's
operations nor methods that are available for regulatory of bank capital. In
Nigeria, the monetary authorities have over the years used the 'following
methods;
I. Fixed minimum capital requirement,
ii. Limitations of lending limit, and
iii. Weighted risklasset ratio
2.5.2 FIXED MINIMUM CAPITAL REQUIREMENT
The end of the free banking era (1952) brought with it an end to
unregulated practice in the provision of commencement capital for banking
business in Nigeria. The fixed minimum capital requirement as a measure
of capital adequacy dictates that a bank should legally maintain at least a
fixed minimum paid-up capital to the tune of certain amount of money.
In Nigeria, the application of this method in measuring capital
adequacy became effective with the enactment of first ever banking Act in
1952. The act stipulated.for the first time, the minimum paid-up capital
requirement for commencement of banking business in Nigeria. The
banking act of 1969 as amended in 1979 stipulated that the paid-up capital
of indigenous banks should be a minimum of Pd600,000, while 811,500,000
was fixed for foreign banks. The same act required merchant banks to
maintain Pd2m.
It is noteworthy to observe that government of Nigeria has continued
to make changes on paid-up capital through other policy adjustments. In
1997, the stipulated minimum paid-up capital for banks .was put at
81500million with 31" December 1998 as the expiry date for full compliance
.by affected banks. This is against N50m and N40m paid up capital fixed for
commercial and merchant banks in 1990 respectively; but by operation
discovered to be inadequate. The essence of this requirement is to ensure
that banks are not under-capitalized thereby putting at risk stockholders
interest and exposing the bank to avoidable risk. This therefore, becomes
sure way to measure capital adequacy of banks. The current minimum
capital requirements stands at N25billion with effect from lS' January 2006.
LIMITATION OF LENDING LIMITS
This aspect of measurement of capital adequacy is clearly an
attempt by monetary authorities to save the banks from risking depositors'
and owners' funds. It is an arrangement whereby the capital of a bank to
lend (large loans) is limited to a certain percentage of the bank funds. The
implementation of the arrangement is that the amount of financial
accornrnodation which a particular commercial bank can offer a
custonier/depositor does not only depend on the stipulated lending limit but
also on the magnitude of paid up capital and reserves.
Before 30Ih June 1991, the maximum loan that any Nigerian bank
could grant to a single customer was 33 X% of the sum of the paid up
capital and statutory reserves of the bank. But since the promulgation of
Banks and Other Financial Institutions Decree (BOFID) of 1991, the limit
has been changed to 20% and 50% for Commercial and merchant banks
respectively. However, there are exceptions, which in most cases require
the approval of the Central Bank of Nigeria (CBN).
2.5.4 WEIGHTED RlSKlASSET RATIO
The risklasset as an index of measurement of capital adequacy
establishes the relationship between risk assets of particular bank and its
capital funds. The implication is that the level of capital cover required by
any asset in the bank's portfolio is directly dependent on the magnitude and
level of risk prevalent in that asset.
It therefore follows that gilt-edged securities as federal governrlient
of Nigeria development loan stock would require less bank cover than
securities issued by an individual firm. At any rate, weighted risklasset ratio
is a complete exposition of whatever class of asset and its risk.
Some authors vary in their approach to classification of the assets
and their various risk levels. Banking literature recognize all of them
probably because they fall within the portfolio of assets of banks. It stiould
be noted that the more risk free an asset the less need for capital cover.
That is why assets classified as liquid like cash, and most money market
instruments whose convertibility into cash is easy require little or no capital
cover. Other assets like normal assets do not require capital cover. Assets
classified as sub-standard require lo%, doubtful debts 50%, bad debts
100% and fixed assets 100% respectively.
2.6 IMPLICATION OF BANK DISTRESS
The adverse effect of bank distress on the economy of any country
is not a respecter of the level of development (Ebhodaghe 1995). Whether
a country is industrialized, developed and big or poor, under-developed and
small, bank failures, if not well managed, portend doom and collapse for
the economy. The devastating effects of bank failures on the only super
power in the world today- the United States of America (USA)- and the
threat it posed to the economy of the first industrial country-Britain- are well
documented in the literature (see Ebhodaghe, 1995).
The government's regulators, members of the public and bank
operators have always resented bank failures due to various reasons.
Governments are particularly concerned in view of the social, political and
economic implications of bank distress. The externalities associated with
bank distress make it distasteful and of serious macro economic
implications unlike what obtains when a non-bank institution fails. For
example, if a brewery company should become insolvent, its demise would
not adversely affect other brewery companies and as a matter of fact they
should benefit by having more customers. However when a bank fails or
becomes distressed, apart from the economy, there may be a spill over of
the problems to the other banks.
Of course, the real or perceived threat of contagion across banks
and the potential for high macroeconomic costs resulting from bank
distress have often led government to adopt a safety net to prevent these
outcomes (Glaessner and Mas, 1995). Some of these adverse effects of
bank distress are reviewed below.
A. EROSION OF PUBLIC CONFIDENCE
About the greatest havoc of bank distress is the erosion of public
confidence in the system especially if the distress is not well managed.
Banking is built on trust and confidence. Once the trust and confidence are
misplaced, banks would no longer be efficient in playing their role of
financial intermediation. The loss of public confidence would no longer be
automatically having many adverse effects. It can easily cause panic and
bank runs, which would threaten the survival of other healthy banks
through systematic risk particularly in the absence of a deposit insurance
scheme or other forms of safety net.
A situation where there is loss of public confidence and bank runs,
demonetisation would be a logical problem. There would be massive
portfolio shift to safer assets such as foreign currencies, government
securities and non- monetary assets as well as capital flight. The
prepondence of the banking public that would not be able to participate in
portfolio shift would not have a safe place to invest part of their wealth.
As this is supposed to be government's responsibility, there will be political
pressure for government to abate the crisis. Equally related to
demonstration is the negative implication for banking culture. Already the
banking culture in Nigeria is poor and low and bank distress would only
exacerbate the situation. As an evidence of this ugly development in
Nigeria, currency outside banks as a proportion of narrow money supply
rose sharply from 42.3% in 1987 before government started to officially
identify the number of distressed banks to 57.4% in 1995(a year when 60
out of the 115 banks were distressed by the authorities) before it declirlecl
to about 50% in1997 (NDIC,1997)
Hitherto, investment in the banking sector had been considered
lucrative. Bank distress would make investors lose their investments in the
banking industry, rather there would be compounded by low profitability for
the remaining banks as loss of public confidence in them would jeopardize
their patronage and earnings.
B. ECONOMIC EFFECTS. ~p4QWt Banks are central to an effective and efficient payments systems in
any country. With bank distress, the payments systems would be perilous b
and at great risk as the link between the real sector and the financial scctor
including international settlement would be greatly impaired. This would
inhibit the intermediation role of banks.
In circumstances where the capacity of banks to perform their main
role of financial intermediation is "impaired" the real sector of the economy
would be adversely affected. Banks are the main means by which
monetary policy is implemented in an economy. With bank failures this
would be hampered and development would be elusive.
Failed banks would be incapacitated from extending new credit. The
healthy banks would equally be constrained from granting credit for fear of
such facilities becoming delinquent. If credits are extended at all, they are
likely to be for short term and mainly to finance commerce and purchase of
foreign exchange. A country where banks become highly speculative and
reckless such as depicted here is dubbed as a "casino" economy. The
effect of these would be to further crowd out of the productive sectors of
manufacturing and agriculture from the credit market. Yet the productive
sector must be galvanized for macroeconomic stability to materialize.
The failure of a large bank or many banks can lead to a sudden
contraction of the money supply as well. This would have very serious
adverse implication for macroeconomic stability as economists, whether
, monetarist or fiscalists, are in accord that the level of money supply has a
positive correlation with the volume of activities in the economy.
Bank failures can hinder effective competition and an efficient
financial intermediation. Competitive banking system will force banks to
operate efficiently if they are to make profits, keep their customers and
remain in business. For this to obtain will depend on, among others, the
number of banks operating in a market, and whether the existing banks are
of an appropriate size and strength for the needs of their customers. Bank
failures can lead to undue concentration. And inefficiency of delivery of
banking services as failed banks are charged and new banks are not given
..free entry.
C. GLOBAL EFFECT.
The primary counterparts of foreign creditors are the banks as they
. are the financial gateway to a country. With bank distress, the international
perception of the banking system would be that of suspicion as it would be
feared that their funds could be locked up and / or lost in the banking
system. In most cases, the international community, except those involved
in criminal practices such as advanced fee frauds popularly known as '419"
in Nigeria and other types of frauds would not extend credit to a country in
which it's banking system is distressed. This would undoubtedly
compromise foreign investment and lead to escalation of capital flight out of
the coimtry.
STUDIES AND MODELS IN PREDICTING BANKS DISTRESS.
Many studies and models have predicted with some degree of
accuracy, the likelihood of success or failure of banks in particular and firms
in general. This review of past studies begins with Altman Multiple
Discriminant Analysis (MDA) Model which is similar to Secret's.
The technique of Multiple Discriminant Analysis (MDA) helps to
combine different ratios into a single measure of the probability of failure
(bankruptcy). MDA can be used to classify companies (banks inclusive), on
the basis of their characteristics as measured by financial ratios, into two
groups: distress or non-distress, failure or non-failure, etc. The empirical
studies of Beaver (1966) and Gupta (1979) identified ratios which have
discriminating power. What .is, however, required from the practical point of
view is the understanding of seriousness posed by low performing ratios
and the combined effect of use of MDA helps to consolidate the effects of
all ratios. MDA constructs a boundary line (a discriminant function), using
historical data of the bankrupt (distressed) and non-bankrupt (non-
distressed) firms. Altman was the first person to apply analysis in finance
for studying bankruptcy (Pardey, 2000).
Altman (1968) after reviewing past studies, discovered that the
works established certain important generalization regarding the
performance and trends of particular measurement, the adaptation of firms,
bgth theoretically and practically, is questionable. He also observed that
ratio analysis presented in this fashion is susceptible to faulty interpretation
and potentially confusing. He therefore chose a Multiple Discriminant
Analysis (MDA) as the appropriate technique as to bridge the gap. In b
classifying firms into bankrupt and non-bankrupt groups, the term
"bankruptcy" was used to describe business failure.
