Effectiveness of the Altman Z-Score model:

Does the Altman Z-Score model accurately capture the effects of

Non-Performing Assets (NPA) in the Indian banking sector?

Author: Asha Harshavardhan Kittur

Supervisor: Magnus Willeson

Examiner: Håkan Locking

Term: VT19

Subject: Bachelor Thesis - Finance

Course code: 2FE34E

II

TABLE OF CONTENTS

Table of contents II

INTRODUCTION 1

1 LITERATURE REVIEW 3

2 PURPOSE 4

3 RECOGNITION OF DISTRESS IN INDIAN BANKING SECTOR 5

3.1 Legal norms of Non-Performing Assets (NPA) 6

3.1.1 Asset classification under Health Code (HC) System 6

3.1.2 Asset classification under Prudential norms 7

3.1.3 Norms Relating to Classification of NPAs 7

3.1.4 Asset Restructuring 9

3.2 Capital Adequacy Ratio (CAR) as a determinant of the stability indicator 10

4 ALTMAN Z-SCORE MODELS 11

5 EMPIRICAL RESULTS 15

5.1 SAMPLE AND DATA 15

5.2 METHODOLOGY 16

5.2.1 Analyzing if the NPA effect is captured by the Z Scores during the NPA-Crisis

period 16

5.2.2 Analyzing if the Z Scores have the capacity to predict the future NPA crisis. 17

6 Results and analysis 18

6.1 Results of the Analysis if the NPA effect is captured by the Z Scores during the NPA-

Crisis period 18

6.1.1 ANALYSIS OF THE FINANCIAL RATIOS 18

6.1.2 ANALYSIS OF ALTMAN’S Z SCORES UNDER TWO DIFFERENT ALTMAN MODELS 19

6.1.3 PREDICTION ACCURACY OF THE ALTMAN’S Z SCORE MODEL 20

6.1.4 PAIRED SAMPLE T-TEST 21

6.2 Results of the analysis if the Z Scores have the capacity to predict the future NPA

crisis. 22

6.2.1 LINEAR REGRESSION TESTS 22

7 LIMITATIONS 22

8 CONCLUSION 23

9 BIBLIOGRAPHY 25

10 ANNEXURES 29

10.1 Summary of Non-performing assets of Public sector banks 29

10.2 Summary of Non-performing assets of Private sector banks 29

10.3 Summary of Capital Adequacy Ratios of Public Sector Banks 30

10.4 Summary of Capital Adequacy Ratios of Private sector banks 31

10.5 Computation of increase/decrease in Financial ratios of Public sector banks 31

10.6 Computation of increase/decrease in Financial ratios of Private sector banks 33

III

10.7 Summary of Z Scores of Public Sector banks using Original Altman Model 34

10.8 Summary of Z Scores of Public Sector banks using Emerging Markets Model 34

10.9 Summary of Z Scores of Private Sector banks using Original Altman Model 35

10.10 Summary of Z Scores of Private Sector banks using Emerging Markets Model 36

10.11 Summary of financial figures of Public Sector banks 36

10.12 Summary of financial figures of Private Sector banks 42

10.13 Computation of increase/decrease in z-scores of both public and private sector

banks 47

10.14 Computation of accuracy of prediction of both public and private sector banks48

10.15 Results of paired sample t-tests 51

10.15.1 Results of the paired sample t-test of Public Sector banks 51

10.15.2 Results of the paired sample t-test of Private Sector banks 51

10.16 Results of Regression 52

10.16.1 Public Sector banks: 52

10.16.2 Private Sector banks: 53

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ABSTRACT

The aim of this study is to measure the effectiveness of Altman’s Z-Score model using Non-per-

forming assets (NPA) as a benchmark stability indicator. To do that, this paper examines if Alt-

man’s Z Score Models capture the decline in financial health of the banks caused by the NPAs,

using a two-fold analysis i.e., in advance through prediction and when the distress period is on-

going. The findings of this paper would suggest that: 1. During the distress period: The Z-Scores

only marginally capture the distress caused by the NPAs, which is in line the findings of Almamy

et al that the predictive ability of the model goes down during the crisis period. (2016) 2. For the

future: The results of the statistical t-tests indicate that, the Z-Scores do not have the predictive

ability to capture the future NPAs. Two different models that are developed by Altman - one for

non-manufacturing firms and the other for the emerging markets, are used to test, if one model is

more suitable than the other to the Indian banking sector. The findings of this paper suggest that,

due to the uniqueness of the Indian banking sector during the NPA crisis, the ‘Emerging market

model’, does not produce any significantly better results. Therefore, there is further scope to de-

velop a tailor-made model suitable to the Indian banking sector.

KEYWORDS: ALTMAN MODEL, Z-SCORES, NON-PERFORMING ASSETS(NPA), INDIA,

EMERGING MARKET

INTRODUCTION

Across the globe, the banking sector acts as the catalyst for the country’s economy. In the wake of

many business failures around the globe, the banking industry is conspicuously under the lens.

There is a great divide between the developed economies and the emerging economies in terms of

accountability and disclosure norms. The term ‘emerging market’, coined by a World Bank econ-

omist, Antoine van Agtmael in the 1980’s, refers to the market activity in countries that are trans-

iting from developing to developed nations (Gangster, 2007). These countries basically have a

large population and resource base and are going through a positive transformation with an aim to

achieve sustainable economic growth. The Morgan Stanley Capital International (MSCI) index is

one of the institutions which is recognized for trading the emerging market phenomenon using

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several metrics. The MSCI index includes the following twenty five (25) markets they consider

emerging: Argentina, Brazil, Chile, China, Colombia, Czech Republic, Egypt, Hungary, India,

Indonesia, Israel, Jordan, Korea, Mexico, Morocco, Pakistan, Peru, Philippines, Poland, Russia,

South Africa, Taiwan, Thailand and Turkey.

Among these, India is one such country which has broken out of its shell and is one of the fastest

growing emerging markets alongside China in Asia, which is working to restructure their economy

keep pace with the globalization, exploring opportunities for trade, technology transfers, and for-

eign direct investment through open door policies. To support such growth, it has developed a

broad spectrum of banking companies. Banking industry in India, is a large cluster of varied bank-

ing institutions which consists of public sector banks, private sector banks, foreign banks, regional

rural banks, urban cooperative banks, rural cooperative banks and co-operative credit institutions.

The banking activities span around many significant small and large industries which are compet-

ing against the odds of global contagion risks and domestic policy failures. One major consequence

of this has been a significant increase in the Non-Performing Assets (NPA), which is spiraled out

of control and the policy makers are struggling hard to have a grip on it. This adversity has mainly

affected the Public sector banks and the Private commercial banks in India, who are the two major

competitive frontrunners and driving forces of the Indian Banking Sector. It is reported that when

the NPA issue arose, the banks did not give a clean account of NPAs in order to avoid legal issues.

In the wake of this, the leading newspaper of India, the Economic Times reported that Raghuram

Rajan, the then Governor of Reserve Bank of India (RBI), the Central banking institution in India,

has issued a stern warning to the banks to refrain from hiding or under-reporting the NPAs. This

would lead to unavailability of reliable data which could deter the investors and consumers from

conducting a fair analysis of the solvency situation of the banks. So, one solution to this could be

using a bankruptcy prediction model that could help do a logical calculation using the financial

reports published by the banks. To assess this, this paper considers the distress to be in the form

of the ongoing NPA crisis in India and thus moves forward holding this as a stability benchmark

indicator of distress with a focus on 20 public sector and 19 private sector banks that are highly

impacted by the NPA crisis. The bankruptcy prediction models used in this study are two of the

models developed by Edward Altman, known as the Altman’s Z-score model and also one other

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variation of this model, developed for the emerging markets as this is the case of Indian banking

industry.

Against this backdrop, Section 1 presents literature review of the various studies done so far. Sec-

tion 2 discusses the purpose of the study conducted through this paper, Section 3 highlights the

recognition of distress in India through the stability indicator chosen for this study, that is the Non-

performing assets(NPAs) and its legal norms as per the Indian law and Section 4 discusses the

different bankruptcy prediction models developed by Altman. In Section 5, the empirical results

in terms of sample, data and methodology are presented. Section 6 discusses the analysis and re-

sults of the study. The limitations of this study are discussed in Section 7 followed by conclusions

in Section 8.

1 LITERATURE REVIEW

Fischer (1936) was the first to use the term discriminant analysis and his method was applied for

exploration of the relationship between a group of independent characters (discriminators) and one

qualitative dependent variable-output. Beaver (1966) explored the predictive ability of financial

ratios. He built univariate discriminant models with the five ratios viz. cash flow to total debt, net

income to total assets, total debt to total assets, working capital to total assets, and current ratio.

In 1968, Altman (1968) developed the Z-score formula with an aim to provide a more effective

financial assessment tool to help lenders and risk analysts in their estimations. He used instead,

multi discriminant analysis since this allowed the use of a model which could treat binary variables

as depended in order to explain the behavior of two different groups. By that time multi-discrimi-

nant analysis was used merely by behavioral and biological sciences (Altman, 1968). Altman

(2000, 2002) repeatedly revised his models to make it available to different economic life condi-

tions and to advance its prediction accuracy figures.

Ever since Beaver (1966) and Altman (1968) pioneered failure prediction studies, many studies

have been carried out on similar lines to either to improve upon or to replicate. In the Indian con-

text, Bandyopadhyay (2006) develops bankruptcy prediction model based upon MDA and logistic

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technique for Indian corporate bond sector. The ratios used in his study measures liquidity, lever-

age, productivity, turnover and other financial variables which measures age, group ownership,

ISO Quality Certification and inter-industry effects of the firms. Bhumia and Sarkar (2011) in

other study on Indian pharmaceutical industry developed model for corporate failure using MDA

technique. The study chooses 16 financial ratios based upon past empirical literature measuring

profitability, solvency, liquidity and efficiency of the firms. Shetty et al. (2012) develops early

warning system for Indian IT/ITES using Data Envelopment Analysis (DEA). Based upon the past

empirical studies ten financial ratios measuring firm’s liquidity, leverage, productivity, and turno-

ver. Kumar and Rao (2015) develops non-linear new Z-score model based upon Person Type-3

distribution for Indian companies. Nayak and Nahak (2011) studied the performance of Indian

public sector banks during post-liberalization period. The study devised performance index for

banks based upon the financial ratios of profitability, financial efficiency, operational efficiency

and financial soundness. The paper applied Principal Component Analysis method to construct

index and ranked different banks over the last 10 years using Edward Altman Model. Sharma &

Mayanka (2013) conducted a study on public and private sector banks of India for the period 2008-

2012 using the original Altman model for non-manufacturing firms. They concluded that the fi-

nancial health of most of the banks were in the “Safe” zone, except for 2 banks. It is important to

note here, that the NPA issue in India, arose after 2012. Parvin, Nitu & Rahman (2016) conducted

a similar study on banks in Bangladesh, using the original model for non-manufacturing companies

and concluded that state-owned banks have a better standing than private banks.

However, in the Indian context, such analysis is still evolving. In India, there is lack of data avail-

ability on distressed companies and there is no central database from where the required data can

be retrieved for study and research purposes. The empirical studies on financial distress prediction

in Indian context are constrained by the fact that the information on such companies is scarce

(Jayadev, 2006).

2 PURPOSE

The purpose of this study is to examine if the Altman’s Z Score Models capture the decline in

financial health of the banks caused by the NPAs, using a two-fold analysis i.e., in advance through

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prediction and when the distress period is ongoing. Hence, in order to facilitate comparison, the

duration of study is chosen as 10 years beginning from the financial year 2009 to until 2018, is

divided into 5 equal years, that is 2009 to 2013 and 2014 to 2018. This has been done to accom-

modate the period before and after the NPA-crisis manifested, presumably in late 2012 and early

2013. Also, two different Altman models, one for the developed nations and another for emerging

markets have been used for this study. The purpose is to test, if one model is better suitable than

the other, for a unique industry in an emerging market, such as the Indian banking sector.

3 RECOGNITION OF DISTRESS IN INDIAN BANKING

SECTOR

Before setting out to evaluate the distress caused by the NPAs in the Indian banking industry, we

should review the evidence about whether there is indeed such a distress. Distress is a form of a

business failure can manifest itself in many ways. One is economic failure, which occurs when a

company is unable to generate the revenue, that would be enough to meet operational costs and

creditors obligations. Second, is a financial failure, which results from a financial distress. Beaver

(1966) defined failure as the inability of a firm to pay its financial obligations as they mature. Spica

and Kristijadi (2003) explain that financial distress can be said to occur, when the company expe-

riences a negative net operating income for several years and when the company does not pay any

dividends for several years. This distress can lead to insolvency or bankruptcy eventually. So,

bankruptcy can be understood as a situation where the financial distress gets out of control and the

liabilities of the company eventually exceed its assets.

