*Prashant Kumar **Kavita,
After the financial crisis and pressure of implementing Basel III norms, financial solvency has become top priority for the banking sector, because there are some factors like failure of management, competition, increasing NPAs, growing incidence of fraud, inability to meet regulatory requirements which create the probability of risk and leads to financial distress. In this context, measuring financial health of a bank has become an imperative need. Bankruptcy risk has always been a matter of concern not only for bankers but for all stakeholders in the business world because the risk can seriously jeopardizes the affairs of the business. Therefore proper assessment of bankruptcy risk is required to smooth functioning of banks and proper implementation of Basel III regulations. It is contemporary to study solvency position of Indian banks. The axle of this study is to predict the financial health and risk of bankruptcy by applying Altman Z Score model in the selected Indian banks. This model highlights that the position of the banks, under study is healthy and comparatively sound. It can be concluded that the selected Indian banks which are under study falls in ‘safe Zone’ as per Z-score criteria and there is not any chance of financial distress.
Keywords: Bankruptcy risk, EBIT, Financial health, financial ratios, Z-score.
Banks play significant role in the financial stability of any economy as banking sector is the main component of financial system. A stable and financially sound banking system leads to economic development of any country. Now these days, financial stability has become the major issue for banking sector because there are some factors such as failure of management, external factors, competition, increasing portfolio of NPA, growing incidence of fraud, inability to meet regulatory requirements which create the probability of risk and leads to financial distress. Banking sector faces various types of risk viz. credit risk, market risk, liquidity risk, foreign exchange risk, political risk, sovereign risk, interest rate risk, operational risk etc. and high intensity of risk leads to business failure (Campbell 2007).
There are five stages of business failure such as incubation, financial embarrassment, financial insolvency, total insolvency and confirmed insolvency (Fitzpatrick 1932). Bankruptcy or insolvency is the form of financial failure refers to where a firm cannot meet its current obligations, when the current obligations exceed the current assets.
Bankruptcy is a severe matter and very common thing among companies and financial institutions. There may be many reasons like changes in market policy, inflation and political reasons which have led to bankruptcy (Movaziri et al, 2012). Bankruptcy can be used as a proxy for measuring economic sustainability. Because it is considered that bankrupt banks have weak position while non-bankrupt banks have strong economic sustainability and long term survival (Amin Jan et al, 2015).
Global financial crisis blessed with inflation, currency deterioration, economic uncertainty, high interest rates and many other uncontrollable factors was enough to break down the resilience of financial sector. The Financial soundness of banking sector is backbone of every economy. In this context, it is very crucial to analyze the financial soundness of domestic banks (Nishi sharma et al, 2013). Bankruptcy prediction is of immense importance to both for lenders as well as for the investors. There are many techniques that have developed to assess the bankruptcy risk. Bankruptcy is a worldwide problem. Bankruptcy histories shows that a company with efficient management, strong financial performance and capable to grow without any distress symptoms, can be turned out to be a sudden bankruptcy. In the period from 2000 to 2011, it has been witnessed a wave of bankruptcies in the giant companies like, Lehman Brothers, Enron give examples to the world that no matter how strong the company is, it can face bankruptcy if it is not well managed. On 30th November 2001, Enron bankruptcy was reported and appealed for bankruptcy protection on the 2nd of December 2001. The other company is Lehman Brothers which was the fourth largest investment bank in US. Lehman filed for bankruptcy protection in 2008 to avoid the possibility of being distressed (Anita et al, 2013). There is a dire need to manage risk of bankruptcy as it is the critical issue for banks. Prediction of bankruptcy is one of the challenging task for every organization (Fawad et al, 2014). In 2008, the largest bankruptcy in U.S. history represents an example for Indian banks to manage their cash flows efficiently .Thus careful