The Altman MDA (also called Z-score) is defined by the' following
discriminant function:
Z = V, XI + V2X2+ . . . . . . + V"X,,
which transforms individual variable values to a single discriminant score of
Z value which is then used to classify the firm,
where V, V2 . . . . . . . . Vn = Discriminant coefficients
XI X2 ........ Xn = Independent Variables
The MDA computes the discriminant coefficients Vj, while the
independent variables XI, are the actual values where j = 1, 2 . . . . . . n
The study showed that if ratios are analysed within a multivariate
framework, it will take on greater statistical significance than the common
technique of sequential ratio comparisons and results were encouraging.
Ako (1999) employed six models (MDA inclusive) to analjlse capital
markets and equity failure in Nigeria. These models are: The Univariate
Analysis Model; The Multiple Discriminant Analysis Model; The Linear
Probability Model; Logit Analysis Model; Probit Analysis Model and Non
parametric Analysis Model. In her procedure, MDA formed linear
combinations of the independent variables (predictor) which served as the
basis for classifying firms into failure and non-failure groups: Thus the
information contained in the actual values or the predictor variables is
summarized in a single index called Z-value or Z-score. She explained
further that to distinguish between the groups, the computed Z-Jalues for
the groups must differ. Therefore each group must have its own equation.
In the light of the above, she specified five (5) discriminantfunctions
(equation). They are as follows;
Z = wo + w, L, + w2R + w3E +W4D +W50P + W6 I S + W7Y
+wsZ + wgT + wlOL + w11 PER + wlzw + w13RR ...... 1
Equation 1 serves as the full discriminant models while Equation 2 is
a semi-full models. Equations 3, 4 and 5 provide further discrirnir~ant
analysis. WO ... .. . W13 are discriminant coefficients upon which the
variables (predictors) such as L, E, OP, PER, Y etc are weighted.
It is important to note that more variables (ratios) were considered
unlike Altman that used few ratios.
The results indicated that issues (Equity) which fail are from
companies with lower profitability, returns, dividend, retention rate and
working capital ratio. Also indicated was that the offer price, earnings,
dividend and income are the variables whose means are most ranked
higher in terms of relative importance (i.e. contribution to overall
discriminant function) The overall discriminant function also fitted the data
reasonably well. Furthermore, the predictive power of the model and its
variants were found to be generally quite high, also suggesting that there
are significant differences in the weights investors attach to factors
influencing their investment decisions.
Using the same MDA, Sinkey (1975) found evidence that support
that the measure of banking factors such as asset composition, loan
characteristics, capital adequacy, sources and uses of revenue, efficiency,
and profitability are good discriminators between the group (i.e. group
mean differences exist). He presented a multivariate statistical analysis of
the balance sheet and income statement characteristic of problem banks in
the years 1969-1972. Newly identified problem banks were matched with
non-problem banks and Multiple Discriminant Analysis was used to test for
group mean as to describe the overlap between groups, and to construct
rules to classify observations (banks) into problem and non-problem
groups.
He used a ten-variable set, the determinant tests which showed that +
both the group mean vectors and group dispersion matrices which were
significantly different in all four years. The chi-square measures of group
overlap indicated that the distribution of the individual vectors of the two
groups overlapped substantially in all four years. The classification results,
which measured the intersection (or overlap) of the two groups, described
groups that are relatively separate even in 1969 and that overlapped less
and less over the next three years. These classification results were quite
encouraging.
Doguwa (1996) explained that Altman et al (1991) identified short
comings associated with the use of MDA model in the prediction of financial
distress in firms. The short comings are;
(i) Predicted values cannot be interpreted as probabilities, since they
are
not constrained to fall between 0 and 1.
(ii) Linear discriminant analysis does allow direct prediction of group
membership, but assumption of multivariate normality of the
independent variables as equal variance-covariance matrices in the
two groups, are required for the prediction rule to be optimal.
As a result of the above short-comings, Doguwa (1996) adopted the
logit model to determine the conditional probability PI, that the ith bank will
fail, given a set of k derived balance sheet ratios: XI1, XI2... . . . Xik for that
bank. The model could be expressed thus: let Yf, Y2....., Yn be independent
binary response variables whose probability functions PI, P2,. ... Pn satisfy
the equation.
P 1 Log - = a, + aj Xij ...... 1
K
1-pi * 1 where I = 1, 2... ... n and pi is given by
Pi = 1
............ .2 1-+ exp (-Bi)
with Bi defined as:
The coefficient a, (j = 1, 2 . . . n) measures the effects on the odds of
failure of a unit change in the corresponding independent variables. The
parameters of equation (2) are estimated by maximizing over the aj's the
log likelihood function:
n n ...... L= Yi log (Pi) + 1 (i-Yi) log (I-pj) . .4
i - I i-1
The result of Doguwa's analysis showed that five discriminatory
variables are important in distinguishing weak from non-problem
commercial banks. These variables are capital adequacy, asset quality,
liquidity, loan category and profitability.
2.8 THE EARLY WARNING MODELS
An early warning model is an established procedure (usually
statistical) for classifying banks into groups (usually failure and non-failure,
distressed or non-distressed). This is usually done using only financial
characteristics of candidate institution. The goal of an early warning model
is to identify an institution's financial weakness at the initial stage in its
process of determination so as to warn interested parties of its potential
failure or distress.
A survey of literature reveals that six (6) statistical models are
employed as early warning models in the prediction of financial distress,
failure or weakness.
The models are as follows:
The univariate Analysis Model:
The Multiple Discriminant analysis Model.
The Linear Probability Model
Logit Analysis Model
Probit Analysis Model
Nonparametric Analysis Model.
MDA and Logit Models have been discussed exhaustively in 2.3
above
At this juncture, it is important to note that Jagtiani et al (2000)
observed that despite the widespread popularity of a logit model as an
effective Early Warning System (EWS) approach, it does have some draw-
back in term of the information that it produces, for example, it is not
possible to determine from the parameter estimates generated by logit
hodels which variables are most ~ ~ s e f u l in predicting capital-inadequate
banks (or alternatively capital-adequate banks). The results only 'indicate
the effectiveness of each variable's ability to discriminate between the two
groups of banks. While the logit model seeks to minimize classification
errors, they do not provide any information about how each-variables
affects Type 1 and 11 errors per se. In addition, logit models are not well
suited to examining interactions between variables.
Beaver (1966) utilized Univariate Analysis model to predict failure.
His work compared a list of ratios individually for failed firms and.a matched
sample of non-failed firms. Observed evidence for five (5) years prior to
failure was cited as conclusive that ratio analysis can be useful in failure
prediction. Ako (1999) quoting Booth (1983) as having employed univariate
analys~s to test four decomposition measures to ascertain the ability of the
attributes, size and stability to discriminate between failed and non-failed
companies. His results concluded that the attributes of most of the
decomposition measures discriminate between failed and non-failed.
However, other researchers have identified its lack of multivariate analysis
as a major short-coming of such studies, i.e, they only consider the
measurements used for group assignments one at a time.
Another model mentioned above is the Probit Analysis Model. This
method, unlike the logit method, avoids the problem of non-normality of the
error term. The intuition behind the probit models is similar to that behind
the logit model. However, for probit, it is argued that if failure is the result of
many independent individually inconsequential additive factors, it is
reasonable to assume the threshold level to be normally distributed (Ako,
1999). This implies that probability is measured by the area under the
standard normal curve which has means = 0 and variance = 1.
Ako (1999) stated that researchers who used this model include
Grablowsky and Tally who used the technique to classify credit application
. of 200 companies using 11 explanatory variables. Their study employed
both Probit and MDA analysis and concluded that Probit analysis was a
variable alternative to MDA as a classification model. Furthermore, Probit
analysis was found to outperform MDA in efficiency principally, because
like the Logit model, Probit model does not require the normality
assumption. Empirical evidence also suggest that Probit Analysis Model
and the Logit Model have similar distribution; both logistic distribution has
slightly thicker tails. However a major problem of using both the Logit and
probit methods is the lack of readily available procedures in many of the
existing statistical packages. Moreover specifications for Probit analysis are
rather complex computationally.
Another model is the Non-Parametric Analysis Model (NM). These
are relatively new approach to classification problem. Their approach
appears to overcome some of the short comings and problems of traditional
MDA and LP models. The Non-parametric models is a modification of MDA
which uses inequalities (instead of equalities) in its maximization
procedures. Moreover, the misclassification errors identified and the
expected cost of misclassification are often smaller than those obtained
with MDA, Logit or Probit Analyses. This latter property and the fact that
different coefficients are obtained for the same variables shed light on the
relative significance and magnitude of the individual variables, as well as on
the interpretation given to results. The NM usually uses forward stepwise
analysis to obtain coefficients for selected variables.
Amongst the notable researchers who used NM include Frydman,
Altman and Kao (FAK, 1985), Marais et al (1984). Both employed NM
namely a recursive partitioning algorithm for classification of bankruptcy
and commercial loans respectively. Their technique was found to
outperform MDA for most empirical results. However, a major short coming
of the recursive partitioning method is that it cannot be used for scoring
observations within the same group as it does not employ a ration scale
unlike the MDA which assigns a score to each observation on a continuous
scale.
2.9 . CRiTlCAL VARIABLES CONSIDERED IN PREVIOUS STUDIES.
Earlier studies such as Sinkey (1 9754, Altman (1 977), Martin (I 977),
Avery and Hanweck (1 984), and Barth et al (1 985), Barr and Siems (1 996)
and Doguwa (1996) analysed the financial characteristics of banks and of
savings and loans associations. Accordingly these studies adopted more or
less the same variables, based on five (5) categories of capital adequacy,
asset quality, management quality, earnings and liquidity (CAMEL) that are
used by the regulators for evaluation process.
Sinkey (1975), finds the loan revenue variable, which is an indicator
of asset quality is the best discriminator. Although the differences in the
means of the management quality, honesty and loan to-capital variables
are also statistically different, the classification accuracy of the model is low
because of the overlap between the problem and non-problem banks
Other variables considered include profitability ratios, capital adequacy and
sources and uses of revenue. Sinkey was the first to apply linear ~nultiple
discriminant analysis to classify banks into either the problem or nor)-
problem groups He reported the following rates of misclassifying a problem
as a non-problem bank for the years: 1969-72; 27%, 28%, 24% and 18%.
