FINANCIAL RATIOS AND THE STATE OF HEALTH OF NIGERIAN BANKS

Onyeiwu Charles University of Lagos, Lagos State, Nigeria Email: chasonyeiwu@yahoo.com Tel: 2348023206015.

And Aliemeke Goodluck Guarranty Trust Bank Plc, Nigeria Email:goodluck.aliemeke@gtbank.com

AbstractThis study investigates the use of financial ratios to ascertain state of health of Deposit Money Banks in the Nigerian financial services sector using the multivariate analysis propounded by (Altman 1968). Twenty-three banks constitute the sample of which eight, have been indicted as weak, by Central Bank of Nigeria within the year of investigation (CBN 2009).The study applied a Multivariate technique to Nigerian banks to ascertain its ability to discriminate between weak and healthy banks. The audit report for year ended 2009 formed the basis of the calculation. The Z score of each of the banks is found to be below 1.80 indicating ill health. However, it is observed that the banks can be disaggregated into two distinct classes i.e. Those with positive Z score which rightly fall into category of banks that have not been indicted by the Central Bank of Nigeria and those with negative Z score which fall into category of banks which have been indicted by CBN within the past one year resulting in 100 percent correct classification. The study provides the regulatory authorities insight on how the Z score can be used to improve their supervisory oversight function.

Keywards: Financial ratios, Z score, Nigerian banks and financial health

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1.1 INTRODUCTION Financial statement analysis is important to the management, owners, personnel, customers, suppliers, competitors, regulatory agencies, tax payers, lenders, academics and others, each having their views in applying financial statement analysis in their evaluations and making judgments about the financial health of organization.One widely accepted method of assessing financial statements is ratio analysis, which uses data from the balance sheet and income statement to produce values that have easily interpreted financial meaning. All banks, banking systems and other financial organizations routinely evaluate their financial health by calculating various ratios and comparing the values to those for previous periods, looking for differences that could indicate a meaningful change in financial condition. Many financial organizations also compare their own ratio values to those for similar organizations looking for differences that could indicate weaknesses or opportunities for improvement.Financial statements analysis is information processing system designed to provide data for decision making. The information is basically derived from published annual financial statements and accounts of the companies.The financial statements analysis and financial ratios is believed to have originated in the United States. (Horrigan 2001), the first course of financial statement analysis could be traced back to the stages of American’s drive to industrialization in the last half of the Nineteenth century. The major development that created the need for a systematic analysis of companies’ financial data are the emergence of the corporation as the main organizational form of business enterprise, resulting in the separation of management from ownership and the fast increasing role of financial institutions (e.g. Banks, investment and insurance companies) as the major suppliers of capital for business expansion requiring formal evaluation of borrowers credit worthiness, consequently, analyzing corporate financial data.The former is to evaluate operational performance (investment analysis) and the latter to determine solvency status (credit analysis). The credit analysis function initially dominate the development of financial statement analysis, as banks began using financial data on a large scale thus, for example, by 1890 it was a routine procedure for commercial bank prospective borrowers to be subjected to credit evaluation but later (Wall 1912) came up with more ratios. 1920 to 1929 was characterized by extensive data collection and the introduction of new ratios. At about this time, the notion of using Profit Margins and turnover was already well developed. There are several ways of classifying ratios and could be based on source of data, what the ratios are meant to measure and users for whom they are primarily computed. A study in 1930s and several ones later conclude that failing firms exhibit significantly different ratio measurements than continuing entities. Therefore, observed evidence for five years prior to failure was cited as conclusive evidence that ratio analysis can be useful in the prediction of failure. For many years Nigerian banking system has been experiencing spate of financial distress which from time to time lead to failure of number of banks with attendant capital loss, employment loss and socially undesirable consequences. It is therefore desirable to find a way of detecting deteriorate financial conditions of Nigerian banks. This study therefore is useful to the regulatory authorities, investors as well as employees.The objective of this study therefore is to apply one of the models known as the Multivariate Analysis (Multiple Disciminant Analysis) to Nigerian banking sector so as to ascertain its ability to discriminate between weak and healthy banks. This paper is to be organized as follows: section one is the introduction, section two is the literature review, section three is the methodology, section four is the findings and section five is the conclusion with policy implication.

