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Using Altman Z-score and Beneish M-score Models to Detect Financial Fraud and Corporate Failure: A Case Study of Enron Corporation

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International Journal of Finance and Accounting 2017, 6(6): 159-166
DOI: 10.5923/j.ijfa.20170606.01
Using Altman Z-score and Beneish M-score Models to
Detect Financial Fraud and Corporate Failure:
A Case Study of Enron Corporation
John MacCarthy
Accounting Department, University of Professional Studies, Accra, Ghana
Abstract The objective of this research is to determine whether Altman Z-score and Beneish M-model could detect
financial fraud and corporate failure of Enron Corporation. Five-year financial information was collected from the US SEC
Edgar database covering the period 1996 to 2000. The Beneish model revealed that the financial statements for the five years
studied were manipulated by management. On the basis of the analysis, the researcher argued that stakeholders would be
better protected when the two models are used simultaneously than when only the Altman Z-score is used. The paper
recommended that Altman Z-score and Beneish M-Model should be used together as an integral part of every audit.
Keywords Altman Z-score, Beneish M-score, Corporate Failure, Financial Statements, Fraud
1. Introduction
Corporate fraud and misconduct remains a constant threat
to public trust in the confidence building of the capital
markets. Fraud is an intentional act, committed to secure an
unfair or unlawful gain or advantage by the perpetrator
(KPMG, 2006). Fraud is any act designed to deceive others,
often resulting in the victim suffering from loss of their
investments. Anytime the financial statement is manipulated,
it creates a disagreement between a company’s financial
performance and related non-financial measures of the
company such as: employee head count, number of retail
outlets, and warehouse space. This creates an inconsistency
that represents a red flag for gatekeepers to suspect fraud in
financial statement prepared (Brazel, Jones & Zimbelman,
2009).
According to the report from Permanent Subcommittee on
Investigation of the U.S. Senate findings, management of
Enron Corp were guilty of the following actions that caused
the collapse of the firm: fiduciary failure, high risk
accounting, inappropriate conflict of interest, extensive
undisclosed off-balance sheet activity, excessive
compensation, and lack of independence of the external
auditors.
Deloitte (2008) revealed that there is connection between
bankruptcy and fraud and there is a high chance that a
company at the verge of bankruptcy would engage in
* Corresponding author:
maccarthy.john@upsamail.edu.gh (John MacCarthy)
Published online at http://journal.sapub.org/ijfa
Copyright © 2017 Scientific & Academic Publishing. All Rights Reserved
fraudulent activities or manipulate the financial statements.
This implies that, prior to the collapse of the company in
2001 there was pressure on the management team to
manipulate earnings in order to show a better picture for the
shareholders. This implies that, at the verge of bankruptcy,
managers are motivated to manipulate their financial
statements to show a better financial picture to their
stakeholders. This creates a linkage between a distressed
company and a fraudulent company; hence, there is the need
to use these two models, Altman Z-score and Beneish
M-score models simultaneously for this study.
Altman Z-score model works well on financial statements
that are not manipulated while Beneish M-score is used to
determine whether the financial statement is manipulated.
Hence, for this analysis to be successful there is the need to
deploy Beneish M-score model prior to the deployment of
Altman Z-score model. Therefore, it is imperative to use
Beneish M-score before Altman Z-score model. To do this,
the Beneish M-score model was first employed to detect
whether the financial statements were manipulated. Then the
Altman Z-score model was used to determine whether Enron
Corp was distressed, and if the financial statements prior to
the collapse were manipulated.
