Description
Despite the copious number of statistical failure prediction models described in the literature, testing of
whether such methodologies work in practice is lacking. This paper examines the performance of the same
companies with solvency for predicting bankruptcy and comparison in both models. This model is
suggested for measuring the values of financial performance (Al-Kassar and Soileau; 2012), and applying
the financial failure model (Z-score) used by Taffler (1983). The data of six companies were examined for
the period 1998-2011
2214-4625/$ – see front matter © 2014 Holy Spirit University of Kaslik. Hosting by Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.aebj.2014.05.010
ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155
Contents lists available at ScienceDirect
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j our nal homepage: www. el sevi er. com/ l ocat e/ aebj
* Corresponding author. Tel.: +962795432337; fax: +0-000-000-0000.
E-mail address: [email protected]
Peer review under responsibility of Holy Spirit University of Kaslik.
Conference Title
Financial performance evaluation and bankruptcy prediction (failure)
* ( )
Dr. Talal A. Al- Kassar
a
, Dr. Jared S. Soileau
b
a
Associate Professor, Accounting Department, Faculty of Economics and Administrative Sciences, Zarqa University, P.O. Box 132222, Zarqa 13110,
JORDAN.
b
Assistant Professor, Department of Accounting, Center for Internal Auditing, Louisiana State University., LA 70803. USA.
A R T I C L E I N F O
Article history:
Received 04 October 13
Received in revised form 15 March 14
Accepted 9 May 14
Keywords:
Financial Performance
Solvency
Bankruptcy
Criteria
Testing
Ranking
A B S T R A C T
Despite the copious number of statistical failure prediction models described in the literature, testing of
whether such methodologies work in practice is lacking. This paper examines the performance of the same
companies with solvency for predicting bankruptcy and comparison in both models. This model is
suggested for measuring the values of financial performance (Al-Kassar and Soileau; 2012), and applying
the financial failure model (Z-score) used by Taffler (1983). The data of six companies were examined for
the period 1998-2011.
The methodology which used at empirical study includes measuring financial performance according to
both models. Then both results have been shown in table (8). The correlations between their results for
both models are shown highly relationship. They were tested by T-test. Therefore, they were classified
and ranked the companies according to these values.
The research also demonstrates the need to include measures of both financial and non-financial
performance in the evaluation as they complement each other. Without both financial and non-financial,
the evaluation process is incomplete and does not provide desired results or the correct image of the
process. The research suggests including comprehensive measures of performance evaluation of projects
by using indicators of adopted criteria. Thus, the application of both models leads to better results and
assists users in maintaining greater objectivity while obtaining more accurate results than from analysis
based on personal evaluation alone.
© 2013 Holy Spirit University of Kaslik. Hosting by Elsevier B.V. All rights reserved.
(*)"This research is funded by the Deanship of Research and Graduate Studies in Zarqa University /Jordan"
© 2014 Holy Spirit University of Kaslik. Hosting by Elsevier B.V. All rights reserved.
148 ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155
1. Introduction
Since the development of the Z-Score, financial innovation has paved the
way for further development of corporate bankruptcy prediction models.
The option pricing model developed by Black and Scholes in 1973 and
Merton in 1974 provided the foundation upon which structural credit
models were built. KMV (Kealhofer, McQuown and Vasicek), Now Part
of Moody's Analytics Enterprise Risk Solutions, was the first to
commercialize the structural bankruptcy prediction model in the late
1980s. Miller (2009) noted that "the Distance to Default is not an
empirically created model, but rather a mathematical conclusion based on
the assumption that a company will default on its financial obligations
when its assets are worth less than its liabilities. It is also based on all of
the assumptions of the Black-Scholes option pricing model, including for
example, that asset returns are log-normally distributed".
There are many dimensions upon which to measure the
performance of a credit scoring system, but the most relevant way to
compare models with different sample sets is by measuring the models'
ordinal ability to differentiate between companies that are most likely to
go bankrupt from those that are least likely to go bankrupt (Bemmann,
2005).
Many governments are interested in establishing investment
projects because of the importance of the role government play in the
efforts to build a stable economic base. This is reflected in many
developing countries which are looking for opportunities to improve their
political, economical, social and cultural aspects. Generally, projects need
a lot of money and resources to finance them. Therefore, finding and
using the best method to control these investments and resources to
achieve development objectives in different fields and avoid insolvency is
of great importance. Gerdin (2005), states that Management Accounting
Systems (MAS) can be considered as "those parts of the formalized
information system used by organizations to influence the behavior of
their managers that leads to the attainment of organizational objectives".
Managers in some organizational contexts are likely to benefit from
accounting information that is detailed and issued frequently, whereas
MAS information in other contexts tends to be general rather than
detailed, and issued less frequently (Gerdin, 2005).
The empirical literature reviewed by Chenhall (2006), for
example, indicates that non-financial performance measures are more
widely adopted in just in time (JIT) and total quality management (TQM)
settings. Other studies like Abdel-Kader and Luther (2008), have
highlighted the need for additional research to increase our understanding
of organizational and environmental factors that explain the development
of management accounting systems, including the use of non-financial
measures. Accounting information plays an important role in individual
and corporate decision making. In particular, a fundamental use of
accounting information is to help different parties make an effective
decision concerning their investment portfolios. Much of the accounting
literature assumes that accounting and financial reporting in a country is a
function of its environment (Belkaoui and AlNajjar, 2006). The
management accounting literature reveals that changes in the environment
and the technology of a company can lead to new decision making and
control problems (Abdel-Maksoud et al., 2010).
1.1. Research objectives
1. To apply a model created by Al-Kassar and Soileau (2012),
this can measure the financial performance of the companies
mathematically.
2. To apply Taffler's model (1983) namely Z-score to measure
financial failure(solvency) of the same companies, and,
3. To see whether there is correlation between the above results
for each company, through testing the values by t-test, and
classify and rank them accordingly.
1.2. Research problems
The research problem focuses on the following:
1. To investigate the correlation between values of financial
performance and failure from each model.
2. In order to avoid personal intervention during the evaluation
process and to use objectively steps to evaluate all companies
by carrying on the comprehensive performance evaluation to
companies by using indicators and criteria adopted.
1.3. Research scope and methodology
The research paper will cover both theoretical and empirical materials.
The theoretical side includes defining of financial performance, criteria,
factor analysis and Taffler's model. While, the empirical side includes the
studying of financial performance values, financial failure values
(company solvency), testing and correlation, and rank and classify the
companies. Different materials, articles, reports, and sites have been used
to assist the research paper. Thus the proposed paper attempts:
1. To measure of financial performance values according to
suggest model.
2. To measure financial failure values according to Taffler's
model.
3. To test the above values.
4. To classify and rank the companies.
1.4. Population of the study
The data for six companies have been used in both models. These
companies 1, 2, and 3, related to a Mill, Transportation, and Heritage and
Museums company respectively. The remaining three, companies 4, 5,
and 6 are for commercial oil companies (petrol stations). The period of the
study is between the years 1998-2011.
The paper is organized as follows: the next section provides a
review of literature and previous studies. Section three begins with the
types of performance evaluation indicators and criteria in both financial
and non-financial groups as internal and external indicators. Section four
presents financial performance formula, computation of financial
performance, the mathematical model and empirical study of six
companies. Section five presents the measuring of financial failure
(solvency). Section six presents the testing of the values of the models.
Finally, section seven provides findings.
2. Literature Review Previous studies
Many studies have been carried out in measuring company performance
and the likelihood of business failure according to various factors.
ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155 149
These studies attempt to avoid the use of potentially biased
personal intervention during the evaluation process by following objective
steps to evaluate companies on a single base measure (see for instance
Altman (1968), Taffler (1977, 1983 and 2005), De Toni and Tonchia
(2001), Bernard et al. (2007), and Al-Kassar and Soileau (2012)).
Concerned with measurement of performance models, De Toni and
Tonchia (2001) used principle components analysis to describe and
evaluate the dimensions and actual state of performance measurement
models in an operations management setting.
Considering social, financial and operational factors, Bernard
et al. (2007) use surface measurements to classify organizations and
establish an overall performance evaluation model for local development
companies.
Altman (1968) developed a measure to predict the likelihood of corporate
bankruptcy based on a set of financial ratios using a multiple discriminant
analysis approach. The Altman model is as follow:
Altman-Z=0.0012(WC) +0.014(RE) +0.033(EBIT) +0.006(MVE)
+0.00999(NCI)
Where:
Altman-Z is the Z-score or predictive measure of corporate
bankruptcy,
WC is the ratio of working capital scaled by total assets,
RE is the ratio of retained earnings scaled by total assets,
EBIT is the ratio of earnings before interest and taxes scaled
by total assets,
MVE is the ratio of market value of equity scaled by the book
value of total debt, and
NCI is the ratio of sales scaled by total assets.