2.1 LITERATURE REVIEW(McDonald and Morris 1984, 1985) present the first extensive studies of the statistical validity of the financial ratio method, one with a single industry and the other with selected firms from the industry. Financial ratios are conventionally analyzed in two ways i.e. time-series and cross-sectional analysis. The former is concerned with the behavior of a given ratio over time, while the latter involves comparisons between the investigated firm’s ratio and those of related firms. Both the time-series and cross-sectional aspects can be combined into one method (the residual analysis). The preceding analysis is the univariate mode i.e. the ratios are examined one at a time while multivariate ratios analysis, is the analysis in which several ratios are measured simultaneously. There are numerous methods that have been applied to indicate a company’s financial health. For example,( McLeay 1986) proposes using a t-distribution model with fat tails for making statistical inferences, (Beaver 1966) presented a univariate model and (Altman 1968) pioneered the use of Multivariate model also known as Multiple Discriminant Analysis which have become most popular in business failure prediction. (Martikainen et-al, 1991) uses a time series approach with transformation analysis to predict financial failure, (Casey 1980) used the human information processing (HIP) method to show that operating cash flow data can lead to more accurate predictions of business

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failure, (Theodossiou 1993) applied a sequential procedure to predict a business tendency towards failure and (Wilcox 1976) applied the Gambler ruin method taken from probability theory to predict business risk.(Kumar and Ganesalingam 2001) applied principal component analysis and cluster analysis to predict the financial distress of major Australian companies.In a bankruptcy study, (Karels and Prakash 1987) say that in applying the multivariate methods (i.e. Multiple Discriminant Analysis), the multivariate normality is more relevant than the univariate normality of individual financial ratios because it reformulates the problem, analyses combined ratios in order to remove possible unclearity and misclassifications.(Watson 1990) examines the multivariate distribution properties of four financial ratios from a sample of approximately 400 Compustat manufacturing firms for cross-sections of 1982 to 1984 and opined that multivariate normality has particular bearing on research and could be useful for bankruptcy prediction. It also has implications on univariate research, since multivariate normality implies univariate normality and it is generally regarded to be superior to the univariate analysis.Few studies have been done in the past to explore the benefits of financial ratios to predict companies’ financial difficulties as an early warning sign. Altman used 66 samples consisting of 33 companies in bankruptcy and another 33 companies not in bankruptcy.By using Multivariate Discriminate Analysis, Altman discovered that profitability, liquidity and solvency ratios are critical to assessing bankruptcy status and his study resulted in 95% accuracy rate a year before the company’s bankruptcy. The predictive power of the model declines subsequently to 72%, 29% and 39% accuracy rate for 2 years, 4 years and 5 years before bankruptcy respectively.A research that applied the Atman model to three Syrian banks in Indonesia for the period 2005-2007 reflected that all the banks were bankrupt (Endri, 2009 Perbanas Quarterly review). Another research by (Hadad, 2004; Rahmat 2002) also predicted all banks bankrupt.