Bankruptcy is often a logical extension of the on-going
misappropriation and mismanagement of a firm’s funds. A
recent empirical evidence collected by the Association of
Certified Fraud Examiners [ACFE], (2008) revealed that
there were about 1,843 global occupational fraud cases
reported between January 8, 2008 and December 31, 2009
and that 25% of the fraud cases caused at least a loss of
USD$ 1 million. It further revealed that, these frauds were
not detected early until at least 18 months on the average
160 John MacCarthy: Using Altman Z-score and Beneish M-score Models to Detect
Financial Fraud and Corporate Failure: A Case Study of Enron Corporation
after they happened. In the case of Enron Corp, fraudulent
activities started in the late 1990s and continued until they
eventually led to the biggest corporate bankruptcy in the
history of the United States of America.
Wells (2001) stated that, the following ratios: DSRI, GMI,
AQI, SGI, DEPI, SGAI, LEVI, and TATAI are critical in
detecting manipulation of financial statements. Enron Corp
was able to deceive both investors and regulators for a very
long time because these models were not quite popular at that
time. However, the question that, the researchers and
academics want answer to is, whether the models are capable
of detecting manipulation and financial distress even when
the auditors failed to carry out their fiduciary duty of care to
the shareholders. Researchers opined that, it is possible to
spot the manipulation and detection of financial statements
by financial analysts and watchdog institutions paid to
protect investors using appropriate models (Nugent, 2003;
Tebogo, 2011).
According to Bratton (2002), Enron flew high at a time
when its stock price peaked at close to $90 in August 2000.
At that time, Enron was among America’s seventh largest
firm by market capitalization and it was rated as “The Most
Innovative Company in America” for five consecutive years
from 1997 to 2001. Enron, led by its founding Chief
Executive Officer (CEO), Kenneth L. Lay, went from State
to State to push local regulators to mandate the unbundling of
vertically integrated utilities and was successful in 24 States.
Enron fought and won battles against protected energy
monopolies and also spent copiously on politics.
2. Literature Review
2.1. Enron’s Financial Earnings Manipulation
There were three major violations spotted under Generally
Accepted Accounting Principles (GAAP) that heralded the
fall of Enron: (1) The off-balance sheet arrangements, (2)
The role of mark-to-market, and (3) The manipulation of
derivatives (Lemus, 2014). Enron’s revenue increased from
$20 billion to $31 billion in 1998 then to $40 billion in 1999
and finally to $100 billion in 2000. This represented over
390% growth within 4 years. Arthur Anderson permitted
Enron to book a present profit based on a projection of power
prices of ten years into the future (Bratton, 2002).
According to Bansal and Kandola (2003) Enron had kept
$600 debt associated with its partnership with Chewco and
the joint energy development investments from its financial
statements. Enron admitted that the earnings in the financial
statements had been overstated because the company failed
to follow the rules on qualifying for Special Purpose Entity
(SPE). According to Petrick and Scherer (2003) and re-cited
by Mahama (2015) Enron relocated many of its assets off the
balance sheet into SPE off the partnership books.
According to Li (2010) one of the major causes of Enron’s
fall was the US Securities Exchange Commission allowing
Enron to use market to market accounting method. Enron
reported $1.41 billion as pretax profit in 2002 using
market-to-market accounting method. Enron manipulated its
derivatives and reported an increase from $1.8 billion to
$10.5 billion. The financial statements of 2000 showed more
than $16 billion in gain from derivatives.
The primary motives behind these violations of earnings
were to remove additional debts to the balance sheets and to
allow a better performance of the company. Enron took full
advantages of accounting limitations in managing its
earnings and balance sheet to portray a rosy picture of
performance. These violations saw Enron share price
peaking at $90 in August and then tumbling to forty cents a
share after the collapse.
2.2. Independence of Enron’s External Auditors
Evidence available after the debacle of Enron revealed
that there were basic weaknesses with the auditor’s
independence. Auditors providing non-audit services has
been one of the most debatable issues in recent times.
According to Hossain (2013) an abnormal high fee charged
by auditors for non-audit services may compromise the
auditor’s independence. Auditor independence is one of the
most important issues in accounting today (Myring & Bloom,
2003). Auditor independence is a fundamental requirement
of any quality audit. The auditor’s independence increases
the effectiveness of the audit to be conducted and provides an
assurance that the audit would be objectively executed.