Lindner and Jung (2014) relate Indian corporate vulnerabilities to increased level of non-perform-

ing and restructured loans in the banking system. Financial Stability Reports published by RBI,

have been highlighting that low debt servicing capability and high indebtedness of some of the

Indian banks could pose a risk to the financial stability. They explicitly say that the Public sector

banks (PSBs) are the ones that are severely affected by Non-Performing Assets (NPA), as com-

pared to the Private commercial banks. This is because, the public sector banks have to mandatorily

lend to the ‘priority sectors’ such as infrastructure and power- the sectors, that have the highest

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value of NPAs. The Public sector banks are a major type of bank in India, where a majority stake

(i.e. more than 50%) is held by a government and the Private commercial banks are the banks,

where majority of the shares or equity, are held by private shareholders. The Financial Stability

Reports of RBI have attributed the rise in NPAs to reasons such as, 1. Global slowdown and tepid

demand for capital 2. Unfriendly bankruptcy laws 3. Willful defaulters 4. Regulatory delays in the

Infra Sector (perhaps the most important): As per the BOT (Build-Operate-Transfer) model of PPP

Infrastructure development in India, private players create Special Purpose Vehicles that makes

each project act as an individual entity. These projects are highly leveraged (on an average 70%)

since the gestation period is long and uncertainty is high, equity funding isn’t attractive to inves-

tors. Therefore, promoters start with high debt and hope to raise equity nearer to the completion of

the project to get higher valuations when uncertainty is lower. Consequently, interest costs shoot

up drastically by this time and at the same time, increases the chances of default of principal as

well. Poghosyan and Cihak (2011) noted that one of the most significant strategic distress factors

in the banking industry are asset quality and capital adequacy. They also argued that asset quality

is the second most important ranked distress factor after leverage.

3.1 LEGAL NORMS OF NON-PERFORMING ASSETS (NPA)

3.1.1 Asset classification under Health Code (HC) System

In India, initially, the RBI had instructed all the Indian banks to uniformly adopt the asset classi-

fication under Health Code (HC) System under which each bank was required to classify its ad-

vances at all branches, including foreign branches, into eight categories with a health code ranging

from 1 to 8 assigned to each borrowal account. They were Code 1: Satisfactory; Code 2: Irregular;

Code 3: Sick: Viable / Under Nursing; Code 4: Sick: Non-Viable / Sticky; Code 5: Advances

Recalled; Code 6: Suit-filed Accounts; Code 7: Decreed Accounts; Code 8: Debts classified by the

bank as Bad and Doubtful. However, this system led to complications and confusions and a lot of

bad loans were under-reported.

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3.1.2 Asset classification under Prudential norms

As per RBI, with effect from April 1, 1994, the HC system was abolished, and prudential norms

were introduced. Under this method, the loan assets of a bank are required to be broadly classified

into only two categories:

1. Performing Assets or Standard Assets: These are the assets, which do not indicate any problem

or weakness regarding repayment of principal and interest. In other words, such assets are the less

risky assets or safe assets.

2. Non-Performing Assets (NPA): These assets on the other hand are loan assets, which cease to

generate income to the bank. It includes borrowers’ defaults or delays in interest or principal re-

payment. According to RBI regulations, a bank can classify a borrower’s account as NPA, only if

the interest charged during any quarter is not serviced fully within 90 days from the end of the

quarter. These assets are then regarded to have well defined credit weaknesses, that could jeopard-

ize the liquidation of debts and may be characterized by distinct possibilities that bank will sustain

some losses. In other words, an NPA may be defined as a credit facility in respect of which the

interest and/or installment of principal has remained unpaid for a specified period.

3.1.3 Norms Relating to Classification of NPAs

RBI has instructed the banks to further classify the NPAs into 3 sub-categories, by considering the

degree of credit weaknesses, risks and extent of dependence on collateral security for realization

of dues.

1. Sub Standard Asset: A substandard asset is one, which has remained NPA for a period of less

than or equal to 12 months.

2. Doubtful Asset: An asset would be classified as doubtful if it has remained in the substandard

category for a period of 12 months.

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3. Loss Asset: A Loss Asset is one where loss has been identified by the bank or internal or

external inspector or auditors or the RBI inspection as non-recoverable and realizable value of

securities is less than or equal to 10% of the outstanding, but the amount has not been written

off wholly.

Once the NPAs are detected, the banks are required to set aside funds as provisions for potential

losses arising out of these loans going bad, which is comparable to the provisions for bad debts

created by business firms. It is a prudent measure to prevent the bank itself from going bankrupt

and thereby wiping out all the deposits made by its customers. Though, this necessary prudence

comes at a cost: this setting aside of capital further restricts the banks’ ability to advance more

loans. This does three things: 1) It hurts banks’ profits deeply, 2) it limits credit availability in the

economy multifold due to the credit multiplier effect forgone and 3) due to lack of supply of loans,

the cost of borrowing rises.

Many studies have been conducted that analyze the banking sector stability, where the impact from

asset quality has been proven. Vigneswara (2015) asserted that banking industry should ensure

proper and high asset quality to achieve banking stability. Ozili (2018) investigated the determi-

nants of banking stability, using NPLs as a stability indicator. Kusa (2013) suggested that many

other ratios can be used to study the performance of banking sector and other related industries

and concluded banking industry will always ensure that non-performing loans are put at their min-

imums to improve quality of assets. In the context of an emerging market, Akhtar and Hayati

(2016) used an empirical Study on Islamic banking system of Pakistan in assessing the effect of

asset quality, income structure and macroeconomic factors on insolvency risk to determine the

insolvency risk in Islamic banking system of Pakistan for the years 2007 to 2015. This paper stud-

ies the effect of Non-performing Assets (NPA) as a stability indicator to assess the credibility

status of a banks in the India. And, a bankruptcy prediction model should be able to recognize its

effects in its output variable.

The charts below give a clear picture of an increase in average NPAs in both the public and private

sectors. Two of the private sector banks, Bandhan Bank and IDFC First Bank are new banks that

were incorporated in the year 2016. Hence, they are not included for comparison purposes.

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3.1.4 Asset Restructuring

To tackle such rising NPAs, banks have the option of asset restructuring. In the context of financial

distress, the definition given in Eichner (2010), who builds on the work of Altman and Hotchkiss

(2006) and Bowman and Singh (1993) says that asset restructuring is aimed at turning the firm

around and overcoming financial distress. Restructuring is also defined as any material discretion-

ary change in a firm’s assets, its capital structure, its operations, or its top management. The

following options are available under this, namely,

a. Asset Restructuring Companies (ARC): These are like the business process outsourcing (BPO)

companies, that specialize in recovering debt. The banks outsource the process of dealing with

NPAs to these ARCs. They ‘sell’ the loan to the ARC at a discount on the value of the loan,

suffer the loss equivalent to the discount, but get the risk off their hands.

b. Corporate Debt Restructuring (CDR): In this case, the bank does not use an ARC but restruc-

tures the NPA on its own, as per the laid norms.

c. Strategic Debt Restructuring (SDR): The banks convert their debt into majority equity in the

company by diluting the other shareholders of the company. Since they now are the major

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shareholders, they will either change the existing management with someone they trust or take

charge themselves in order to take decisions that will lead the company back to profitability.

Regarding the Indian banks, the Government of India has come to the banks’ aid by announcing

various loan recovery schemes such as 5.25 scheme for infra projects, S4A scheme (Scheme for

sustainable structuring of stressed assets) etc. Also, various initiatives by the Government such as

the Insolvency and bankruptcy code of 2016, recapitalization of banks, creation of bank bureaus,

joint lender forums etc. are some of the other domestic ways implemented so far, to tackle the

NPA issue. The effects of this have been visible in the financial statements, where the banks are

seen to be adequately capitalized. This is a good sign which can be a sign of a countermeasure put

in place to keep the banks functioning.

3.2 CAPITAL ADEQUACY RATIO (CAR) AS A DETERMINANT OF THE

STABILITY INDICATOR

When the NPAs in the balance sheet increase, the CAR of the banks decrease signaling the im-

pending risk of financial distress. Zhang, Xie, and Lu (2015) also indicated that non-performing

loan ratio representing asset quality and capital adequacy are the most influential indicators pre-

dicting financial distress. CAR also known as capital to risk-weighted assets ratio and it measures

a bank's financial strength, by using its capital and assets value. Generally, a bank with a high

capital adequacy ratio is considered safe and likely to meet its financial obligations. So, the impact

of NPAs can be cross verified with the CAR and therefore Capital Adequacy Ratio (CAR) is also

one such determinant of the stability indicator chosen for the study.

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4 ALTMAN Z-SCORE MODELS

In 1968, Edward I Altman (1968) developed and presented a multi-discriminant model which com-

puted a Z-score, as an assessment tool to assist risk analysts. He considered that univariate predic-

tion models served in most cases as indicators and not as predictors of bankruptcy. The formula

uses a statistical technique known as multiple discriminant analysis (MDA), by which Altman

attempted to predict defaults by use of the following five accounting ratios (Hayes et al., 2010).

The final discriminant function estimated by Altman in 1968, is as follows (Altman et all,2014)

𝑍 = 1.2𝑋1 + 1.4𝑋2 + 3.3𝑋3 + 0.6𝑋4 + 1.0𝑋5………Equation 1

Where,

X1 = Working capital/Total assets

X2 = Retained Earnings/Total assets

X3 = Earnings before interest and taxes/Total assets

X4 = Market value of equity/Book value of total liabilities

X5 = Sales/Total assets

0.00

5.00

10.00

15.00

Trend of Average

Capital Adequacy

Ratio of Public

Sector Banks

0.00

5.00

10.00

15.00

20.00

2009201020112012201320142015201620172018

Trend of Average

Capital Adequacy

Ratio of Private Sector

Banks

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Z = Overall Index

Cut off limits:

Z > 2.99 - “Safe” Zone

1.8 < Z < 2.99 - “Grey” Zone

1.9 Z < 1.80 - “Distress” Zone

However, the above original Z-Score Model was based on the market value of the firm and was

thus applicable only to publicly traded companies. In 1983, Altman emphasized that the Z-Score

Model is a publicly traded firm model and ad hoc adjustments are not scientifically valid. There-

fore, Altman (1983) re-estimated the model substituting the book value of equity for the market

value in X4. Using the same data, Altman extracted the following revised Z-Score Private Firm

Model:

𝑍 = 0.717𝑋1 + 0.847𝑋2 + 3.107𝑋3 + 0.420𝑋4 + 0.998𝑋5…..Equation 2

Where,

X1 = Working Capital /Total Assets

X2 = Retained Earnings/Total Assets

X3 = Earnings Before Interest and Taxes/Total Assets

X4 = Book Value of Equity/ Total Liabilities

X5 = Sales/ Total Assets

Cut off limits:

Z > 2.90 - “Safe” Zone

1.23 < Z < 2.90 - “Grey” Zone

Z < 1.23 - “Distress” Zone

Altman, improving upon the work done by himself (1977), developed a four-variable Z-Score

model in collaboration with Hotchkiss (2006) for non-manufacturing firms. He excluded the

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Sales/Total assets ratio X5 from this revised model, because of a potential industry effect. The

industry effect is more likely to take place when this kind of industry-sensitive variable (asset

turnover) is included into the model. (Altman 1983).

𝑍 = 6.56𝑋1 + 3.26𝑋2 + 6.72𝑋3 + 1.05𝑋4……..Equation 3

Cut off limits:

Z > 2.6 - “Safe” Zone

1.1 < Z < 2.6- “Grey” Zone

Z < 1.1 - “Distress” Zone

However, this model did not cater to the needs of the emerging markets. Recognizing this need,

Altman tweaked the model equation by additionally adding a constant of 3.25 to its existing equa-

tion. Heine & Altman (2011) explains that the constant term of +3.25 was added, to standardize

the scores with a score of zero (0) equated to a D (default) rated bond. However, the set of cut off

limits remain the same as above, to categorize the firms into solvency zones.

𝑍 = 3.25 + 6.56𝑋1 + 3.26𝑋2 + 6.72𝑋3 + 1.05𝑋4…..Equation 4

Cut off limits:

Z > 2.6 - “Safe” Zone

1.1 < Z < 2.6- “Grey” Zone

Z < 1.1 - “Distress” Zone

As regards to the emerging markets, there is one more model developed by Altman (2017) and it

is called Z” Score Model for Manufacturers, Non-Manufacturer Industrials; Developed and

Emerging Market Credits. Here, the model equation remains the same as above in the emerging

market model, but the cut off limits are different. Altman, Hatzell and Peck (1995) have applied

this enhanced Z" Score model to emerging market corporates, specifically Mexican firms that had

issued Eurobonds denominated in U.S. dollars.

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𝑍′′ = 3.25 + 6.56𝑋1 + 3.26𝑋2 + 6.72𝑋3 + 1.05𝑋4……..Equation 5

Cut off limits:

Z’’ > 5.85 - “Safe” Zone

4.35 < Z’’ < 5.85- “Grey” Zone

Z’’ < 4.35 - “Distress” Zone

Ratios applicable to both the models:

Z = the score and X = the independent variables (ratios of)

X1: Working Capital/Total Assets

X2: Retained Earnings/Total Assets

X3: EBIT/Total Assets

X4: Book Value Equity/Total liabilities

X5 Sales/Total Assets ratio

• X1 Working capital / Total asset

Working capital is a common measure of a company's liquidity, efficiency, and overall health.