According to Altman (1968), a minimum Altman-Z score of
1.8 is necessary to avoid failure, but only with a z-score of 3.0 or more is
the company fairly safe. Using the following modified Z-Score model,
Taffler (1983) studied solvency among UK companies:
Z-Scr=C0+ 0.053*(PBT/CL) + 0.13*(CA/TL) + 0.18*(CL/TA) +
0.16*(NCI)
Where:
Z-Scr: Taffler’s solvency z-score for UK companies,
C0: constant,
PBT/CL is the ratio of profit before taxes scaled by current
liabilities,
CA/TL is the ratio of current assets scaled by total liabilities,
CL/TA is the ratio of current liabilities scaled by total assets,
NCI ('no credit' interval) is calculated as the difference
between the quick assets and current liabilities scaled
by the daily operating expenses [(quick assets –current
liabilities)/daily operating expenses] as a measure of
short term liquidity. More specifically, the ratio
indicates the number of days which a company can
continue to finance operations from its existing quick
assets if revenues are cut-off.
Based on the Taffler (1983) model, the coefficient percentages
C1 to C4 contribute 0.53, 0.13, 0.18, and 0.16 respectively, to the models
operation. Companies with a ZT-Score above a certain threshold (i.e. Z-
Scr=0) were predicted not fail during the next year.
Al-Kassar and Soileau (2012) present a mathematical financial
performance model based on factor scores for three companies in Jordan
and test the results by plotting and ranking. The results are then correlated
and tested, in order to classify and rank companies by value. In summary,
the research presents a linkage between a model suggested by the Al-
Kassar and Soileau (2012) and Taffler’s Z-score model. Based on a 25
year sample period within their study, Taffler and Agarwal (2005)
conclude their Z-score model possesses forecasting ability.
This paper attempts to reconcile and measure the correlation
between both models financial performance and bankruptcy prediction
(financial failure). Therefore, to evaluate results for the same companies
by following objective steps to evaluate them on a single base measure,
and in order to avoid personal intervention during the evaluation process.
3. Types of Performance evaluation indicators and criteria
Jowett, and Rothwell (1988) noted that financial targets provide readily
measurable objectives that are frequently already available. They contends
that such financial measures may be achievable by simply exploiting the
monopoly power of the industry, through either high prices or lower
quality of goods or services and proposed additional performance
indicators as one solution to this problem.
In general, the indicators are divided into the following categories
based on work by Parks and Glendinning (1981), Jowett and Rothwell
(1988), and BSA (1996) as follows:
3.1. Internal indicators
It includes measures of information related to production and service from
inside the organization. These measures include:
i. Production indicators.
ii. Productivity indicators.
iii. Financial Indicators.
iv. Marketing indicators.
v. Personnel indicators.
vi. Special Indicators, (which related to the nature of
the project).
3.2. External indicators
It includes measures that attempt to capture information that the
organization does not have control over yet may impact the organizational
results. Such factors include:
i. Economic indicators
ii. Social indicators
iii. Political indicators
iv. Environmental Indicators.
4. Measuring of Financial Performance
Al-Kassar and Soileau Model:To measure mathematically the financial
performance it is necessary to know the components. Courtis (1978)
indicates that total performance can be divided into three main groups,
namely, profitability, managerial performance and liquidity (or solvency);
as shown in Figure (1).
150 ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155
Fig. 1- Financial Ratios Categoric Framework.
Therefore, we have the following relation:
FP = ?(P + MP + L)
Where: FP = Financial Performance
P = the average of Profitability of relevant ratios.
MP = the average of managerial performance of relevant ratios.
L = the average of liquidity of relevant ratios.
To solve the above relation it is necessarily to use Factor
Analysis to analyze the interrelationships of a set of variables using
multivariate methods. Al-Kassar and Soileau (2012) have used factor
analysis,
†
to identify the interrelationships between the sets of variables;
however, their mathematical model for factor analysis might be expressed
as:
FP(Y) = c1*r1 + c2*r2 + c3* r3 + … + cn*rn + C
Where: FP = financial performance of a company.
c1 = raw coefficient score for ratio 1.
r1 = ratio 1, n = number of ratios.
C = constant.
Computer programs can be used to obtain the values of
standardized scores for each factor and ratio. Therefore, the total value of
1
SPSS is Statistical Package for Social Science.
all the factors represents the value of financial performance. The final
stage in calculating the equation can be achieved by running a computer
program to obtain a single value for each of the six companies that
incorporates all of the financial performance ratios. Table 1 provides these
measures of financial performance for six companies. Companies 1, 2,
and 3, which exhibit negative results, are for a Mill, Transportation, and
Heritage and Museums company respectively. The remaining three,
companies 4, 5, and 6 are for commercial oil companies (petrol stations)
and have positive results in each year. The period study's is between 1998-
2011 as shown below in Table 1 (Financial performance values) and
Figure 2.
Fig. 2- Financial Performance Values.
5. Measuring of Financial Failure (Solvency)
Taffler's Model: When applying the above model to the six companies
(financial performance) presented in Table 1, the model reflects the
likelihood of the observed company's failure according to the summary of
the percentages of the four selected ratios as opposed to considering each
ratio in isolation. In their 2005 study, Taffler and Agarwal indicate that
the four key dimensions of the firm's financial profile measure:
profitability, working capital position, financial risk, and liquidity.
Through factor analysis and the use of the Mosteller-Wallace criterion,
Taffler and Agarwal (2005) it is possible to evaluate the relative
contribution of each component ratio to the overall Z-score. This analysis
indicates that profitability alone accounts for approximately 53% of the
discriminant power, while the other three balance sheet measures together
provide the remaining proportion.
The ratios are in factor analysis form, without personal
intervention, and the results commensurate with the power of the model.
Taffler and Agarwal (2005) indicate that over a 25 year period, the z-score
model possesses true forecasting ability. Table 2 provides the ratios
definitions as well as the calculated ratio coefficients and ratio coefficient
percentages for all six companies previously referenced in Table 1 and
Figure 2.
Table 1- Financial Performance Values.
Year/company
No. 1 2 3
4
5
6
1998
-5.743 1.593 0.516 5.443 -1.746 0.828
1999
-3.415 2.679 -0.110 5.599 -1.671 1.318
2000
-3.585 1.755 -0.455 2.857 -1.472 0.628
2001
-3.410 0.793 0.106 0.882 0.463 0.215
2002
-3.354 -1.411 -0.994 0.391 0.152 0.362
2003
-3.887 -1.986 -1.458 0.327 1.666 0.613
2004
-2.524 -2.313 -1.704 -0.244 1.592 0.955
2005
-4.053 -1.451 -1.705 0.911 2.131 0.895
2006
-4.945 -0.892 -1.199 0.659 1.139 3.659
2007
-6.030 0.093 -2.454 0.507 2.231 5.173
2008
-3.173 -1.796 -2.475 0.182 0.038 5.187
2009
-2.957 -1.091 -2.280 0.226 0.238 5.266
2010
-1.980 -1.025 -2.155 0.218 0.258 5.313
2011
-1.755 -1.041 -2.320 0.202 0.287 4.895
ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155 151
Three of the four ratios presented in Table 2 (PBT/CL, CA/TL, and NCI)
are negatively associated with risk of insolvency, therefore the greater the
ratio, the lower the risk of insolvency. However, a higher value of CL/TA
is indicative of greater risk of insolvency. This study confirms the
importance of the relationship between profitability and current liabilities,
where the specific weight of this indicator is 53%. The researchers agree
on the importance of including a profitability measure to capture
significant changes in the likelihood of financial failure without over
dependence on the direct relationship between current assets and current
liabilities.
Table 3- Financial Failure Values.
Company Number
Year 1 2 3
4 5 6
1998
-4.25 -3.20 0.90 6.88 -1.50 1.77
1999
-1.54 3.00 1.20 7.01 -1.30 2.35
2000
-2.19 1.30 1.50 4.12 -0.90 1.88
2001
-2.30 -0.80 2.10 2.31 0.93 1.02
2002
-2.22 -1.10 2.40 2.10 0.68 1.08
2003
-1.52 -1.40 -2.60 2.22 2.73 1.54
2004
-1.91 -4.50 -2.70 1.98 2.91 1.83
2005
-2.41 -4.60 -2.90 3.62 3.85 1.91
2006
-2.62 -4.20 -2.40 3.75 2.74 2.19
2007
-3.80 5.20 -2.80 4.97 3.92 3.22
2008
-1.20 -3.10 -2.10 2.06 1.02 3.56
2009
-2.90 -3.40 -2.30 2.01 1.11 4.28
2010
-2.75 -3.60 -2.50 1.32 1.42 4.38
2011
-2.62 -3.20 -2.80 1.10 2.69 4.24
Fig. 3- Financial Failure Graph.