Any factors that threaten an auditor’s independence would
eventually impair the objectivity of the auditor’s
independence. The professional code of ethics outlined in the
International Federation of Accountants required that
auditors should identify potential threats and apply
safeguards to eliminate or reduce threats to an acceptable
level.
There were many activities between Enron and Arthur
Anderson that threatened the independence of the auditor to
play the role of the first “gatekeeper” of Enron shareholders
and other stakeholders. Enron top officers in charge of
accounting matters were previously Anderson’s accountants.
Enron usually hired Anderson employee on a regular basis.
The financial statements of 2001 revealed that $25 million
and $27 million were paid to Anderson as audit fees and
consulting fees respectively.
Enron and Arthur Anderson used unacceptable accounting
practices which misinformed the shareholders and other
stakeholders of their investments in Enron.
2.2.1. Theoretical Framework: Altman Z-score Model
This literature review provides insight into the theories
and models for predicting corporate failure and manipulation
of financial statements. Altman Z-score is a Multiple
Discriminant Analysis (MDA) or a quantitative model used
to distinguish between surviving and failing companies
(Robinson & Maguire, 2001) based on information gathered
from published financial statements. Altman Z-score model
is the quantitative model used to predict financial corporate
International Journal of Finance and Accounting 2017, 6(6): 159-166 161
distress.
Altman Z-score has the ability to discriminate between
companies that are financially distressed and those that are
not financially distressed. The model used financial figures
from financial statements and grouped them into five
different variables for analysis. These ratios or independent
variables are used to predict the probability that, the firm
would go into bankruptcy in at most two years.
The model uses the under-mentioned formula to detect
bankruptcy with reference to these weights assigned to X1,
X2, X3, X4 and X5:
Z- Score = 1.2X1 +1.4X2 + 3.3X3 + 0.4X4 + 1.0X5 (1)
Where
X1 = Net working capital
Total assets
X2 = Retained earnings
Total assets
X3 = EBIT
Total assets
X4 = Market value of equity
Book vlue of liabilities (Total debts)
X5 = Sales
Total assets
The independent variables for the model are X1, X2, X3,
X4 and X5 which are used to determine the dependent
variable, the Z-score in equation (1). The outcome Z-score
value is obtained and compared with the cut-off shown in
Table 1 that is non-distress, grey and distress dependents on
the score obtained. Altman Z-score has high degree of
accuracy in predicting corporate financial distress in the
USA as well as in the emerging markets (Altman, Hatzell &
Peck, 1995). The initial Z-score model was designed for the
manufacturing sectors that have high capital intensity and
therefore, was not suitable for the non-manufacturing sector.
One of the ratios in the model uses sales/total assets that
can skew the results in sectors that are not capital intensive.
The model was a linear combination of several independent
variables that give cut-off scores estimating the financial
health of a company. A low total assets figure reduces these
ratios and the result in lower Z-score. This creates the
impression that the company is financially distressed when it
is not. This was the main problem that the original model
faced with a sector with low total assets which made it
unsuitable for use in the non-manufacturing sector.
Table 1. Altman Z-score Model
Altman Z-score Meaning of the cut-off points
Z > 2.67 Non-distress Zones
1.81 <Z < 2.67 Grey Zones
Z < 1.81 Distress Zones
Source: Adapted from “Business Bankruptcy Prediction Models” by Anjum,
2012, p. 216
The original Altman Z-score was later modified to take
care of this shortcoming, and now the model can be used for
both manufacturing and non-manufacturing, private
companies and for those listed on the emerging markets.