Total assets show the overall assets of banks including both short and long-term. The WC/TA ratio

is a sign of a bank’s liquidity and ability to meet creditor's short-term obligations.

• X2 Retained earnings / Total assets

Retained earnings is the amount carried out to the coming years from net earnings. Accumulated

Retained Earnings to Total Asset (TA) is the ratio that measures the accumulated profitability of

the banks.

• X3 Operating earnings / Total assets

Earnings before Interest and Taxes (EBIT) show the operating profit of banks. EBIT to Total Asset

measures the operating efficiency of an organization. The value of this ratio indicates the capacity

of the firm to generate satisfactory earnings to pay off its fixed obligation like interest.

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• X4 Book value of equity / Total liabilities

This is the ratio of Book value of shareholder’s Equity to total liabilities. This ratio indicates the

long-term financial soundness of the banks. Having 1:1 equity debt mix is considered as quite

good, whereas excessive debt represents the danger of insolvency.

• X5 Sales/Total Assets ratio

This is the standard capital-turnover ratio illustrating the sales generating ability of the assets of a firm. It

refers to the capability of management in dealing with competitive conditions. This ratio was dropped in

the Z”-Score model.

In this paper, the author uses the Altman model equation 3 developed for the non-manufac-

turing firms and Altman model equation 4 developed for the emerging markets to calculate

and compare the Z-Scores. This is done in order to analyze, if one model suits the Indian

banking sector better than the other.

5 EMPIRICAL RESULTS

In this section, the sample, data and methodology of the empirical study are presented.

5.1 SAMPLE AND DATA

The data sample consisted of 39 Indian banks, of which 20 are public sector banks and 19 private

sector banks. The stability indicator chosen as the benchmark of distress for the purposes of this

paper are the NPAs. The nature of data used is Secondary data, as the financial figures used in the

construction of financial ratios are taken from the audited and published financial statements of

the respective banks. These figures are denominated in Crores of INR (Indian Rupees). The year

ending date of all financial documents used in this process was the 31st March for each year of

reference, as in India a financial year begins from 1st April to 31st March. However, for simplicity

purposes, the financial year ending 31st March 20XX is referred to as “20XX”. Also, the NPA and

CAR figures are collected from the statistical tables published by the Reserve Bank of India. There-

fore, such data types include pooled data categories, namely the combination of time series data

(time series data) and slices of latitude (cross section).

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5.2 METHODOLOGY

This paper aims at analyzing if the effect of rising NPAs can be captured by the Altman Z Scores. A two-

fold analysis can be performed to test this. They are:

5.2.1 Analyzing if the NPA effect is captured by the Z Scores during the

NPA-Crisis period

To analyze if the Z Scores have captured the rising effect of NPAs, the ten year longer study from

2009 to 2018 is divided 5 equal years, that is 2009 to 2013 (First study period) and 2014 to

2018(Second study period). This is because, the NPA-crisis manifested towards the end of 2013,

hence in order to facilitate comparison, the duration of study has been split into 5 years before the

NPA crisis and 5 after the NPA crisis began, to test if its effect is visible in the Z Scores.

In order to calculate the Z Scores, total of 1,508 ratios consisting of liquidity ratios(X1), profita-

bility ratios(X2), operational efficiency ratios(X3) and long-term capital adequacy ratios (X4)

were computed for both Public and Private Sector Banks. Altman’s Z score is the output variable

of a combination of weighted financial ratios. Toto (2011), in his book says that bankruptcy as a

condition, shows early indications through financial statements if analyzed carefully. Also, two

sets of Z Scores were computed, where one set was computed using the original Altman model

equation 3 developed for the non-manufacturing firms and the other set using the Altman model

equation 4 developed for the emerging markets. The resulting Z scores were measured against the

respective cut off limits provided by the model in order to categorize the banks into safe/grey/dis-

tress zones.

To test the effect of NPAs on the Altman’s Z Scores, an inverse relationship between changes in

the Z Scores and changes in NPAs was analyzed. As Siraaj KK (2014) notes in his Doctoral thesis,

NPA can impact the banks in 6 major ways namely, erosion of profit, increasing provisions, in-

creasing intermediation costs, increasing spread, declining reserves and surpluses and increase

market borrowings (Ranked in order). A prolonged continuity of all these factors lead to bank-

ruptcy. All these factors are inherently measured through the X1, X2, X3 & X4 ratios used by the

Altman model and consequently, the impact of NPAs that arose in study period 2 must be visible

in the Z-Scores in study period 2. If a Z-Score model is correctly developed, its component ratios

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typically reflect certain key dimensions of corporate solvency and performance. The power of such

a model results from the appropriate integration of these distinct dimensions weighted to form a

single performance measure, using the principle of the whole being worth more than the sum of

the parts (Agarwal & Taffler, 2005). Thus, if a bank has registered a growth in NPAs, the corre-

sponding Z-Score must go down indicating its impact for the Altman model is to be believed as a

robust indicator of identifying distress. This was followed by a comparative analysis between the

Z Scores of the first study period and the second study period.

Further, to test the statistical accuracy of the Z Score computations and analysis, a paired sample

t-test was conducted. This test was conducted to test, if there was a significant difference between

the Z Scores of the first and second study period. The findings are discussed in the ‘Results’ sec-

tion.

5.2.2 Analyzing if the Z Scores have the capacity to predict the future NPA

crisis.

This analysis incorporates a futuristic point of view, by testing if the Z Scores computed for the

first study period that is before the NPA crisis manifested, show any signs of a potential crisis in

the form of NPAs. This is particularly important for a country like India, where there is no a

central depository of information, from where an investor can access reliable data on companies.

In case, the Z scores indicate signs of an imminent distress ahead in the form of NPAs, there will

be ample time for governments to put in place pre-emptive measures and checks, to prevent such

an issue from arising and which can be beneficial in safeguarding many investments from going

bankrupt.

To do so, a regression was run using the Z Scores of the first study period as the independent

variable ‘X” and the NPA numbers of the second study period as the dependent variable ‘Y’ for

both public and private sector banks. Before doing so, the dependent variable ‘Y’, that is the NPA

numbers, were divided by their respective total assets before being in order to normalize the bank

size effect, as some larger banks have larger NPAs and this could distort the analysis.

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6 RESULTS AND ANALYSIS

The results and the two-fold analysis are presented in this section.

6.1 RESULTS OF THE ANALYSIS IF THE NPA EFFECT IS CAPTURED BY

THE Z SCORES DURING THE NPA-CRISIS PERIOD

6.1.1 ANALYSIS OF THE FINANCIAL RATIOS To measure the impact of NPAs during the first 5 years of the NPA-crisis, the above ratios were

individually analyzed. The impact of NPAs across the public sector was clearly visible in the prof-

itability and operational efficiency ratios. When compared to the study period 2009-13, study pe-

riod 2014-18 as regards to the public sector banks, indicates an average of 73% overall decline in

the profitability ratios and an average of 15% overall decline in the operational efficiency ratios.

Similarly, the long-term capital adequacy ratios show an overall decline of an average 2%.

Whereas, the liquidity ratios show a marginal overall increase of an average 0.11%. The impact of

NPAs across the private sector banks, was not as severe as the public sector banks. In fact, liquid-

ity, operational efficiency and long-term capital adequacy ratios of the private sector banks show

an average of overall increase of 4%, 6% and 5% respectively. However, the profitability ratios

show an overall decline of an average 5%.

Such a reduction in profitability ratio to a large extent, can be attributable to the NPAs, as NPAs

block the money flow into profitable projects. Thus, it not only affects the current profit but also

the future streams of profit, which may lead to a loss of a long-term beneficial opportunity. NPAs

bring down the Return on Investment (ROI) as well, which adversely affects the earnings of the

banks. Also, this ratio is measured through the Retained earnings. When you combine the retained

earnings with the total assets, the ratio gives a measure of how much the company relies on debt.

If a company has little to no retained earnings, then it must start seeking external funds to con-

tinue with its operations. In this case, retained earnings of public sector banks went down on an

average from 1543 Crores during the period 2009-13 to 846 Crores during the period 2014-18,

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which is a decline of an average of 45%. Now, the only solutions left for this problem would be

either to resort to debt or dilution.

6.1.2 ANALYSIS OF ALTMAN’S Z SCORES UNDER TWO DIFFERENT ALTMAN’S

MODELS Altman’s Z Scores were computed using both the Altman’s model equations 3 & 4 mentioned

above. Under both the models, it was found that the Z Scores of all the Public and Private sector

banks were found to be surprisingly well above the highest respective cut off limit of 2.6, indicat-

ing that all the banks are in the ‘Safe’ zone and that there is no imminent danger of bankruptcy.

Under the original model that excludes the intercept value of +3.25, the Z scores of all banks were

greater than 4. And, under the emerging market model, the intercept value of +3.25 pushed the Z

scores further up to scores greater than 8. Analyzing this in combination with the highest cut off

limit of 2.6 indicates that, perhaps the cut off limits need to be re-visited and redefined. Altman et

all (2014), concluded in his study on using the models in the international context, that considering

practical applications, it is obvious that while a general international model works reasonably well,

for most countries the classification accuracy may be somewhat improved with country-specific

estimation. In a country model, the information provided even by simple additional variables may

help boost the classification accuracy to a much higher level.

One way to do that may be to reduce the intercept value of +3.25 to adjust for the relatively higher

growth rate countries among the emerging economies, like India. In the emerging market model

equation 4, the additional +3.25 constant added proves to be very generic and cannot be applied

as an umbrella value to all emerging economies. So, it would be ideal if each emerging market is

given a relative weightage apt for its pace of growth.

However, even before the intercept value of +3.25 is added, the Z scores are already higher than 4

resulting in the categorization all the banks in the safe zone. This is because, even though there is

a huge NPA crisis in the Indian banking sector, the banks are well capitalized, and this has helped

the banks in maintaining healthy working capital levels. This is visible in the X1 ratio which has

one of the highest weightages of 6.56. Also, the operating earnings are not greatly affected as the

NPA loss adjustments are made in the appropriation of the operating earnings. This is visible in

the X3 ratio, which has the highest weightage of 6.72. These two ratios which are least affected by

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the NPA crisis, have contributed majorly to the overshooting of the Z scores in the Indian banks.

On the other hand, the decline in the profitability of the banks due to the NPA crisis is clearly

visible in the X2 ratio through retained earnings. During the study, it was found that retained earn-

ings of most of the banks were Zero. This metric should ideally bring down the Z Scores, but the

weightage given to this ratio is 3.26, therefore it was offset by the other high weighted ratios. So,

based on this, it is safe to say that, the Z scores of Indian banking sector need to be analyzed using

a different set of cut off limits as they tend to give a false projection of a stable solvency position,

which is far from the truth.

Also, additionally one can also experiment with the Z’’ Score model (Equation 5) developed for

the emerging market credits to analyze this given situation. The model equation 5 is same as the

model equation 4 but the cut off limits are different. The highest cut off limit for the Z’’ Score

model is 5.85 for a bank to be classified in a “Safe” from bankruptcy zone and as mentioned above,

the Z scores computed using these model equations 4&5 are greater than 8. Hence, this model also

does not hold good for the Indian banking industry.

6.1.3 PREDICTION ACCURACY OF THE ALTMAN’S Z SCORE MODEL As mentioned earlier, in order to consider the Z Scores as a robust indicator of identifying distress

caused by NPAs, the Z Scores of banks must go down for a corresponding increase in NPAs of

that bank and vice versa. On comparing the changes in the NPA with the changes in Z Scores, we

found the following results:

Banking sector

Accuracy of results

Percentage of accuracy of Z

Score in capturing the NPA

effect

Public Sector Banks 12 out of 20 banks 60%

Private Sector Banks 3 out of 17 banks 18%

Total 15 out of 37 banks 41%

The results indicated by these samples do not however address the bankruptcy issue per se. They

are the results of the tests to check how much of the distress has seeped into the financial ratios,

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which are the main inputs for the Z-Score. As seen above, only 41% of the banks showed the

impact of NPAs, which is not a very impressive number. Because, the MDA model derived by

Altman, had the accuracy of prediction of 95% in the first year prior to failure, 72% in the second

year and paltry 36% in the 5th year (Altman, 1968). This could mean that, as regards to the banking

sector that has its unique set of challenges, there is still room to improve the model equations to

incorporate the distress elements. However, it is important to note that the models are not a sub-

stitute calculation for the probability of default. It provides a fair overall estimate of probability of

default and not certainty.

6.1.4 PAIRED SAMPLE T-TEST The paired sample t-test, sometimes called the dependent sample t-test, is a statistical procedure

used to determine whether the mean difference between two sets of observations is zero. Hence,

the Z-Scores for the two study periods are averaged and are used to conduct a paired sample T-

Test with the following hypothesis:

• Null Hypothesis(H0): μd = 0

The null hypothesis (H0) assumes that the true mean difference (μd) is equal to zero.

• Alternative Hypothesis(H1): μd ≠ 0 (two-tailed)

The two-tailed alternative hypothesis (H1) assumes that the true mean difference (μd) is not

equal to zero

Conclusion of the paired t-test performed at 5% level of significance: The results of the paired

t-test returned a P value of 0.068064 for the public sector banks and 0.062504 for the Private sector

banks. The P-values are greater than the significance level of 5% under both the cases for a two-

tailed test, thus the null hypothesis cannot be rejected. Thus, Z-scores for Public and Private Banks

do not significantly differ when compared during both the study periods.