2.2 Multiple Discriminant AnalysisThe Multivariate model (Multiple Discriminant Analysis) will be applied to Nigerian banks in this study to ascertain its suitability in predicting the financial conditions of banks in Nigeria.(Altman 1968) coined a multivariate Z-score analysis to assess financial health and to predict bankruptcy. It has been considered a powerful diagnostic tool that forecasts bankruptcy.(Altman 2000) describes Multivariate method (Multiple Discriminant Analysis) as a statistical technique used to classify an observation into one of several a priori groupings dependent upon the observation’s individual characteristics. It makes predictions on problem where the dependent variable is in qualitative form e.g. healthy or unhealthy, bankrupt or non-bankrupt.The analysis under multivariate method is transformed into one simplest dimension, the discriminating function of the firm. The model is defined as: Z = V1X1 + V2X2 + V3X3 + ….VnXn transforming the individual variable value to a single discriminant score or Z value which is then used to classify the object where V1, V2….Vn are discriminant coefficients and X1,X2…..Xn are independent variable.The Z score is a linear analysis in that five measures are objectively weighted and summed up to arrive at an overall that then becomes the basis for classification of firms into one of the priori groups (healthy and unhealthy). The variables in the Z score are X1, X2, X3, X4 and X5 representing current assets/current liabilities, working capital/total assets, retained earning/total assets, earning before interest and tax/total assets, market value of equity/total liabilities and sales/total assets respectively.

Ratio Working Capital to Total Asset measures the net liquid assets (i.e. current assets less current liabilities) of a company to its total capitalization.

Ratio Retained Earning to Total Assets measures the cumulative profitability over time of a company. Infant company would show an adverse RA/TA ratio because it has no cumulative profits overtime.

Ratio Earning before Interest and Tax to Total Assets measures the true productivity of the company’s assets. It is a very significant ratio for studies involving business failure.

Ratio of Market Value of Equity to Total Liabilities shows how much the firm’s equity can reduce in value before the liabilities exceed the assets and the company becomes insolvent. Equity involves the combination of market value of preference and common shares.Ratio Gross Earning to Total Assets measures the firm’s assets utilization. It measures the management capacity in dealing with competitive conditions.

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3.1 METHODOLOGYThe area of study is the commercial banking sector of the Nigerian economy. The selected samples are twenty-three banks in the sector. The design of the study is based on financial ratios using Multivariate Statistical Methodology (i.e. Multiple Discriminant Statistical Methodology) by applying Altman’s Z-score model. The Multivariate technique (Multiple Disciminant Analysis) is applied to a sample of twenty-three banks for a period of one year using the published accounts available for the banks in 2009 i.e. Post-CBN Audit Results-September 2009. It is important to point out that eight of these banks have been indicted in 2009 by the Central Bank of Nigeria and we want to see the effectiveness of the Z score in classifying these banks. This is to differentiate the selected banks in two mutually exclusive groups i.e. weak and healthy banks. The twenty-three banks represent the population of the Nigerian banking sector.The basic source of data for this research work is secondary data. This data is extracted from classification made by BGL Securities, an Asset Management and investment company, (www.bglplc.com). The banks are ranked alphabetically. (See Appendix 1)

The study is designed to measure the performance of banks in Nigeria through the use of Altman’s financial ratios. The financial ratios to be calculated are

X1= Working Capital to Total Assets.X2= Retained Earning to Total Assets.X3= Earning Before Interest and Tax to Total Assets.X4= Market Value of Equity to Book Value of Total Liabilities.X5= Gross Earning to Total Assets.Z= Overall Index.

(Altman 2000) gave criteria for classifying banks as weak, healthy and very healthy. The standard Z score model given below has been tested and proved to be effective for many years in different countries.Model Z= 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 0.999X5Altman advise that variables X1 to X4 must be calculated as absolute percentage values e.g. the bank whose net current assets to current liabilities (X1) is 15% should be included as 15.0% and not 0.15. Only variable X5 should be expressed in different way i.e. S/TA ratio of 300% should be included as 3.0.The model discriminate the sampled unit(s) in three categories in terms of Z-score output in relation to the financial performance.