The model, for some reasons, appears to generate a lot of
mixed emotions; some are in favour of it while others are
against it. Grice and Ingram (2001), however, indicated that
the model’s accuracy is significantly lower in recent periods
than reported in Altman’s study. Most criticisms against the
model are its over-reliance on accounting data; inadequate
recognition of cash-flow as a relevant component; lack of
consideration on non-financial ratios; focus on failure rather
than sustainability of the business; the need for
industry-specific or geography specific model types and the
danger of flexible interpretation or manipulation of financial
results leading to “window dressing” or inappropriate
favorable report of financial position or performance
(Wilkinson, 2009).
The first limitation of Altman Z-score is the need for
industry-specific or geography specific model types.
Specific industries have different characteristics; hence it
would not be feasible to apply a general model for all
industries. Another limitation of the model is the assumption
that financial ratios taken from public financial information
will be accurate. Meanwhile most often, companies in
financial distress manipulate their financial statements to
show better performance. Therefore, errors in these
secondary data will affect the level of accuracy of the
outcomes and will not be suitable for the present purpose
(Panneerselvam, 2008).
2.2.2. Beneish M-score Model
Professor Messod Beneish developed Beneish M-score
model in 1999 as a complementary forensic tool to Altman
Z-score model with the aim of protecting shareholders,
creditors and bankers in their analyses. Beneish M-score is a
financial forensic tool often used to detect areas of possible
manipulation on the company’s financial statements by
forensic accountants, auditors and regulators (particularly
the SEC).
Beneish model is used to discriminate between companies
that have manipulated their financial statements. The score is
determined from eight independent variables and an
intercept to detect whether the company’s earnings have
been manipulated by management. The eight variables were
taken from the company’s financial statements and used to
determine M-score of this study. An M-score obtained that is
greater than -2.22 is an indication that the company’s
financial statements may have been manipulated
(Warshavsky, 2012). Hence, if the score obtained from the
computation of the eight variables from Enron’s financial
statements is greater than the cut-off point of negative 2, then
it implies that, the financial statements were manipulated. A
score suggests that, the financial statements prepared by
management should be investigated further or have to be
investigated further for financial fraud.
M-score model is a probability model, and such cannot
detect 100% manipulation. Beneish identified that it is
possible to determine 76% manipulators accurately and
162 John MacCarthy: Using Altman Z-score and Beneish M-score Models to Detect
Financial Fraud and Corporate Failure: A Case Study of Enron Corporation
17.5% inaccurately as non-manipulator. The eight variables
used to develop the Beneish M-score model are:
DSRI: Day Sales in Receivable Index
GMI: Gross Margin Index
AQI: Assets Quality Index
DEPI: Depreciation Index
SGAI: Sales, General and Administrative Expenses Index
LVGI: Leverage Index
TATAI: Total Accruals to Total Assets Index
Beneish used the under-stated model to detect
manipulation of the financial statements based on these
weights in equation (2):
M- Score = -4.84 + 0.92*DSRI + 0.528*GMI
+ 0.404*AQI +0.892*SGI +0.115*DEPI
0.172*SGAI +4.679*TATA 0.327*LEVI (2)
DSRI =
Accounts receivable (cy))
Sales (cy)
Accounts receivable (py)
Sales (py)
DSRI is used to measure the changes made in respect of
receivables consistent with the changes made in respect of
sales. A DSRI score of 1.031 or below indicates that, the
financial statements in respect of the DSRI were not
manipulated but a score of 1.465 and above indicates that,
the financial statements in respect of the DSRI have been
manipulated or an indication that, the company has changed
its credit terms and now granting more credit than before.
When this does not show a fair consistent trend then it
suggests that either more sales are made on credit terms
rather than through cash sales or the company is having
difficulty in the collection of cash from trade debtors. A
rising DSRI might be the perfect legal activity of a company
extending more credit to customers and companies that
overstated revenue. Therefore, a sharp rise in the DSRI score
provides signals to the forensic investigators that, the
financial statements are manipulated or terms of credit have
changed.