When the ratio of Profit before Tax to Current Liabilities is low, the
company has a higher risk of being able meet the needs of current
payments with operating cash flows. Therefore, the ratio is negatively
associated with the risk of insolvency. Table 4, indicates that companies 1,
2, and 3 were in negative position as a result of operating losses, while the
others (companies 4, 5, and 6) are in better position. Company 1 from the
year 2008 and company 2 from 2009 become positive through 2011.
Alternatively, company 3 begins with a positive value from 1998 to 2002
then takes a negative position, indicating increasing risk. Therefore, lower
value indicates that company is approaching the barrier of financial
failure.
Table 4- Profit before Tax/Current Liabilities.
Company Number
Year 1 2 3
4
5
6
1998
-0.75
-1.25
1.6
2.5
-0.6
1.2
1999
-0.25
1.75
1.5
2.04
-0.5
1.6
2000
-0.15
0.9
1.9
2.1
-0.2
1.3
2001
-0.1
0.8
1.5
2.3
0.3
1.1
2002
-0.3
-0.7
1.3
2.09
0.27
1.1
2003
-0.16
-0.8
-0.9
2.1
1.24
1.2
2004
-3.2
-0.9
-1
1.9
1.35
1.55
2005
-2.3
1.1
0.8
2.8
2.68
1.65
2006
-1.9
-1.8
-1.2
2.9
2.5
1.89
2007
-1.7
1.4
-1.09
3.25
2.8
2.4
2008
1.5
-1.2
-1.01
2.1
1.1
2.45
2009 1.6 0.9 -1.05
2.2
1.2
3.15
2010 1.1 0.8 -1.01
1.8
1.3
3.3
2011 1.3 0.5 -0.85
1.5
1.9
3.25
Table 2- Financial indicators used to measure the financial failure.
Purpose Ratio
Coefficient
percentages
Ratio
Coefficients
Ratios
Lower value indicates that
company is approaching
the barrier of financial
failure
53% 12.18 PBT/CL
Lower value indicates that
company is approaching
the barrier of financial
failure
13% 2.50 CA/TL
Higher value indicates that
the company is
approaching the barrier of
financial failure
18% 10.68 CL/TA
Lower value indicates that
the company is
approaching the barrier of
financial failure .
16% 0.029 NCI ('no
credit'
interval)
Intercept Term 3.2 CO
100% Total
152 ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155
Fig. 4- Profit Before Tax/ Current Liability.
Similar to the Profit before Taxes to Current Liabilities ratio, Current
Assets to Total Liabilities presented in Table 5 and Figure 5 provides an
additional measure of financial solvency risk. Therefore, when the ratio is
low the company is higher risk. Companies 1-3 have low values while
companies 4-6 have high values. Therefore, lower values indicate that, the
company is approaching the barrier of financial failure.
Fig. 5- Current Assets/ Total Liabilities.
The ratio of Current Liabilities to Total Asset has a positive association
with insolvency, indicating greater risk of business failure. As can be
noted in in Table 6 and Figure 6, companies 1-3 show higher vales than
companies 4-6. As higher value indicates that the company is approaching
the barrier of financial failure, companies 1-3 are in a more risky position.
The ratio of Sales to Total Assets is negatively associated with the
probability of financial failure, similar to ratios associated with tables 4
and 5. The lower values are very risky to the company. Beginning in
2004, the position of company 1 began to improve, but is still in a fairly
poor position in 2011. Alternatively, Company 2, 3, and 6 improved
whereas 4 and 5 seem to have experienced higher variability.
Table 5- Current Assets/Total Liabilities.
Company Number
Year 1 2 3
4 5 6
1998
0.4 0.53 0.6 2.6 1.25 1.8
1999
0.54 0.58 0.64 2.7 1.22 2.98
2000
0.64 0.54 0.53 2.2 1.18 1.45
2001
0.58 0.72 0.72 1.8 0.98 1.02
2002
0.62 0.7 0.88 1.5 1.41 1.11
2003
0.6 0.9 0.79 1.4 1.65 1.54
2004
0.6 0.7 0.71 1.2 1.47 2.04
2005
0.5 0.6 0.78 1.9 2.4 2.01
2006
0.6 0.9 0.89 1.7 1.8 3.53
2007
0.3 0.73 0.74 1.6 2.6 4.34
2008
0.6 0.84 0.76 1.1 1.15 4.23
2009 0.58 0.72 0.73
1.2 1.22 4.33
2010 0.64 0.68 0.70
1.1 1.3 4.45
2011 0.52 0.75 0.79
1.2 1.34 4.28
Table 6- Current Liabilities/Total Assets.
Company Number
Year 1 2 3
4 5 6
1998
0.8 0.42 0.38 0.15 0.23 0.25
1999
0.78 0.39 0.36 0.12 0.21 0.12
2000
0.69 0.41 0.41 0.035 0.15 0.35
2001
0.57 0.21 0.23 0.038 0.034 0.44
2002
0.56 0.23 0.16 0.046 0.041 0.38
2003
0.88 0.24 0.11 0.053 0.043 0.28
2004
0.55 0.36 0.12 0.068 0.039 0.18
2005
0.58 0.33 0.21 0.035 0.031 0.17
2006
0.75 0.38 0.14 0.047 0.040 0.022
2007
0.83 0.26 0.12 0.041 0.025 0.015
2008
0.72 0.31 0.23 0.061 0.035 0.012
2009 0.61 0.29 0.18
0.057 0.029 0.010
2010 0.57 0.35 0.21
0.059 0.030 0.011
2011 0.64 0.28 0.23
0.062 0.033 0.016
ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155 153
Fig. 6- Current Liabilities/Total Assets.
Table 7- NCI (Sales/Total Assets).
Company Number
Year 1 2 3
4 5 6
1998
0.3 2.4 1.9 3.01 0.81 1.21
1999
0.41 1.16 1.94 3.02 0.76 1.89
2000
0.5 1.15 2.3 1.25 0.75 1.1
2001
0.61 2.1 2.2 2.02 2.0 0.78
2002
0.8 1.8 2.8 1.9 2.1 1.05
2003
0.78 1.3 2.9 1.2 3.2 1.4
2004
1.31 1.2 3.1 1.4 3.1 1.5
2005
0.85 1.1 2.8 2.0 3.4 1.45
2006
0.8 1.3 2.99 1.8 2.7 2.54
2007
0.5 2.4 3.4 1.77 3.5 4.10
2008
1.4 2.6 2.6 1.1 1.1 4.15
2009 1.5 2.3 2.3
1.33 1.3 4.28
2010 1.4 2.4 2.5
1.4 1.0 4.35
2011 1.4 2.5 2.6
2.1 1.4 4.01
Fig. 7- NCI.
Both models (Financial performance model by Al-Kassar and Soileau
(2012) and Taffler's (1983) model) were applied for six companies, it
should be noted that the mean value of the financial performance (Y) is
zero. This means that any Y value of a company above zero is classified
as above average and those below zero are classified as below average.
6. Study analysis and correlation
The analysis of liquidity via financial indicators helps to provide early
warning of increased risk of financial failure. Thus, it is noted that there
are two directions in the analysis of liquidity. The first focuses on the
direct relationship between current assets and current liabilities which
considers the use of current assets as a main source of meeting current
liabilities. This trend contrasts with the continuity of an organization and
its profits. Since profitability depends on the use of productive assets,
including current assets, disposing of current assets to pay current
liabilities prevents the continuity of the organization to continue to
perform operations.
The second trend based on the main function of financial
management of the project which is to estimate the financial needs of the
activity, and the provision of necessary sources of funding and investing
them to achieve profit and therefore the continuity of the project. To
determine the imbalance between these elements leads to financial failure.
It should consider the narrow concept of liquidity, which links the direct
relationship between current assets and current liabilities.