attention to the impact of bankruptcy risk level on bank’s profitability is necessary because intense risk puts serious threat to banks and increasingly level of risk may create a chance of closing down the bank’s operations. Financial soundness is of prime importance in the current crisis and financial scams scenario in the banking sector (Parul Chotalia 2014). In the wake of the financial crisis of 2008, Basel-III accord was released in 2010. The New Basel Capital Accord (popularly known as Basel-III) is desirable regulation and major agenda for the commercial banks in India and across the world. The main focus of Basel-III is on the estimation of capital requirements which would ensure financial stability and determine the common standards of banking regulation. From the perspective of Basel III, to maintain the higher capital requirements and to comply with Basel III norms are the concerned areas for banks. Thus, Indian banking sector need to predict bankruptcy risk and analyze their financial statements because the risk of bankruptcy directly hits the financial strength and earnings of banks. Therefore proper assessment of bankruptcy risk is required to smooth functioning of banks and proper implementation of Basel III regulations. The prediction of business failure is an important step for taking timely corrective and remedial measure for protecting business from the problem of bankruptcy. The basic concern of prediction is to evaluate the terms of credit and ensure repayment safely (Roli Pradhan 2014). The problem of business failure is attributed to both financial and non-financial causes such as poor planning, inefficient management and fraud. There is need for predicting financial failure on time for taking curative measures in relation to financial investments (Venkata Ramana et al, 2012).
There are many different models to forecast the complex problem of bankruptcy. There may be many internal credit rating model used for bank which improves their current predictive power of financial risk factors and explained how banks assess the credit worthiness of the borrowers and how can they identify the defaulters so as to improve their credit evaluation process (Kishore Navin 2011). Many internal and external users of financial statements like banks, credit rating agencies, underwriters, auditors, policy makers and regulators analyze company’s financial position. For this purpose different approaches and models are used. During financial and economic crisis selection of model for bankruptcy prediction is essential. For example when bank financially assists a company, bank predicts risk of bankruptcy of that company prior to financial help. The prediction models are used to check the bankruptcy and can be applied to modern economy to predict distress and bankruptcy of one, two and three years in advance (Sanobar Anjum 2012). But the most influential model is Altman Z score model due to most acceptable and widely used. The well-known Altman model developed by Edward Altman in 1968 called Z score model has been identified as independent variables (financial ratios) as well as the relative weight of each variable which represents dependent variable (Z) through an analytical study of a sample of US companies in 1968 (Ali Abusalah et al, 2012).
ALTMAN’S Z-SCORE MODEL
The Z-score model was constructed by Edward I. Altman in 1968 (Assistant Professor of finance at New York University). It is a multivariate formula and powerful diagnostic tool that measures the probability of bankruptcy within a two year period with proven high degree of accuracy. This model is known as bankruptcy prediction model and has gained popularity since 1985 (Altman 1968). Altman used 22 variables from the financial reports of 66 publicly held manufacturing companies in USA with assets of more than $1 million. The 66 companies were categorized into 2 groups, 33 failed and 33 successful. Altman’s Z-value is derived through a multiple discrete analysis (MDA). The discriminant analysis was applied to calculate the coefficients for Z-score equation. Altman first compiled 22 variables describing the standard ratio categories. He reduced his selection to five ratios. This model is also called multiple discriminant analysis model (MDA). Z score analysis is capable of predicting default through combining various financial ratios (M Jayadev 2006). Altman model may be used as an indicator and evidence to determine the firm’s bankruptcy and credibility. Altman’s z-score model predicts the corporate default and measure financial distress status of companies. Z-score is calculated by multiplying the coefficients by each of financial ratio. Linear combination of 5 common financial ratios has been widely used to predict default risk. Altman’s model has found 95.0% accuracy rate and also called Zeta. This model is internationally accepted. The Z score original model was developed in 1968 for manufacturing firms. Altman again devised the Z score to be adapted for private companies in 1983.This model was further developed to create the Z Score model for emerging market companies and for non-manufacturers in 1993. This model kept the first four variables. Altman’s models are:
- Original model for manufacturing firms
- Revised model for privately held firms
- Revised four model for non-manufacturing or emerging market
Table 1 ALTMAN’S INDEX
|RATIOS||Original Model 1968||Revised model
|Revised four model
|X1 = WC/TA||1.21||0.717||6.56|
|X4 = MVE/TL||0.60.||0.42||1.05|
|X5 = S/ TA||1.09||0.998||N/A|
Table 2 Altman’s benchmark
|Score 1968||Score 1983||Score 1993||Interpretation|
|Z > 2.99||Z > 2.90||Z > 2.60||Non-bankrupt firms, Safe zone|
|1.81 < Z < 2.99||1.23 < Z <2.90||1.10 <Z < 2.60||Difficult to predict, Grey zone|
|Z < 1.81||Z < 1.23||Z <1.10||Distress zone, bankrupt firms|
Bankruptcy predictions are based on accounting ratios and other financial variables. Linear discriminant analysis was the first statistical method applied to explain which firms entered in bankruptcy (Richard et al, 2014). The most widely used tool for financial analysis is financial ratios. Financial analysis discloses the financial performance of firm and indicates the possible causes standing behind the deterioration of financial performance (Obaid Saif 2011). Ratios have been using for many years by investors, creditors, lenders, stockholders, auditors and others who may get substantial losses as a result of business failure. Researchers have used financial ratios to construct business failure prediction models. Ratio analysis is used in various part of the world for measuring financial accuracy and creditworthiness of the firms (Vineet et al, 2014).
Financial ratios are the significant component of financial analysis to evaluate and analyze the financial statements. Altman used five standard ratios in Z score viz. liquidity ratio, profitability ratio, leverage ratio solvency ratio, activity ratio. Financial ratios are used to assess profit and risk and provide the basis for estimating the results of business operations and explaining how well a business is doing (Khalid Al-Rawi et al, 2008). Financial ratios are good indicator of the probability of bankruptcy. While analyzing the ratios, formulas are used to determine the financial position of the firms. Ratio predicts the financial soundness of the firms whether a firm is going to bankrupt or not (Bashar 2015). Financial ratios have been used for making comparison among the firms in same industries. Efficient performing firms have been identified through their financial analysis and higher performance of firms makes their transition towards adopting new regulations easily (Ravi Chandran 2015).
VARIABLES USED IN ALTMAN’S Z-SCORE MODEL
X1 Working Capital/Total Assets
This is the most valuable variable to predict bankruptcy. This liquidity ratio calculates the ability of the firm to finance its short term obligations. A decreasing figure will suggest the higher chance of bankruptcy. This ratio is the measure of liquid asset of firm in relation to total capitalization. WC (working capital) = current assets – current liabilities.
X2 Retained Earnings/Total Assets
This variable indicates the ability of a firm to accumulate earnings using its assets. The higher the ratio the better as it suggests the firm can accumulate earnings. A young firm will usually display a very low RE/TA as it has not had the time to build up cumulative profits hence the incidence of failure is much higher in a firm’s earlier years (Altman, 1968).
X3 Earnings before Interest and Taxes/ Total Assets
This indicates company’s profitability and company’s assets. The decreasing ratio indicates the firm is not earning and decreasing the profit on each investment.
X4 Book Value Equity/ Total Liabilities
This expresses the financial leverage i.e. the proportion of equity. It is directly related to solvency position of firm. It calculates how much the firm’s market value would decline before the liabilities exceeds the assets and firm becomes insolvent. If the market value of equity is below the total debt the firm becomes insolvent.
X5: Sales/Total assets
The sales of a firm depict the manufacturing capability of companies’ assets. In Altman’s model this financial ratio did not deliver any statistical significance but he still found it to be useful to default prediction because of the relationship to other variables in the model (Altman 1968).