GMI =
Sales Cost of sales (cy)
Sales (cy)
Sales Cost of sales (py)
Sales (py)
GMI is used to measure the ratio of a prior year’s GMI to
that of the current year review. The GMI score of 1.041 or
lower indicates that gross profit of the current period is not
manipulated but a score of 1.193 indicates that gross profit of
the company is manipulated (Harrington, 2005).
Warshavsky opined that, earning quality is a very important
aspect for evaluating a company’s financial health. This,
therefore, create temptation to manipulate earnings when
things are not going on well.
AQI =
Total assets (cy)PP + E (cy)
Total assets (cy)
Total assets (py)PPE + E (py)
Total assets (py)
AQI is used to measure the proportion of total assets of the
current year to the previous year. According to Pustylnick
(2009), when an AQI ratio greater than 1.0 is an indication
that some expenses or intangible assets have been capitalized
and others have been deferred for the future. An increase in
AQI indicates that additional expenses are being capitalized
to avoid writing-off to the statement of comprehensive
income in order to preserve profit (Harrington, 2005).
SGI =Sales (cy)
Sales (py)
SGI is used to measure sales in the current year over the
sales of a previous year. SGI is used to measure the sales
figure in the current year. A score of 1.134 or below is an
indication of non-manipulation and a score above 1.607
indicates that, the sales figures have been manipulated.
Harrington (2005) observed that, companies with high
growth rate find themselves highly motivated to commit
fraud when the trends reverse. In such situations, there is a
potential increase beyond a certain percentage that may
cause suspicion (Pustylnick, 2009).
DEPI =
Depreciation exp. (cy)
Depcreiation exp. (cy) + PP + E (cy)
Depreciation exp. (py)
Depcreiation exp. (py) + PP + E (py)
DEPI is used to measure the ratio of the depreciation
expense against the company’s value of PPE in the current
year against that of the previous year. A DEPI ratio of 1.001
or lower is an indication that, DEPI has not been manipulated.
According to Beneish (1999), a score above 1.077 is an
indication that, the assets value has been revalued or the
useful life of the assets has been extended or adjusted
upward.
SGAI =
Sales,distribution and administration cost (cy)
Sales (cy)
Sales,distribution and administration cost (py)
Sales (py)
SGAI is used to measure the ratio of sales, general and
administrative expenses for the current year over the
previous year. A score of 1.001 or below is an indication that
SGAI has not been manipulated. According to Beneish
(1999) a disproportionate increase in sales indicates a
negative signal about the company future prospects.
According to Lev and Thiagarajan (1993) a disproportional
increase in SGAI is an indication of a negative signal about
the company’s future prospects. A positive relation gives an
indication that there is probability of manipulation.
Leverage Index (LEVI) = Total Liabilities
Total assets
LVGI is used to measure the company’s ratio in terms of
total debt to total assets for the current year divided over the
previous year’s ratio. A LEVI greater than 1 implies that
there is an increase in leverage position in the company and
that the company has taken more debt to operate or to run the
business for the period under review.
International Journal of Finance and Accounting 2017, 6(6): 159-166 163
Total Accruals to Total Assets Index (TATAI) =
Working capital Depreciation
Total assets
TATAI is used to measure the ratio of change in working
capital accounts other than cash and less depreciation. The
growth of TATAI usually indicates that goodwill and
amortization numbers in the financial statements have being
tampered with. A mean score of 0.018 is an indication that
there is non-financial manipulation while a mean score of
0.031 and above is an indication that the financial data have
been tampered with.
Table 2. Beneish M-score Model
Index Non-Manipulator Manipulator
DSRI 1.030 1.460
GMI 1.041 1.190
AGI 1.040 1.250
SGI 1.134 1.610
DEPI 1.001 1.077
SGAI 1.001 1.041
LVGI 1.037 1.111
TATAI 0.018 0.031
Source: Source: Adapted from Beneish M-score model
The Beneish model identifies between 38% and 76% of
the manipulated reporting companies correctly and
misclassified between 3.5% and 17.5% of the manipulated
companies as non-fraudulent companies (Beneish, 1999).