This is in line with the results of the analysis made in this study, that no significant difference is

noticed between the two study periods and the impact of NPAs is not significantly visible in the Z

Scores of the second study period.

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6.2 RESULTS OF THE ANALYSIS IF THE Z SCORES HAVE THE CAPAC-

ITY TO PREDICT THE FUTURE NPA CRISIS. This analysis attempts to address the issue of dearth of reliable data on NPAs in an emerging

economy like India. As mentioned earlier, this regression tests if the Z Scores of the first study

period indicate any signs of a potential NPA crisis.

6.2.1 LINEAR REGRESSION TESTS The following are the hypothesis for the regression tests:

Null Hypothesis(H0): Altman’s Z-Score is not a predictor or future NPAs

Alternative Hypothesis(H1): Altman’s Z-Score is a predictor of future NPAs

Conclusion of the paired t-test performed at 5% level of significance: R-squared measures the

proportion of the variation in your dependent variable (Y) explained by your independent variables

(X) for a linear regression model. In case of Public sector banks only 11% of the future NPAs is

explained by the Z Scores and in case of private sector banks, only 14% of NPAs is explained by

the Z Scores. Thus, as changes in the predictors are related to changes in the response variable, we

can say that this model does not explain much of the response variability in NPAs. Further, the

results of the regression test returned a P value of 0.144991 for the public sector banks and

0.144266 for the Private sector banks which is not statistically significant. Thus, the null hypoth-

esis cannot be rejected, and it can be concluded that Altman’s Z Score is not a predictor of future

NPAs.

7 LIMITATIONS

The reports are based as per the audited annual financial reports, which are published by the com-

pany. Only past ten years financial statements i.e., from 2009 to 2018 are used for the analysis. All

the information provided by the company is assumed to be correct. The study of comparison and

analysis is restricted to Public and Private Sector Banks part of the Indian Banking Sector and not

in entirety. Calculations are based on the summarized year end information which may not be a

true reflection of the overall year’s results. The study reflects the financial position of the company

and it cannot show a thorough picture of the activities of the banks.

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Also, it is important to remember that the public sector banks are the banks, where the majority of

shareholding belongs to the Governments. Hence, public sector banks could get some financial

assistance from the Governments to facilitate recovery as opposed to the private sector much eas-

ily. Therefore, there is a possibility that the signs of solvency position based on these ratios may

be reversed when such financial aid materializes. Hence, in such a dynamic scenario, using histor-

ical data could not present an accurate picture of the banks. Eisenbeis (1977) has pointed out the

insufficient value of information used in prediction models like Altman’s where the prediction of

a firm’s future financial status is only based on analysis of past data.

8 CONCLUSION

In this paper, the financial health of the Indian Public and Private sector banks was studied with

data spreading across 10 years from 2009 to 2018 using two different Altman’s Z Score models.

Empirical studies show that the emerging market model is used less frequently in the Indian con-

text and this paper tested both the models on the same set of data. This paper finds that, as regards

to the solvency categorization of the banks, both the Altman’s models that were used resulted in

similar findings despite having different cut off limits, because the Z scores classified all the banks

as Safe from bankruptcy.

Also, the prevalent distress due to the NPAs in the Indian banking sector, especially of the Public

sector banks is found to be marginally reflected in the ratios chosen by the Altman model. As

regards the Z Score, the results from this study indicate that there were no major deviations from

the Z Scores of the pre-NPA crisis period. This result is consistent with the findings of Almamy et

al. (2016) that prediction accuracy of these traditional models decreases during the period of crisis.

Also, with a futuristic viewpoint, the regression tests performed on the sample set used in this

paper, concluded that the Z Scores cannot be regarded as the predictors of future NPAs. Thus, one

should proceed with caution in using the Z Scores alone to get an early indication of a potential

distress through the NPAs, as they may not be able to predict them in advance.

This paper contributes to the literature, towards the research on the effects of deterioration of sta-

bility indicators such as NPAs, which are unique to financial firms. Perhaps, Altman’s models

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could explicitly incorporate NPAs as a direct variable in their equations applicable to the banking

industry. This is more important in the case of the emerging markets like India, where everything

seems calm on the surface while the undercurrent of the NPAs may be damaging the banking

structure and the country’s economy.

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https://economictimes.indiatimes.com/news/economy/policy/raghuram-rajan-warns-of-action-

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https://www.msci.com/emerging-markets Accessed on June 4th 2019

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10 ANNEXURES

10.1 SUMMARY OF NON-PERFORMING ASSETS OF PUBLIC SECTOR BANKS

Non-performing Assets

Public Sector Banks 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Allahabad Bank 1078 1222 1648 2059 5137 8068 8358 15385 20688 26563

Andhra Bank 368 488 996 1798 3714 5858 6877 11444 17670 2812436

Bank of Baroda 1843 2401 3153 4465 7983 11876 16261 40521 42719 56480

Bank of India 2471 4883 4812 5894 8765 11869 22193 49879 52045 62328

Bank of Maharashtra 798 1210 1174 1297 1138 2860 6402 10386 17189 18433

Canara Bank 2168 2590 3089 4032 6260 7570 13040 31638 34202 47468

Central Bank of India 2316 2458 2394 7273 8456 11500 11873 22721 27251 32874

Corporation Bank 559 651 790 124 2048 4737 7107 14544 17045 22213

Dena Bank 621 642 842 957 452 2616 4393 8560 12619 16361

Indian Bank Ltd 459 510 740 1851 3565 4562 5670 8827 9865 11990

Indian Overseas Bank 1923 3611 3090 3920 6608 9020 14922 30049 35098 38180

Oriental Bank of India 1058 1469 1921 3580 4184 5618 7666 14702 22859 26134

Punjab & Sindh Bank 161 206 724 763 1537 3554 3082 4229 6298 7802

Punjab National Bank 2507 3214 8720 13466 18880 25695 55818 55370 86620 86620

State Bank of India 18414 23533 25326 39676 51189 61605 56725 98173 112343 223427

Syndicate Bank 1595 2007 2599 3183 5325 4611 6442 13832 17609 25759

UCO Bank 1540 1666 3150 4086 7130 6621 10265 20908 22541 30550

Union Bank of India 1923 2671 3623 5450 6314 2564 13031 24171 33712 33712

United Bank of India 1020 1372 1356 2176 2964 7118 6553 9471 10952 16552

Vijaya Bank 699 995 1259 1718 1533 1986 2443 6027 2443 7526

10.2 SUMMARY OF NON-PERFORMING ASSETS OF PRIVATE SECTOR BANKS

Non-performing Assets

Private Sector Banks 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Axis Bank Ltd 898 1318 1599 1806 2393 3146 4110 6088 81055 49566

Bandhan Bank 0 0 0 0 0 0 0 19 86 373

City Union Bank Ltd 102 94 112 124 173 293 336 512 682 857

DCB Bank 290 319 264 242 215 138 186 197 254 369

Dhanlaxmi Bank 64 78 67 104 380 486 558 459 316 469

Federal Bank 590 821 1148 1301 1554 1087 1058 1668 1727 2796

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HDFC Bank Ltd 1988 1817 1694 1999 2335 2989 3438 4393 30848 42754

ICICI Bank Ltd 9649 9481 10034 9475 9608 10506 15095 26221 42159 53240

IDBI Bank 1436 2129 2785 4551 6450 9960 12685 24875 44753 55588

IDFC First Bank 0 0 0 0 0 0 0 3058 1542 1779

Indus Ind Bank 255 256 266 347 458 621 563 777 1055 1705

Jammu & Kashmir Bank 559 462 519 517 644 783 2464 4369 6000 6007

Karnataka Bank Ltd 443 550 702 685 639 836 944 1180 1582 2376

Karur Vysya Bank 206 235 228 321 286 279 678 511 1484 3016

Kotak Mahindra Bank 689 767 603 614 758 1059 1237 2838 3579 3825

Laxmi Vilas Bank 144 325 158 308 460 546 455 391 640 2694

RBL Bank 17 28 22 33 26 78 111 208 357 567

South India Bank 261 211 230 267 434 433 643 1562 1149 1980

YES Bank 85 60 81 84 94 175 313 749 2019 2627

10.3 SUMMARY OF CAPITAL ADEQUACY RATIOS OF PUBLIC SECTOR BANKS

Capital Adequacy Ratios

Public Sector Banks 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009

Allahabad Bank 8.69 11.45 11.02 10.45 9.96 11.03 12.83 12.96 13.62 13.11

Andhra Bank 11.00 12.38 11.58 10.63 10.78 11.76 13.18 14.38 13.93 13.22

Bank of Baroda 12.13 12.24 13.18 12.61 12.28 13.30 14.67 14.52 14.36 14.05

Bank of India 12.94 12.14 12.01 10.73 9.97 11.02 11.95 12.17 12.94 13.01

Bank of Maharashtra 11.01 11.18 11.20 11.94 10.79 12.59 12.43 13.35 12.78 12.05

Canara Bank 13.22 12.86 11.08 10.56 10.63 12.40 13.76 15.38 13.43 14.10

Central Bank of India 9.04 10.94 10.40 10.90 9.87 11.49 12.40 11.64 12.23 13.12

Corporation Bank 9.23 11.32 10.56 11.09 11.65 12.33 13.00 14.11 15.37 13.61

Dena Bank 11.09 11.39 11.00 10.93 11.14 11.03 11.51 13.41 12.77 12.07

Indian Bank Ltd 12.55 13.64 13.20 12.86 12.64 13.08 13.47 13.56 12.71 13.98

Indian Overseas Bank 9.26 10.49 9.67 10.11 10.78 11.85 13.32 14.55 14.78 13.20

Oriental Bank of Commerce 10.50 11.64 11.76 11.41 11.01 12.04 12.69 14.23 12.54 12.98

Punjab & Sindh Bank 11.25 11.05 10.91 11.24 11.04 12.91 13.26 12.94 13.10 14.35

Punjab National Bank 9.20 11.66 11.28 12.21 11.52 12.72 12.63 12.42 14.16 14.03

State Bank of India 12.60 13.11 13.12 2.40 12.44 12.92 13.86 11.98 13.39 14.25

Syndicate Bank 12.24 12.03 11.16 10.54 11.41 12.59 12.24 13.04 12.70 12.68

UCO Bank 10.94 10.93 9.63 12.17 12.68 14.15 12.35 13.71 13.21 11.93

Union Bank of India 11.50 11.79 10.56 10.22 10.80 11.45 11.85 12.95 12.51 13.27

United Bank of India 12.62 11.14 10.08 10.57 9.81 11.66 12.69 13.05 12.80 13.28

Vijaya Bank 13.90 12.73 12.58 11.43 10.56 11.32 13.06 13.88 12.50 13.15

31(54)

10.4 SUMMARY OF CAPITAL ADEQUACY RATIOS OF PRIVATE SECTOR BANKS

Capital Adequacy Ratios

Private sector Banks 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009

Axis Bank Ltd 16.57 14.95 15.29 15.09 16.07 17.00 13.66 12.65 15.80 13.69

Bandhan Bank 31.48 26.36 29.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00

City Union Bank Ltd 16.22 15.83 15.58 16.52 15.01 13.98 12.57 12.75 13.46 12.69

DCB Bank 16.47 13.76 14.11 14.95 13.71 13.61 15.41 13.25 14.85 13.30

Dhanlaxmi Bank 13.87 10.26 7.51 9.59 8.67 11.06 9.49 11.80 12.99 15.38

Federal Bank 14.70 12.39 13.93 15.46 15.14 14.73 16.64 16.79 18.36 20.22

HDFC Bank Ltd 14.82 14.55 15.53 16.79 16.07 16.80 16.52 16.22 17.44 15.69

ICICI Bank Ltd 18.42 17.39 16.64 17.02 17.70 18.74 18.52 19.54 19.41 15.53

IDBI Bank 10.41 10.70 11.67 11.76 11.68 13.13 14.58 13.64 11.31 11.57

IDFC Bank 18.00 18.90 22.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Indus Ind Bank 15.03 15.31 15.50 12.09 13.83 15.36 13.85 15.89 15.33 12.55

Jammu & Kashmir Bank 11.42 10.80 11.81 12.57 12.69 12.83 13.36 13.72 15.89 14.48

Karnataka Bank Ltd 12.04 13.30 12.03 12.41 13.20 13.22 12.84 13.33 12.37 13.48

Karur Vysya Bank 14.43 12.54 12.17 14.62 12.59 14.41 14.33 14.41 14.49 14.92

Kotak Mahindra Bank 18.22 16.77 16.34 17.17 18.83 16.05 17.52 19.92 18.35 20.01

Laxmi Vilas Bank 9.81 10.38 10.67 11.34 10.90 12.32 13.10 13.19 14.82 10.29

RBL Bank 15.33 13.72 12.94 13.13 14.64 17.11 23.20 56.41 34.07 42.30

South India Bank 12.70 12.37 11.82 12.01 12.42 13.91 14.00 14.01 15.39 14.76

YES Bank 18.40 17.00 16.50 15.60 14.40 18.30 17.90 16.50 20.60 16.60

10.5 COMPUTATION OF INCREASE/DECREASE IN FINANCIAL RATIOS OF PUBLIC SECTOR BANKS

X1 X2 X3 X4

Liquidity Ratio Profitability Ratio Operational Efficiency Ratio Capital Adequacy Ratio