The group mean vectors and group dispersion matrices are significantly
different in all the four years, and the differentials increase over time. The
findings suggest that problem banks appear to be different from non-
problem banks, and the difference is increase over time. However, chi-
square measures of group overlap indicate that the distributiohs of the
individual bznks' characteristics overlap substantially and accordingly
Sinkey commented that the "descriptive" classification results were "better"
than might expected.
Ako (1999) listed the following financial ratios as good discriminators
between failed and non-fail firms in her analysis of capital market and
equity failure in Nigeria:-
Liquidity Ratios
Profitability Ratios
Leverage Ratios
Activity Ratios
Returns and Market Ratios.
Altman (1977), concluded that operating income and its trend are the
most important discriminators. He also foulid that net worth and real estate-
owned variables to be important. The reason was that these variables
reflect the capital, profitability and asset quality of a financial institution
Martin (1977), found that the variables representing earnings, loall
quality and capital were useful in distinguishing problem banks from non-
problem banks. In using the logit analysis, he used the following variables.
Net Income to Total Assets; Gross charge offs to Net operating income;
commercial and industrial loans to Total loans; and Gross capital Risk
Assets. Furthermore he found that misclassification rates depend on the
observation period and the combination of variables included into the
model. He selected combinations of variables in 1970 and 1974 and
reported the following ranges of misclassification errors: 1975-1 976, 1971 -
72 for failed banks: 4.0% - 13.0%, 42.0%-50.0% while survived banks had
9.0%-11.0%. 15%-33.0%: The implications of his results are that, first,
earnings and capital adequacy are of relevance only when the level of loan
losses is high, and, secondly, statistical early warning models are of most
interest in periods of moderate adversity, rather than in times that are
better or substantially worse. In terms of performance, the classification
accuracies are similar between discriminant and logit models.
Avery and Hanweck (1984), interpret that bank size is critical in a
bank's survival as it indicates its ability to raise capital and reflects the
reluctance of regulators to close a failing but large bank. They also
concluded that local banking variables are not important in explaining bank
closures because of the unexpected signs of the coefficients.
Barth et al (1985), found that the variables representing capital
adequacy, asset quality, earnings and liquidity are the only Statistically
significant variables. However, they interpret size as an indicator of greater
liquidity as they believe that a larger financial institution has a greater ability
to borrow as to alleviate liquidity problems.
Bar and Siems (1996) applied six variables selected for failure
prediction models. These variables were:
- Equity capital to Total loans for capital adequacy;
- Non performing loans to Total Assets for Asset Quality;
- . DEA efficiency score for Management Quality;
- Net income to Total Assets for Earnings Ability;
- Large Dollar Deposits to Total Assets for liquidity;
- . Percentage change in Residential construction for Local Economic
Conditions.
Their research revealed that a coriiparison of the One-Year-Ahead
and Two-Year-Ahead mean values gave insight into differences between.
The surviving and failing populations. Except for liquidity measure, the gap
between the mean values for survivors and failures widens as failures
approaches. Also there is a difference in mean values for the survivors, but
a noticeable deterioration for failures (except for the liquidity variables).
They concluded that one might expect the IYA model to predict failure with
greater accuracy than the 2YA model. The standard probit methodology
was used to develop models that would classify banks as either survivors
or failures.
Doguwa (1996), in his Logit Regression Approach, used a set of
variables expressed in ratio form (percentages) in identifying problems
banks in Nigeria. These include variables CAR and CLR to measure capital
adequacy; LRWA, LQR, LCR, LTA and BDL for quality and risk of a bank's
portfolio; LAS LR and LDR reflect in various ways the liquidity position of
the bank and its ability to respond to liquidity pressures. Others included
variables SFR and TKA for source of funds; LSL for loan category and
lastly ROA and ROC, for retained profit (loss) for earnings. The resulis
showed that five variables are important i r ~ distinguishing weak from sound
commercial banks. These variables are Capital Over Risk Weighted Assets
(CAR), Risk Weighted Assets to Total assets (LRWA), Treasury Bills and
Certificates to Total current liabilities (LAS), Loans and Advances to states
and local governments to Total Loans (LSL) and Retained. Profit (or loss)
to Total Assets (ROA). One interesting result was that four explanatory
variables (discriminatory factors) in the analysis bear close resemblances
to the CAMEL components, while the loan category factor indicates that
increased loans to states and local governments have contributed to the
distress condition of commercial banks.
Cole and Gunther (1995), applying a split-population survival time
. model, separated the determinants of bank failure from factors that
influence the survival time of failed banks.
They found that the basic indicators of a bank's condition, such as
capital, troubled assets and net income, are significant in explaining the
timing of a bank failure. However, many other variables typically included in
bank failure models, such as measures of bank liquidity, are not associated
with failure time.
REFERENCES
Adeyemi, K.S. (1 993), "Symptoms and causes of Bank Failures", presented at the
Workshop on Bank Reconstruction, Otta, Nigeria.
Ako, R.M, (1999), "The Capital Market and Equity Failure in Nigeria", CBN
E C O I I O I ) ~ ~ a11d Financial Review, Vo1.37 No.3, 77-1 10.
Altman, E.I. (1968), "Financial Ratios, Discriminant Analysis and the Prediction of
Corporate Bankruptcy", Journal of Finance Vo1.23, 589-610.
Altman, E.I. (1977), "Predicting Performance in the Savings and Loan Associatiorl
Industry", Journal of Monetary Economics, Vo1.3, 443-456..
Altrnan, E. I., et al (1 %I) , "Application of Classification Techniques in Business,
Banking, and Finance; Contemporary studies", Economic and Financial
Analysis Vo1.3 (JAI, Greenweih, CT).
Avery, R and G. Hanweck (1984), "A Dynamic Analysis of Bank Failures, Bank
Structure and Competition", Conference Proceedings, Federal Reserve
Bank of Chicago, 380-395.
Barr, R.S. and Siems, T.F. (1996), "Bank Failure Prediction Using DEA to
Measure Management Quality", Financial Industry Studies, Federal
Reserve Bank of Dallas, Texas 75201.
Earth, J.D, et al (1985), "Thrift Institution Failures: Estimating the Regulators
Closure Rule", Research in Financial Service Vol.1, 1-23.
b
Beaver, W.H (1996), "Financial Ratios and Predictors of failure", Empirical
Research in Accounting, Selected Studies 71-1 11.
Benston, George J. (1986), "Perceptive on Safe and Sound Bank: Past, Present
and Future", MIT Press Cambridge, Mass. 8-16.
Cole, R. and Gunther, J. (1995), "Separating the Likelihood and Timing of Bank
Failure", Journal of Banking and Finance, Vol. l9(6), 1073-1 089.
Doguwa, Sani, 1. (1 996), "On Early Warning Models for the ldentification of
Problem Banks in Nigeria", CBN Economic and Financial Review, Vo1.3
(110. I ) , 462-487.
Ebhodaghe, J.U. (1995), "Causes and Environmental Effects of Bank failures in
Nigeria" NDlC Quarterly, Vo1.5 No.3
Frydman, H., et a1 (FAK, 1985), "Introducing Recursive Partitioning for financial
Classification: The case of Financial Distress'' Journal of Finance, PP, 269-
29.
Glaessner, T.0. And Mas, 1. (1995), "Incentives and the Resolution of Bank
Distress", The World Bank Observer, Vol. 10 No.1
Gupta, L.C. (1 979) "Financial Ratios as forewarning indicators of corporate
Sickness", Bombay: lClCi
Hanweck, G.A. (1985), "Predicting Bank Failure", Research Papers in Banking
and Financial Economics" (Financial Section, Division of Research and
Statistics, Board of Governors of the Federal Reserve System) November.
Jagtiani, J.A., et a1 (2000). "Predicting Inadequate Capitalization: Early Warning
System for Bank Supervision", Policy Studies, Federal Reserve Bank of
Chicago, September 2000 (S&R - 200 - 10R).
b
Jimoh, A. (1993), "The Role of Early Models in Identification of Problem Banks:
Evidence from Nigeria", Nigerian Financial Review, Vo1.6, 29-40.
Lane, W. R., et al (1 986), "An Application of the COX Proportional Hazards Model
to Bank Failure", Journal of Banking and Finance, Vol.10, 51 1-531.
Looney, S.W., et al (1989), "An Examination of Misclassifications with Bank
Failure Prediction Models", Journal of Economic and Business, Vo1.41. 327-
336.
Marais. M 1- et al (1 984), "The Experimental Design of Classification Models. An
Applmtion of Recursive Partitioning and Boots trapping to corttnterci:ll
bank loan classification", Journal of Accounting Research Supplerlierit
PP 87-1 14.
Martin, D. (1977), "Early-warning of Bank failure: A Logit Regression Approach",
J0i11'rlal of Balking and Finance, Vol.1, 249-276.
NDlC (1995), "Distress in the Nigerian Financial Services Industry", Vo1.5 No.3.
Nyong, M.A (1994), "Bank Supervision and the Safety and Soundness of the
Banking: An Early Warning Model Applied to Nigeria Data", CBN Economic:
and Fir~ancinl Review, Vo1.32, 41 9-434.
Oguneleye, G.A. (1993), "Manifestations and Management of Distress in the
Financial Service Industry", Paper presented at FITC'S 2"d bank Directors'
Workshop held at the Lagos Sheraton Hotel.
Ologun, S.O. (1994), "Bank Failure in Nigeria: Genesis, Effects and Remedies",
CBN Economic and Financial Review, Vo1.6 (N0.3) 16 - 29.
Onyia, A.C. ( I 998) "Business Finance in Nigeria" Pg.47-54
Pandey, I.M. (2000), "Financial Management" 185-1 86.
. Secrit, H. (1 938), "National Bank Failure and Non-failure: An Autopsy and
Diagnoisis", Principal Press, Blownigton W.
Sinkey, Jr. J. F. (1975), "A Multivariate Statistical Analysis of Characteristics of
Problem Banks", Journal of Finance 30 ( I ) , 21 - 36.
Tam, K. and Kiang (1 992), "Managerial Application of Neural Networks: The case
Of Bank Failure Prediction", Management Science Vo1.38, 416 - 430.
Thomson, J.B. (1991), "Predicting Bank Failure in the 1980sV, Economic Review,
Federal Reserve Bank of Cleveland, 27,9 - 20.
West, R . C . (1985), "A Factor-Analytic Approach to Bank Condition",
Journal of Banking and Finance, 9, 253 - 266.