3. Empirical Analysis
3.1. Sample and Data Research Methods
This paper adopted quantitative research methodology.
This research method was used to collect secondary data
needed to test the research hypotheses in order that the
research questions can be answered or to “uncover” the true
financial position of Enron prior to the failure using
mathematical-based methods (Naoum, 1998). Secondary
data was collected from the US SEC Edgar database from the
period 1997 to 2001 in order to answer the research questions
and hypothesis of the study. The analytical tools used for this
study were Altman Z-score and Beneish M-model. Excel and
SPSS were the software used to assist the analysis of the
study. The data were analysed based on the assumption that
they were normally distributed and linearly distributed.
3.2. Hypotheses
The study seeks to test the following hypotheses:
H01: The financial statements published by Enron
Corporation showed signs of corporate distress prior to the
failure.
HA1: The financial statements published by Enron
Corporation did not show signs of corporate distress prior to
the failure.
H02: The financial statements published by Enron
Corporation were manipulated prior to the failure.
HA2: The financial statements published by Enron
Corporation were not manipulated prior to the failure.
3.3. Findings
The hypotheses of the study are tested and the outcome
presented in this section. It is important to organize data into
suitable variables or ratios before the application of the two
models for the analyses. The hypothesis for this study is
tested using parametric statistical tool of Multiple
Discriminant Analysis (MDA). Both Altman Z-score and
Beneish M-score are MDA and are made up of several
independent variables and dependent variables which
intercept (i.e., residue). The basic assumption is that data are
normally distributed, linearly distributed and auto-correlated
in order to test the research hypotheses in the use of MDA.
Table 2 showed that data is normally distributed.
Table 3. Shapiro-Wilk’s Test of Normality
Shapiro-Wilk
Statistic df Sig.
Z-score 0.831 5 0.141
M-score 0.850 5 0.193
Source: Researcher’s SPSS Version 21 Computation
Table 3 revealed that Shapiro test (p>0.5) on data were
normally distributed for the statistical analyses. The study
failed to reject the null hypothesis because the sig values of
0.141 and 0.193 for Z-score and M-score respectively were
above the p-value of 0.05. This test is used to verify whether
the normality of data assumption is satisfied and tested at 5%
significance level. If the data used for the analysis were
normally distributed, then the p-value of each variable XI,
X2, X3, X4 and X5 would be greater than the chosen level of
significance, 5%. From the table, the p-value for each
variable is greater than 5% (p > .05). This means that the data
normality assumption is satisfied for all data. Therefore, a
valid conclusion is established that the data used for the
analysis were normally distributed.
Table 4. Durbin-Watson’s Test of Autocorrelation
Model R R.
square Adj. R.
square Std. E Durbin-Watson
A-score 1.00 1.00 1.468
M-score 1.00 1.00 1.943
Source: Researcher’s SPSS Version 21 Computation
Durbin-Watson test is used to determine whether there is
autocorrelation in the residue of time series regression. The
statistical range from 0 to 4 is an indication of a negative
correlation. A value around 2 is an indication that there is no
autocorrelation in the data and therefore the null hypothesis
(H0) should be rejected as the Durbin-Watson value of 1.468
and 1.943 for Altman Z-score and Beneish M-score
respectively as shown in Table 4.
164 John MacCarthy: Using Altman Z-score and Beneish M-score Models to Detect
Financial Fraud and Corporate Failure: A Case Study of Enron Corporation
Analyzing the financial statements using Altman Z-score
provides the basis for understanding and evaluating the
results of business operations and explains how well a
business has done. In this light, Altman Z-score was used to
determine the bankruptcy status of Enron Corp and the result
is shown in Table 5.