Public

Sector

Banks

Avg

2009-

13

Avg

2014-

18

Increase/

(Decreas

e)

Avg

2009-

13

Avg

2014-

18

Increase/

(Decreas

e)

Avg

2009-

13

Avg

2014-

18

Increase/

(Decreas

e)

Avg

2009-

13

Avg

2014-

18

Increase/

(Decreas

e)

Allahabad

Bank 66.3% 69.6% 3.3% 0.7% 0.1% -0.6% 6.4% 5.1% -1.4% 5.4% 4.7% -0.7%

Andhra

Bank 73.2% 70.1% -3.1% 0.8% 0.2% -0.6% 6.9% 5.7% -1.1% 5.9% 5.2% -0.7%

Bank of

Baroda 75.2% 77.4% 2.2% 0.9% 0.2% -0.7% 5.5% 4.3% -1.2% 6.2% 6.1% 0.0%

Bank of

India 72.4% 75.3% 2.8% 0.7% 0.1% -0.5% 5.8% 4.3% -1.5% 5.2% 4.8% -0.3%

32(54)

Bank of

Maharash

tra 66.0% 70.2% 4.2% 0.4% 0.1% -0.3% 5.9% 5.3% -0.6% 4.2% 5.0% 0.8%

Canara 68.8% 69.2% 0.3% 0.8% 0.2% -0.6% 6.7% 5.7% -1.0% 5.4% 5.0% -0.4%

Central

Bank of

India 66.5% 64.6% -1.9% 0.4% 0.0% -0.3% 6.1% 5.0% -1.2% 3.8% 4.8% 0.9%

Corporati

on Bank 66.8% 68.2% 1.4% 0.8% 0.1% -0.6% 6.5% 5.9% -0.6% 5.4% 4.9% -0.6%

Dena

Bank 69.0% 67.2% -1.8% 0.7% 0.1% -0.6% 6.3% 5.1% -1.2% 4.7% 5.9% 1.2%

Indian

Bank Ltd 66.6% 67.5% 0.9% 1.1% 0.4% -0.7% 6.9% 6.0% -0.9% 7.3% 6.9% -0.4%

Indian

Overseas

Bank 71.3% 67.7% -3.6% 0.3% 0.0% -0.2% 4.6% 4.9% 0.2% 43.5% 5.1% -38.4%

Oriental

Bank 67.4% 68.6% 1.2% 0.6% 0.1% -0.5% 6.9% 5.5% -1.3% 6.3% 5.6% -0.7%

Punjab

National

Bank 69.2% 70.9% 1.7% 0.8% 0.2% -0.6% 6.5% 4.4% -2.2% 6.0% 6.0% 0.0%

Punjab&Si

ndh 66.1% 67.3% 1.2% 0.7% 0.2% -0.5% 7.3% 6.4% -0.9% 4.5% 5.1% 0.6%

State

Bank of

India 66.9% 67.5% 0.6% 0.7% 0.3% -0.3% 5.8% 5.1% -0.8% 6.4% 6.5% 0.1%

Syndicate

Bank 74.7% 73.5% -1.2% 0.5% 0.2% -0.2% 6.1% 5.2% -0.9% 3.3% 4.2% 0.9%

UCO Bank 69.1% 63.3% -5.8% 0.4% 0.2% -0.3% 5.3% 4.8% -0.5% 4.0% 4.3% 0.3%

Union

Bank of

India 69.5% 71.6% 2.1% 0.7% 0.2% -0.5% 6.2% 5.7% -0.5% 4.7% 5.0% 0.3%

United

Bank of

Ind 64.6% 59.4% -5.3% 0.4% 0.1% -0.3% 5.8% 5.1% -0.6% 4.7% 4.4% -0.3%

Vijaya

Bank 66.8% 68.8% 2.0% 0.5% 0.3% -0.2% 6.7% 6.4% -0.3% 4.9% 4.9% 0.0%

33(54)

10.6 COMPUTATION OF INCREASE/DECREASE IN FINANCIAL RATIOS OF PRIVATE SECTOR BANKS

X1 X2 X3 X4

Liquidity Ratio Profitability Ratio Operational Efficiency Ratio Capital Adequacy Ratio

Private

Sector

Banks

Avg

2009-

13

Avg

2014-

18

Increase/

(Decreas

e)

Avg

2009-

13

Avg

2014-

18

Increase/

(Decreas

e)

Avg

2009-

13

Avg

2014-

18

Increase/

(Decreas

e)

Avg

2009-

13

Avg

2014-

18

Increase/

(Decreas

e)

Axis Bank

Ltd 64.4% 70.8% 6.4% 1.2% 0.9% -0.2% 6.5% 6.1% -0.4% 9.0% 10.7% 1.6%

Bandhan

Bank 0.0% 63.4% 63.4% 0.0% 2.2% 2.2% 0.0% 5.5% 5.5% 0.0% 13.6% 13.6%

City Union

Bank Ltd 71.1% 75.0% 3.9% 1.2% 1.3% 0.1% 8.0% 8.0% 0.1% 7.6% 10.6% 3.1%

DCB Bank 63.0% 67.8% 4.9% 0.4% 1.0% 0.6% 5.6% 6.7% 1.1% 9.4% 9.6% 0.1%

Dhanlaxmi

Bank 68.1% 63.2% -4.9% 0.4% 0.0% -0.4% 9.1% 8.7% -0.5% 6.2% 5.3% -0.9%

Federal

Bank 66.7% 70.5% 3.7% 1.0% 0.7% -0.3% 7.2% 6.8% -0.4% 11.3% 9.6% -1.7%

HDFC Bank

Ltd 61.3% 68.8% 7.5% 1.2% 1.4% 0.2% 6.3% 6.9% 0.6% 9.8% 11.1% 1.3%

ICICI Bank

Ltd 62.2% 68.9% 6.7% 0.9% 1.0% 0.2% 6.6% 6.0% -0.5% 15.1% 13.8% -1.3%

IDBI Bank 65.7% 65.0% -0.7% 0.5% 0.1% -0.4% 1.9% 4.0% 2.1% 5.3% 6.0% 0.7%

IDFC First

Bank 0.0% 30.5% 30.5% 0.0% 0.4% 0.4% 0.0% 3.4% 3.4% 0.0% 9.7% 9.7%

Indus Ind

Bank 66.3% 72.0% 5.7% 1.4% 2.0% 0.6% 11.4% 10.6% -0.8% 8.2% 12.0% 3.8%

Jammu &

Kashmir

Bank 62.4% 68.2% 5.8% 1.6% 0.7% -0.9% 10.9% 8.3% -2.6% 7.4% 7.7% 0.3%

Karnataka

Bank Ltd 60.4% 67.3% 6.9% 0.6% 0.6% 0.0% 7.3% 7.0% -0.4% 7.6% 7.3% -0.3%

Karur

Vysya Bank 68.1% 74.6% 6.5% 1.5% 0.9% -0.6% 11.3% 10.3% -1.1% 7.9% 8.9% 1.0%

Kotak

Mahindra

Bank 60.0% 69.0% 9.0% 1.4% 1.5% 0.1% 7.1% 7.2% 0.1% 14.2% 15.4% 1.3%

Laxmi Vilas

Bank 69.4% 70.7% 1.3% 0.4% 0.4% -0.1% 6.8% 6.5% -0.3% 6.2% 5.7% -0.5%

RBL Bank 65.7% 63.6% -2.0% 0.7% 0.7% 0.0% 5.4% 6.2% 0.8% 21.0% 10.1% -10.9%

34(54)

South India

Bank 69.5% 71.6% 2.1% 0.8% 0.5% -0.3% 7.2% 6.9% -0.2% 5.9% 6.3% 0.4%

YES Bank 59.6% 65.5% 5.9% 1.2% 1.3% 0.1% 7.6% 7.6% 0.0% 7.4% 9.2% 1.8%

10.7 SUMMARY OF Z SCORES OF PUBLIC SECTOR BANKS USING ORIGINAL ALTMAN MODEL

Public Sector Banks 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009

Allahabad Bank 4.5705 5.0752 5.0488 5.1714 4.9516 5.0206 4.9513 4.9018 4.6878 4.7450

Andhra Bank 4.8237 4.9572 5.0874 5.2116 5.1419 5.3144 5.4269 5.4324 5.3396 5.2395

Bank of Baroda 5.1211 5.4424 5.4041 5.6115 5.6101 5.3380 5.6196 5.4848 5.3131 5.2108

Bank of India 5.0802 5.1995 5.3168 5.4525 5.3630 5.4190 5.2768 5.0458 5.0897 5.2461

Bank of Maharashtra 4.7218 5.0455 5.2499 5.0829 4.9611 4.9786 4.8569 4.7253 4.7257 4.6298

Canara Bank 4.9544 4.9673 4.9414 4.9932 5.0388 4.9374 5.0992 5.1646 5.0440 5.0210

Central Bank of India 4.4308 4.7316 4.6199 4.6673 4.6694 4.9562 4.9865 4.8629 4.8376 4.4978

Corporation Bank 4.5649 5.0776 5.0101 5.0620 4.9130 4.9462 5.0066 4.8513 4.7645 4.9464

Dena Bank 4.5199 4.6524 4.9721 5.0046 4.9345 4.8546 5.1626 5.1217 4.9561 5.0188

Indian Bank Ltd 4.7884 4.6476 4.9710 5.1465 5.0503 5.0800 5.0284 4.8568 4.8722 4.8839

Indian Overseas Bank 4.5363 4.7184 4.7682 4.9576 5.1314 5.1839 5.8418 5.5576 5.3442 5.3519

Oriental Bank of India 4.5887 5.1617 4.9720 4.9016 5.0584 5.0032 4.9558 4.7242 5.0519 5.1023

Punjab & Sindh Bank 4.7205 4.9041 5.0381 5.0290 4.8409 4.9702 5.0358 4.9267 4.7467 4.8276

Punjab National Bank 4.7386 5.0035 5.0906 5.1509 5.0815 5.0544 5.0783 5.0934 5.0469 5.0607

State Bank of India 4.4205 4.5399 5.1131 4.9733 5.1901 5.1562 5.0994 4.8082 4.6711 4.5970

Syndicate Bank 4.9869 5.3255 5.2251 5.2855 5.2885 5.3854 5.3698 5.3589 5.3489 5.3472

UCO Bank 4.1191 4.4121 4.2701 4.9097 4.8950 5.0617 5.0369 4.9601 4.6707 4.9944

Union Bank of India 4.9572 5.1049 5.3463 5.2216 5.0934 5.1380 5.2292 5.0623 4.8947 4.9488

United Bank of India 4.3250 4.1662 4.3673 4.2722 4.3056 4.8457 4.9322 4.7690 4.3364 4.5531

Vijaya Bank 5.3516 4.9593 4.9645 4.8822 4.8648 5.0716 4.9491 4.7795 4.7385 4.9520

10.8 SUMMARY OF Z SCORES OF PUBLIC SECTOR BANKS USING EMERGING MARKETS MODEL

Public Sector Banks 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009

Allahabad Bank 7.8205 8.3252 8.2988 8.4214 8.2016 8.2706 8.2013 8.1518 7.9378 7.9950

Andhra Bank 8.0737 8.2072 8.3374 8.4616 8.3919 8.5644 8.6769 8.6824 8.5896 8.4895

Bank of Baroda 8.3711 8.6924 8.6541 8.8615 8.8601 8.5880 8.8696 8.7348 8.5631 8.4608

35(54)

Bank of India 8.3302 8.4495 8.5668 8.7025 8.6130 8.6690 8.5268 8.2958 8.3397 8.4961

Bank of Maharashtra 7.9718 8.2955 8.4999 8.3329 8.2111 8.2286 8.1069 7.9753 7.9757 7.8798

Canara Bank 8.2044 8.2173 8.1914 8.2432 8.2888 8.1874 8.3492 8.4146 8.2940 8.2710

Central Bank of India 7.6808 7.9816 7.8699 7.9173 7.9194 8.2062 8.2365 8.1129 8.0876 7.7478

Corporation Bank 7.8149 8.3276 8.2601 8.3120 8.1630 8.1962 8.2566 8.1013 8.0145 8.1964

Dena Bank 7.7699 7.9024 8.2221 8.2546 8.1845 8.1046 8.4126 8.3717 8.2061 8.2688

Indian Bank Ltd 8.0384 7.8976 8.2210 8.3965 8.3003 8.3300 8.2784 8.1068 8.1222 8.1339

Indian Overseas Bank 7.7863 7.9684 8.0182 8.2076 8.3814 8.4339 9.0918 8.8076 8.5942 8.6019

Oriental Bank of India 7.8387 8.4117 8.2220 8.1516 8.3084 8.2532 8.2058 7.9742 8.3019 8.3523