USES OF Z-SCORE
Altman’s model still exists and used by the financial institutions to measure creditworthiness of the companies Z score is a beneficial analytical tool and the application of Altman’s failure prediction model is not constrained by geographical boundaries (Oforegbunam et al, 2011). Altman’s model provides credibility to the valuation process. It helps in evaluating the reliability statistically and providing insight into relative performance and financial viability (Altman 2000). The Z-score is the best measure for evaluating the financial soundness of a firm that shows the lower the score higher the chance of failure. The importance of Z-score can be identified by a number of studies. Altman’s Z-score model has been used to predict the financial distress in a number of sectors like empirical analysis examined 21 textile companies listed in the Karachi stock exchange, during the period 2000 to 2010. These result for bankrupted and non-bankrupted show that Altman model can give good predictions (Fawad Hussain et al, 2014), predicted the risk of bankruptcy in cement companies (N VenkataRamana et al, 2012), Measured the financial health of Indian Logistic industry (Vikas Tyagi 2014), Indian Steel industry (M.S.Ramaratnam et al, 2010), Automobile Industry of India (Sarbapriya Ray et al, 2011), Sugar Manufacturing Units (Ramana Reddy et al 2013), Seed industry in India (Praveena et al, 2012).
Z score has been used as a tool to measure credit risk (Sairani et al, 2014), Altman score is applied to test credit worthiness of company. It can be concluded from the study that the banks face risk more consciously. The model calculates the financial soundness of corporate house in terms of Z values. Z score has originally been devised to signal the probability of bankruptcy of manufacturing firms. But it has been frequently updated to make it applicable for private companies, non-manufacturers and service industries. The model presents for more than 70% accuracy in predicting bankruptcy (Nishi et al, 2013). This study contributes to the field of accounting and finance, specifically on bankruptcy prediction in a developing country. The study is limited to only fifteen quoted firms including Food & Beverages, Manufacturing, Printing, Insurance, Trading in Ghana (Kingsley Opoku Appiah 2011). The empirical analysis by (M Sulphey 2013) examined 220 companies of BSE small cap for financial solvency using Z score. The result showed that only 79 companies were in safe zone. 117 companies were difficult to predict and 24 are the bankrupt firms. The study proved the efficiency of Altman model in predicting failures. The wide usage of the Z-Score Model as a measure of financial distress in the economic and financial research points out that it is widely accepted because it is a simple and consistent measure of calculating bankruptcy.
A popular risk measure in the banking and financial solvency related literature that reflects a banks probability of insolvency is the Z-score. Its widespread use is because of its simplicity and it can be calculated using only accounting information (Laetitia Lepetit 2015).
CRITICS OF Z-SCORE
This model does not always have the same accuracy to different business entities. This model is criticized for discriminating only among three borrower behavior; high, indeterminate, and low default risk. The weights in the Z-score model will be constant or not over any but very short periods, there is no reason to expect. The model ignores important factors (such as qualitative and macroeconomic factors) that may play a significant role in the default or non-default decision.
OBJECTIVES OF THE STUDY
- To analyze the financial soundness and risk of bankruptcy in selected Indian public sector and private sector banks.
- To compare the financial soundness of selected Indian public sector and private sector banks.
MATERIAL AND METHODS
The present study is an attempt to analyze bankruptcy risk in banking sector through the application of Altman Z-score which helps in forecasting the financial health of bank. In order to achieve the objectives of research, a descriptive and analytical approach has been used. Five banks were selected from public sector and five banks were selected from private sector. The present study predicts Z score for 10 Indian banks for a period of 5 years from 2011-2015. Public sector Banks namely state Bank of India, Bank of Baroda, Canara Bank, Punjab National Bank and Union Bank of India and Private sector Banks namely ICICI, Axis Bank, Yes Bank, IndusInd bank and Kotak Mahindra bank were chosen. Data for the present study were collected from secondary sources including bank’s annual report and The Economic Times (newspaper) website for last 5 years to generate the financial ratios. The period of the study is 2011-2015. Altman Z-score model for non-manufacturer or emerging markets (1993) has been used in this study.