Table 2CATEGORY Z-score VALUE IMPLICATIONS

I Below 1.80 Weak Performance/Bankruptcy zone

II 1.80 – 3.00 Healthy Performance

III Above 3.00 Very Healthy/Sound Performance

4.1FINDINGSOn application of financial figures of variables extracted from data on Table 1 (see appendix 1), the ratios X1, X2, X3, X4 and X5 value of each bank are obtained as shown in table 3 (See Appendix 2).

a. Working Capital to Total Assets Ratio

In this ratio, highest percentage came from FCMB with 33% which means it is having lower financial difficulties and higher liquidity compare with other banks while lowest percentage came from Wema bank with -88% meaning it is having higher financial difficulties and lower liquidity than the rest of the banks

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b. Retained Earning to Total Assets

In this ratio, highest percentage is 5% scored by Citibank meaning that Citibank has high profitability by utilizing its assets in the sector more effectively while the lowest percentage of -70% is scored by Finbank.

c. Earning Before Interest and Tax to Total Assets

In this ratio, Citibank scored highest percentage with 6% which means that the bank maximized its productivity by using its assets more effectively while Finbank scored lowest with -55%.

d. Market Value of Equity to Book Value of Liabilities

Standard Chartered bank had highest percentage with 64% which means that the bank could guarantee payment of its liabilities from its equities while Bank PHB and Oceanic Bank scored lowest percentage with 7% meaning that the banks can not guarantee payment of their liabilities from their equities.

e. Gross Earning to Total Assets

Finbank had the highest percentage with 35% which means that the bank assets are well utilized while Oceanic Bank had the lowest with 5%.

On application of ratios X1, X2, X3, X4 and X5, the Z value of each bank is obtained as shown in table 4 (See Appendix 3)

According to table 4 above, all banks scored less than 1.80 which is the benchmark. The implication of this is that all the banks are weak, have financial difficulties and in bankruptcy zone using Altman’s Z-score model. The table is further divided into banks with negative and positive scores and arranged in descending order as reflected in table 5a and 5b.

Table 5a (Banks with Positive Scores)

S/N BANK 1.2*WC/TA 1.4*RE/TA 3.3*EBIT/TA 0.6*MVE/BVL 0.999*GE/TA TOTAL1.2X1 1.4X2 3.3X3 0.6X4 0.999X5 Z

1 STANBIC IBTC 0.35 0.02 0.11 0.30 0.13 0.912 STANDARD

CHARTERED 0.16 0.05 0.16 0.38 0.11 0.863 FIDELITY BANK 0.39 0.01 0.03 0.34 0.06 0.824 STERLIG BANK 0.22 -0.03 0.10 0.30 0.20 0.795 FCMB 0.39 0.00 0.00 0.22 0.09 0.706 CITIBANK 0.21 0.06 0.20 0.08 0.10 0.667 ECOBANK 0.16 -0.03 0.06 0.31 0.11 0.618 GTBANK 0.23 0.02 0.07 0.17 0.12 0.609 ZENITH BANK 0.21 0.01 0.09 0.13 0.12 0.5610 ACCESS BANK 0.26 -0.03 0.08 0.12 0.10 0.5411 SKYE BANK 0.15 -0.03 0.10 0.08 0.18 0.4712 FIRST BANK 0.17 0.00 0.01 0.15 0.06 0.3913 UBA 0.08 -0.01 0.08 0.12 0.13 0.3914 DIAMOND BANK 0.18 0.00 0.01 0.12 0.08 0.3815 UNITY BANK 0.01 -0.05 -0.11 0.17 0.14 0.16

TOTAL 3.17 -0.01 0.99 2.99 1.73 8.84

5

AVERAGE 0.21 -0 0.07 0.20 0.12 0.60

Table 5b (Banks with Negative scores)