Punjab & Sindh Bank 7.9705 8.1541 8.2881 8.2790 8.0909 8.2202 8.2858 8.1767 7.9967 8.0776

Punjab National Bank 7.9886 8.2535 8.3406 8.4009 8.3315 8.3044 8.3283 8.3434 8.2969 8.3107

State Bank of India 7.6705 7.7899 8.3631 8.2233 8.4401 8.4062 8.3494 8.0582 7.9211 7.8470

Syndicate Bank 8.2369 8.5755 8.4751 8.5355 8.5385 8.6354 8.6198 8.6089 8.5989 8.5972

UCO Bank 7.3691 7.6621 7.5201 8.1597 8.1450 8.3117 8.2869 8.2101 7.9207 8.2444

Union Bank of India 8.2072 8.3549 8.5963 8.4716 8.3434 8.3880 8.4792 8.3123 8.1447 8.1988

United Bank of India 7.5750 7.4162 7.6173 7.5222 7.5556 8.0957 8.1822 8.0190 7.5864 7.8031

Vijaya Bank 8.6016 8.2093 8.2145 8.1322 8.1148 8.3216 8.1991 8.0295 7.9885 8.2020

10.9 SUMMARY OF Z SCORES OF PRIVATE SECTOR BANKS USING ORIGINAL ALTMAN MODEL

Private Sector Banks 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009

Axis Bank Ltd 5.1848 5.3031 5.4391 5.0572 4.9915 4.7631 4.7774 4.8373 4.7885 4.8108

Bandhan Bank 6.3814 6.3326 5.8269 5.1743 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

City Union Bank Ltd 5.6682 5.6892 5.6961 5.5254 5.5017 5.5529 5.4155 5.3014 5.0933 5.2400

DCB Bank 5.2445 4.9786 5.1435 4.9211 4.8741 4.7674 4.7475 4.5098 4.2903 4.7625

Dhanalaxmi Bank 4.6401 4.8239 4.8990 4.7213 4.8393 5.0341 5.1208 5.2213 5.2281 5.1749

Federal Bank 5.4301 5.2885 5.3381 5.0877 4.8524 5.0329 5.1242 5.0234 4.9657 4.9116

HDFC Bank Ltd 5.3441 5.0896 5.3230 4.9541 4.9861 4.7504 4.4838 4.6550 4.6713 4.3668

ICICI Bank Ltd 5.1787 5.3469 5.2484 4.9432 4.8114 4.6679 4.5241 4.6307 4.6560 5.0612

IDBI Bank 4.2311 4.6791 4.7619 4.5781 4.7434 4.8272 4.5604 4.6181 4.2121 4.3332

IDFC Bank 3.6249 3.7844 4.3101 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Indus Ind Bank 5.6098 5.6694 5.5981 5.6736 5.6150 5.5349 5.5402 5.0963 5.0170 5.0602

Jammu & Kashmir Bank 5.3465 4.8639 5.3331 5.0178 5.1066 4.9738 4.8914 4.6363 4.9949 5.2761

Karnataka Bank Ltd 5.3447 4.7638 5.0649 4.9304 4.7833 4.8765 4.6200 4.4937 4.4665 4.3170

Karur Vysya Bank 5.5892 5.7005 5.8135 5.7763 5.6579 5.3577 5.4336 5.3422 5.2611 5.4040

Kotak Mahindra Bank 5.3205 5.5393 5.0826 5.0468 5.1044 4.7716 4.8373 4.5456 4.3658 4.5050

36(54)

Laxmi Vilas Bank 4.8656 5.2465 5.3316 5.2506 5.0377 5.2926 5.0204 4.8852 4.9460 5.2782

RBL Bank 5.2318 5.0385 4.4073 4.4890 4.4371 4.1416 4.9029 5.1287 4.9739 5.4172

South India Bank 5.3781 5.1569 5.4028 5.0556 5.2349 5.2807 5.3694 4.9928 5.0409 4.9506

YES Bank 5.4089 5.3216 4.9362 4.6376 4.3981 3.9848 4.2375 4.7150 4.9455 4.7791

10.10 SUMMARY OF Z SCORES OF PRIVATE SECTOR BANKS USING EMERGING MARKETS MODEL

Private Sector Banks 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009

Axis Bank Ltd 8.4348 8.5531 8.6891 8.3072 8.2415 8.0131 8.0274 8.0873 8.0385 8.0608

Bandhan Bank 9.6314 9.5826 9.0769 8.4243 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

City Union Bank Ltd 8.9182 8.9392 8.9461 8.7754 8.7517 8.8029 8.6655 8.5514 8.3433 8.4900

DCB Bank 8.4945 8.2286 8.3935 8.1711 8.1241 8.0174 7.9975 7.7598 7.5403 8.0125

Dhanlaxmi Bank 7.8901 8.0739 8.1490 7.9713 8.0893 8.2841 8.3708 8.4713 8.4781 8.4249

Federal Bank 8.6801 8.5385 8.5881 8.3377 8.1024 8.2829 8.3742 8.2734 8.2157 8.1616

HDFC Bank Ltd 8.5941 8.3396 8.5730 8.2041 8.2361 8.0004 7.7338 7.9050 7.9213 7.6168

ICICI Bank Ltd 8.4287 8.5969 8.4984 8.1932 8.0614 7.9179 7.7741 7.8807 7.9060 8.3112

IDBI Bank 7.4811 7.9291 8.0119 7.8281 7.9934 8.0772 7.8104 7.8681 7.4621 7.5832

IDFC Bank 6.8749 7.0344 7.5601 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Indus Ind Bank 8.8598 8.9194 8.8481 8.9236 8.8650 8.7849 8.7902 8.3463 8.2670 8.3102

Jammu & Kashmir Bank 8.5965 8.1139 8.5831 8.2678 8.3566 8.2238 8.1414 7.8863 8.2449 8.5261

Karnataka Bank Ltd 8.5947 8.0138 8.3149 8.1804 8.0333 8.1265 7.8700 7.7437 7.7165 7.5670

Karur Vysya Bank 8.8392 8.9505 9.0635 9.0263 8.9079 8.6077 8.6836 8.5922 8.5111 8.6540

Kotak Mahindra Bank 8.5705 8.7893 8.3326 8.2968 8.3544 8.0216 8.0873 7.7956 7.6158 7.7550

Laxmi Vilas Bank 8.1156 8.4965 8.5816 8.5006 8.2877 8.5426 8.2704 8.1352 8.1960 8.5282

RBL Bank 8.4818 8.2885 7.6573 7.7390 7.6871 7.3916 8.1529 8.3787 8.2239 8.6672

South India Bank 8.6281 8.4069 8.6528 8.3056 8.4849 8.5307 8.6194 8.2428 8.2909 8.2006

YES Bank 8.6589 8.5716 8.1862 7.8876 7.6481 7.2348 7.4875 7.9650 8.1955 8.0291

10.11 SUMMARY OF FINANCIAL FIGURES OF PUBLIC SECTOR BANKS

Public Sector

Banks Criteria 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009

Total Assets

25333

4

23774

3

23651

6

22758

9

22051

5

20442

9

18295

8

15129

1

12170

4

9765

8

Total Liabilities

24715

9

22604

7

22510

0

21579

5

20956

7

19393

3

17331

1

14364

8

11582

0

9267

9

Allahabad Bank Working Capital

17033

4

16990

9

16795

4

16165

0

14865

2

13977

8

12285

3

10205

3 77829

6303

9

37(54)

Retained

Earnings 0 0 0 528 1031 889 1568 1138 965 653

EBIT 5035 11769 11867 15141 15072 14122 12523 8923 7490 6282

Book Value of

Equity 6176 11696 11416 11794 10949 10497 9647 7643 5884 4979

Total Assets

24217

1

22312

3

20056

0

18517

0

16739

2

14632

7

12457

8

10910

9 90431

6852

9

Total Liabilities

23218

8

21260

6

18956

5

17581

9

15865

4

13788

6

11709

9

10261

7 86021

6488

2

Andhra Bank Working Capital

17007

8

15358

5

13978

0

13190

8

11797

7

10542

0 91640 81767 66714

4914

2

Retained

Earnings 0 174 507 517 370 1006 1035 963 805 438

EBIT 6179 12855 13318 13049 11294 10924 9404 6837 5614 4646

Book Value of

Equity 9982 10517 10994 9351 8737 8441 7479 6492 4410 3647

Total Assets

71999

9

69487

5

67137

6

71498

9

65950

4

54713

5

44732

1

35839

7

27831

7

2266

72

Total Liabilities

67660

4

65457

2

63117

8

67515

3

62351

9

51516

6

41984

5

33735

4

26321

0

2137

93

Bank of Baroda Working Capital

52872

9

53720

1

52100

4

56746

5

52284

6

40858

5

35037

0

27509

5

20625

1

1632

89

Retained

Earnings 0 1051 0 2685 3633 3585 4306 3605 2508 1893

EBIT 25337 31159 24623 35197 32472 28713 25383 18734 14997

1331

1

Book Value of

Equity 43394 40303 40199 39834 35984 31968 27477 21044 15106

1288

0

Total Assets

61142

6

62845

3

61176

1

61869

8

57319

0

45260

3

38453

5

35117

3

27496

6

2259

32

Total Liabilities

58143

4

60337

5

58074

8

59080

6

54700

9

42986

7

36481

0

33520

1

26216

5

2141

47

Bank of India Working Capital

44905

9

46822

7

46777

2

47417

8

43164

4

34246

0

28053

1

24852

5

19553

1

1620

27

Retained

Earnings 0 0 0 1384 2402 2144 2276 2115 1375 2586

EBIT 18933 25092 22281 33881 30625 25893 23745 17436 14616

1501

3

Book Value of

Equity 29991 25078 31014 27891 26180 22735 19726 15971 12801

1178

5

38(54)

Total Assets

15666

2

15932

4

16109

0

14629

6

13654

5

11705

4 91244 76464 71074

5904

3

Total Liabilities

14784

8

15194

4

15359

8

13924

5

13024

2

11177

7 86897 72937 68670

5697

8

Bank of

Maharashtra Working Capital

10671

9

11439

3

11778

4

10215

0 92737 79631 61189 50291 46522

3754

1

Retained

Earnings 0 0 101 365 301 608 302 235 352 311

EBIT 4441 6744 9592 9604 9196 7923 5355 3983 4008 3546

Book Value of

Equity 8814 7380 7491 7051 5715 4689 4348 3527 2404 2065

Total Assets

61999

9

58760

2

55697

0

54876

2

49226

6

41267

4

37446

7

33623

5

26505

2

2196

46

Total Liabilities

59091

8

55929

0

53081

2

52230

8

46814

4

38983

0

35384

2

31829

3

25251

3

2096

06

Canara Bank Working Capital

44028

8

40565

7

38331

6

37366

9

33860

6

27498

8

25828

9

23915

9

18309

5

1502

27

Retained

Earnings 0 1122 0 2162 1926 2298 2790 3543 2598 1741

EBIT 22528 33158 31073 37584 33666 29871 27244 20267 16893

1497

4

Book Value of

Equity 29079 28312 26159 26453 24122 22845 20625 17942 12539

1004

0

Total Assets

32686

8

33398

3

30601

8

31247

2

29022

6

26834

2

23005

9

21013

1

18310

1

1480

17

Total Liabilities

31201

3

31992

0

29163

1

29682

9

27627

8

25489

7

21950

5

20321

1

17737

1

1436

14

Central Bank of

India Working Capital

20842

5

22363

1

19655

2

19888

2

18715

8

18250

1

15793

0

14223

3

12225

8

9114

1

Retained

Earnings 0 0 0 522 0 751 384 1115 974 491

EBIT 9623 14558 16153 20052 16938 17443 14627 11554 11069 9152

Book Value of

Equity 14855 14063 14387 15643 12331 11829 10555 6919 5730 4403

Total Assets

22240

7

24839

5

23538

0

22648

0

22234

7

19359

2

16364

4

14354

2

11169

2

8692

2

Total Liabilities

21220

8

23634

6

22469

4

21599

5

21226

2

18402

6

15536

8

13640

4

10591

7

8202

6

Corporation Bank Working Capital

14410

7

17371

3

16441

1

15630

7

14984

3

12986

2

11083

6 96069 72771

5845

4

39(54)