CHAPTER THREE
RESEARCH METHODOLOGY
RESEARCH DESIGN
Testing the effectiveness of the CAMEL Rating Model in Predicting
bank distress or non-distress condition, requires an appropriate research
design. According to lkeagwu (1998), a good research design makes
explicit and integrated procedure for selecting a sample of data for
analysis, content categories, and units to be placed into the categories,
comparison between categories, and the classes of inference which may
be drawn from the data. In the light of the above, the research utilized a
survey approach. Furthermore, both the historical and documentary study
method of data collection were adopted. This is based on the financial
records of banks sourced from regulatory authorities - NDIC and CBN.
in this study a sample of 12 banks was selected. This has been a
common practice by many researchers when studying firms or institutions
in an industry. For instance, Beaver (1966), adopted selective design to
analyse financial ratios as predictors of failures. Altman (1968), selected
some firms and predicted corporate bankruptcy. See also Ako (1999),
Sinkey (1 975), Jimoh (1 993) and Doguwa (1996).
3.2 SOURCES OF PRIMARY AND SECONDARY DATA
The data for thrs study was obtai*ied from three main sources:
Annual Reports and Statements of Accounts of the Banks under study,
*Central Bank of Niclcria Statistic:al Oulliorl and Nigerian Deposit Insurance
Corporation, (NDIC) Annual Report and Statement of Account.
3.3 METHOD OF DATA (:OI.I-ECTION
Data for ttlrt; r,ll~cly was collected t l i i ~ugh personal visits to the Head,
Regional and Branch Off~ces of the banks affected. Offices of Regulatory
authorities in Lagm A t x ~ j a and Enl-~g~r, wele consulted. Another method of
data collection t l ~ l t \ . / . 1 ? ;1va11al,lc to tlie research was the internet. For
instance, a list of reystered comrnerc~al banks that operated during the
period of study was down-loaded from the official website of the NDIC.
3.4 POPULATION AND SAMPLE SELECTION
Currently, there are 89 commercial banks in Nigeria. A total of 12
banks were selected for the study. Six out of the twelve banks were placed
under the distressed group because they have been "on and off" the
clearing system which indicates clear signals of distress. To avoid bias, the
study utilized random sampling techniques in the selection of the other six
placed under the non-distressed group. Names of the banks were coded
and placed in a container. Thereafter, sampling without replacement was
done. The banks include Union Bank of Nigeria Plc, FSB International
bank, Gulf bank, First bank of Nigeria Plc, All states Trust Bank, Hallmark
Bank of Nigeria Plc, Societal Generale Bank, United Bank for Africa, Inter-
continental Bank of Nigeria Plc, Triumph Bank, NAL Bank and Bank of the
North.
3.5 THEORETICAL MODEL FOR DATA ANALYSIS
In predicting bank distress and non-distress, the Multiple
Discriminant Analysis (MDA) was used. This framework is similar to that
employed by Altman (1 968), Altman (1 977), Sinkey (1 975), Eisel~beis
(1 977) and Ako (1 999).
In this work, the model captured the CAMEL variable which are the
indices used in the assessment of the distress and non-distress banks
studied.
The choice of this model is informed by its advantages especially as
pointed out by Altman (1968). Furthermore, the MDA as a statistical
. technique can be used to classify an observation into one of several a priori
groupings dependent upon the observations' individual characteristics. It is
used primarily to classify and lor make prediction in problems where
dependent variables appear in qualitative form, e.g. male or female,
bankrupt or non-bankrupt, problem or non-problem, distressed or non-
distressed. Meyer and Pifer (1970) confirmed thus; Multiple discriminant
analysis is a statistical technique used to determine the varial~le that
distinguish several a priori groups to classify observations into one of these
groups.
3.6 RATIONALE FOR USING MULTIPLE DISCRIMINANT ANALYSIS (MDA)
IN THIS STUDY
After a careful consideration of the nature of the problem and the
objectives of this study, MDA was chosen as the appropriate statistical
technique. MDA has been utilized in a variety of disciplines since its first
application in the 1930s (Fisher, 1936). During those earlier years, it was
used mainly in the biological and behavioural sciences (Cochran 1964).
The pioneering work utilizing MDA in a financial context was
performed by Duran (1941) in revaluating the credit worthiness of I ~ w r l c : ~
loan applicants. More recently this method had been applied successfirlly
to financial problems such as consumer credit and Forgy (1963) analyzed
several technique including MDA model, in the evaluation of good and bad
installment loans. Walter (1959) utilized MDA model to classifying high and
low price earnings ratio firms, anc! Smith (1965) applied the technique in
the classification of firms into standard investment categories. When data
are collected for groups (distressed and non-distressed), MDA then
attempts to derive a linear combination of these characteristics which "best"
discriminated between the groups. If a particular bank has characteristics
(financial ratios) which can be quantified for all the banks in the analysis,
the MDA determines a set of discriminant coefficients. The MDA technique
considers an entire profile of characteristics common to banks (e.g. CAMEL
Rating) as their interactions of these properties, unlike the Univariate study
which can only consider the measurements used for group assignment,
one at a time.
Another reason for using MDA is that when utilizing a
comprehensive list of financial ratios in accessing a bank's distress
potential, there is reason to believe that some of the measurements will
have a high degree of correlation or colloniearity with each other. While this
aspect necessitates careful selection of predictive variables, it also has the
advantage of yielding a model with a relatively small number of selected
measurements that has the potential of conveying a great deal of
information. Perhaps a primary advantage of MDA in dealing with
classification problems is the potential of analyzing the entire variable
profile of the bank simultaneously rather than sequentially examining its
individual characteristics.
However, it is instructive to emphasize that MDA is not necessarily
the best for predicting bank distress in Nigeria. There are indeed some
critical situations where other techniques or models could be used to
produce better results than the MDA. But for resource allocation problem of
the type is involved in this research work, the MDA is more preferred
because of its flexibility, ease of use and implementation amongst other
factors
3.7 MODEL SPECIFICATION
The Multiple Discriminant Analysis Model (MDA) is given by the
equation below:
Z=aX1 +bX2 +cX3 9dX4 +ex5
Where Z= Discriminant Score (discriminating between distress and non-
distress banks).
XI = Capital Adequacy
X2 = Asset Quality
X3 = Management Competency
X4 = Earnings Strength
X5 = Liquidity.
a,b,c,d,e, = Discriminant Coefficient.
The discriminant coefficients are the official values of the CAMEL
variable. According to financial system regulators (NDIC and CBN), these
are the recommended weights attached to the CAMEL rating, see below;
TABLE Ill
OFFICIAL WEIGHTS ALLOCATED TO CAMEL VARIABLES
--- VARIABLES I COMPONENT WEIGHT("/~)-~ .- . . - - - -----I-- -. - - - Capital Ratio 25 - I .---- Asset Quality Ratio 25 -1
I -. - -
Management Ratio 15 --I -.. --- -- Earning Strength Ratio t-- 20 - --I - - -
iiquidity Ratio 1
15 -.-- - - -7
SOURCE: NDlC Quarterly Vol. 6 (No. 3 & 4) SepflDec, 1996.
AND CBN'S BANK RATING SYSTEM MANUAL 1994.
3.6.1 VARIABLES DEFINITION
Capital Adequacy Ratio, XI = EC
Where EC is Equity Capital and TA is Total Asset.
Asset Quality Ratio, X2 = PDA
TL
Where PDA is Provision for Doubtful
Accounts and TL for Total Loans.
Management Competency Ratio,
X 3 = X I + X 2 + X 4 + X 5
4
the average Capital adequacy, asset quality, Earning strength and
liquidity.
Earning strength ratio, - X4 = EBI'T
7-A
Where EBlT is earning before interest and tax and TA is total assets.
Liquidity Ratio, X5 = TL
TCL
Where TL is total loans and TCL is total current liabilities.
3.7 TECHNIQUES OF DATA ANALYSIS
Two techniques were employed in the analysis of the data namely:
MDA and Multiple Regression Analysis. The MDA treated data related to
objectwe one (1) while Multiple Regression Analysis treated objectives two
to five (2-5) consistent with Ako (1999), two equations are provided for the
two groups- Distressed groups and non-distressed groups. See equation
below.
................ ZDG = aX1 + bX2 +cX3 +dX4 e x 5 . . (1)
.................. ZNK - ayl +by2 +cy3 +dy4 +eys... (2)
Equation (1) took care of the distressed group (DG) while equation
(2) took care of the non-distressed group (NDG). For the purpose of
distinction, Y was introduced into the equation.
This technique was supplemented with Multiple Regression
Analysis. The Multiple Regression Equation is given by the fnllowing
equation:
Y= PC, +- p, + PT+ p2X21 + ................... P k XKI + EI
In this analysis the dependent variable Y is the Z-score which is the
predicting factor or variable XI X2 X3... . . . . . . . . . .Xk represent the CAMEL
variables (the independent variables). Regression was performed using
compi~ter package called "SPSS".
3.8 STATEMENT OF NULL AND ALERNATIVE HYPOTHESES
I. I I,: There is no significant relationship between capital b
adequacy and the predicting of bank distress.
t i : There is significant relationship between capital adequacy
and predicting of bank distress.
2. I : There is no significant relationship between Asset Quality and
predicting of bank distress.
HI: There is significant relationship between Asset Quality and
predicting of bank distress.
3. H,: There is no significant relationship between Management
competence and predicting of bank distress.
HI. There is significant relationship between Management
competency and predicting of bank distress.
4. H,: There is no significant relationship between Earnings
Strength and Predicting Bank Distress.
HI: There is significant relationship between Earnings
Strength and Predicting Bank Distress.
3.9 HYPOTHESES TEST STATISTICS
To test the individual discriminating ability of the variables, two
hypotheses tests statistics were used. These were the "F-test" and " r-test".
T-test was conducted to see the significant relationship betwcwl t l ~
independent variables (CAMEL) and the predicting factor - the Z-value.
Thus the Z-score value is a function of capital adequacy, Asset quality,
Management quality, Liquidity and Earnings Strength.
REFERENCES:
Ako, R.M (1999), "The Capital Market and Equity Failure in Nigeria". CBN
Econolriic and Financial Review, Vo1.37 No3,77-110.
Altman, E.I. (1968), "Financial Ratio Discriminant Analysis and Prediction of
Corporate Bankruptcy", Journal of finance, 569-610
Altman, E.I. (1977), "Predicting Performance in the Savings and Loan Association
industry, " Joumal of Monetary Economics, 443-466.