This means that the analysis of financial performance assists
financial management and top management of the companies to give
greater attention to important ratios. From the plotting of the 25 ratios,
there are seven ratios that show they have a direct impact and the most
powerful in the calculation of the values of financial performance,
namely: (Al-Kassar and Soileau, 2012):
1. Sales/ Working Capital.
2. Sales/Accounts Receivable.
3. Current Assets/Total Liabilities.
4. Current Assets/Current Liabilities.
5. Current Liabilities/Total Assets.
6. Cash/Current Liabilities.
7. Profit before Tax/Current Liabilities.
Consistent with the 1983 Taffler model, some ratios have an
impact in determining the values and the power of the overall model as
well. It is also helpful to improve their policies by adopting the standard
levels such as current assets to current liabilities to be (2:1), decreasing
current liabilities, changing policy regarding customers by reducing credit
policy and collecting debt from customers within shorter periods. This
might lead the companies to safeguard their financial performance and
move to be an improved position far away from increased likelihood of
financial failure.
It is important to measure the profit impact to the future
payment of current liabilities. Thus, contribution of current assets to cover
operating expenses without depending on external sources of funding
should be shown. Furthermore, it is essential to analyze the contribution
of current assets to cover the total liabilities, and the specific weight of the
current liabilities to total assets. Therefore, results from the four ratios of
the Taffler model (1983) show maximum value of the first, second and
fourth, and the low value for the third one, give the best indicator to move
away from the likelihood of bankruptcy in the near future for an
organization.
Thus, the correlation between the values of financial
performance (FP) and the values of financial failure (X) for the companies
1, 2 and 3 which have poor results are tested by (t-test) and results reveal
the following:
154 ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155
Table 8- Correlation Values.
Company 1 2 3 4 5 6
Correlation
Coefficient (r) 0.539 0.601 0.821 0.880 0.966 0.929
P-Value 0.047 0.023 0.000 0.000 0.000 0.000
T calculated 2.217 2.604 4.981 6.418 14.418 8.695
T scheduled 1.771 1.771 1.771 1.771 1.771 1.771
Rank 6 5 4 3 1 2
The values indicate a strong correlation lies between (0.7 and
0.9) for companies. Where -values are determined by the value of the
correlation coefficient.
Using t-test: then it indicates the following hypothesis,
Ho: ƒÏ = 0 does not exist any relationship.
H1: ƒÏ‚ 0 there is a relationship.
It appears in the table above, and after testing each of the calculated
values of t and t scheduled at a significance level 0.05. As the calculated
values of t greater than the scheduled, therefore, will reject Ho in support
of H1, there is a relationship. The rank and classification of the companies
are shown in Table 8. The negative results should be observed because it
is beginning to fail. Also it should deal more accurately with the
companies that get negative results because it is possible to fail in the
coming year. But normally, they received subsidies from the State to help
them for next periods.
7. Findings
The constructs and uses a model to mathematically measure
organizational financial performance and likelihood of financial failure.
Values of both financial performance and financial failure are used to
correlated am evaluate through the graph of company ratios selected.
Results of the models are tested and demonstrate with acceptable values
of significance, 0.05, and the null hypothesis rejected in favour of the
alternative that a relationship exists between financial performance and
likelihood of organizational failure. The comprehensive performance
models used in the evaluation process of these companies requires both
financial and non-financial measures to complement each other. Without
following such a process, the analysis of these companies is not complete
and may yield unreliable results. Therefore, we recommend applying both
evaluations and using indicators and criteria adopted to reach a more
reliable result. Therefore, it is recommended to follow both models to
obtain accurate results and maintain objective from personal intervention.
REFERENCES
Abdel-Kader, M., & Luther, R., (2008). The impact of firm characteristics on
management accounting practice: a UK-based empirical analysis.
The British Accounting Review, 40(1), 2-27.
Abdel-Maksoud, A., Cerbioni, F., Ricceri, F., & Velayutham, S. (2010).
Employee morale, non-financial performance measures
deployment of innovative managerial practices and shop-floor
involvement in Italian manufacturing firms. The British Accounting
Review, 42 (1), 36-55.
Agarwal, V., & Taffler, R. J. (2005). Twenty-five years of z-score in the UK:
do they really work? Accounting and Business Research, 37 (4), 1-
36.
Al-Kassar, T., & Soileau, J. (2012). Design and Applied Mathematical Model
of Measuring Financial Performance Evaluation: Jordan Results.
Oil, Gas & Energy Quarterly, 60(3) 621-636.
Aharoni, Y. (1981). Performance Evaluation of state-owned Enterprises: A
Process Perspective, (note). Management and Finance, 27 (11)
1340-1347.
Altman, E. I. (1968). Financial Ratios Discriminant Analysis and the
Prediction of Corporate Bankruptcy. Journal of Finance, 23, 4,
589-609.
Belkaoui, A., & AlNajjar, F. (2006). Earnings opacity internationally and
elements of social, economic and accounting order. Review of
Accounting and Finance, 3 (3), 130-144.
Bemmann, Martin, Improving the Comparability of Insolvency
Predictions (June 23, 2005).
Dresden Economics Discussion Paper Series No. 08/2005. Available at
SSRN:http://ssrn.com/abstract=731644.
Bernard F., Desrochers, J., Martel, D., & Prefontaine, J. (2007). Modeling the
Performance Evaluation of Local Investment and Economic
Development Corporations. Journal of Business & Economic
Research, Sept. 5 (9), 55-76.
Chenhall, R. H. (2006). The contingent design of performance measures. In
Bhimani (Ed.). Contemporary issues in management accounting,
92-116. Oxford: Oxford University Press.
Courtis, J.K. (1978). Modeling a Financial Ratios Categoric Framework.
Journal of Business Finance and Accounting, 5 (4), 371-386.
De Tone, A., & Tonchia, S. (2001). Performance Measurement Systems-
Models, Characteristics and Measures. International Journal of
Operations & Production Management, 21 (1/2), 46-71.
Gerdin, J. (2005). Managing accounting system design in manufacturing
departments: an empirical investigation using a multiple
contingencies approach. Accounting, Organizations and Society,
30 (1) 99–126.
Glautier, M., & Underdown, B. (1986). Accounting theory and Practice, UK.
Miller, W. (2009). Introducing the Morningstar Solvency Score, A Bankruptcy
Prediction Metric. Morningstar, Inc. December 2009, Electronic
copy available at:http://ssrn.com/abstract=1516762.
Jowett, P., & Rothwell, M. (1988). Performance Indicators in Public Sector.
UK, (also White Paper 1987).
Perks, R., & Glendinning, R.. (1981). Little Progress Seen in Published
Performance Indicators. Management Accounting, Dec 28-30.
Taffler, R., & Tisshaw H. (1977). Going, going, gone – four factors which
predict. Accountancy, March, 50-54.
Taffler, R. J. (1983). The Z-Score Approach to Measuring Company Solvency.
The Accountant's Magazine, March, 22-24.
The Board of Supreme Audit (BSA), internal studies, 1986, 1998 unpublished.
ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155 155
Appendix A:
The following are the ratios referred to in the text as R1-R25:
Profitability Ratios
Return on investment ratios:
R1= EBIT/TA = Earnings before Tax scaled by total assets
R2= NP/TA = Net Profit scaled by total assets
R3= NP/NW = Net Profit scaled by net worth
Profit margin ratio:
R4= NP/Sales = Net Profit scaled by sales
Capital turnover ratios:
R5= Sales/TA = Return on Sales computed as Sales scaled by total assets
R6= Sales/NW = Sales scaled by net worth
R7= Sales/WC = Sales scaled by working capital
Managerial Ratios
Credit Policy ratio:
R8= Sales/AR = Sales scaled by accounts receivable (AR Turnover)
Inventory ratios:
R9= INV/COGS = Inventory scaled by the cost of goods sold
R10= INV/WC = Inventory scaled by working capital
R11= INV/Sales = Inventory scaled by sales
Administration ratios:
R12= Op. Exp./TA = Operating Expenses scaled by total assets
R13= COGS/Sales = Cost of goods sold scaled by sales
Asset-equity structure ratios:
R14= NW/TA = Net Worth scaled by total assets
R15= Debt/WC = Debt scaled by working capital
R16= LTA/NW = Long-term assets scaled by new worth
R17= LTA/TA = Long-term assets scaled by total assets
R18= WC/SALES = Working capital scaled by sales
Solvency Ratios
Short-term liquidity ratios:
R19= CA/CL = Current assets scaled by current liabilities (Current Ratio)
R20= WC/TA = Working capital scaled by total assets
R21= Quick Assets/CL = Quick assets scaled by current liabilities (Quick
Ratio)
Long-term solvency ratio:
R22= Debt/NW = Total debt scaled by net worth
Cash flow ratios:
R23= CF/CL = Cash flows from operations scaled by current liabilities
R24= CF/TA = Cash flows from operations scaled by total assets
R25= CF/WC = Cash flows from operations scaled by working capital
doc_100985108.pdf
Despite the copious number of statistical failure prediction models described in the literature, testing of
whether such methodologies work in practice is lacking. This paper examines the performance of the same
companies with solvency for predicting bankruptcy and comparison in both models. This model is
suggested for measuring the values of financial performance (Al-Kassar and Soileau; 2012), and applying
the financial failure model (Z-score) used by Taffler (1983). The data of six companies were examined for
the period 1998-2011
2214-4625/$ – see front matter © 2014 Holy Spirit University of Kaslik. Hosting by Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.aebj.2014.05.010
ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155
Contents lists available at ScienceDirect
ScienceDirect
j our nal homepage: www. el sevi er. com/ l ocat e/ aebj
* Corresponding author. Tel.: +962795432337; fax: +0-000-000-0000.