S/N BANK 1.2*WC/TA 1.4*RE/TA 3.3*EBIT/TA 0.6*MVE/BVL 0.999*GE/TA TOTAL1.2X1 1.4X2 3.3X3 0.6X4 0.999X5 Z

1 AFRIBANK -0.20 -0.24 0.10 0.06 0.15 -0.122 UNION BANK -0.09 -0.31 -0.38 0.06 0.10 -0.633 INTERCONTINENTAL -0.28 -0.57 -0.04 0.05 0.15 -0.704 SPRING BANK -0.77 -0.23 -0.77 0.11 0.09 -1.575 BANK PHB -0.46 -0.54 -1.13 0.04 0.29 -1.806 OCEANIC BANK -0.15 -0.43 -1.38 0.04 0.05 -1.877 WEMA BANK -1.06 -0.54 -1.28 0.22 0.33 -2.338 FINBANK -0.29 -0.98 -1.80 0.10 0.35 -2.63

TOTAL -3.30 3.84 -6.68 0.68 2.41 -11.65AVERAGE -0.41 -0.48 -0.84 0.09 0.30 -1.46

From the above tables, 15 banks had a positive score which is 65% of the population and 8 banks had a negative score and classified as being very weak i.e. is 35% of the population.From Table 5a, it is observed that only 8 banks surpass the group industry average of 0.60 representing 35% of the whole population.According to CBN classification of banks under distress which have earlier been classified, there is 80% correct classification of banks under financial distress which is in table 5b.It can be inferred from above discussion and tables that banks in Nigeria have not performed well on all parameters which prove that the overall financial performance of the banks are not quite well and all the banks have to make improvements on different fronts. It is also observed that Union Bank which is termed as “Government Bank” and one of the old generation banks falls under banks with negative scores (financial distress).

4.2Descriptive StatisticsThis statistics points out some basic characteristics of each of the groups; healthy and weak bank. The healthy banks report higher mean in the ratios of the independent various except in case of X5 where the weak banks group mean is higher than the healthy bank group mean. Also ratios of the independent variables of the weak banks reflect greater fluctuation than the healthy banks ratios in all case but one ie.X4, being the only ratio where there is greater fluctuating in healthy bank group.

5.1CONCLUSIONThe aim of this study is to investigate the use of financial ratios to ascertain state of health of Deposit Money Banks in the Nigerian financial services sector using the multivariate analysis propounded by( Altman 1968). Twenty-three banks constitute the sampled, of which, eight, have been indicted as weak, by Central Bank of Nigeria, within the year of investigation (CBN 2009). However, the Z score of each of the banks is below 1.80, indicating that, all the twenty three banks are unhealthy. This result agrees with earlier study of Syrian banks in Indonesia, (Endri 2009). However, it is observed that the banks can be disaggregated into two distinct classes i.e. Those with positive Z score which rightly fall into category of banks that have not been indicted by the Central Bank of Nigeria and those with negative Z score which fall into category of banks which have been indicted by CBN within the past one. Following this categorization, it can be concluded that the Z score is a useful tool to identify banks with deteriorating financial conditions in Nigeria, because, it enabled 100 percent right classification of the banks. This result is to be interpreted with caution and conclusion can be taken after different technique for assessing bank health confirms the result. 5.2POLICY IMPLICATION

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The result of this study points out that financial statement of Nigerian banks have good information content that can enable an investor monitor the health of his banks and make useful contribution at the AGM. It provides the regula-tory authorities additional insight on how the Z score can be used to improve their supervisory oversight function. With different techniques and tools of this nature, the banks can identify when basic financial data is signaling change in direction and strategy to keep the bank healthy and profitable.

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REFERENCESAltman, E. I. (1968). ‘ Financial ratios, discriminant analysis and the prediction of corporate bankruptcy.’ Journal of Finance, 23(4):589-609. Altman, E. I. (2000). ‘Predicting financial distress of companies: revisiting the Z score model.’ retrieved from www.z score.pdf. 5-10 Beaver, W. H. (1966). ‘Financial ratios as predictors of failure’. Journal of Accounting Research (Supplement), 4(3):71-111..BGL Banking Report, Companies Reports, BGL Research. www.bglplc.com Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research (Supplement), 4(3):71-111.Casey, C. J. (1980). ‘Variation in Accounting Information Load: The effect on loan officers’ Predictions of bankruptcy’. The Accounting Review, LV (1).Endri, 2009, ‘The Financial Performance Analysis using Altman Z score’; Perbanas Quarterly Review.Foster, G. (1978), ‘financial statement analysis’. Prentice-Hall, first Ed.Ganesalingam, S and Kumar, K. ( 2001), ‘Forecasting credit rating using ANN and statistical techniques’ epublication.bond.edu.Hadad, (2004), ‘The financial performance analysis using Altman Z scores’- Indonesia Central Bank.