Beaver, W. t i . (1 966), "Financial Ratio as Predictors of failure" Empirical Research
in Accounting: Selected Studies (The Institute of professional Accounting,
University of Chicago), 71 -1 11
Doguwa, S.I. (1996), "On Early Warning Model for the identification of problem
Banks in Nigeria1', CBN ~'conomic and Financial Review Vol. 34, No1.462-
487
Eisenbeis, R.A. (1977), Pitfalls in the application of discriminant analysis in Business,
Finance and Economics. The journal of Finance, June,875-900.
Fisher, R.A. (1 936), "The Use of Multiple measurements in Taxonomic Problems",
Annals of Eugenics No7 (September) PP179-188.
Ikeagwu, Ebui K. (1998)," Groundwork of Research (Methods and Procedures)
"Institution of development Studies.
Jimoh, A. (1993), "The Role of Early Warning Models in Identification of problern
banks
. Evidence from Nigeria" Nigeria Financial Review, Vo1.6, 29-40.
Meyer, P.A, and H.W Pifer (1970), "prediction of Bank Failure", The Journal of
finance 25, September, 853-868.
Myers H, and Forgy, E.W. (1963), "Development of Numerical Credit Evaluation
Systems", Journal of American Statistical Association Vo150 (September),
PP797-806.
Sinkey, J. F .Jr. (1975), "Statistical Analysis of the Characteristics of problem Banks",
The Journal of Finance No I, March, 12-36.
Smith, K.V. (1 965), "Classification of Investment Securities Using MDA", Institute
Paper No. 101.
Walter, J.E. (1959), "A Discriminant Function for Earnings Price Ratios of Large
Industrial Corporations", Review of Economics and Statistics, Vol.XL1, PP44-52
NDlC (1994) CBN- RATING SYSTEM MANUAL 1994.
CHAPTER FOUR
DATA PRESENTATION AND ANALYSIS
INTRODUCTION
In this chapter the objective is to present and analyze data collected,
and test the respective hypotheses formulated earlier in the study, using
the appropriate test statistics.
PRESENTATION OF DATA
Data presented in Tables 4.1,4.2 and 4.3 were analyzed to ariswer
research questions formulated to guide the study. This enables the
researcher to achieve the objective of the research, which stipulates the
prediction of distress and non-distress of commercial banks in the Nigerian
banking industry. Data in Table 4.4 is a descriptive statistics for each bank
studied.
DATA ANALYSIS AND PRESENTATION . Table 4.2 presents changes from the fifth year to the second year to
distress situation.
CAPITAL ADEQUACY Xq
A comparative analysis of tables 4.2a and 4.2b show that this
variable declined in mean terms for the distressed group from fifth to
second years prior to distress while it maintained a steady rise in the non-
distressed group in the study period. Specifically it slumped from 11.35 to
9.84 in the fifth and fourth years respectively, giving a decline of 1.51%. In
the same period, X I increased from 8.20 to 8.71 for the non-distressed
group. In the third year prior to distress, X I declined further from 9.84 to
9.14 and finally to 8.10, while it increased from 8.71 to 9.1 8 and finally to
9.79 in the second year.
In the light of the above scenario, we therefore corroborate the view
that as banks approach distress their nehvorth is gradually eroded. This
gradual erosion of networth leads to a continuous decline in capital
adequacy ratio until a point where it becomes intolerable except re-
capitalization option is adopted.
ASSET QUALITY X2
Table 4.2a and 4.2b showed that X2 deteriorated on the average for
the distressed group from fifth to second year prior to distress. It
deteriorated from 3.42 in year five to 0.69 in second year prior to distress.
At the same period the non-distressed group increased from 4.61 from fifth
year to 6.21 in the second year prior to distress.'
The determination of X2 for the distress group could have been as a
result of the uncomfortable banks portfolio structure in which cosmetic
mismanagement is adopted which might lead to non-disclosure of
delinquent exposures in the financial statement. In addition to this, the
increased spate of fraud and forgeries in the Nigerian banking system has
contributed to this poor state.
MANAGEMENT COMPETENCY X3
Management competency which measures management's
effectiveness in co-ordinating the other four variables XI, X2, X4 and X5 also
characteristically depicted contradictory changes for the two groups in the
study period. X3, in the distressed group had a steady decline from year five
to year two prior to distress, while a tremendous change was made in the
non-distress group for the same period as shown in tables 4.2a and 4.2b
respectively.
The result is very obvious since X3 depends on "the other four
variables. Additionally, as the four ratios decline, management embarks on
cosmetic mismariagernent which causes it to disregard some prudential
regulations. b
EARNINGS STRENGTH, Xq
Earning strength X4. measures the profitability of banks. A
comparative analysis of X4 from the tables 4.2a and 4.2b showed that this
ratio declined 01, tlie average for the distressed groups while it improved for .
the non-distressed in the study period. Specifically, it diminished from 6.88
in year five to 4.63 in year two prior to distress while that of non-distress
increased from 9.59 to 11.26 in the same period.
This result is in line with past research findings, which proved
profitability as a key variable in determining the survival or demise of a firm.
LIQUIDITY POSITION, X5
Liquidity position X5 measures bank's ability to service its deposit
liabilities.
A comparative analysis of this ratio for the distress and non-
distressed group in the study period showed that X5 declined from 55.59 in
year five to 34.21 in year four while that of the non-distressed group slightly
rose from 30.27 to 31.77 in the same period. (See tables 4.2a and 4.2b for
further assessment).
4.3.1 COMPUTING "CRITICAL VALUE" TO DETERMINE DISTRESS AND
NON-DISTRESS GROUP,
Computation of the "Critical Value" was done using Table 4.1.0 as
shown below:
TABLE 4.1.0
VARIBALES MEANS
VARIABLES DISTRESS GROUP MEAN (11=6)- -----I- NON-DISTRESSED
GROUP MEAN (n=6)
9.50%
5.35%
18.12%
10.39%
The discriminant f\rriction is represented thus:
Z = ax1 +bX2 -I cX.1 I (1x4 +ex5
For Distressed Groirl), we have
ZDG = ax1 + bX2 -t cX,, +dXn ex5
For Non-Distressed ( ;roup, we have.
ZNoG = aY1 +by2 +cY3 +dY4 +eY5
Z,, = .25 (0.0961)+ .25(O.O2l4)+.l5(O.l224)+.2O(O.O574)+. I5(O.3351)
= 0.0240 + 0.0054 + 0.0184 +0.0115 . C " 3 = 0.1096
ZNDG = .25(0.0950) + .25(0.0535) +.I 5(O.I812) +.20(0.1039) + . I 5(0.3229)
= 0.0236 + 0.0134 + 0.0272 +0.0208 +0.0484 =0.1334
Determination of "Cut-Off Point" or "Critical Value".
(0.1096 +0.1334) + 2 = 0.1215 or 12.15%
Note: The result showed that all banks having a Z-score of greatel
that 0 1215 clearly fall into the category of non-distressed group, while
those banks having a Z-value of less than 0.1215 conveniently fall into the
distressed group.
TABLE 4.1 . I
VALUE OF CAMEL VERIABLES (2001 - 2004)
YEAR DISTRESSED GROUP - - Hallmark Bank
Gulf Bank
All States Trust Bank
Triumph Bank
Societal Generale Bank
Bank of the North
NON-DISTRESSED GROUP
First Bank of Nig. Plc
UBA
FSB INT'L
Intercontinental Bank
NAL Bank PIC
Union Bank Of Nig. PIC -
DISTRESSED GROUP
Hallmark Bank
Gulf Bank
All States Trust Bank
Triumph Bank
Societal Generale Bank
Bank of the North
NON-DISTRESSED GROUP
First Bank of Nig. PIC
UBA
FSB INT'L
Intercontinental Bank
NAL Bank PIC
Union Bank Of Nig. PIC
Hallmark Bank
Gulf Bank
All States Trust Bank
Triumph Bank
Societal Generale Bank
Bank of the North
NON-DISTRESSED GROUP
First Bank of Nig. PIC
UBA
FSB INT'L
Intercontinental Bank
NAL Bank PIC
Union Bank Of Nig. PIC
- -- - -
Hallmark Bank
Gulf Bank
All States Trust Bank
Triumph Bank
Societal Generale Bank
Bank of the North
NON-DISTRESSED GROUP
First Bank of Nig. PIC
UBA
FSB INT'L
Intercontinental Bank
NAL Bank Pic
Union Bank Of Nig. PIC
Note: All figures are in % UI ated.
SOURCE: Calculated from Banks' Annual Report and Statement of Accounts
TABLE 4.2a
AVERAGE RATIO FOR THE DISTRESSED GROUP FOR STUDY PERIODS . ..
VARIABLES I FIFTH YEAR
/ Ratio ( change ' - - Ratio
- -. . 9.84
2 19 - - 13.08
6 .74 -
34 21 -
-- - Change a . - - - - -. - -1 51 - - --
-0.93 - -- -0.06
- - -- -0.14 -
-21.38 - - -- ->
THIRD YEAR 1 SECOND YEAR
RW Changea 1 Ratio I Changea
1 I I 1. I. SOURCE: Calculated fro111 Batiks' Annual Report and Statement of Accounts
*
TABLE 4.2b
AVERAGE RATIO FOR THE NON-DISTRESSED GROUP FOR THE STUD,Y PERIODS
1 VARIABLES I FIFTHYEAR I FOURTH YEAR I THIRD YEAR I SECOND YEAR 1 I / Ratio I Changea i Ratio / Changea i Ratio / Changea Ratio I Changea I
I I I I- _ NOTE: All ~ ' i iu res Are in sb ~Aless oth'erwise staied
SOURCE: Adapted from Appendix
RESULTS
Judging from the critical value of 12.15%, banks having 12.15% Z-score
and above do not fall into the distressed group, while those below 12.15% Z-score
fall into distressed group.