The revised Z-score is as:
Z = 6.56 X1 + 3.26 X2 + 6.72 X3 + 1.05 X4
Z = overall score
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
Altman’s Z score value
Z Score > 2.60 shows firms are in safe zone, Z < 1.10 reflects firms are in distress zone, 1.10 <Z< 2.60 indicates firms are in grey zone and difficult to predict.
Two hypotheses have been formulated according to the objectives of study:
Null hypothesis H0: Banks are likely in financial distress and going to bankrupt within twelve months.
Alternate hypothesis H1: Banks are not likely in financial distress and not going to bankrupt with in twelve months.
H’0: There is no difference between the financial performance of public sector banks and private sector banks.
H’1: There is difference between the financial performance of public sector banks and private sector banks
RESULTS AND DISCUSSIONS
This study was intended to identify the risk of bankruptcy in selected Indian public and private sector banks. Average Z-score have been calculated for 5 Indian public sector and private sector banks from 2011 – 2015. The score would help to identify the financial viability of the banks. This can be presented as:
Figure1 Average Z-score of banks (2011-2015)
It can be seen from the graph that all 10 banks comprise five public sector and five private sector banks come under safe zone. The Z score value of selected Indian banks shows that no banks are going to bankrupt. All banks are in safe zone as their Z score values are more than 1.1
Figure 2 Z score value for public sector banks
Public sector banks secured Z score value more than 2.6 means no banks are in distress zone, all banks are safe. This shows bank under observations are not facing bankruptcy. SBI secured highest value among public sector banks in 2015.
Figure 3 Z score value for private sector banks
The graph indicates all five private banks are in safe zone as their Z score value is greater than 2.6. IndusInd bank got highest value among private sector banks. In comparison of last year, Z score value has decreased for IndusInd, Axis bank, Kotak and ICICI bank, but banks position is in safe zone.
Table3 Z SCORE BASED RANKS
|Sr.no||BANKS||Average z score 2015||Z score rank 2015||Average z score 2014||Z score rank 2014|
|1||Bank of Baroda||5.5348||2||5.4345||2|
|2||State bank of India||6.2698||1||5.7104||1|
|3||Punjab national bank||5.0451||6||5.0623||5|
|5||Union bank of India||5.0774||5||5.0315||6|
|10||Kotak Mahindra bank||4.8898||7||4.9236||8|
Altman model assigns highest rank to SBI among 10 Indian banks. The second rank is assigned to Bank of Baroda which is followed by IndusInd bank. However, other banks are also in safe zone as they secure more than 2.6 score. Z score for Yes bank is the least which is followed by Axis bank.
Figure4 Z score value for Indian public and private sector bank
Z score value of selected Indian banks in 2015 is more than 2.6. In 2015 SBI got highest value among all banks. But as compared to 2014 few banks show decreasing trend such as PNB, Axis bank, Yes bank and Kotak bank. Z score value for IndusInd bank, ICICI, Union Bank of India, Canara bank, SBI and Bank of Baroda has increased as compared to 2014. The graph shows that public sector banks have secured greater score than private sector banks it means public banks are financially sounder than private banks. Although private banks are in safe zone and their financial performance is satisfactory.
H0: null hypothesis banks are in financial distress and going to bankrupt within twelve months has been rejected while the alternative hypothesis has been accepted.
The calculated Z score indicates that each bank has got score more than 1.1. So banks are in safe zone. The average Z-score reveals that no banks are going to bankrupt, as all banks are financially healthy.
H’0: The second null hypothesis there is no difference between the performance of public sector banks and private sector banks has been rejected.
Alternative hypothesis has been accepted that there is difference between the performance of public sector banks and private banks. The greater score for public banks shows that public sector banks are financially sounder than private sector banks. Financial performance of public banks is better than private banks.