Horrigan J.O. (2001), ‘Methodological Implications of non-normally distributed financial ratios: a comment’, Journal of Business Finance and Accounting, 573-593.Karel, G and Prakash, A.( 1987), ‘Multivariate Normality and Forecasting of Business Bankruptcy,’ Journal of Business Finance C3 Accounting 14(4).Maleay, S. (1986), ‘ Distributional Characteristics of Ratios- Evidence from Turkish firms.’ www.eurojournal.comMartikainen, T. and Ankelo, T (1991),’On the instability of financial pattern of failed firms and the predictability of corporate failure’ Economic Ltters 35/2, 209-214.McDonald B and Morris, M (1984), ‘The Statistical Validity of Ratio Method in Financial Analysis: An Empirical Examination’, Journal of Finance and Accounting,(1st quarter) 547-555.Theodossiou, P. T. (1993). Predicting shifts in the mean of a multivariate time series process: An application in predicting business failure. Journal of the American Statistical Association, 88(422):441-449Wall,. (1912) ‘A short history of financial ratio analysis’ (Unpublished)Watson, C. (1990),’Multivariate distributional properties and transformation of financial ratios, Accounting Review 65/3, 682-695 .Wilcox, J. (1976). A gambler’s ruin prediction of business failure using accounting data. Sloan Management Review, pages 1-10. Spring copy of the journal.

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APPENDIX

Appendix 1

9

Table 1

S/

N

BANK WORKI

NG

CAPITA

L

TOTAL

ASSETS

RETAIN

ED

EARNIN

G

EBIT MARKE

T

VALUE

OF

EQUITY

BOOK

VALUE OF

LIABILITI

ES

GROSS

EARNIN

G

N’m N’m N’m N’m N’m N’m N’m

1 ACCESS

BANK

139,987 647,692 (11,759) 16,672 95,335 485,244 64,326

2 AFRIBANK (68,945) 422,194 (71,226) 12,315 50,786 479,067 64,238

3 BANK PHB (279,684) 735,653 (282,801) (252,931

)

63,690 903,355 214,400

4 CITIBANK 36,646 206118 9,336 12,733 22,352 166,211 19,945

5 DIAMOND

BANK

96,522 651,173 114 1,119 106,102 538,831 53,184

6 ECOBANK 55,674 409280 (8,905) 6,866 175,903 338,752 45,137

7 FIDELITY

BANK

108,172 334,757 2,208 2,777 115,852 202,065 18,561

8 FINBANK (41,802) 172,558 (120,686) (94,357) 44,067 256,624 59,542

9 FIRST BANK 293,186 2,033,204 2,163 3,189 427,839 1,725,176 128,148

10

1

0

FCMB 123,256 376,756 (479) (479) 88,943 247,701 35,206

11 GTBANK 207,464 1,072,362 16,091 21,379 246,233 884,746 127,475

1

2

INTERCONTI

NENTAL

BANK

(187,794) 801,614 (328,450) (10,765) 89,019 1,054,275 119,770

1

3

OCEANIC

BANK

(118,661) 927,504 (286,037) (387,155

)

