The

paper attempts to study the fundamentals of credit

analysis primarily by the

appliance of Altman Z Score and its limitations in an Indian context. Altman Z-score,

developed in 1967 is the result of a credit potency test that measures a company’s probability of going bankrupt. It is built on five ratios that

are computed from financial data collected from in company’s annual financial

statements. The ratios measure various criteria like profitability,

leverage, liquidity, solvency and activity to analyse whether a company has a elevated degree of

likelihood of being bankrupt. The lower the value, the higher the

likelihood that the firm is headed toward bankruptcy.

The companies analysed form a part of the list of defaulters

sent by the Reserve Bank of India (RBI) to banks in 2017, which are then

referred to National Company Law Tribunal for bankruptcy proceedings. By applying

Altman Z-Score to past data from the defaulters’ list the paper aims to study the effectiveness

of the Altman z-score to predict bankruptcy or financial distress 1-2 years before to the insolvency

proceedings.

The malleability of Altman Z-Score’s formula enables

calculation and comparison of the score across industries. It would also help

ease the burden of bad loans clogging the banking system with early prediction

of defaulters.

Literature Review

Aasen

MR (2011) also conducted a similar study on the Oslo stock exchange, addressing

the financial distress on manufacturing firms caused due to the financial

crisis. While the probability of default for the sample enterprise did increase

noticeably in the course

of the crisis, research also indicated Z-score’s capability to predict

bankruptcy while the crisis was going on had worsened.

Alareeni,

B., & Branson, J. (2013) also looked into the effectiveness of the Altman

Z-score formula and found it relevant in assessing distressed industrial firms

in Jordan. However the model could not effectively differentiate amongst

distressed and unstressed companies in the service sector.

Alkhatib,

K., & Al Bzour, A. E. (2011) studied the effect of financial ratios in

prediction of bankruptcy in Jordanian listed companies using the Altman and

Kida models for the years 1990-2006. The results when compared showed that the

Altman Z-score model was more favourable with a higher predictive ability of

five years prior to liquidation compared to the Kida model.

Balasundaram,

N., (2009) sought to analyse the financial stability of listed manufacturing firms

in Sri Lanka using Altman Z-score in the years 2003-07.

Bellovary,

J. L., Giacomino, D. E., & Akers, M. D. (2007) also reviewed bankruptcy

estimation models from 1930-2007 and discussed the predictive ability of

Atman’s Z-score mode. Its accuracy drops to from 95% to 36% from one year to

five years before failure. However, there is a diverse set of definitions of

failure used for prediction studies, which prove to be a limitation.

C, S.

(2016). gauges the financial stability of NIFTY 50 companies using the Altman Z

Score Algorithm. Financial companies are excluded for better suitability. In

conclusion, out of the 50 companies – 26 companies are interpreted as safe. 9

companies have a neutral Z-Score. Moreover, 5 companies can be ruled out as

financially distressed. Oil and gas sector, Electric generation and metals are

industries that show a lower score.

Celli,

M. (2015) also successfully applied Altman’s Z-score formula to data collected

from a population of 102 firms on the Italian stock exchange to predict

defaults.

Chadha,

P. (2016) measures the financial operation of listed firms on the Kuwait Stock

Exchange after the financial crisis using Altman’s Z-score model

and Zmijewski’s bankruptcy model. However lack of financial information led to

some scores being incomplete and inconclusive.

Chouhan

V, Chandra B, Goswami S (2014) – Main aim was to study the substantiality of

Altman Z-Score in the modern times. Primarily,

the Z score is computed for 10 firms chosen for a time period of five years.

Later, divided according to z scores, finally the significance of the level of

change in the ratio is measured with One sample Komogrov-Smirnow test. In

conclusion, changes in z-score are not significant in any companies.

Elmabrok,

Ali & Kim-Soon, Ng. (2012). The sample size is 44. In conclusion, Altman

Zscore is declared as relevant and useful as a financial analysis tool.

Edward

I. Altman formulated the Altman Z-score in 1968 which estimates and predicts

the likelihood of a firm entering insolvency or bankruptcy within a years period

using various financial ratios. Altman’s formula forecasted with 95%

correctness which sample companies filed for insolvency within the following 365

days.

Gerantonis,

N., Vergos, K., & Christopoulos, A. G. (2009) analysed whether the Altman

Z-score model could correctly predict company failures in Greece during the

period 2002-2008. Results showed that the Z-score model could predict

bankruptcy upto 3 years prior to the event.

Grice,

S. and Ingram, R.W. (2001) evaluated the abiloity of Altman’s Z-score model

using a ratio wise sample of financially stable and unstable organizations from

different years, sectors, and financial statuses than studied by Altman. While

the model was seen to be responsive to industry differentiations, the general

accuracy was noticeably higher for manufacturing industry than

non-manufacturing firms.

Guarau

T. (2013) provides for a recalibrated Z-score model for Japan due to

differences arising from accounting and financial divergences and corporate

governance. While the empirical evidence using a sample of 132 companies does

show support for the calibrated model, it can only be used in the Japan setting

and is limited to public companies.

In the

Indian context, Sajjan, R. (2016) aims to understand the likelihood of

bankruptcy of selected manufacturing and non-manufacturing firms for 5 years

from 2011 to 2015 are listed on BSE & NSE. While results showed most of the

firms to be in the Distress Zone, the paper does suffer from a limitation of

data points with the study covering only 6 companies.

Kishore

R. and Kishore K. (2012) analysed three venerable models for assessing the distress

of Texmo Industries, Coimbatore using Altman’s Z-score model, O-score and

Zmijewski’s model. The study was based on data for five years: 2005-2006 to

2009-10. While the Z-score correctly predicted the increased probability during

the recession years, Ohlson’s O-score analysis showed higher correlation with

traditional analysis.

Meeampol

S, Lerskullawat P, Wongsorntham A, Srinammuang P, Rodpetch V, et al. (2014)

applied the Z-score and the Emerging market Z-score model to listed companies

on Stock Exchange of Thailand which were highly effective when 2 years of data

was used instead of one. Results showed that the Z-score model fit the sample

better as compared to the Emerging market Z-score model.

M.M.,

Sulphey & S, Nisa. (2013) analyzed a relatively large sample of 220

companies of BSE Small Cap for financial worthiness with the help of Z score.

The results showed that a large number of companies were in the ‘grey’ or

‘distress’ zones.

Niresh,

J. A. and Pratheepan, T. (2015), TThis paper focuses on Trading Sector

companies in Sri Lanka. 71% companies were in financial distress and 29 were a

part of the neutral zone.