Table 5. Altman Z-score Calculation for Enron Corporation
Years 1996 1997 1998 1999 2000
X1 0.020 0.01 (0.01) 0.02 0.04
X2 0.174 0.11 0.11 0.11 0.07
X3 0.253 0.08 0.18 0.20 0.13
X4 0.565 0.469 0.663 0.922 0.724
X5 0.823 0.90 1.06 1.20 1.54
Score 1.84 1.58 2.00 2.45 2.49
Source: Enron Corp Data from US SEC Edgar (1996-2000)
Analyzing the financial statements using Beneish model
provides a basis to determine whether the financial
statements used to analyze H01 was manipulated. In this light,
Beneish M-score was used to determine whether financial
statements were manipulated and the result is shown in Table
6.
Table 6. Beneish Model
Years 1996 1997 1998 1999 2000
DSRI 1.141 0.489 0.974 1.146* 1.365
GMI 0.791 0.691 1.068 0.855 0.466
AQI 1.136 1.236 1.125 1.064 0.771
SGI 1.446 1.526 1.542 1.283 2.513*
DEPI 1.056 0.983 1.173* 1.046 0.901
SGAI 0.798 0.911 0.770 1.013 0.378
TATAI 0.061* 0.018* 0.050* 0.051* 0.017
LVGI 1.053 1.025 1.023 0.908 1.354*
Score -1.70 -2.46 -1.65 -1.87 -1.11
Source: Enron Corp Data from US SEC Edgar (1996-2000)
*This indicates possibility that earnings were manipulated when compared to
Beneish (1999).
3.4. Discussion of Results
Table 5 shows the computation of Enron Corp Z-score
from the secondary data collected from 1996 to 2000.
Altman Z-score calculation revealed that, the company was
financially distressed as far back as 1996 and remained in
distressed zone in 1997 before moving out of the distress
zone to the grey zone from 1998 to 2000. The scores of 2.00,
2.45 and 2.49 for 1998, 1999 and 2000 respectively showed
that, the company was in grey zone. The results provided the
evidence that, Altman Z-score was not a sufficient model to
have caught the failure of Enron Corp especially if the
financial statements were manipulated. Altman Z-model
requires companies must remain distressed for three years,
and then go into bankruptcy after two years. However, in this
particular case, Enron moved out of distress zone in the third
year to grey zone making it difficult to predict the
bankruptcy, especially if its financial statements were
manipulated.
The average score for the five-year period was 2.07 which
is an indication that the company was not distressed but in
the distressed zone. Therefore, using year-on-year variables
to determine distress revealed that Enron was distressed for
only two years but by the third year which was critical in the
determination of distress, the company moved out of
distressed zone to grey zone. Secondly, using the average of
the first three years also confirmed that, the company was not
distressed but in the grey-zone. Therefore, the null
hypothesis (H01) is not rejected.
Table 6 showed that, the M-score for the year 1997 was
negative 2.46, a figure below the benchmark M-Score for
non-manipulated earnings. The four-year score in Table 6
was above the mean score of a non-manipulated figure of
negative 2.22 with the exception of 1997. However, a
detailed overview of the eight variables in 1997 also revealed
that TATAI was manipulated. This implies that all the
five-year financial statements were manipulated.
This indicates that earnings of 1997 were not manipulated,
but immediately after 1997 till 2000, the M-score was below
negative 2.22, an indication that the earning figures were all
manipulated from 1998 to 2000 to conceal the picture that
the company was distressed.
A closer look at Table 6 revealed that only one variable is
manipulated in 1996 and 1997 but the manipulation became
more intense after 1998, where two variables were
manipulated and then finally three variables in 2000.
Consistently, Beneish M-model showed that, most of the
independent variables were manipulated prior to the collapse.
The manipulation started gradually from 1996 with only one
variable TATAI, then it rose to two in 1998 (TATAI and
DEPI). The manipulation of the independent variable
remained two out of the eight variables manipulated in 2000,
representing over 25% of the financial statement prepared by
the management of Enron Corp.