72,663 995,448 46,831

1

4

SKYE BANK 76,094 607,246 (13,922) 19,038 64,099 512,296 106,698

1

5

SPRING

BANK

(64,274) 99,636 (16,313) (23,304) 33,963 180,348 9,294

1

6

STANBIC

IBTC

95,954 327,382 3,718 11,011 127,125 250,287 42,799

1

7

STANDARD

CHARTERED

30,666 228,577 7,767 11,422 122,850 191,731 24,151

1

8

STERLING

BANK

34,347 190,983 (4,584) 5,977 81,660 161,115 37,768

1

9

UBA 98,957 1,562,112 (7,292) 36,044 267,294 1,386,700 198,148

2

0

UNION BANK (79,324) 1,015,528 (222,863) (118,455

)

106,594 1,153,269 97,506

2

1

UNITY BANK 1,275 261,062 (8,728) (8,728) 65,259 229,022 35,932

11

2

2

WEMA BANK (66,622) 75,696 (29,433) (29,433) 46,445 127,693 25,286

2

3

ZENITH

BANK

289,075 1,686,915 16,391 47,158 302,409 1,352,494 199,000

Source: Company Reports, BGL Research (Post-CBN Audit Results-September 2009)

Appendix 2

Table 3. Altman Approach Financial Ratios

S/N BANK WC/TA RE/TA EBIT/TA MVE/BVL GE/TA

X1 X2 X3 X4 X5

1 ACCESS BANK 0.22 -0.02 0.03 0.20 0.10

2 AFRIBANK -0.16 -0.17 0.03 0.11 0.15

3 BANK PHB -0.38 -0.38 -0.34 0.07 0.29

4 CITIBANK 0.18 0.05 0.06 0.13 0.10

5 DIAMOND BANK 0.15 0.00 0.00 0.20 0.08

6 ECOBANK 0.14 -0.02 0.02 0.52 0.11

7 FIDELITY BANK 0.32 0.01 0.01 0.57 0.06

8 FINBANK -0.24 -0.70 -0.55 0.17 0.35

9 FIRST BANK 0.14 0.00 0.00 0.25 0.06

10 FCMB 0.33 0.00 0.00 0.36 0.09

11 GTBANK 0.19 0.02 0.02 0.28 0.12

12

12 INTERCONTINENTAL

BANK -0.23 -0.41 -0.01 0.08 0.15

13 OCEANIC BANK -0.13 -0.31 -0.42 0.07 0.05

14 SKYE BANK 0.13 -0.02 0.03 0.13 0.18

15 SPRING BANK -0.65 -0.16 -0.23 0.19 0.09

16 STANBIC IBTC 0.29 0.01 0.03 0.51 0.13

17 STANDARD

CHARTERED 0.13 0.03 0.05 0.64 0.11

18 STERLING BANK 0.18 -0.02 0.03 0.51 0.20

19 UBA 0.06 0.00 0.02 0.19 0.13

20 UNION BANK -0.08 -0.22 -0.12 0.09 0.10

21 UNITY BANK 0.00 -0.03 -0.03 0.28 0.14

22 WEMA BANK -0.88 -0.39 -0.39 0.36 0.33

23 ZENITH BANK 0.17 0.01 0.03 0.22 0.12

Appendix 3

Table 4. Z-Score

S/

N

BANK

1.2*WC/

TA

1.4*RE/

TA

3.3*EBIT/

TA

0.6*MVE/

BVL

0.999*GE/

TA

TOTA

L

1.2X1 1.4X2 3.3X3 0.6X4 0.999X5 Z

1 ACCESS BANK 0.26 -0.03 0.08 0.12 0.10 0.54

2 AFRIBANK -0.20 -0.24 0.10 0.06 0.15 -0.12

13

3 BANK PHB -0.46 -0.54 -1.13 0.04 0.29 -1.80

4 CITIBANK 0.21 0.06 0.20 0.08 0.10 0.66

5 DIAMOND

BANK 0.18 0.00 0.01 0.12 0.08 0.38

6 ECOBANK 0.16 -0.03 0.06 0.31 0.11 0.61

7 FIDELITY

BANK 0.39 0.01 0.03 0.34 0.06 0.82

8 FINBANK -0.29 -0.98 -1.80 0.10 0.35 -2.63