Retained

Earnings 0 561 0 467 449 1148 1205 1116 936 714

EBIT 8736 15857 13240 16070 14416 13593 11776 8129 6747 5763

Book Value of

Equity 10199 12049 10686 10484 10085 9566 8276 7138 5775 4897

Total Assets

12101

8

12978

3

13344

2

12992

1

12486

3

11344

3 87387 70839 57589

4846

5

Total Liabilities

11290

8

12319

6

12723

0

12248

0

11772

1

10853

4 83095 67380 55196

4651

6

Dena Bank Working Capital 78634 84276 93320 89160 84574 75075 61905 50188 39198

3356

4

Retained

Earnings 0 0 0 215 436 648 699 538 455 389

EBIT 3278 6498 6618 8383 7731 7549 5659 4168 3597 2925

Book Value of

Equity 8110 6587 6212 7440 7142 4909 4292 3459 2393 1949

Total Assets

25271

6

21823

3

20371

0

19283

6

18722

6

16282

3

14141

9

12171

8

10138

9

8405

4

Total Liabilities

23688

9

20377

2

19023

2

18027

8

17569

1

15198

4

13178

2

11339

2 94342

7817

4

Indian Bank Ltd Working Capital

16906

5

13861

3

13867

3

13556

2

12903

2

11218

6 95707 79841 66384

5489

8

Retained

Earnings 969 1125 640 804 950 1297 1433 1388 1275 1034

EBIT 11927 12652 12753 12860 12364 11195 10081 7959 6905 6007

Book Value of

Equity 15827 14462 13478 12558 11536 10439 9637 8327 7047 5880

Total Assets

24796

8

24716

7

27443

7

28563

7

27489

9

24465

6

24465

6

24465

6

24465

6

2446

56

Total Liabilities

23679

6

23558

8

26118

3

27170

2

26054

2

23234

7

23234

7

23234

7

23234

7

2323

47

Indian Overseas

Bank Working Capital

16569

1

16441

6

18249

2

19439

8

19399

7

17450

3

17450

3

17450

3

17450

3

1745

03

Retained

Earnings 0 0 0 0 451 380 693 762 516 1087

EBIT 3816 11148 14406 18666 17950 16172 14171 9486 8057 8726

Book Value of

Equity 11170 11579 13254 13934 14356 12309

16913

0

13452

8 96707

9267

0

Total Assets

23397

9

25346

1

23754

2

23051

4

22030

2

20109

4

17799

9

16176

2

13783

4

1125

83

40(54)

Total Liabilities

22359

7

24078

8

22410

8

21735

6

20752

3

18899

5

16691

4

15155

2

13051

3

1061

30

Oriental Bank of

India Working Capital

15497

0

18501

7

16231

6

15391

9

15140

4

13589

4

11878

7

10396

1 95286

7792

0

Retained

Earnings 0 0 134 398 912 1062 913 1202 908 703

EBIT 6794 11990 15010 15509 15470 14535 13019 9947 8954 8004

Book Value of

Equity 10382 12673 13434 13157 12779 12099 11085 10211 7321 6452

Total Assets

11382

3 96695

10258

1 97848 94624 80489 72905 68550 56699

4128

3

Total Liabilities

10848

7 91426 97513 93037 90444 76591 69386 65501 54570

3966

6

Punjab & Sindh

Bank Working Capital 76680 64724 70379 66828 61919 53693 49153 46199 36560

2671

6

Retained

Earnings 0 201 269 97 241 271 406 484 509 434

EBIT 4226 6416 7240 7152 6953 6378 5876 4424 3768 3104

Book Value of

Equity 5336 5269 5068 4812 3975 3698 3519 3049 2129 1617

Total Assets

76684

0

72082

3

66779

3

60333

4

55074

8

47896

5

45822

0

37835

4

29665

8

2469

40

Total Liabilities

72945

0

68272

7

63232

8

56425

4

51626

1

44771

7

43185

5

35831

6

28042

7

2338

00

Punjab National

Bank Working Capital

53381

2

50756

5

48520

3

43129

5

38671

3

32917

5

31734

7

26625

8

20458

5

1695

78

Retained

Earnings 0 1325 0 2449 2975 271 4152 3724 3202 2473

EBIT 13498 34294 26375 33717 31768 33559 30099 21743 18849

1705

9

Book Value of

Equity 37390 38096 35465 39080 34487 31248 26366 20038 16231

1314

0

Total Assets

34547

50

27059

70

22629

70

20480

80

17945

70

15687

00

13374

10

12246

90

10539

60

9650

43

Total Liabilities

32604

70

25492

70

21187

00

19196

40

16762

90

14698

20

12534

60

11597

10

98800

7

9070

95

State Bank of

India Working Capital

21617

90

17102

40

16117

00

14060

30

12895

10

11129

20

93694

0

81812

3

67287

9

6042

87

Retained

Earnings 0 8387 7961 10481 8604 11284 9366 6364 7241 7297

41(54)

EBIT

13011

8

12851

3

12057

7

11669

6

10324

3 95277 81714 63822 61249

5709

6

Book Value of

Equity

19428

0

15670

0

14427

4

12843

8

11827

6 98880 83951 64986 65949

5794

8

Total Assets

32397

7

29907

3

30796

7

30313

5

25186

1

21512

2

21512

2

21512

2

21512

2

2151

22

Total Liabilities

31058

5

28648

5

29724

2

29099

5

24096

4

20555

7

20555

7

20555

7

20555

7

2055

57

Syndicate Bank Working Capital

23294

9

22270

5

22693

3

22308

3

18545

7

16065

4

16065

4

16065

4

16065

4

1606

54

Retained

Earnings 0 359 0 1218 1369 1604 1090 838 659 758

EBIT 10835 17380 16192 18089 14722 13228 13228 13228 13228

1322

8

Book Value of

Equity 13392 12589 10726 12140 10898 9566 8036 6657 5223 4595

Total Assets

21625

6

23149

1

24493

1

24627

5

23961

4

19894

0

18106

8

16389

0

13780

8

1119

96

Total Liabilities

20878

2

22221

2

23570

9

23433

6

22897

5

18980

2

17294

9

15692

1

13304

7

1085

01

UCO Bank Working Capital

12793

5

14322

9

14672

1

16655

5

16237

7

13892

7

12844

8

11405

5 89209

7875

0

Retained

Earnings 0 0 0 922 1254 501 754 789 901 452

EBIT 6459 10658 10914 14934 13681 12788 8635 8109 7489 5579

Book Value of

Equity 7473 9278 9221 11939 10639 7315 8119 6968 4761 3495

Total Assets

49010

3

45368

0

40631

4

38273

0

35463

4

31239

7

26256

6

23635

1

19550

9

1613

38

Total Liabilities

46734

8

43270

2

38600

4

36439

4

33761

8

29659

7

24946

7

22516

0

18670

2

1542

83

Union Bank of

India Working Capital

34932

4

32455

2

30083

9

27379

1

24767

7

22005

0

18917

8

16627

6

13135

4

1092

00

Retained

Earnings 0 555 1216 1407 1442 1683 1340 1666 1805 1468

EBIT 16803 24108 25651 26423 23537 20646 16948 13192 11943

1043

2

Book Value of

Equity 22755 20978 20310 18336 16905 15689 12438 10555 8303 6549

Total Assets

14482

5

14113

1

12951

7

12302

8

12510

5

11461

5

10201

0 90043 77011

6204

1

42(54)

Total Liabilities

13711

0

13470

6

12460

2

11779

4

12043

1

10935

6 97071 85679 73560

5943

2

United Bank of

India Working Capital 88652 81699 78294 70633 74829 76533 69112 59542 45828

3905

3

Retained

Earnings 0 220 0 256 0 314 544 451 258 185

EBIT 5395 6586 6945 8285 6480 7055 6330 4841 4268 3397

Book Value of

Equity 7715 6425 4915 5234 3874 4459 4939 4363 3451 2609

Total Assets

17784

8

15507

0

14556

9

14278

1

13748

2

11098

2 95812 82013 70222

6238

2

Total Liabilities

16458

7

14407

4

13574

1

13362

2

12904

1

10568

6 90837 77490 67059

5956

4

Vijaya Bank Working Capital

13361

1

10565

1 99141 94456 91566 76943 64318 54172 45433

4174

2

Retained

Earnings 562 588 382 322 273 462 459 403 401 218

EBIT 9270 9788 9481 10381 9071 7750 6734 4505 4453 4655

Book Value of

Equity 9837 7321 6531 5923 5639 4096 4975 4523 3163 2818

10.12 SUMMARY OF FINANCIAL FIGURES OF PRIVATE SECTOR BANKS

Private Sector

Banks Criteria 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009

Total Assets 691458

60155

9

52564

2

46197

8

38328

7

34058

6

28565

1

24274

6

18068

1

14775

9

Total Liabilities 628012

54579

7

47247

8

41730

1

34506

7

30747

8

26284

2

22374

7

16463

6

13754

4

Axis Bank Ltd Working Capital 507236

44263

2

38482

9

31202

0

25349

7

21357

9

18153

3

16024

0

11731

7 95706

Retained

Earnings 276 2281 8224 6254 5285 4351 3563 2812 2037 1452

EBIT 27284 31917 36549 32311 28038 25069 20265 13727 10485 9934

Book Value of

Equity 63445 55763 53165 44677 38220 33108 22809 18999 16044 10215

Total Assets 43536 28704 18475 502 0 0 0 0 0 0

Total Liabilities 34154 24258 15140 0 0 0 0 0 0 0

Bandhan Bank Working Capital 34926 22936 14480 393 0 0 0 0 0 0

Retained

Earnings 2430 1112 275 1 0 0 0 0 0 0

43(54)

EBIT 4200 3298 1115 2 0 0 0 0 0 0

Book Value of

Equity 9382 4446 3335 502 0 0 0 0 0 0

Total Assets 39937 35271 31252 27871 24994 22977 18351 14592 11559 9251

Total Liabilities 35775 31701 28200 25176 22969 21336 17108 13585 10734 8590

City Union Bank Ltd Working Capital 30649 26970 23781 20364 18210 17014 13249 10422 7877 6469

Retained

Earnings 574 503 374 328 292 274 238 181 122 98

EBIT 2762 2668 2566 2413 2200 1968 1531 1080 874 740

Book Value of

Equity 4163 3570 3052 2696 2025 1647 1243 1007 826 661

Total Assets 30248 24071 19142 16150 12936 11291 8687 7425 6151 5958

Total Liabilities 27692 22124 17402 14617 11838 10345 7884 6862 5611 5419

DCB Bank Working Capital 21777 16205 13233 10600 8411 7238 5560 4611 3677 3860

Retained

Earnings 223 200 195 191 151 102 55 21 0 0

EBIT 1804 1586 1340 1122 911 734 544 376 245 361

Book Value of

Equity 2556 1947 1740 1534 1098 946 804 563 540 538

Total Assets 12289 12336 12466 14355 14691 13831 14681 14271 8087 5643

Total Liabilities 12289 11682 11958 13631 13954 13101 13952 13426 7647 5219

Dhanlaxmi Bank Working Capital 7493 7685 8142 8994 9510 8880 9745 10226 5749 3780

Retained

Earnings 0 12 0 0 0 3 0 22 20 51

EBIT 643 770 690 743 760 1008 1032 681 422 366

Book Value of

Equity 749 654 508 724 736 731 728 845 440 424

Total Assets 138533

11520

9 91430 83140 74971 71302 60627 51699 43822 38977

Total Liabilities 126326

10627

2 83344 75407 68025 64943 54926 46596 39138 34657

Federal Bank Working Capital 104493 83814 66697 55991 47822 47607 41150 35179 28945 25337

Retained

Earnings 686 831 357 815 671 687 621 440 381 415

EBIT 7514 6931 5960 6560 5929 5387 4775 3207 3122 2793

Book Value of

Equity 12204 8937 8086 7733 6946 7044 5701 5103 4685 4320

Total Assets

106399

0

86396

2

70893

9

59057

6

49165

8

40039

0

33797

2

27742

9

22255

7

18335

9

44(54)

Total Liabilities 957693

77449

9

63626

2

52856

7

44817

9

36417

6

30804

7

25205

2

20103

7

16871

2

HDFC Bank Ltd Working Capital 772363

58904

1

50489

2

38843

7

32636

4

25115

1

20064

7

17525

7

14110

9

10609

7

Retained

Earnings 14164 11203 9837 8173 6867 5448 4134 3141 2388 1818

EBIT 66844 58306 51268 41403 35425 29004 22503 15204 12075 12210

Book Value of

Equity 106295 89462 72678 62010 43479 36214 29924 25376 21520 14646

Total Assets 882659

77515

9

72385

6

64878

4

59688

2

53726

3

48949

6

40667

8

36386

7

37985

0

Total Liabilities 780508

67825

6

63694

5

56836

2

52367

6

47056

1

42909

3

35158

7

31224

8

33031

7

ICICI Bank Ltd Working Capital 635086

56518

6

51515

6

42309

6

37818

0

32861

6

29189

3

25081

7

22379

3

25417

6

Retained

Earnings 5828 8331 6808 8270 7162 5994 4590 3554 2697 2518

EBIT 39374 43698 43711 45866 41666 37599 31606 23715 22935 27840

Book Value of

Equity 102150 96903 86911 80422 73207 66701 60403 55091 51618 49533

Total Assets 350819

36226

5

37487

2

35652

7

32940

8

32281

7

29036

3

25361

4

23376

9

17255

4

Total Liabilities 334663

34511

9

35275

8

33387

2

30748

2

30334

5

27279

0

24094

3

22554

4

16511

2

IDBI Bank Working Capital 228896

24176

5

25096

1

22029

9

21108

9

21067

2

19534

9

17342

0

14726

0

11138

9

Retained

Earnings 0 0 0 751 964 1412 1646 1304 815 678

EBIT -5205 13422 16983 23693 22317 22313 2630 2281 1045 985

Book Value of

Equity 16155 17146 22114 22650 21922 19470 17573 12671 8226 7443

Total Assets 126707

11235

5 83333 0 0 0 0 0 0 0

Total Liabilities 111451 97678 69704 0 0 0 0 0 0 0

IDFC Bank Working Capital 58756 53889 48607 0 0 0 0 0 0 0

Retained

Earnings 602 1020 383 0 0 0 0 0 0 0

EBIT 7990 7535 3268 0 0 0 0 0 0 0

Book Value of

Equity 15256 14677 13629 0 0 0 0 0 0 0

45(54)