TABLE 4.3a
BANK
First bank
Hallmark bank
UBA
FSB Int'l
Intercontinental
Gulf Bank
All States
Triumph Bank
NAL Bank PIC
Societal Generale bank
Union Bank
Bank of the North
SCORE
12.20%
9.80%
13.68%
14.99%
15.62%
9.06%
10.14%
7.57%
12.50%
10.73%
19.30°h
11.18%
REMARK
Non-distressed
Distressed
Non-Distressed
Non-Distressed
Non-Distressed
Distressed
Distressed
Distressed
Non-Distressed
Distressed
Non-Distressed
Distressed
SOURCE: Calculated from Banks' Annual Report and Statement of Accollnts
TABLE 4.3b
DISTRESSED GROUP NON-DISTRESSED GROUP - -
I 1 1 First Bank of Nigeria
I I .
3 1 All States Trust Bank ( 3 1 FSB INT'L I I .~
) m m p h Bank - -. - .- - 1 4 1 Intercontinental
I I
I-~oci~al Generale Bank 1 5 1 NAL Bank PIC
l - ~ / ~ a n k 6 m North 1 I 1 6 1 Union Bank
I I I 1 - SOURCE: Result from 4.3a
4.4 PRESENTATION OF REGRESSION RESULTS
The independent variables: Capital Adequacy, Asset Quality,
Management quality, Earnings strength and liquidity were tegressed
against the predicting factor (Z-score) to assess the impact of each variable
on the ability of the Z-score to predict distressed and non-distressed
condition of banks. This was done to elicit evidence relevance to the
analysis of objectives two to five.
The table below is a summary of the regression results.
TABLE 4.4 THE REGRESSION RESULTS
Pearson
Correlation
Significance
(I -tailed)
N
Dependent variables Z
Independent variables: Capital Adequacy (C), Asset Quality (M) Earnings strength
(E), and liquidity (L).
N. Observation: 48
R~ = 0.065
DW Test = 2.223
Correlation is significant ( I -tailed) at 1 % level
t-statistics are in parentheses.
TABLE 4.5
COEFFICIENTS a
MODEL
1
(constant)
C
A
M
E
L
Unstandardized
Coefficients -- -
Beta -- Std. Error -- 88.938
. I90
,154
1 94
4.862
2.329
L a. Dependent Variables
Standardized
Coefficients
Beta
4.5 DISCUSSION OF FINDINGS
Data in Table 4.4 show that the goodness of fit test or the coefficient
of correlation ( R ~ ) has a value of 0.065. This suggests a weak correlation
between the dependent variable (z-score) and the independent variables
(capital adequacy C, asset quality A, management efficiency M, earnings
strength E and liquidity L). A statistical value of 0.5 and above would have
suggested a perfect correlation. This variation not with-standing, the
variables CAMEL provide an effective means in predicting bank distress or
noh-distress as shown in the subsequent analysis.
Data in Table 4.30 show the average value of the variables-CAMEL
The table further differentiates distress from non-distress group. On the
basis of this presentation; a "cut-off point" was obtained to determine the
fate of the bank. That is whether the bank will go distress or not. The critical
value obtained was 12.15 percent. This critical value is known a. thc
z-score. A bank having a z-score greater than 12.15 percent cleat ly fall into
the category of non-distressed, while those banks having from below
12.15percent conveniently fall into the distressed group. The classification
of banks into distressed and non-distressed group, problem and non-
problem bank, failed and non-failed bank, justifies the use of the Multiple
Discriminant Analysis (MDA) .
Table 4.3a shows the respective z-score obtained by individrlal
bank. The following banks that fell into the non-distressed group include:
First Bank, UBA, FSB International, Intercontinental, NAL Bank plc and
Union 13ank. Their corresponding scores were as follows: 12.20%, 13.68'%,
14 99%, l5.62%, 12.50% and 1930%.
The distressed banks and their respective scores are: Hallmark
9.80%, Gulf 9.06%, All States 10.14%, Triumph 7.57%, Societal Generate
10.73% and bank of the North 11.18%.
Analysis, using the CAMEL ratios, exhibits a deteriorating trend as
distress approaches evidencing different financial characteristics. For
instance in the distressed group in Table 4.2a, the value capital adequacy
in fifth year prior to distress was 11.35%, 4'h year 9.84%, 3rd year 9.14'/0
and 2"d year 8.10%. From the table it can be deduced that the value of the
ratios diminishes as distress approaches. Under the non-distressed group,
table 4.2b, the opposite is the case. For instance the value of capital
adequacy in the years studied were: 8.20%, 8.7I0h, 9.18% and 9.74%.
From the foregoing, there is a progressive appreciation in the value of the
variables. These findings are in line with Altman (1968). See also for
instance the works of Myer and Pifer (1 970), Altman (1 977), Sinkey (1 975)
and Ako (1999).
At this juncture it is pertinent to mention that the research was
designed to determine the impact of CAMEL ratios on the predicting
variables (z-score). The regression estimates show that all components of
the CAMEL Rating Model have a significant relationship with the variable
(z-score). Apart from earnings strength all other components of CAMEL
,rating model negatively correlated with the predicting variable. A positive
correlation between earnings strength and z-score shows that earnings
. strength has an overwhelming influence on the success or failure of any
bank. Therefore, for a bank to remain in business, it must remain profitable
as to meet present and future obligations. A negative correlation between
capital adequacy and the z-score indicates that a bank may remain
profitable regardless of the size of capital.
4.6 TEST OF HYPOTHESES
Four hypotheses were formulated for the study. Each of them was
tested as follows:
HYPOTHESIS 1
H,: There is no significant relationship between capital
adequacy and the prediction of bank distress.
' HI: There is significant relationship between capital adequacy
and prediction of bank distress.
Degree of freedom: N-1=48-1=47
0.01 level of significant is 2.42
Table value of to.99 =2.42
Calculated value of t= 0.354.
DECISION RULE: Accept Ho if the calculated value of t is greater than the
table value, otherwise reject. ,
Since-0.354 < 2.42, we reject the null hypothesis (Ho). Therefore we can
conveniently state that there is a significant relationship between capital adequacy
and the prediction of bank distress.
HYPOTHESIS 11
HO: Asset Quality has no significant impact on the prediction of bank
distress.
HI: Asset Quality has significant impact on the prediction of bank
distress.
Degree of freedom N - 1 = 48-1 = 47
0.01 level of significance is 2.42
Table value of t 0 . ~ 9 b
Calculated value o f t = 0 -.401
DECISION RULE: Accept H, if the calculated value of t is greater than the
table value; otherwise reject.
Since - 0.401<2.42, we reject the null hypothesis (H,). Therefore we can say that
Asset Quality has significant impact on the prediction of bank distress.
HYPOTHESIS Ill
H,: Management quality does not significantly affect the prediction of
distress in the banking industry.
HI: Management quality significantly affects the prediction of distress in
the banking industry.
Degree of freedom N - 1 =48-1=47
0.01 level of significance is 2.42
Table value of =2.42
Calculated value o f t = 0 -.463
DECISION RULE: Accept H, if the calculated value of t is greater than the table
value, otherwise reject.
Since - 0.463~2.42, we reject the null hypothesis (H,). here fore we can
say that management quality significantly affect the prediction of distress in the
banking industry.
HYPOTHESIS IV
H,:
HI:
, b
There is no significant relationship between Earnings strength and
bank distress.
There is significant relationship between Earnings strength and bank
distress.
Degree of freedom N - 1 =48-1=47
0.01 level of significance is 2.42
Table value of to 99 =2.42
Calculated value of t = 0.612
DECISION RULE: Accept t i , , if the calculated value of t is greater than the
table value; otherwise reject.
Since 0.612~2.42, we reject the null hypothesis (H,). Therefore we can
conveniently state the there is significant relationship between Earnings strength
and bank distress.
SUMMARY OF FINDINGS
The Research revealed the following.
The financial condition of a bank continues to deteriorate as it approaches
its distressed period. This is evident in the diminishing value of the CAMEL
ratios as shown in table 4.2a.
The distress or non-distressed condition of a bank is determined by the
discriminant value (Z-score). The higher the value of Z-score, the greater
the chances of such a bank surviving and vice-versa.
These findings are in agreement with the result presented by
Altman (1 968).
That management competence significantly affects the prediction of
distress in the banking industry.
The finding also showed that there is significant relationship between
Earnings strength and bank distress.
From the above result, (CAMEL) variables are good indicators of
financial condition of banks.
The result equally showed that all banks having a Z-score of greater than
12.5% clearly fell into the category of non-distressed group, while those
banks having a Z-value of less than 12.15% conveniently fell into the
distressed group.
Despite the fact that Asset Quality also negatively affects the prediction or
othetwise it however has significant impact on the predication of bank
distress.
TABLE 4.6
DESCRIPTIVE STATISTICS FOR ALL BANKS
Valid N (Listwise)
TABLE 4.7
MODEL SUMMARY
Minimum
-568.00
1-87
- . I4
12.92
6.78
5.60
Model
1
Maximum
11 57.00
1044.00
22.68
66.71
1065.00
1320.00
R
.255a
Mean - ..
16.0633
31.0800
8.061 3
32.9000
37.401 0
39.4600
Std. Deviation -- 1 87.3902
149.3780
5.9987
12.7019
1 5 1.6670
1 88.7932
R-square Adjusted
F R q u a r e
Std. Error of
The Estimate
Durbin-
Wdtson
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 SUMMARY
The study attempted a test of the effectiveness of CAMEL fiatirlg
Model in predicting bank distress in Nigeria. Considering the fact that bank
distress has a multiple effect on the economy, there is need for a robust
predictive model that will serve as an early warning signal in order to
minimize the occurrence of distress.
Data for this study were obtained mainly from published annud
reports and accounts of banks between the periods 2001-2004. A total of
12 banks were selected for the study. Six out of the twelve banks selected
were placed under the distressed group because they have been "on and
off' the clearing system which indicates clear signals. To avoid bias, the
study utilized random sampling technique in the selection of the other six
placed under the selection of the other six placed under the non-distressed
group. Names of the banks were coded and placed in a conhiner
Thereafter, sampling without replacement was done. In orde~ to a( t t~evo
the objectives of the study, two statistical tools were employed in the
analysis of data: Multiple Discriminant Analysis (MDA) and Multiple
Regression analysis. The regression was performed using SPSS. MDA
model was initially used by Altman in 1968 to predict corporate bankruptcy
and popularly known as Altman z-score. For this reason, this theoretical
framework has come to be known as Altman z-score. In the sccond
analysis, CAMEL were regressed against the z-score (the d q w 11 lei 1 1
variable or predicting factor) in order to determine the impact OII tho
prediction of distress.