The prediction of business failure is very crucial for financial managers, analysts, investors and other users of financial statements. Z score model is useful to estimate the financial soundness of any entity. The financial ratio is the most significant factor in bankruptcy prediction. In the present study it has been tried to know whether selected Indian banks are in distress zone or not. The efficiency of Altman model has been highlighted in the present study. The study estimates Z score value for 10 Indian banks comprising five public and five private sector banks. Conclusively it has been witnessed that by using Altman model for a period of 5 years, all banks are financially sound as they all got Z value more than 2.60.There is difference between the financial performance of public sector banks and private banks as public banks have secured greater Z score value than private banks. This shows that the public banks are financially sounder than private banks (Deepak et al, 2014). The attainment of greater performance would determine safe credit norms, better management of earnings, assets, capital that would easily absorb the risk exposure and ascertain the stability and long term survival of banks. The present study would help the banks to put themselves on the track of Basel-III. It can be concluded from the study that Edward Altman model is a useful tool for investors, managers and other stakeholders to predict the financial failure that can evaluate bankruptcy risk of organizations. The present study is expected to provide efficient framework to policymakers as well as bankers while making investment decision.
Based on the result and conclusion from the present study, the following recommendations should be given as a consideration to Indian banks for effective management and good performance. Basel norms should be given special concerns specially capital regulations that may strengthen the risk absorbing capacity of banks. In order to improve risk analysis practices, efforts should be made to strengthen the risk management system of banks. The adoption of sound management practice and corporate governance will definitely reduce the chance of bank failure. The special training efforts should be made to enhance the capabilities of staff members. Banks should not only rely on Altman model or financial ratios as a tool to predict bankruptcy but also other tools should be considered. Banks should identify and evaluate the factors that determine the probability of default. Banks should evaluate Z score on regular basis
SUGGESTIONS FOR FUTURE RESEARCH
In present study an attempt has been made to predict the bankruptcy in selected Indian banks using Altman model, One can use other tools to predict bankruptcy. The Altman model for bankruptcy prediction can be used in other sectors. This type of study can be explored in future studies as Bankruptcy risk puts bank in distress zone or leads to failure. Further research can be done to extent observation years or sample used. The research can be done on testing the efficacy of various bankruptcy risk models and compare them to find out the best model. The analysis of this study can be repeated for other economies using the same methodology.
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|SBI in cr.||2015||2014||2013||2012||2011|
|Reserves and surplus||127691.65||117535.68||98199.65||83280.16||64351.04|
|X1=working capital/Total assets||0.7359917||0.760405||0.7566421||0.74680128||0.7314546|
|X2=Retained earnings/Total assets||0.5519635||0.864619||0.0667659||0.06637963||0.05753397|
|X3=Earnings before interest and taxes/Total assets||0.1626988||0.0189342||0.0211324||0.02516614||0.014566|
|X4=Book value of equity/ total liabilities||0.0003907||0.0004402||0.0004651||0.00053486||0.00056773|
|Reserves & Surplus||38708.4||35533.25||32323.43||26028.37||19720.99|
|X1=working capital/Total assets||0.7148526||0.7250178||0.712727||0.71709722||0.73150614|
|X2=Retained earnings/Total assets||0.0433586||0.0556698||0.0696839||0.05853415||0.05388293|