Therefore, the null hypothesis (H02) is rejected to imply
that because the financial statements of Enron Corp were
manipulated to hide the true financial position of the
company, Altman Z-score model failed to predict the true
picture that Enron was distressed.
4. Conclusions and Recommendations
This paper concludes that the financial statements were
manipulated to hide the debt of the company, inflate profits
with the intention to support the stock price, so that the
company’s value would be overstated. Enron took advantage
of accounting limitation in managing its earnings and
balance sheet to portray a glowing picture for its
performance (Heavly & Papelu, 2003). The independence of
Arthur Anderson and his firm was threatened by other
professional services they were rendering which made lose
sight of their mandate. A manipulated financial statement
International Journal of Finance and Accounting 2017, 6(6): 159-166 165
could not be detected by Altman Z-score model to predict the
bankruptcy accurately alone without the Beneish M-score
model due to the fact that the earnings were manipulated or
earnings were managed.
There is a high probability that the company that is faced
with financial distress would manipulate their earnings by
changing depreciation rates, delay in recognition of expenses,
recording sales early or creating other accounting tricks that
favour the company in order to show a better picture than
what actually pertains in the company. These manipulations
may sometimes not be illegal, but rather used to disguise the
actual picture of the company. Enron Corp’s financial
statements including notes of disclosures did not show the
true financial picture of Enron Corp (i.e., a company that was
having financial and economic difficulty).
The outcome obtained from Altman Z-score on Enron
Corp revealed that three out of the five years (i.e. 1998, 1999
and 2000) were in the grey zone while 1996 and 1997 were
distressed. The company jumped out of distress in the third
zone which confirmed the likelihood that the financial
statements after 1997 had been manipulated to improve the
firm’s performance.
The auditor, Arthur Anderson, whose responsibility it was
to see if the financial statements were manipulated, was
accused of applying negligent standards on their audit
because of conflict of interest over significant consulting
fees from Enron (Heavly & Papelu, 2003). Conflict of
interest and lack of adequate oversight responsibilities on the
part of auditors and the Board of Enron contributed to the
firm’s collapse causing so much loss to the investors. There
should have been suspicions after 1997, and forensic tools
such as Beneish M-score model could have been used to spot
manipulations by the senior management.
Enron Corp’s failure, thus, could have been detected and
prevented earlier using Beneish model, and also if the auditor
was diligent rather than being busy providing consultancy
work at the expense of doing quality audit to the stakeholders.
The analysis performed by the Beneish model revealed that
Enron Corp manipulated the financial statements to gain
advantage.
Mulford and Comiskey (1996) defined earnings
management as the active manipulation of accounting results
for the purpose of creating an altered impression of the
business performance. The Beneish model if applied well by
the auditor can provide potential ‘red flags’ for further
investigations to be carried out. This revelation could trigger
better audit work in order to show a true position of Enron.
If there should be any lesson learnt from Enron’s case then
it is obvious we cannot rely upon the professional
gatekeepersauditors, analysts and others whom the market
has long trusted to filter, verify and assess complicated
financial information. According to Coffee (2002), Enron’s
failure demonstrates that the gatekeeper failed and the
critical issue is how that failure could be ratified.
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... The quantitative model could predict corporate financial distress. Maccarthy (2017) emphasised the strength of Altman Z-score in identifying financially distressed and non-financially distressed companies. The model utilises information from the financial statements and categorises the data into five independent variables to be analysed. ...
... The model's applicability has restrictions that call for geographical or industry-specific model types. Specific industries with different characteristics cause limitations; hence, the model might be incompatible with all industries with exact prediction accuracy (Maccarthy, 2017;Sanobar, 2022). Meanwhile, the Z-score accuracy is influenced by the financial statement figures. ...
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... hinting manipulation. So, we reject the null hypothesis H0(1B) and conclude that the five factored Beneish M-Score could detect the earnings manipulation of Bhushan Steel Ltd. Similar results were obtained by Kukreja et al. (2020) andMacCarthy (2017). ...
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