The results show that all the observed CAMEL ratios exhibit a
deteriorating trend as distress approaches evidencing different financial
characteristics. Thus, change in the value of the ratio becomes significarlt
in the second and third years of distress period. The regression wtitl)4cn.
show that all components of the CAMEL Rating Model have a significant
relationship with the variable (z-score). Apart from earnings strength, all
other components of CAMEL rating model negatively correlated with the
predicting factor.
RECOMMENDATIONS
Based on the research findings, the following recommendations
were made:
1. Since CAMEL Rating is a yardstick or benchmark for assessing the
financial condition of a bank, there should be regular assessment of
the financial performance of banks by regulators so as to dictate
early symptoms of financial deterioration.
2. The management of every commercial bank should endeavor to
maintain a considerable level of profitability as a measure of its
earnings strength. By so doing each bank will be able to meet its
obligations.
3. Since liquidity appears to be a strong indicator of financial distress,
banks should endeavor to roll out more products through aggressive
marketing and rendering more services in order to enhance deposit
mobilization. This is so because one of the most important liclr~idily
to banks is deposit mobilization.
4. The regulatory authority should carry out routine supervision and
examination to ascertain the financial condition of commercial
banks. This will enable them to detect early signal of deterioration,
thereby maintaining public trust and confidence in the industry.
5.3 .- CONCLUSION
Based on the entire research findings, the following conclusions
were made: Capital Adequacy, Assets Quality, Management Competence,
. Earnings Strength and Liquidity as components of CAMEL are important
indicators of the financial condition of a bank. Thus distress or non-distress
is determined by CAMEL Assessment. The most important is liquidity and
its source depends on deposits from customers. A bank's earning capacity
reduces once its deposits base in weak.
CAMEL Rating Model can predict distress accurately at least up to 4
years and the accuracy diminishes with increase in lead-time. ?'he rrloc.1
important conclusions are: (i) all CAMEL ratios show a deter~oratiry trerid
as distress approaches and (ii) serious change in the ratio ocwrrcd
between the third and second years prior to distress. The doyee of
seriousness is measured by the constant change in the value of l l i ~ !
CAMEL ratio. Therefore
CAMEL rating, when integrated with the more analytical discriminant
analysis in a multivariate context, provides a good index of predicting bank
distress.
LWOEQW 5.4 SUGGESTED AREA FOR FURTHER STUDY
Picking some samples of healthy and failed banks and 11sitlc1 1 1 1 0
MDA Model to see if CAMEL detects distress, sooner or lafcr
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CBN BANK RATING SYSTEM MANUAL 1994
APPENDIX 1
HALLMARK BANK
YEAR Z-VALUE < ' A M 12 L
2004 8.98 .25(9.93) I .25(0.60) t- . 15(9.86)4- .20( I0 53) 1 I i ( l : 2003 9.64 .25( 1 1.0 1 ) 1- .25(0.83)+ . 15(1 0.50)+ .20( 1 1 57) 1 . I :(I : ; I \ 2002 9.59 .25(.42) I .25(1.34)+ . 15(1.34)+ .20( 10.17) I . I i ( l : : { o )
200 1 10.97 .25(8.2) 1 X(2.04) + .15( 12.26)+ .20( 14.46) 1 . I y(24 1 1 ) SOURCE: Calc~~latctl I'roa~ Ihnk's Ar~nanl Rcport arrd State~nent of Accor~nts
BANK OF THE NORTH
YEAR Z-VALUE <: A M 15 I ,
2004 1 1.33 .25(642) 1 .25(0.40)4 .15( 13.83)+ .20(0.27) 1 .I i(42 i L ) 2003 12.26 .25(6.50) 1 .25(1.79)+ . 15(14.70)+ .20(8.90) 1 . I 5(4 I . 17) 2002 10.99 .25(6.57) 1 .25(-0.13)+ .15(13.12)+ .20(10..37) I . l s(3.5 0 j )
200 1 10.14 .25(1.87) t .25(4.39)+ . l5(l I .91)+ .20(l I .5 I ) I . I $(20 ! : 4 ) SOURCE: Calculated from Ihnk's Annual Report nnd Str~temr~~t of / 2c .cwl r ls
FIRST BANK OF NIGERIA
YEAR
2004 2003 2002 200 1
YEAR
2004 2003 2002 200 1
Z-VALUE C A M E 1 ,
12.98 .25(12.36)-1 .25(14.44) t . 15(14.39)+ .20(14.44) 1 . I ; (28 7 0 )
11.13 .25(7.8 1) I- .25(6.94)+ . I S(l I .94)+ .20( l(l.05) 1 . I ;( I8 '?(I) 1 1.70 .25(6.66)+ .25(4.87)+ .15( 13.03)+ .20( 15.06) I . I \ (24 0 I )
12.75 .25(8.03)+ .25(9.70)+ .15(13.74)+ .20(13.07) 1 . I !(2! : 5 )
SOURCE: Calculated from Bank's Annual Report and Ststcr~rcnf vf \ O w r B '
UNION BANK OF NIGERIA PLC
Z-VALUE C A M 15 I ,
23.30 .25(18.56)-1 .25(1.85)+ . I 5(26.97)+ .20(20.77) 1- .I .?(Mi.'/ I ) 18.5 1 .25(1 1 .52) + .25(0.94)+ . 15(2 1.79)+ .20( 18.30) 1 .1.?(56. )'I) 18.27 .25(1 I .42)+ .25(0.90)+ . I 5(2 1.19)+ .20(22.68)+- . I .5(119.7.5) 17.12 .25(1 1.20)+ .25(0.89)+ . 15(19.89)+ .20(2.0.22)+ . I 5(117.10)
SOURCE: Calculated from Rnak's Annual Report a ~ ~ d Statcn~cnt of Accounts
UBA PLC
YEAR Z-VALUE C A M 15 I ,
20.04 1 1.95 .25(6.85)4- .25(8.90)+ .15(13.04)+ .20(11.80) 1 .I S(24.6 1 ) 2003 15.44 .25(4.92)+ .25(7.99)+ . 15(45.29)+ .20(1 1.13) I- .I S(2 1.25) 2002 13.20 .25(4.50)+ .25(4.44)+ . I S(32.09) t .20(10.23) I- .15( 12.92) 200 1 14.63 .25(5.65)+ .25(5.80)+ . 15(42.34)+ .20(1 5.59)-I- . 15(15.30)
SOURCE: Calci~lated front Dank's Annual Report and Statement of Accounts
F S B INTERNATIONAL B A N K P L C
YEAR
2004 2003 2002 200 1
YEAR
2004 2003 2002 200 1
YEAR
2004 2003 2002 200 1
YEAR
2004 2003 2002 200 1
* '
YEAR
2004 2003 2002 200 1
%-VALUE C A M E II,
1 2.22 .25(12.60)+ .25(5.17)+ . 15(17.63)+ .20(4.48) I- .15(48.25) 14.84 .25(14.64)+ .25(5.43)+ . 15(16.79)+ .20(4.75) 1 .I S(112.3 1)
151.77 .25(14.63)+ .25(6.42)+ . 15(16.36)+ .20(6.38) 1 .I T(78.0,') 13.13 .25(12.62)+ .25(6.00)+ , 15(17,18)+ .20(7.6 1 ) I . I 5(/1?.4(')
S0111<( '15: Calculatecl from Rank's Annual Report and Statement of Acxwr i i f -
INTER-CONTINENTAL B A N K P L C
Z-VALUE C A M E I,
13.93 .25(15.66)+ .25(7.83)+ . 15(15.06)+ .20(5.49)+ . l5(3 I .26) 12.68 .25(9.29)+ .25(8.35)-1 . 15(14. I I)-+- .20(6.53)-1- . 15(32.26) 16.68 .25(1 1.8 I)+ .25(9.03)+ . 15(18.92)+ .20(8.20) I-. .I S(46.62) 10.20 .25(13.48)+ .25(9.53)+ . 15(22.03)+ .20(7.8 1) 1. . I 3( 57.2.1)
SOUI<('E: Calculated from Bank's Annual Report and Statement of Accrwiils
GULF B A N K
Z-VALUE C A M E I,
9.03 .25(5.86)+ .25(4.54)+ . 15(10.58)+ .20(1 .OR)+ . 15(30.87) 9.97 .25(6.43)-t .25(5.58)-1 . 15(1 1.45)-t .20(3.50) I- .15(30.29) 7.80 .25(4.38)+ .25(4.77)+ . 15(8.97)+ .20(2.90)-t . 15(23.84) 9.43 .25(5.7 I ) + .25(4.32)+ . 15(11 .05)+ .20(2.63) 1- . I 7 3 1.35)
SOURCE: Calculated from Dank's Annual Report and Statement of Accounts
A L L STATES TRUST B A N K
Z-VALUE C A M E I
10.59 .25(7.53)+ .25(0.93)+ . 15(9.98)+ .20(2.22)+ . I j(29.23) 9.23 .25(5.61)-1- .25(-0.05)+ . 15(8.74)+ .20(1.4 1)-1- .15(27.98) 10.88 .25(5.93)+ .25(0.59)+ . 15(1 1 .O I)+ .20(3.05)-1- .I S(34.45) 9.87 .25(3.93)+ .25(0.50)+ . 15(6.78)+ .20(1.52) 1- .15(2 1.15)
SOURCE: Calculated from Bank's Annual Report and Statement of Ac~or111ts
TRIUMPH B A N K
Z-VALUE C A M E 1.)
SOURCE: Calculated from k k ' s ~ d n u a i ~ e ~ o r t a n d ' ~ t a temknt of Accoi~~ifs
Y EM
2004 2003 2002 200 1
YEAR
2004 2003 2002 200 1
NAL BANK PLC
ZVALUE C A M I3 1 .