E-mail address: [email protected]
Peer review under responsibility of Holy Spirit University of Kaslik.
Conference Title
Financial performance evaluation and bankruptcy prediction (failure)
* ( )
Dr. Talal A. Al- Kassar
a
, Dr. Jared S. Soileau
b
a
Associate Professor, Accounting Department, Faculty of Economics and Administrative Sciences, Zarqa University, P.O. Box 132222, Zarqa 13110,
JORDAN.
b
Assistant Professor, Department of Accounting, Center for Internal Auditing, Louisiana State University., LA 70803. USA.
A R T I C L E I N F O
Article history:
Received 04 October 13
Received in revised form 15 March 14
Accepted 9 May 14
Keywords:
Financial Performance
Solvency
Bankruptcy
Criteria
Testing
Ranking
A B S T R A C T
Despite the copious number of statistical failure prediction models described in the literature, testing of
whether such methodologies work in practice is lacking. This paper examines the performance of the same
companies with solvency for predicting bankruptcy and comparison in both models. This model is
suggested for measuring the values of financial performance (Al-Kassar and Soileau; 2012), and applying
the financial failure model (Z-score) used by Taffler (1983). The data of six companies were examined for
the period 1998-2011.
The methodology which used at empirical study includes measuring financial performance according to
both models. Then both results have been shown in table (8). The correlations between their results for
both models are shown highly relationship. They were tested by T-test. Therefore, they were classified
and ranked the companies according to these values.
The research also demonstrates the need to include measures of both financial and non-financial
performance in the evaluation as they complement each other. Without both financial and non-financial,
the evaluation process is incomplete and does not provide desired results or the correct image of the
process. The research suggests including comprehensive measures of performance evaluation of projects
by using indicators of adopted criteria. Thus, the application of both models leads to better results and
assists users in maintaining greater objectivity while obtaining more accurate results than from analysis
based on personal evaluation alone.
© 2013 Holy Spirit University of Kaslik. Hosting by Elsevier B.V. All rights reserved.
(*)"This research is funded by the Deanship of Research and Graduate Studies in Zarqa University /Jordan"
© 2014 Holy Spirit University of Kaslik. Hosting by Elsevier B.V. All rights reserved.
148 ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155
1. Introduction
Since the development of the Z-Score, financial innovation has paved the
way for further development of corporate bankruptcy prediction models.
The option pricing model developed by Black and Scholes in 1973 and
Merton in 1974 provided the foundation upon which structural credit
models were built. KMV (Kealhofer, McQuown and Vasicek), Now Part
of Moody's Analytics Enterprise Risk Solutions, was the first to
commercialize the structural bankruptcy prediction model in the late
1980s. Miller (2009) noted that "the Distance to Default is not an
empirically created model, but rather a mathematical conclusion based on
the assumption that a company will default on its financial obligations
when its assets are worth less than its liabilities. It is also based on all of
the assumptions of the Black-Scholes option pricing model, including for
example, that asset returns are log-normally distributed".
There are many dimensions upon which to measure the
performance of a credit scoring system, but the most relevant way to
compare models with different sample sets is by measuring the models'
ordinal ability to differentiate between companies that are most likely to
go bankrupt from those that are least likely to go bankrupt (Bemmann,
2005).
Many governments are interested in establishing investment
projects because of the importance of the role government play in the
efforts to build a stable economic base. This is reflected in many
developing countries which are looking for opportunities to improve their
political, economical, social and cultural aspects. Generally, projects need
a lot of money and resources to finance them. Therefore, finding and
using the best method to control these investments and resources to
achieve development objectives in different fields and avoid insolvency is
of great importance. Gerdin (2005), states that Management Accounting
Systems (MAS) can be considered as "those parts of the formalized
information system used by organizations to influence the behavior of
their managers that leads to the attainment of organizational objectives".
Managers in some organizational contexts are likely to benefit from
accounting information that is detailed and issued frequently, whereas
MAS information in other contexts tends to be general rather than
detailed, and issued less frequently (Gerdin, 2005).
The empirical literature reviewed by Chenhall (2006), for
example, indicates that non-financial performance measures are more
widely adopted in just in time (JIT) and total quality management (TQM)
settings. Other studies like Abdel-Kader and Luther (2008), have
highlighted the need for additional research to increase our understanding
of organizational and environmental factors that explain the development
of management accounting systems, including the use of non-financial
measures. Accounting information plays an important role in individual
and corporate decision making. In particular, a fundamental use of
accounting information is to help different parties make an effective
decision concerning their investment portfolios. Much of the accounting
literature assumes that accounting and financial reporting in a country is a
function of its environment (Belkaoui and AlNajjar, 2006). The
management accounting literature reveals that changes in the environment
and the technology of a company can lead to new decision making and
control problems (Abdel-Maksoud et al., 2010).
1.1. Research objectives
1. To apply a model created by Al-Kassar and Soileau (2012),
this can measure the financial performance of the companies
mathematically.
2. To apply Taffler's model (1983) namely Z-score to measure
financial failure(solvency) of the same companies, and,
3. To see whether there is correlation between the above results
for each company, through testing the values by t-test, and
classify and rank them accordingly.
1.2. Research problems
The research problem focuses on the following:
1. To investigate the correlation between values of financial
performance and failure from each model.
2. In order to avoid personal intervention during the evaluation
process and to use objectively steps to evaluate all companies
by carrying on the comprehensive performance evaluation to
companies by using indicators and criteria adopted.
1.3. Research scope and methodology
The research paper will cover both theoretical and empirical materials.
The theoretical side includes defining of financial performance, criteria,
factor analysis and Taffler's model. While, the empirical side includes the
studying of financial performance values, financial failure values
(company solvency), testing and correlation, and rank and classify the
companies. Different materials, articles, reports, and sites have been used
to assist the research paper. Thus the proposed paper attempts:
1. To measure of financial performance values according to
suggest model.
2. To measure financial failure values according to Taffler's
model.
3. To test the above values.
4. To classify and rank the companies.
1.4. Population of the study
The data for six companies have been used in both models. These
companies 1, 2, and 3, related to a Mill, Transportation, and Heritage and
Museums company respectively. The remaining three, companies 4, 5,
and 6 are for commercial oil companies (petrol stations). The period of the
study is between the years 1998-2011.
The paper is organized as follows: the next section provides a
review of literature and previous studies. Section three begins with the
types of performance evaluation indicators and criteria in both financial
and non-financial groups as internal and external indicators. Section four
presents financial performance formula, computation of financial
performance, the mathematical model and empirical study of six
companies. Section five presents the measuring of financial failure
(solvency). Section six presents the testing of the values of the models.
Finally, section seven provides findings.
2. Literature Review Previous studies
Many studies have been carried out in measuring company performance
and the likelihood of business failure according to various factors.
ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155 149
These studies attempt to avoid the use of potentially biased
personal intervention during the evaluation process by following objective
steps to evaluate companies on a single base measure (see for instance
Altman (1968), Taffler (1977, 1983 and 2005), De Toni and Tonchia
(2001), Bernard et al. (2007), and Al-Kassar and Soileau (2012)).
Concerned with measurement of performance models, De Toni and
Tonchia (2001) used principle components analysis to describe and
evaluate the dimensions and actual state of performance measurement
models in an operations management setting.
Considering social, financial and operational factors, Bernard
et al. (2007) use surface measurements to classify organizations and
establish an overall performance evaluation model for local development
companies.
Altman (1968) developed a measure to predict the likelihood of corporate
bankruptcy based on a set of financial ratios using a multiple discriminant
analysis approach. The Altman model is as follow:
Altman-Z=0.0012(WC) +0.014(RE) +0.033(EBIT) +0.006(MVE)
+0.00999(NCI)
Where:
Altman-Z is the Z-score or predictive measure of corporate
bankruptcy,
WC is the ratio of working capital scaled by total assets,
RE is the ratio of retained earnings scaled by total assets,
EBIT is the ratio of earnings before interest and taxes scaled
by total assets,
MVE is the ratio of market value of equity scaled by the book
value of total debt, and
NCI is the ratio of sales scaled by total assets.