9 FIRST BANK 0.17 0.00 0.01 0.15 0.06 0.39

1

0

FCMB

0.39 0.00 0.00 0.22 0.09 0.70

11 GTBANK 0.23 0.02 0.07 0.17 0.12 0.60

1

2

INTERCONTINE

NTAL BANK -0.28 -0.57 -0.04 0.05 0.15 -0.70

1

3

OCEANIC

BANK -0.15 -0.43 -1.38 0.04 0.05 -1.87

1

4

SKYE BANK

0.15 -0.03 0.10 0.08 0.18 0.47

1

5

SPRING BANK

-0.77 -0.23 -0.77 0.11 0.09 -1.57

1

6

STANBIC IBTC

0.35 0.02 0.11 0.30 0.13 0.91

1 STANDARD 0.16 0.05 0.16 0.38 0.11 0.86

14

7 CHARTERED

1

8

STERLING

BANK 0.22 -0.03 0.10 0.30 0.20 0.79

1

9

UBA

0.08 -0.01 0.08 0.12 0.13 0.39

2

0

UNION BANK

-0.09 -0.31 -0.38 0.06 0.10 -0.63

2

1

UNITY BANK

0.01 -0.05 -0.11 0.17 0.14 0.16

2

2

WEMA BANK

-1.06 -0.54 -1.28 0.22 0.33 -2.33

2

3

ZENITH BANK

0.21 0.01 0.09 0.13 0.12 0.56

Statistics

Describing

the

Characteristi

cs of Healthy

Banks

Z X1 X2 X3 X4 X5

Mean 0.589333 0.211333 -0.000667 0.066000 0.199333 0.115333

Median 0.600000 0.210000 0.000000 0.080000 0.170000 0.110000

Maximum 0.910000 0.390000 0.060000 0.200000 0.380000 0.200000

Minimum 0.160000 0.010000 -0.050000 -0.110000 0.080000 0.060000

Std. Dev. 0.209164 0.105347 0.030814 0.073173 0.100603 0.038705

15

Skewness -0.251456 0.200243 0.383062 -0.512831 0.485382 0.627542

Kurtosis 2.392327 2.725250 2.497171 3.756432 1.768753 3.074783

Jarque-Bera 0.388867 0.147422 0.524863 1.015106 1.536469 0.988018

Probability 0.823301 0.928940 0.769179 0.601967 0.463831 0.610175

Sum 8.840000 3.170000 -0.010000 0.990000 2.990000 1.730000

Sum Sq. Dev. 0.612493 0.155373 0.013293 0.074960 0.141693 0.020973

Observations 15 15 15 15 15 15

The

characteristics

of unhealthy

banks

Z X1 X2 X3 X4 X5

Mean -1.456250 -0.412500 -0.480000 -0.835000 0.085000 0.188750

Median -1.685000 -0.285000 -0.485000 -0.950000 0.060000 0.150000

Maximum -0.120000 -0.090000 -0.230000 0.100000 0.220000 0.350000

Minimum -2.630000 -1.060000 -0.980000 -1.800000 0.040000 0.050000

Std. Dev. 0.885082 0.338051 0.244014 0.679369 0.060474 0.117161

Skewness 0.217073 -0.995722 -0.963606 0.116907 1.535057 0.341797

Kurtosis 1.730828 2.628494 3.253792 1.692875 4.187927 1.482978

Jarque-Bera 0.599760 1.367954 1.259519 0.587748 3.612257 0.922886

Probability 0.740907 0.504606 0.532720 0.745370 0.164289 0.630373

Sum -11.65000 -3.300000 -3.840000 -6.680000 0.680000 1.510000

Sum Sq. Dev. 5.483588 0.799950 0.416800 3.230800 0.025600 0.096087

Observations 8 8 8 8 8 8

16

17