case studies in advanced financial statement...
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CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
Different analytical techniques are used in different types of organizations to determine the
possibility of financial statement fraud, embezzlement or other types of fraudulent schemes that
create unusual relationships in financial statements. This session uses case studies to walk you
through the process of analyzing data and, more importantly, how to interpret the results of the
tests. In addition, the case studies show how to use visual techniques to easily explain the testing
and its results. Key concepts include the Beneish M-Score Model, Z-Score analysis, CRO and
cash flow analysis, Sloan's Accruals, Dechow-Dichev Accrual Quality, Jones Nondiscretionary
Accruals, Piotroski's F-Score Analysis, Lev-Thiagarajan's 12 Signals, and an overview of
Benford's Law.
PAMELA MANTONE, CFE, CPA, MAFF, CITP
Director – Forensic Investigations
Elliott Davis Decosimo
Pam Mantone specializes in litigation support services with emphasis on forensic accounting
and fraud examinations. She has performed forensic and fraud auditing services for
organizations, including the gathering of forensic evidence and testifying to findings. Mantone
also provides consulting services regarding implementation of fraud prevention and fraud
detection internal control systems. Her experience includes conducting and supervising audits of
local banks, credit unions, local nonprofit organizations, and HUD audits. She manages and
performs external and internal audits of financial institutions. Her book, Using Analytics to
Detect Possible Fraud: Tools and Techniques, was published in 2013 and provides a common
source of analytical techniques used in forensic accounting investigations.
“Association of Certified Fraud Examiners,” “Certified Fraud Examiner,” “CFE,” “ACFE,” and the
ACFE Logo are trademarks owned by the Association of Certified Fraud Examiners, Inc. The contents of
this paper may not be transmitted, republished, modified, reproduced, distributed, copied, or sold without
the prior consent of the author.
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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Annual ACFE Global Fraud Conference ©2016 1
NOTES Case Studies
Advanced financial analysis of financial statements
provides a wealth of information for those who want to
know that the financial information is reasonable and free
from material errors, whether it is a CFO or CEO
reviewing their own financial statements or a forensic
accountant trying to find whether the financial statements
have been misstated. While the tools and techniques
covered in the presentation are not sufficient by
themselves for prosecution, they do provide a means of
finding areas that have unusual variations and require
further investigation. They are valid for any type of
organization, whether it is a manufacturing or wholesale
distribution company, nonprofit organizations,
governmental entities, or service organizations. The tools
and techniques covered in the presentation include the
following:
Cash Flow and the Net Income Ratio
Operating Performance Ratio
Vertical Analysis
Lev-Thiagarajan’s 12 Signals
Piotroski’s F-Score Model
Beneish M-Score Model
Dechow-Dichev Accrual Quality
Sloan’s Accruals
Jones Nondiscretionary Accruals
Overview of Benford’s Law
Z-score Analysis
By using these tools and techniques, the forensic
accountant will be building a road map of areas that
require additional investigative work by defining areas of
higher risk and the possibility of fraudulent activity. The
calculations of the various techniques from the case
studies are produced in a visual form using various types
of graphs rather than the use of numbers. Visual
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES representation often provides the most effective means of
interpreting the results of the calculations, as well as
“teaching tools” when providing testimony in cases.
Detailed evidence is essential for prosecution, and
multiple methods are necessary in all investigations.
Choose analytical procedures that meet the needs of the
engagement and apply results to benchmarks. Begin with
preliminary ratios such as liquidity, debt and profitability
ratios, and then move to more advanced analytical
techniques. Ratios measure the ending balances in the
financial statement to the prior year’s ending balances but
do not measure the changes in those balances, while
indices do. Although limited in use when compared to
using indices, ratios are part of the foundation for more
advanced forensic analyses of financial information.
Cash flow statements are probably the most
misunderstood in a set of financial statements, but they
provide a wealth of information for the reviewer. Cash
flow statements provide the foundation for understanding
the relationships between the various account balances on
the balance sheet. For example, when a receivable is paid,
then cash increases. Cash flow statements cannot be
altered easily to hide fraudulent transactions within a set
of financial statements. If cash flow statements are
missing from a set of financial statements, it is easier to
falsify the amounts on the balance sheet and income
statement. In the case of a small manufacturing company,
cash flow statements were not part of the financial
statements provided monthly to the shareholders.