According to Altman (1968), a minimum Altman-Z score of
1.8 is necessary to avoid failure, but only with a z-score of 3.0 or more is
the company fairly safe. Using the following modified Z-Score model,
Taffler (1983) studied solvency among UK companies:
Z-Scr=C0+ 0.053*(PBT/CL) + 0.13*(CA/TL) + 0.18*(CL/TA) +
0.16*(NCI)
Where:
Z-Scr: Taffler’s solvency z-score for UK companies,
C0: constant,
PBT/CL is the ratio of profit before taxes scaled by current
liabilities,
CA/TL is the ratio of current assets scaled by total liabilities,
CL/TA is the ratio of current liabilities scaled by total assets,
NCI ('no credit' interval) is calculated as the difference
between the quick assets and current liabilities scaled
by the daily operating expenses [(quick assets –current
liabilities)/daily operating expenses] as a measure of
short term liquidity. More specifically, the ratio
indicates the number of days which a company can
continue to finance operations from its existing quick
assets if revenues are cut-off.
Based on the Taffler (1983) model, the coefficient percentages
C1 to C4 contribute 0.53, 0.13, 0.18, and 0.16 respectively, to the models
operation. Companies with a ZT-Score above a certain threshold (i.e. Z-
Scr=0) were predicted not fail during the next year.
Al-Kassar and Soileau (2012) present a mathematical financial
performance model based on factor scores for three companies in Jordan
and test the results by plotting and ranking. The results are then correlated
and tested, in order to classify and rank companies by value. In summary,
the research presents a linkage between a model suggested by the Al-
Kassar and Soileau (2012) and Taffler’s Z-score model. Based on a 25
year sample period within their study, Taffler and Agarwal (2005)
conclude their Z-score model possesses forecasting ability.
This paper attempts to reconcile and measure the correlation
between both models financial performance and bankruptcy prediction
(financial failure). Therefore, to evaluate results for the same companies
by following objective steps to evaluate them on a single base measure,
and in order to avoid personal intervention during the evaluation process.
3. Types of Performance evaluation indicators and criteria
Jowett, and Rothwell (1988) noted that financial targets provide readily
measurable objectives that are frequently already available. They contends
that such financial measures may be achievable by simply exploiting the
monopoly power of the industry, through either high prices or lower
quality of goods or services and proposed additional performance
indicators as one solution to this problem.
In general, the indicators are divided into the following categories
based on work by Parks and Glendinning (1981), Jowett and Rothwell
(1988), and BSA (1996) as follows:
3.1. Internal indicators
It includes measures of information related to production and service from
inside the organization. These measures include:
i. Production indicators.
ii. Productivity indicators.
iii. Financial Indicators.
iv. Marketing indicators.
v. Personnel indicators.
vi. Special Indicators, (which related to the nature of
the project).
3.2. External indicators
It includes measures that attempt to capture information that the
organization does not have control over yet may impact the organizational
results. Such factors include:
i. Economic indicators
ii. Social indicators
iii. Political indicators
iv. Environmental Indicators.
4. Measuring of Financial Performance
Al-Kassar and Soileau Model:To measure mathematically the financial
performance it is necessary to know the components. Courtis (1978)
indicates that total performance can be divided into three main groups,
namely, profitability, managerial performance and liquidity (or solvency);
as shown in Figure (1).
150 ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155
Fig. 1- Financial Ratios Categoric Framework.
Therefore, we have the following relation:
FP = ?(P + MP + L)
Where: FP = Financial Performance
P = the average of Profitability of relevant ratios.
MP = the average of managerial performance of relevant ratios.
L = the average of liquidity of relevant ratios.
To solve the above relation it is necessarily to use Factor
Analysis to analyze the interrelationships of a set of variables using
multivariate methods. Al-Kassar and Soileau (2012) have used factor
analysis,
†
to identify the interrelationships between the sets of variables;
however, their mathematical model for factor analysis might be expressed
as:
FP(Y) = c1*r1 + c2*r2 + c3* r3 + … + cn*rn + C
Where: FP = financial performance of a company.
c1 = raw coefficient score for ratio 1.
r1 = ratio 1, n = number of ratios.
C = constant.
Computer programs can be used to obtain the values of
standardized scores for each factor and ratio. Therefore, the total value of
1
SPSS is Statistical Package for Social Science.
all the factors represents the value of financial performance. The final
stage in calculating the equation can be achieved by running a computer
program to obtain a single value for each of the six companies that
incorporates all of the financial performance ratios. Table 1 provides these
measures of financial performance for six companies. Companies 1, 2,
and 3, which exhibit negative results, are for a Mill, Transportation, and
Heritage and Museums company respectively. The remaining three,
companies 4, 5, and 6 are for commercial oil companies (petrol stations)
and have positive results in each year. The period study's is between 1998-
2011 as shown below in Table 1 (Financial performance values) and
Figure 2.
Fig. 2- Financial Performance Values.
5. Measuring of Financial Failure (Solvency)
Taffler's Model: When applying the above model to the six companies
(financial performance) presented in Table 1, the model reflects the
likelihood of the observed company's failure according to the summary of
the percentages of the four selected ratios as opposed to considering each
ratio in isolation. In their 2005 study, Taffler and Agarwal indicate that
the four key dimensions of the firm's financial profile measure:
profitability, working capital position, financial risk, and liquidity.
Through factor analysis and the use of the Mosteller-Wallace criterion,
Taffler and Agarwal (2005) it is possible to evaluate the relative
contribution of each component ratio to the overall Z-score. This analysis
indicates that profitability alone accounts for approximately 53% of the
discriminant power, while the other three balance sheet measures together
provide the remaining proportion.
The ratios are in factor analysis form, without personal
intervention, and the results commensurate with the power of the model.
Taffler and Agarwal (2005) indicate that over a 25 year period, the z-score
model possesses true forecasting ability. Table 2 provides the ratios
definitions as well as the calculated ratio coefficients and ratio coefficient
percentages for all six companies previously referenced in Table 1 and
Figure 2.
Table 1- Financial Performance Values.
Year/company
No. 1 2 3
4
5
6
1998
-5.743 1.593 0.516 5.443 -1.746 0.828
1999
-3.415 2.679 -0.110 5.599 -1.671 1.318
2000
-3.585 1.755 -0.455 2.857 -1.472 0.628
2001
-3.410 0.793 0.106 0.882 0.463 0.215
2002
-3.354 -1.411 -0.994 0.391 0.152 0.362
2003
-3.887 -1.986 -1.458 0.327 1.666 0.613
2004
-2.524 -2.313 -1.704 -0.244 1.592 0.955
2005
-4.053 -1.451 -1.705 0.911 2.131 0.895
2006
-4.945 -0.892 -1.199 0.659 1.139 3.659
2007
-6.030 0.093 -2.454 0.507 2.231 5.173
2008
-3.173 -1.796 -2.475 0.182 0.038 5.187
2009
-2.957 -1.091 -2.280 0.226 0.238 5.266
2010
-1.980 -1.025 -2.155 0.218 0.258 5.313
2011
-1.755 -1.041 -2.320 0.202 0.287 4.895
ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155 151
Three of the four ratios presented in Table 2 (PBT/CL, CA/TL, and NCI)
are negatively associated with risk of insolvency, therefore the greater the
ratio, the lower the risk of insolvency. However, a higher value of CL/TA
is indicative of greater risk of insolvency. This study confirms the
importance of the relationship between profitability and current liabilities,
where the specific weight of this indicator is 53%. The researchers agree
on the importance of including a profitability measure to capture
significant changes in the likelihood of financial failure without over
dependence on the direct relationship between current assets and current
liabilities.
Table 3- Financial Failure Values.
Company Number
Year 1 2 3
4 5 6
1998
-4.25 -3.20 0.90 6.88 -1.50 1.77
1999
-1.54 3.00 1.20 7.01 -1.30 2.35
2000
-2.19 1.30 1.50 4.12 -0.90 1.88
2001
-2.30 -0.80 2.10 2.31 0.93 1.02
2002
-2.22 -1.10 2.40 2.10 0.68 1.08
2003
-1.52 -1.40 -2.60 2.22 2.73 1.54
2004
-1.91 -4.50 -2.70 1.98 2.91 1.83
2005
-2.41 -4.60 -2.90 3.62 3.85 1.91
2006
-2.62 -4.20 -2.40 3.75 2.74 2.19
2007
-3.80 5.20 -2.80 4.97 3.92 3.22
2008
-1.20 -3.10 -2.10 2.06 1.02 3.56
2009
-2.90 -3.40 -2.30 2.01 1.11 4.28
2010
-2.75 -3.60 -2.50 1.32 1.42 4.38
2011
-2.62 -3.20 -2.80 1.10 2.69 4.24
Fig. 3- Financial Failure Graph.