|X3=Earnings before interest and taxes/Total assets||0.01981489||0.0212655||0.0235145||0.01792757||0.01885658|
|X4=Book value of equity/ total liabilities||0.0006149||0.0006764||0.000762||0.00076277||0.00086561|
|Bank of Baroda||2015||2014||2013||2012||2011|
|Reserves & Surplus||39391||35555||31546.92||27064.47||20600.3|
|X1=working capital/Total assets||0.81925515||0.81479184||0.76739409||0.803746761||0.789068489|
|X2=Retained earnings/Total assets||0.0385537||0.00563796||0.05925060||0.062085721||0.059070578|
|X3=Earnings before interest and taxes/Total assets||0.01431454||0.01447893||0.01690196||0.015540912||0.017011849|
|X4=Book value of equity/ total liabilities||0.00064037||0.00067116||0.00079356||0.000945997||0.001126368|
|Reserves & Surplus||31384.04||29158.85||24434.79||20181.82||17498.46|
|X1=working capital/Total assets||0.7133910||0.7205251||0.69079741||0.712773491||0.736365663|
|X2=Retained earnings/Total assets||0.1945416||0.18307680||0.06093203||0.055251927||0.053304415|
|X3=Earnings before interest and taxes/Total assets||0.0130793||0.0142306||0.01468768||0.012303889||0.015422355|
|X4=Book value of equity/ total liabilities||0.0089429||0.0009658||0.00110469||0.001212805||0.001349482|
|Union Bank Of India||2015||2014||2013||2012||2011|
|Reserves & Surplus||19125.10||17734.05||16588.39||12437.68||10555.35|
|X1=working capital/Total assets||0.7398458||0.72115574||0.72672435||0.746685789||0.734438403|
|X2=Retained earnings/Total assets||0.0375089||0.05548580||0.05441403||0.048696635||0.046185647|
|X3=Earnings before interest and taxes/Total assets||0.0156548||0.01510445||0.01831264||0.014441989||0.013524879|
|X4=Book value of equity/ total liabilities||0.0017091||0.00214581||0.00232173||0.002590134||0.002779929|
|Reserves & Surplus||11262.25||6761.11||5449.05||4323.65||3446.93|
|X1=working capital/Total assets||0.63646133||0.5981232||0.5388227||0.58910759||0.66394979|
|X2=Retained earnings/Total assets||0.03747289||0.0357372||0.0292884||0.02869625||0.02327488|
|X3=Earnings before interest and taxes/Total assets||0.02517666||0.0261914||0.0228604||0.0222707||0.02053668|
|X4=Book value of equity/ total liabilities||0.003236||0.003514||0.0038279||0.00519218||0.00615253|
|Reserves & Surplus||79262.26||72051.73||65547.84||59250.09||53938.82|
|X1=working capital/Total assets||0.6886350||0.6754697||0.651171||0.640023||0.642713|
|X2=Retained earnings/Total assets||0.0351477||0.1591541||0.129885||0.129914||0.138217|
|X3=Earnings before interest & taxes/Total assets||0.0320957||0.0296391||0.026155||0.018422||0.017491|
|X4=Book value of equity/ total liabilities||0.0018874||0.0020683||0.002295||0.002533||0.002952|
|Reserves & Surplus||10101.03||8506.3||7096.67||4043.72||3350.92|
|X1=working capital/Total assets||0.7531528||0.7321772||0.713365||0.727011||0.678039|
|X2=Retained earnings/Total assets||0.0131047||0.136657||0.099663||0.072487||0.07626|
|X3=Earnings before interest and taxes/Total assets||0.029395||0.0307916||0.025833||0.022065||0.020839|
|X4=Book value of equity/ total liabilities||0.0051566||0.0063655||0.007493||0.00858||0.010786|
|Reserves & Surplus||44202.41||37750.64||32639.91||22395.34||18588.28|
|X1=working capital/Total assets||0.6982229||0.6861348||0.647853||0.655391||0.683312|
|X2=Retained earnings/Total assets||0.015001||0.1436104||0.099007||0.080854||0.079266|
|X3=Earnings before interest and taxes/Total assets||0.0299533||0.0310079||0.028219||0.023438||0.022892|
|X4=Book value of equity/ total liabilities||0.0010609||0.0012717||0.001419||0.001492||0.001751|
|KOTAK MAHINDRA BANK||2015||2014||2013||2012||2011|
|Reserves & Surplus||13754.91||11889.93||9091.19||7610.41||6464.95|
|X1=working capital/Total assets||0.687330||0.68438||0.637374||0.651152||0.633047|
|X2=Retained earnings/Total assets||0.019457||0.194014||0.11237||0.120584||0.135198|
|X3=Earnings before interest and taxes/Total assets||0.029632||0.0305887||0.026656||0.025728||0.024487|
|X4=Book value of equity/ total liabilities||0.0038177||0.0045716||0.004614||0.005868||0.007705|