However, it does not take much effort to build a cash flow
statement to determine whether the financial statements
are accurate. The cash flow statements in the presentation
of this company were prepared using the information from
the company’s tax returns.
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES Important note to remember with cash flow statements is
that they should never be “plugged” to balance other than
for rounding issues. If a cash flow statement does not flow
correctly, there are noncash transactions in the accounting
records that have not been recognized and properly stated
as such.
The cash flow and net income ratio is very simple to use
and quite effective when graphing cash from operations to
net income. The formula, net income from operations
minus cash flow from operations divided by net income
from operations, should have its numerator at
approximately zero or a negative number. Remember that
depreciation and amortization expenses are subtracted
from income and not cash flows, so the net income from
operations number should be less than the cash flow from
operations number.
When using a dual axis chart, such as the one on the
following page representing net income and cash realized
from operations (CRO), it becomes very easy to find the
unusual pattern in the information provided in the
financial statements. CRO and net income should follow
each other rather than going in opposite directions.
-0.50
0.00
0.50
1.00
1.50
2.00
$0
$500,000
$1,000,000
$1,500,000
$2,000,000
$2,500,000
YR 2 YR 3 YR 4 YR 5
Net Income CRO
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES The CRO and net income comparison in the chart was
prepared from the financial statements of a wholesale
supply company. While CRO should flow the same as net
income, from YR 4 to YR 5, net income has not changed,
while CRO is increasing rapidly. This is definitely not “the
norm” and should be a warning sign that further
investigation is required.
Another useful ratio, the operating performance ratio, is
often a very good first step for analyzing net income and
sales. The formula is quite simple—net income divided by
net sales. Larger values are desirable, and adding fictitious
revenues dramatically increases the ratio. If this ratio
increases significantly, then other analytical tests and
statistical tests measure and confirm the excessive change.
While horizontal analysis is a “standard practice” in
comparing financial statements from year to year, the
vertical analysis provides a better picture of the operations
of a company. Often called common-sizing, all of the other
accounts in the income statement are measured on a
percentage of sales revenue. This method investigates the
relationships between the accounts and allows the
reviewer to focus on percentage changes from year to year
compared to numerical differences. The method also
allows the reviewer to focus on changes in operations
from year to year and removes external factors such as
competition decreasing sales. The percentages should
remain reasonably constant from period to period unless
there are operational changes internally.
While the techniques listed above are more common
analytical tools for financial information, there are two
tools that provide a new way to look at financial
statements and take a relatively short time to prepare: Lev-
Thiagarajan’s 12 Signals and Piotroski’s F-Score Model.
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES Both models were used previously to measure different
components of financial information. Lev-Thiagarajan’s
12 Signals was used to measure values of corporate
securities, while Piotroski’s F-Score Model was used to
measure a stock’s financial strength. Both models provide
a simple system of analyzing financial statements using a
point method based on simple calculations. While
Piotroski’s F-Score Model relies only on a “1 or 0” point
system, the Lev-Thiagarajan’s 12 Signals allows for a “not
applicable” selection as well. It is best to ignore the
signals that are not applicable to the financial statements.
The focal point for Lev-Thiagarajan’s Twelve Signals is
the direction in which the positives and negatives flow
from year to year, as shown in the chart in the presentation
representing the calculations of a manufacturing company.
In the case of this manufacturing company, the negatives
tend to increase from YR 2 through YR 4. Then in YR 5,
the company reverses the trend and had more positives
than negatives. This change becomes the focus of further
investigative analysis. In the first three years of analysis,
the accountant was embezzling funds and hiding the
missing cash throughout various accounts in the financial
statements.
While Piotroski’s F-Model uses nine variables compared
to the 12 Signals of Lev-Thiagarajan’s 12 Signals, the
model offers more in terms of analyzing not only the
financial statements but also the operating efficiency of a
company. Secondly, this model does not require any
market values, so it is useful for private company financial
statements. There are four components of the model that
measure profitability, three components that measure
liquidity, and three components that measure the operating
efficiency.