When the ratio of Profit before Tax to Current Liabilities is low, the
company has a higher risk of being able meet the needs of current
payments with operating cash flows. Therefore, the ratio is negatively
associated with the risk of insolvency. Table 4, indicates that companies 1,
2, and 3 were in negative position as a result of operating losses, while the
others (companies 4, 5, and 6) are in better position. Company 1 from the
year 2008 and company 2 from 2009 become positive through 2011.
Alternatively, company 3 begins with a positive value from 1998 to 2002
then takes a negative position, indicating increasing risk. Therefore, lower
value indicates that company is approaching the barrier of financial
failure.
Table 4- Profit before Tax/Current Liabilities.
Company Number
Year 1 2 3
4
5
6
1998
-0.75
-1.25
1.6
2.5
-0.6
1.2
1999
-0.25
1.75
1.5
2.04
-0.5
1.6
2000
-0.15
0.9
1.9
2.1
-0.2
1.3
2001
-0.1
0.8
1.5
2.3
0.3
1.1
2002
-0.3
-0.7
1.3
2.09
0.27
1.1
2003
-0.16
-0.8
-0.9
2.1
1.24
1.2
2004
-3.2
-0.9
-1
1.9
1.35
1.55
2005
-2.3
1.1
0.8
2.8
2.68
1.65
2006
-1.9
-1.8
-1.2
2.9
2.5
1.89
2007
-1.7
1.4
-1.09
3.25
2.8
2.4
2008
1.5
-1.2
-1.01
2.1
1.1
2.45
2009 1.6 0.9 -1.05
2.2
1.2
3.15
2010 1.1 0.8 -1.01
1.8
1.3
3.3
2011 1.3 0.5 -0.85
1.5
1.9
3.25
Table 2- Financial indicators used to measure the financial failure.
Purpose Ratio
Coefficient
percentages
Ratio
Coefficients
Ratios
Lower value indicates that
company is approaching
the barrier of financial
failure
53% 12.18 PBT/CL
Lower value indicates that
company is approaching
the barrier of financial
failure
13% 2.50 CA/TL
Higher value indicates that
the company is
approaching the barrier of
financial failure
18% 10.68 CL/TA
Lower value indicates that
the company is
approaching the barrier of
financial failure .
16% 0.029 NCI ('no
credit'
interval)
Intercept Term 3.2 CO
100% Total
152 ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155
Fig. 4- Profit Before Tax/ Current Liability.
Similar to the Profit before Taxes to Current Liabilities ratio, Current
Assets to Total Liabilities presented in Table 5 and Figure 5 provides an
additional measure of financial solvency risk. Therefore, when the ratio is
low the company is higher risk. Companies 1-3 have low values while
companies 4-6 have high values. Therefore, lower values indicate that, the
company is approaching the barrier of financial failure.
Fig. 5- Current Assets/ Total Liabilities.
The ratio of Current Liabilities to Total Asset has a positive association
with insolvency, indicating greater risk of business failure. As can be
noted in in Table 6 and Figure 6, companies 1-3 show higher vales than
companies 4-6. As higher value indicates that the company is approaching
the barrier of financial failure, companies 1-3 are in a more risky position.
The ratio of Sales to Total Assets is negatively associated with the
probability of financial failure, similar to ratios associated with tables 4
and 5. The lower values are very risky to the company. Beginning in
2004, the position of company 1 began to improve, but is still in a fairly
poor position in 2011. Alternatively, Company 2, 3, and 6 improved
whereas 4 and 5 seem to have experienced higher variability.
Table 5- Current Assets/Total Liabilities.
Company Number
Year 1 2 3
4 5 6
1998
0.4 0.53 0.6 2.6 1.25 1.8
1999
0.54 0.58 0.64 2.7 1.22 2.98
2000
0.64 0.54 0.53 2.2 1.18 1.45
2001
0.58 0.72 0.72 1.8 0.98 1.02
2002
0.62 0.7 0.88 1.5 1.41 1.11
2003
0.6 0.9 0.79 1.4 1.65 1.54
2004
0.6 0.7 0.71 1.2 1.47 2.04
2005
0.5 0.6 0.78 1.9 2.4 2.01
2006
0.6 0.9 0.89 1.7 1.8 3.53
2007
0.3 0.73 0.74 1.6 2.6 4.34
2008
0.6 0.84 0.76 1.1 1.15 4.23
2009 0.58 0.72 0.73
1.2 1.22 4.33
2010 0.64 0.68 0.70
1.1 1.3 4.45
2011 0.52 0.75 0.79
1.2 1.34 4.28
Table 6- Current Liabilities/Total Assets.
Company Number
Year 1 2 3
4 5 6
1998
0.8 0.42 0.38 0.15 0.23 0.25
1999
0.78 0.39 0.36 0.12 0.21 0.12
2000
0.69 0.41 0.41 0.035 0.15 0.35
2001
0.57 0.21 0.23 0.038 0.034 0.44
2002
0.56 0.23 0.16 0.046 0.041 0.38
2003
0.88 0.24 0.11 0.053 0.043 0.28
2004
0.55 0.36 0.12 0.068 0.039 0.18
2005
0.58 0.33 0.21 0.035 0.031 0.17
2006
0.75 0.38 0.14 0.047 0.040 0.022
2007
0.83 0.26 0.12 0.041 0.025 0.015
2008
0.72 0.31 0.23 0.061 0.035 0.012
2009 0.61 0.29 0.18
0.057 0.029 0.010
2010 0.57 0.35 0.21
0.059 0.030 0.011
2011 0.64 0.28 0.23
0.062 0.033 0.016
ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155 153
Fig. 6- Current Liabilities/Total Assets.
Table 7- NCI (Sales/Total Assets).
Company Number
Year 1 2 3
4 5 6
1998
0.3 2.4 1.9 3.01 0.81 1.21
1999
0.41 1.16 1.94 3.02 0.76 1.89
2000
0.5 1.15 2.3 1.25 0.75 1.1
2001
0.61 2.1 2.2 2.02 2.0 0.78
2002
0.8 1.8 2.8 1.9 2.1 1.05
2003
0.78 1.3 2.9 1.2 3.2 1.4
2004
1.31 1.2 3.1 1.4 3.1 1.5
2005
0.85 1.1 2.8 2.0 3.4 1.45
2006
0.8 1.3 2.99 1.8 2.7 2.54
2007
0.5 2.4 3.4 1.77 3.5 4.10
2008
1.4 2.6 2.6 1.1 1.1 4.15
2009 1.5 2.3 2.3
1.33 1.3 4.28
2010 1.4 2.4 2.5
1.4 1.0 4.35
2011 1.4 2.5 2.6
2.1 1.4 4.01
Fig. 7- NCI.
Both models (Financial performance model by Al-Kassar and Soileau
(2012) and Taffler's (1983) model) were applied for six companies, it
should be noted that the mean value of the financial performance (Y) is
zero. This means that any Y value of a company above zero is classified
as above average and those below zero are classified as below average.
6. Study analysis and correlation
The analysis of liquidity via financial indicators helps to provide early
warning of increased risk of financial failure. Thus, it is noted that there
are two directions in the analysis of liquidity. The first focuses on the
direct relationship between current assets and current liabilities which
considers the use of current assets as a main source of meeting current
liabilities. This trend contrasts with the continuity of an organization and
its profits. Since profitability depends on the use of productive assets,
including current assets, disposing of current assets to pay current
liabilities prevents the continuity of the organization to continue to
perform operations.
The second trend based on the main function of financial
management of the project which is to estimate the financial needs of the
activity, and the provision of necessary sources of funding and investing
them to achieve profit and therefore the continuity of the project. To
determine the imbalance between these elements leads to financial failure.
It should consider the narrow concept of liquidity, which links the direct
relationship between current assets and current liabilities.
