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
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
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.
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.
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.
N., (2009) sought to analyse the financial stability of listed manufacturing firms
in Sri Lanka using Altman Z-score in the years 2003-07.
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.
(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.
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
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.
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.
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.
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
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.
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
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.
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.
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
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.
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
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.