12.94 .25(24.92) 1 .25(-9.90)+ . 15(14.94)+ .20(4.82)+ . lf;(39.'! ' ) 12.33 .25(13.62) t .25(-5.68)+ . 15(15.3 1)-t .20(0.05)+ .15(52.30) 12.92 .25(14.03)+ .25(-6.86)+ .15(16.26)+ .20(0.33)+ .15(57.52) 1 1.78 .25( 16.42)i- .25(-4.09)+ . 15(13.87)+ .20(2.64)+ .I S(40.5 1 )
SOURCE: Calculated from Ilank's Annual Report and Statement of Accorlnts
SOCIETAL GENERALE BANK PLC
ZVALUE C A M li I ,
10.96 .25(11.92)+ ,25(2.28)+ .15(12.47)+ .20(3.07) 1 . I 5 ( I .6") 10.46 .25(12.27)+ .25(5.21)+ .15(11.28)+ .20(5.06) 1 . I ?( ' 1 . 5" ) 9.87 .25(10.44)+ .25(4.56)+ .15(10.77)+ .20(5.86) 1 . I .i( ' 2 I 11.61 .25(1 1 .03)+ .25(6.98)+ . 15(12.56)+ .20(7.65)-1- . I S(24.50)
SOURCE: Calculated front Bank's Annual Renort and Sfafen~el~t of Accor~nts
APPENDIX 2
DETERMINING 'I'HE CRITICAL VALUES OF BANKS
FIRST BANK OF NIG. PLC
YEAR Z-VALUE C A M E L
2004 12.98 12.36 2.25 14.39 14.44 28.50 2003 11.13 7.8 1 6.94 1 1.94 14.05 , 18.96 2002 1 1.70 0.66 4.87 13.03 1 5.66 24.91 200 1 12.75 8.03 9.70 13.74 13.67 23.55 Average 12.20
SOURCE: Calculatetl horn Uank's Arrnual Report and Statement of Accounts
UNION BANK NIG.PLC
YEAR Z-VALUE C A M E L
2004 23.30 18.56 1.85 26.97 20.77 66.71 2003 18.5 1 1 1.52 0.94 2 1.79 18.39 56.29 2002 , 18.27 1 1.42 0.90 21.19 22.68 49.75 200 1 17.12 1 1.20 0.89 19.89 20.22 47.19 Average 19.30
SOURCE: Calculated fron~ Ua~~k's Amual Report and Statement of Accounts
UBA (UNITED BANK FOR AFRICA PLC)
YEAR Z-VALUE C A M E , L
2004 1 1.95 6.85 8.90 15.04 1 1.80 24.6 1 2003 15.44 4.92 7.99 45.29 11.13 2 1.25 2002 13.20 4.50 4.44 32.09 10.23 12.92 200 1 14.63 5.65 5.80 42.34 15.59 15.30 Average 13.68
SOURCE: Calculated from Uank's Annual Report and Statement of Accounts
FSB INTERNATIONAL BANK PLC
YEAR , Z-VALUE C A M E L
2004 15.22 12.60 5.17 17.63 4.48 48.25 2003 14.84 14.64 5.43 16.79 4.25 42.33 2002 14.77 14.63 6.42 16.36 6.38 38.02 200 1 15.13 12.62 6.00 17.18 7.6 1 42.49 Average 14.99
SOURCE: Calculated fron~ Bank's Annual Report and Statement of Accounts
INTER-CONTINENTAL BANK I'LC
YEAR %-VALUE C A M E 1,
2004 . 1 3 -03 1 5.66 7.83 15.06 5.49 3 1.26 2003 12.68 9.29 8.35 14.1 1 6.53 32.26 2002 16.08 11.81 9.03 18.92 8.20 46.62 200 1 10.20 13.48 9.53 22.03 7.8 1 57.21 Average 15.02
SOURCE: Calculated from Dank's A n ~ ~ u a l Report and Statement of Acco~rnfs
G U L F B A N K
YEAR %-VALUE C A M E 1,
2004 0.03 5.86 4.54 10.58 1.03 30.87 2003 9.97 6.43 5.58 1 1.45 3.50 30.29 2002 7.80 4.38 4.77 8.97 2.90 23.84 200 1 9.43 5.71 4.32 1 1.05 2.63 ' 31.55 Average 9.06
SOURCE: Calculated from ,Bank's Annual Report and Statement of Accounts
A L L STATES TRUST B A N K
YEAR ZVALUE C A M E L
2004 10.59 15.66 7.83 15.06 5.49 3 1.26 2003 9.23 9.29 8.35 14.1 1 6.53 32.26 2002 10.88 11.81 9.03 18.92 8.20 46.62 200 1 9.87 13.48 9.53 22.03 7.8 1 57.24 Average 10.14
SOURCE: Calculatctl fro111 Dank's Annoal Rcport and Statement of Accounts
TRIUMPH B A N K
YEA8 Z-VALUE C A M E L
2004 8.43 7.53 0.93 9.98 2.22 29.23 2003 7.18 5-61 (0.05) 8.74 1.4 1 27.98 2002 . 9.0G 5.93 0.59 11.01 3.05 34.45 200 1 5.60 3.93 0.50 6.78 1.52 21.15 Average 7.57
SOIlI<( '15: Calculated from Ihak's Anneal Report and Statement of Accounts
NAL BANK PLC
YEAR Z-VALUE C A M E L
2004 12.94 24.92 (9.90) 14.94 4.82 39.92 2003 12.33 13.52 (5.08) 15.31 0.95 52.36 2002 12.93 14.03 (6.86) 16.26 0.33 57.52 200 1 1 1.78 16.42 (4.09) 13.87 2.64 40.5 1
Average 12.50 SOURCE: Calcl~la fed fro111 Ihnk's Aur111al Report and Statement of Awounts
SOCIETAL GENERAL BANK PLC
YEAR Z-VALUE C A M E L
Average 10.73 SOURCE: Calculated frolll l3a11k's A I I I ~ I I R ~ Report and Statement of Accounts
HALLMARK BANK OF NIG. PLC
YEAR Z-VALUE C A M E L
Average 9.80 SOURCE: Calculated fiw111 I h ~ ~ l i ' s Alln11:rl Rcport and Statement of Accounts
BANK OF THE NORTH
YEAR Z-VALUE C A M E L b
2004 1 1.33 6.12 0.40 13.83 6.27 42.52 2003 12.26 6.50 1.79 14.70 8.96 4 1.17 2002 10.99 6.57 (0.13) 13.12 10.37 35.62 200 1 10.14 1.87 4.39 11.91 1 1.5 1 ' 29.84 Average 11.18
SOURCE: Calculated fl-0111 I h ~ ~ l i ' s A11nu;11 Report and Statement of Accounts
APPENDIX 3
DESCRIPTIVE STATISTICS FOR EACH BANK
Maximum Mean Minimum Std.
BANKS FIRST BANK OF NIGERIA A
PLC
HALLMARK BANK OF
NIGERIA
UBA UNITED BANK FOR
AFRICA
FSC INTERNATIONAL
BANK PLC
INTER-CONTENENTIAL
BANK PLC
Deviation 3.16
2.50
.86
3.97
1.05
.87
' .47
3.59
1.77
8.71
3.1 1
2.73
2.03
1 .O3
2.36
5.36
14.58
653.00
GULF BANK
ALL STATE TRUST BANK A
TRIUMPH BANK
NAL BANK PLC
SOCIETAL GENERAL
BANK
UNION BANK INT'L PLC A 2.04
C 11.01
E 16.47
L 24.11
M 1065.00
Z 10.97
BANK OF THE NORTH A 4.39
C 6.57
E 11.51
L 42.42
M 14.70
Z 12.26
SOURCE: Data run-down using SPSS
APPENDIX 4 - LIST OF BANKS OPERATING IN THE NIGERIAN BANKING INDUSTRY AS AT
JULY 2005.
1 ACB International Bank PIC
2. Access Bank (Nig) PIC
3. Afribank Nigeria PIC
4. African lnternational Ltd (Merchant Bankers)
5 African International Bank Ltd
6. African Express Bank Pic
7 All States Bank PIC
8. Assurance Bank PIC
9. Bank of the North PIC
10. Bond Bank Ltd
11 Broad Bank of Nigeria Ltd
12. Capital Bank lnternational
13 Centre - Point Bank PIC
14. Chartered Bank PIC
15. Citizens Bank Ltd
16. City Express Bank Pic
17. Continental Trust Bank Ltd
18. Co-operative Bank PIC
19. Co-operative Development Bank PIC
20 Devcom Bank Ltd
21. Diamond Bank Ltd
22. Eagle Bank Ltd
23. Ecobank (Nig) PIC
24. Eko international Bank Ltd
25. Equitorial Trust bank Ltd
26. Equity Bank Ltd
27. FBN (Merchant Bankers) Ltd
28. Fidelity Bank PIC
29. First Atlantic Bank PIC
30. First Bank of Nigeria PIC
31. First City Monument Bank PIC
32. First Interstate Bank PIC
33. Fortune lnternational Bank PIC
Fountain Trust Bank PIC
FSB lnternational Bank PIC
Gateway Bank Plc
Globa Bank PIC
Guarantee Bank Trust Bank PIC
Guardian Trust Bank PIC
Gulf Bank of Nigeria PIC
Habib Nigeria Bank Ltd
' Hallmark Bank PIC
Indo-Nigeria Bank Ltd
Inland Bank (Nig) PIC
Intercity Bank PIC
Intercontinental Bank PIC
IMB lnternational Trust Bank PIC
lnternational Trust Bank PIC
Investment Banking & Trust Company Ltd
Leadbank PIC
Liberty Rank PIC
Lion Bank of Nigeria PIC
Magnum Trust Bank PIC
Manny Bank Nigeria PIC
Marina lnternational Bank Ltd
MBC lnternational Bank Ltd
Metropolitan Bank PIC
Midas Bank PIC
Nol Bank PIC
National Bank of Nigeria Ltd
NBM Bank Ltd b
New African Merchant Bank plc
New Nigerian Bank PIC
Nigeria-American Bank Ltd
Nigerian lnternational Bank (Citigroup)
NUB lnternational Bank Ltd
Oceanic Bank lnternational (Nig) Limited
Omega Bank PIC
Pacific Bank PIC
Platinur~i Dank Ltd
Prudent Bank PIC
Regent Bank PIC
Reliance Bank Ltd
Society General Bank Nig. PIC
Society Bancaire Nig. Ltd
Stanbic Dank Nig. Ltd
Standal t l Chartered Bank Nig. Ltd
Standard rrust Bank PIC
Trans International Bank PIC
Triumph Bank PIC
Tropical Commercial Bank PIC
Trust Bank of Africa Ltd
Union M~rchant Bank Ltd
Union Balk of Nigeria PIC
United Bank for Africa PIC
Universal 'Trust Bank PIC
Wema Bank PIC
Zenith Bank PIC
Source: NDIC, Information on Insured Banks Available on:
httplwww.ndic-nigeria.com