This means that the analysis of financial performance assists
financial management and top management of the companies to give
greater attention to important ratios. From the plotting of the 25 ratios,
there are seven ratios that show they have a direct impact and the most
powerful in the calculation of the values of financial performance,
namely: (Al-Kassar and Soileau, 2012):
1. Sales/ Working Capital.
2. Sales/Accounts Receivable.
3. Current Assets/Total Liabilities.
4. Current Assets/Current Liabilities.
5. Current Liabilities/Total Assets.
6. Cash/Current Liabilities.
7. Profit before Tax/Current Liabilities.
Consistent with the 1983 Taffler model, some ratios have an
impact in determining the values and the power of the overall model as
well. It is also helpful to improve their policies by adopting the standard
levels such as current assets to current liabilities to be (2:1), decreasing
current liabilities, changing policy regarding customers by reducing credit
policy and collecting debt from customers within shorter periods. This
might lead the companies to safeguard their financial performance and
move to be an improved position far away from increased likelihood of
financial failure.
It is important to measure the profit impact to the future
payment of current liabilities. Thus, contribution of current assets to cover
operating expenses without depending on external sources of funding
should be shown. Furthermore, it is essential to analyze the contribution
of current assets to cover the total liabilities, and the specific weight of the
current liabilities to total assets. Therefore, results from the four ratios of
the Taffler model (1983) show maximum value of the first, second and
fourth, and the low value for the third one, give the best indicator to move
away from the likelihood of bankruptcy in the near future for an
organization.
Thus, the correlation between the values of financial
performance (FP) and the values of financial failure (X) for the companies
1, 2 and 3 which have poor results are tested by (t-test) and results reveal
the following:
154 ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155
Table 8- Correlation Values.
Company 1 2 3 4 5 6
Correlation
Coefficient (r) 0.539 0.601 0.821 0.880 0.966 0.929
P-Value 0.047 0.023 0.000 0.000 0.000 0.000
T calculated 2.217 2.604 4.981 6.418 14.418 8.695
T scheduled 1.771 1.771 1.771 1.771 1.771 1.771
Rank 6 5 4 3 1 2
The values indicate a strong correlation lies between (0.7 and
0.9) for companies. Where -values are determined by the value of the
correlation coefficient.
Using t-test: then it indicates the following hypothesis,
Ho: ƒÏ = 0 does not exist any relationship.
H1: ƒÏ‚ 0 there is a relationship.
It appears in the table above, and after testing each of the calculated
values of t and t scheduled at a significance level 0.05. As the calculated
values of t greater than the scheduled, therefore, will reject Ho in support
of H1, there is a relationship. The rank and classification of the companies
are shown in Table 8. The negative results should be observed because it
is beginning to fail. Also it should deal more accurately with the
companies that get negative results because it is possible to fail in the
coming year. But normally, they received subsidies from the State to help
them for next periods.
7. Findings
The constructs and uses a model to mathematically measure
organizational financial performance and likelihood of financial failure.
Values of both financial performance and financial failure are used to
correlated am evaluate through the graph of company ratios selected.
Results of the models are tested and demonstrate with acceptable values
of significance, 0.05, and the null hypothesis rejected in favour of the
alternative that a relationship exists between financial performance and
likelihood of organizational failure. The comprehensive performance
models used in the evaluation process of these companies requires both
financial and non-financial measures to complement each other. Without
following such a process, the analysis of these companies is not complete
and may yield unreliable results. Therefore, we recommend applying both
evaluations and using indicators and criteria adopted to reach a more
reliable result. Therefore, it is recommended to follow both models to
obtain accurate results and maintain objective from personal intervention.
REFERENCES
Abdel-Kader, M., & Luther, R., (2008). The impact of firm characteristics on
management accounting practice: a UK-based empirical analysis.
The British Accounting Review, 40(1), 2-27.
Abdel-Maksoud, A., Cerbioni, F., Ricceri, F., & Velayutham, S. (2010).
Employee morale, non-financial performance measures
deployment of innovative managerial practices and shop-floor
involvement in Italian manufacturing firms. The British Accounting
Review, 42 (1), 36-55.
Agarwal, V., & Taffler, R. J. (2005). Twenty-five years of z-score in the UK:
do they really work? Accounting and Business Research, 37 (4), 1-
36.
Al-Kassar, T., & Soileau, J. (2012). Design and Applied Mathematical Model
of Measuring Financial Performance Evaluation: Jordan Results.
Oil, Gas & Energy Quarterly, 60(3) 621-636.
Aharoni, Y. (1981). Performance Evaluation of state-owned Enterprises: A
Process Perspective, (note). Management and Finance, 27 (11)
1340-1347.
Altman, E. I. (1968). Financial Ratios Discriminant Analysis and the
Prediction of Corporate Bankruptcy. Journal of Finance, 23, 4,
589-609.
Belkaoui, A., & AlNajjar, F. (2006). Earnings opacity internationally and
elements of social, economic and accounting order. Review of
Accounting and Finance, 3 (3), 130-144.
Bemmann, Martin, Improving the Comparability of Insolvency
Predictions (June 23, 2005).
Dresden Economics Discussion Paper Series No. 08/2005. Available at
SSRN:http://ssrn.com/abstract=731644.
Bernard F., Desrochers, J., Martel, D., & Prefontaine, J. (2007). Modeling the
Performance Evaluation of Local Investment and Economic
Development Corporations. Journal of Business & Economic
Research, Sept. 5 (9), 55-76.
Chenhall, R. H. (2006). The contingent design of performance measures. In
Bhimani (Ed.). Contemporary issues in management accounting,
92-116. Oxford: Oxford University Press.
Courtis, J.K. (1978). Modeling a Financial Ratios Categoric Framework.
Journal of Business Finance and Accounting, 5 (4), 371-386.
De Tone, A., & Tonchia, S. (2001). Performance Measurement Systems-
Models, Characteristics and Measures. International Journal of
Operations & Production Management, 21 (1/2), 46-71.
Gerdin, J. (2005). Managing accounting system design in manufacturing
departments: an empirical investigation using a multiple
contingencies approach. Accounting, Organizations and Society,
30 (1) 99–126.
Glautier, M., & Underdown, B. (1986). Accounting theory and Practice, UK.
Miller, W. (2009). Introducing the Morningstar Solvency Score, A Bankruptcy
Prediction Metric. Morningstar, Inc. December 2009, Electronic
copy available at:http://ssrn.com/abstract=1516762.
Jowett, P., & Rothwell, M. (1988). Performance Indicators in Public Sector.
UK, (also White Paper 1987).
Perks, R., & Glendinning, R.. (1981). Little Progress Seen in Published
Performance Indicators. Management Accounting, Dec 28-30.
Taffler, R., & Tisshaw H. (1977). Going, going, gone – four factors which
predict. Accountancy, March, 50-54.
Taffler, R. J. (1983). The Z-Score Approach to Measuring Company Solvency.
The Accountant's Magazine, March, 22-24.
The Board of Supreme Audit (BSA), internal studies, 1986, 1998 unpublished.
ARAB ECONOMICS AND BUSINESS JOURNAL 9 (2014) 147–155 155
Appendix A:
The following are the ratios referred to in the text as R1-R25:
Profitability Ratios
Return on investment ratios:
R1= EBIT/TA = Earnings before Tax scaled by total assets
R2= NP/TA = Net Profit scaled by total assets
R3= NP/NW = Net Profit scaled by net worth
Profit margin ratio:
R4= NP/Sales = Net Profit scaled by sales
Capital turnover ratios:
R5= Sales/TA = Return on Sales computed as Sales scaled by total assets
R6= Sales/NW = Sales scaled by net worth
R7= Sales/WC = Sales scaled by working capital
Managerial Ratios
Credit Policy ratio:
R8= Sales/AR = Sales scaled by accounts receivable (AR Turnover)
Inventory ratios:
R9= INV/COGS = Inventory scaled by the cost of goods sold
R10= INV/WC = Inventory scaled by working capital
R11= INV/Sales = Inventory scaled by sales
Administration ratios:
R12= Op. Exp./TA = Operating Expenses scaled by total assets
R13= COGS/Sales = Cost of goods sold scaled by sales
Asset-equity structure ratios:
R14= NW/TA = Net Worth scaled by total assets
R15= Debt/WC = Debt scaled by working capital
R16= LTA/NW = Long-term assets scaled by new worth
R17= LTA/TA = Long-term assets scaled by total assets
R18= WC/SALES = Working capital scaled by sales
Solvency Ratios
Short-term liquidity ratios:
R19= CA/CL = Current assets scaled by current liabilities (Current Ratio)
R20= WC/TA = Working capital scaled by total assets
R21= Quick Assets/CL = Quick assets scaled by current liabilities (Quick
Ratio)
Long-term solvency ratio:
R22= Debt/NW = Total debt scaled by net worth
Cash flow ratios:
R23= CF/CL = Cash flows from operations scaled by current liabilities
R24= CF/TA = Cash flows from operations scaled by total assets
R25= CF/WC = Cash flows from operations scaled by working capital
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