Total Assets 221895

17886

0

14022

8

10939

3 87190 73425 57673 45691 35430 27615

Total Liabilities 198430

15860

3

12292

7 99154 78555 66018 53161 41874 33267 26186

Indus Ind Bank Working Capital 161978

13124

5 99988 78975 61320 50573 40326 29560 22761 17649

Retained

Earnings 3172 2868 1989 1578 1225 902 698 485 277 104

EBIT 15262 12703 10533 8980 7491 6326 4847 3092 2353 2078

Book Value of

Equity 23464 20256 17301 10240 8634 7407 4511 3817 2163 1429

Total Assets 89687 82019 80268 76086 78620 71743 60270 50508 42547 37693

Total Liabilities 84145 76977 73844 69975 72896 66879 56177 47030 39536 35070

Jammu & Kashmir

Bank Working Capital 66682 55947 56937 48393 50096 43963 36636 29170 27187 25688

Retained

Earnings 202 -1632 333 407 946 812 643 492 405 328

EBIT 3871 2668 4825 5230 5834 5347 4198 3104 2729 2620

Book Value of

Equity 5541 5042 6424 6110 5724 4865 4093 3479 3010 2623

Total Assets 70483 64238 56701 52026 47169 41601 36378 31731 27068 22883

Total Liabilities 65489 59519 53010 48637 44117 38744 33779 29302 25236 21316

Karnataka Bank Ltd Working Capital 52475 41345 38667 34156 30126 26894 22360 19200 16115 12804

Retained

Earnings 241 452 320 357 236 272 180 147 114 195

EBIT 3876 4163 4217 4089 3541 3325 2657 1993 1898 1843

Book Value of

Equity 4994 4719 3691 3389 3052 2857 2598 2429 1833 1567

Total Assets 65536 60431 56225 51837 50377 46747 37646 28235 21943 17061

Total Liabilities 59272 55395 51652 47591 47051 43662 34938 26120 20323 15711

Karur Vysya Bank Working Capital 49205 45155 42583 38653 36746 31578 26041 19424 14628 11643

Retained

Earnings 300 606 397 306 288 402 351 287 272 172

EBIT 3903 4432 4574 4401 4228 3809 2985 2012 1619 1367

Book Value of

Equity 6264 5036 4573 4246 3326 3085 2708 2115 1620 1350

Total Assets 265067

21473

6

19238

6

10601

2 87585 83694 65667 50851 37436 28712

Total Liabilities 227586

18712

0

16842

7 91871 75310 74247 57721 44054 32951 24898

46(54)

Kotak Mahindra

Bank Working Capital 189190

15952

6

13076

6 69523 57652 51549 41061 30234 21572 16868

Retained

Earnings 3962 3309 2006 1791 1442 1306 1042 777 533 251

EBIT 16435 14721 12607 8329 7319 6809 5268 3280 2209 1973

Book Value of

Equity 37482 27616 23959 14141 12275 9447 7946 6796 4485 3814

Total Assets 40462 35318 28732 24705 20653 17667 16163 13301 10499 8308

Total Liabilities 38302 33352 27140 23228 19676 16730 15284 12490 9760 7854

Laxmi Vilas Bank Working Capital 28312 25283 20833 17553 14119 12521 10989 8988 7099 6040

Retained

Earnings 0 256 126 97 50 62 73 77 25 38

EBIT 1302 2444 2134 1876 1515 1506 1167 723 661 535

Book Value of

Equity 2160 1967 1592 1478 977 937 879 812 739 454

Total Assets 61857 48689 39160 27104 18197 12962 7207 3232 2087 1707

Total Liabilities 55174 44354 36172 24874 16313 11357 6065 2148 1735 1366

RBL Bank Working Capital 44065 33162 23261 16336 10768 7074 4758 2198 1412 1229

Retained

Earnings 546 446 243 170 68 78 59 8 13 20

EBIT 3707 3173 2353 1697 1143 759 374 113 114 120

Book Value of

Equity 6683 4335 2988 2229 1884 1606 1142 869 247 237

Total Assets 82765 74366 63222 59161 55007 49795 40380 32820 25534 20379

Total Liabilities 77764 69764 59514 55710 51770 46929 38357 31127 24067 19094

South India Bank Working Capital 62030 52591 46638 40400 38694 35493 29334 22424 17501 13578

Retained

Earnings 261 393 267 227 401 407 333 237 189 162

EBIT 4727 4772 4557 4388 4345 3809 3134 2100 1735 1465

Book Value of

Equity 5001 4602 3708 3451 3236 2866 2023 1694 1467 1286

Total Assets 312446

21506

0

16526

3

13617

0

10901

6 99104 73626 59007 36383 22901

Total Liabilities 286687

19300

6

15147

7

12449

0

10189

4 93297 68949 55213 33293 21277

YES Bank Working Capital 232159

15281

9

10785

6 82152 61384 50480 40050 37463 24312 14247

Retained

Earnings 3591 3330 2108 1624 1327 1080 840 640 425 304

EBIT 18725 15671 12733 10994 9591 8001 6142 3887 2308 1958

47(54)

Book Value of

Equity 25758 22054 13787 11680 7122 5808 4677 3794 3090 1624

10.13 COMPUTATION OF INCREASE/DECREASE IN Z-SCORES OF BOTH PUBLIC AND PRIVATE SEC-

TOR BANKS

Average increase / (decrease) in Z Scores using – Altman’s Emerging model equation

Public Sector

Banks

Avg

2009-

13

Avg

2014-

18

Increase/

(Decrease)

Private Sector

Banks

Avg

2009-

13

Avg

2014-

18

Increase/

(Decrease)

Allahabad

Bank 8.0591 8.2230 0.16 Axis Bank Ltd 8.0454 8.45 0.40

Andhra Bank 8.5289 8.3394 (0.19)

City Union

Bank Ltd 8.5706 8.87 0.30

Bank of

Baroda 8.5510 8.6712 0.12 DCB Bank 7.8655 8.28 0.42

Bank of India 8.3737 8.5552 0.18

Dhanlaxmi

Bank 8.4058 8.03 (0.37)

Bank of

Maharashtra 7.9784 8.2566 0.28 Federal Bank 8.2616 8.45 0.19

Canara Bank 8.2381 8.2221 (0.02)

HDFC Bank

Ltd 7.8355 8.39 0.55

Central Bank

of India 8.0205 7.9292 (0.09) ICICI Bank Ltd 7.9580 8.36 0.40

Corporation

Bank 8.1376 8.1790 0.04 IDBI Bank 7.7602 7.85 0.09

Dena Bank 8.2462 8.0730 (0.17)

Indus Ind

Bank 8.4997 8.88 0.38

Indian Bank

Ltd 8.1196 8.1973 0.08

Jammu &

Kashmir Bank 8.2045 8.38 0.18

48(54)

Indian

Overseas

Bank 8.6317 8.1326 (0.50)

Karnataka

Bank Ltd 7.8047 8.23 0.42

Oriental

Bank of India 8.2026 8.1976 (0.01)

Karur Vysya

Bank 8.6097 8.96 0.35

Punjab &

Sindh Bank 8.1506 8.1671 0.02

Kotak

Mahindra

Bank 7.8551 8.47 0.61

Punjab

National

Bank 8.2375 8.2699 0.03

Laxmi Vilas

Bank 8.3345 8.40 0.06

State Bank of

India 7.9932 8.1489 0.16 RBL Bank 8.1629 7.97 (0.19)

Syndicate

Bank 8.6035 8.4995 (0.10)

South India

Bank 8.3769 8.50 0.12

UCO Bank 8.0614 7.8613 (0.20) YES Bank 7.7824 8.19 0.41

Union Bank

of India 8.2069 8.3936 0.19

United Bank

of India 7.8011 7.6303 (0.17)

Vijaya Bank 8.0388 8.2657 0.23

10.14 COMPUTATION OF ACCURACY OF PREDICTION OF BOTH PUBLIC AND PRIVATE SECTOR BANKS

Accuracy of prediction (NPA effect in the Z Scores)

Public

Sector

Banks

Increase/

(Decreas

e)

in NPA

Increase/

(De-

crease) in

Z Score

Accuracy

in

predictio

n

Private

Sector

Banks

Increase/

(Decreas

e) in NPA

Increase/

(De-

crease) in

Z Score

Accuracy

in

predictio

n

49(54)

Allahaba

d Bank 13583 0.10

In-

accurate

Axis Bank

Ltd 27190 0.40

In-

accurate

Andhra

Bank 569384 (0.31)

Accurate City

Union

Bank Ltd 415 0.30

In-

accurate

Bank of

Baroda 29603 0.04

In-

accurate DCB Bank (37) 0.42

Accurate

Bank of

India 34298 0.07

In-

accurate

Dhanlax

mi Bank 319 (0.37)

Accurate

Bank of

Maharas

htra 9931 0.23

In-

accurate Federal

Bank 584 0.19

In-

accurate

Canara

Bank 23156 (0.07)

Accurate HDFC

Bank Ltd 14918 0.55

In-

accurate

Central

Bank of

India 16664 (0.20)

Accurate

ICICI

Bank Ltd 19795 0.40

In-

accurate

Corporati

on Bank 12295 0.02

In-

accurate IDBI Bank 26102 0.09

In-

accurate

Dena

Bank 8207 (0.21)

Accurate Indus Ind

Bank 628 0.38

In-

accurate

Indian

Bank Ltd 6758 (0.02)

In-

accurate

Jammu &

Kashmir

Bank 3384 0.18

In-

accurate

Indian

Overseas

Bank 21624 (0.63)

Accurate Karnatak

a Bank

Ltd 780 0.42

In-

accurate

50(54)

Oriental

Bank of

India 12953 (0.03)

Accurate Karur

Vysya

Bank 938 0.35

In-

accurate

Punjab &

Sindh

Bank 4314 0.01

In-

accurate

Kotak

Mahindra

Bank 1821 0.61

In-

accurate

Punjab

National

Bank 52667 (0.05)

In-

accurate

Laxmi

Vilas

Bank 666 0.06

In-

accurate

State

Bank of

India 78827 (0.02)

In-

accurate

RBL Bank 239 (0.19)

Accurate

Syndicate

Bank 10709 (0.14)

Accurate South

India

Bank 873 0.12

In-

accurate

UCO

Bank 14662 (0.42)

Accurate

YES Bank 1096 0.41

In-

accurate

Union

Bank of

India 17442 0.09

In-

accurate

United

Bank of

India 8351 (0.40)

Accurate

Vijaya

Bank 2844 0.11

Accurate

51(54)

10.15 RESULTS OF PAIRED SAMPLE T-TESTS

10.15.1 Results of the paired sample t-test of Public Sector banks

Variable 1 Variable 2

Mean 5.028002551 4.935592726

Variance 0.047689525 0.066035542

Observations 20 20

Pearson Correlation 0.606704459

Hypothesized Mean Difference 0

df 19

t Stat 1.934647621

P(T<=t) one-tail 0.034032355

t Critical one-tail 1.729132812

P(T<=t) two-tail 0.068064711

t Critical two-tail 2.093024054

10.15.2 Results of the paired sample t-test of Private Sector banks

Variable 1 Variable 2

Mean 4.03067999 4.870110512

Variance 6.66024839 0.998895639

Observations 19 19

Pearson Correlation 0.826573355

Hypothesized Mean Differ-

ence 0

df 18

t Stat

-

1.985784534

P(T<=t) one-tail 0.031252226

t Critical one-tail 1.734063607

P(T<=t) two-tail 0.062504451

t Critical two-tail 2.10092204

52(54)

10.16 RESULTS OF REGRESSION

10.16.1 Public Sector banks: SUMMARY OUTPUT

Regression Statistics

Multiple R 0.337978

R Square 0.114229

Adjusted R Square 0.06502

Standard Error 1.261204

Observations 20

ANOVA

df SS MS F Significance F

Regression 1 3.692317 3.692317 2.321284 0.144991

Residual 18 28.63144 1.590636

Total 19 32.32376

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.272372 0.308543 0.882767 0.389 -0.37585 0.920597 -0.37585 0.920597

X Variable 1 -2.06368 1.354495 -1.52358 0.144991 -4.90936 0.782012 -4.90936 0.782012

53(54)

10.16.2 Private Sector banks:

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.369592

R Square 0.136599

Adjusted R Square 0.079038

Standard Error 0.173608

Observations 17

ANOVA

df SS MS F Significance F

Regression 1 0.071526 0.071526 2.373146 0.144266

Residual 15 0.452093 0.03014

Total 16 0.523619

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.555139 0.060172 9.225795 1.43E-07 0.426884 0.683393 0.426884 0.683393

X Variable 1 -0.26106 0.169462 -1.5405 0.144266 -0.62226 0.100143 -0.62226 0.100143

54(54)