Forensics Journal - Stevenson University 2015 | Page 62

STEVENSON UNIVERSITY

found to be 80-90% accurate in predicting bankruptcy one year prior
to the event” (The Altman Z-Score, 2011).

Therefore, the M-Score model could help the forensic analyst
determine if the subject company presents the typical profile
of a potential earnings manipulator.

The Z-Score calculation for public companies incorporates five
individual ratios, which are weighted and combined into one index.
The calculation assesses working capital to total assets (T1), retained
earnings to total assets (T2), earnings before interest and taxes to total
assets (T3), market value of equity to book value of total liabilities
(T4) and sales to total assets (T5) (The Altman Z-Score). The Z-Score
calculation appears as follows:

F-Score
The Fraud Score (F-Score) is another model that is used to assess
companies for potential fraud. This model is not as popular as the
M-Score in the business or academic community, but some consider
it useful as an initial filter for predicting fraud. The model was
actually created to use as a screening tool to help identify potential
companies capable of outperforming the stock market. The F-Score
was developed by Joseph Piotroski when he was an Associate Professor
of Accounting at the University of Chicago’s School of Business.
Piotroski is now an Accounting Professor at Stanford University’s
Graduate School of Business (Joseph D. Piotroski, n.d.). The model
analyzes nine variables within three categories; (1) Profitability,
(2) Leverage, Liquidity and Source of Funds, and (3) Operating
Efficiency (Croft, 2011). The highest possible score is nine (Lipton,
2009).

1.2*T1 + 1.4*T2 + 3.3*T3 + 0.6*T4 + 1.0*T5
(The Altman Z-Score, 2011).
M-Score
While the Z-Score can be used as one measure of financial health,
other models have been developed to identify companies that
might be engaging in earnings manipulation. One such model is
the M-Score. The M-Score is a mathematical model developed
in 1999 by Messod Daniel Beneish, Professor of Accounting at
Kelly School of Business, Indiana University. The model consists
of eight individual ratios which are weighted and then combined
into one index. The index is then used as a performance measure to
evaluate the subject company for potential earnings manipulation. A
calculated index that exceeds -2.22 is considered a strong indicator of
earnings manipulation. If the index is less than -2.22 the company is
not considered to be an earnings manipulator. The individual ratios
comprising the index are Days’ Sales in Receivable Index (DSRI),
Gross Margin Index (GMI), Asset Quality Index (AQI), Sales
Growth Index (SGI), Depreciation Index (DEPI), Sales, General and
Administrative Expenses Index (SGAI), Total Accruals to Total Assets
Index (TATI) and Leverage Index (LVGI) (The Bemish M-score,
2011; The Trustees of Indiana University, 2014; Jun, 2014; ACFE
Like A Laser, 2013). The M-Score calculation appears as follows:

Under the Profitability category the analysis is focused on reviewing
the quality of Net Income, Operating Cash Flow, Return on Assets
and Quality of Earnings. Generally, the score assigned to variables
within the profitability increase if they are positive, or outperform
prior year results. For example, the Net Income variable and
Operating Cash flow variable only require positive results in the
current year to be assigned a score of one whereas, both the Return
on Assets and Quality of Earnings variables need to exceed prior year
figures to be assigned a score of one. Under the Leverage, Liquidity
and Source of Funds category the F-Score model analyzes the extent
of decreases in leverage and increases in liquidity from current to prior
year. Lower leverage and higher liquidity from prior to current signals
a relatively healthier company. Therefore, each of the two variables
would be assigned a score of one if current year results outperformed
prior year results. This category also assesses the extent of dilution
from current to prior year. For this variable, less dilution is better than
higher dilution. Therefore, a score of one is assigned to companies
that issue no new equity. Under the Operating Efficiency category,
the analysis involves reviewing the company’s gross margin and asset
turnover. Each one of these metrics are assigned a score of one if the
current year performance exceeds the prior year performance. For the
overall F-Score Model, under each category, the score assigned to each
variable is combined into an overall score (Croft, 2011).

M = -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*LVGI
(The Beneish M-score, 2011).
The underlying concept behind the M-Score model is that certain
characteristics are present in companies to manipulating their
earnings. The characteristics were determined based on research
performed by Professor Beneish. According to Professor Beneish:
A company that is likely to manipulate its earnings fits
into a typical profile: its sales are increasing quickly; it
shows deterioration in the quality of its assets and in its
gross margins; and it uses aggressive accounting practices.
Companies with these traits also tend to look like high
performers in the stock market – but, time after time,
they are unable to sustain those results (IU Inc., 2014).

The F-Score model requires direct access to the company’s books
and records otherwise it is difficult to use this data analysis to make
meaningful assessments about the company’s true financial condition.
However, “for publicly-traded companies, you can get access to
a company’s publicly-reported financial statements, such as their
annual reports or the 10-K statements they file with the United
States Securities and Exchange Commission (SEC). And with that

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