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES Profitability
Current year income variable
Current year operating cash flow variable
Comparison of current year income to current year
operating cash flow variable
Comparison of prior year net income to prior year
operating cash flow variable
Liquidity
Current year ratio of LTD to total assets compared
to prior year ratio
Comparison of current year current ratio to prior
year current ratio
Current year outstanding shares compared to prior
year outstanding shares
Operating efficiency
Comparison of current year gross margin to prior
year growth margin
Comparison of the percentage increase in sales to
the percentage increase in total assets
The best method to analyze Piotroski’s F-Score model is
to use a graphical representation of the points by the three
components as noted in the chart below from the analysis
of a primary government.
0
1
2
3
4
YR 2YR 3
YR 4YR 5
3 3
4 4
0 0
1 1
0
1 1
1
Profitability Liquidity Operating Efficiency
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES The chart poses an interesting question with just a simple
analysis: How does a company show profitability in Year
2 without liquidity and very low levels of operating
efficiency? If a company is experiencing growth in its
infrastructure and increases in fixed assets are confirmed
in the financial statement comparison over the prior year,
the results of that initiative would be similar to the results
noted in Year 2 of the chart. Otherwise, the question
becomes an issue of possible anomalies in the financial
statements. In the case of this municipality, large but well-
hidden embezzlement activity had been occurring over the
course of several years. Fixed assets that did not exist
were posted to the government-wide financial statements,
and depreciation expense was based on these “imagined”
assets.
Beneish M-Score
The Beneish M-Score model provides a wealth of
information about an organization’s financial statements
by using indices to measure changes from period to
period, whether the periods are monthly, quarterly, or
annually. While both the Lev-Thiagarajan 12 Signals and
Piotroski’s F- Score Model use a simple point system, this
model uses a weighted average methodology in its
calculations. When using the formula’s overall
calculations, if the M-Score is greater than a -2.22, the
score suggests a higher probability of financial statement
manipulation. The one item to remember, though, is the
movement of negative numbers in order to accurately
determine the possibility of financial statement
manipulation. For example, a -2.21 is actually greater than
a -2.22 while a -2.23 is actually less than the -2.22.
– Less Zero More +
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES In addition to the overall M-Score formula, each
component of the formula provides its own analytic to
assess the possibility of unusual variations in the financial
statements. Each component also has its own benchmark
for comparison. The components consist of the following:
Days Sales in Receivable Index (DSRI)
Gross Margin Index (GMI)
Asset Quality Index (AQI)
Sales Growth Index (SGI)
Depreciation Index (DI or DEPI)
Sales and General Administrative Expenses (SGAI or
SGAEI)
Leverage Index (LI or LVGI)
Total accruals to total assets (TATA)
The general benchmark for all of the components, with the
exception of TATA, is 1. The general benchmark of
TATA is zero. Calculations might vary slightly from the
benchmarks, but remember that the Beneish Model
measures changes in small increments, and multiple
periods of financial statements are necessary to measure
the changes accurately.
The movement of the component calculation related to its
benchmark provides the following information about the
financial statements:
When DSRI is greater than 1, it suggests that accounts
receivable and sales are not maintaining a stable
relationship and increases the possibility of potential
earnings manipulation.
When GMI is greater than 1, it indicates a decline in
gross profit in the current year and might suggest
exploitation of inventory or cover-up of embezzlement
activities.
When AQI is greater than 1, it indicates the possibility
of deferring costs, such as incorrectly capitalizing
costs.
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES
-1.16
-3.23
-2.42
0.27
YR 1 YR 2 YR 3 YR 4
When SGI is greater than 1, it indicates faster rates of
growth. While this by itself is good for a company, it
also represents higher risk elements. A company might
be growing faster than it is able to maintain its internal
control structure.
When DEPI is greater than 1, it indicates that the rate
of depreciation has slowed by changing the useful
lives of equipment or changing depreciation methods.
SGAI is usually stable, and disproportionate decreases
might suggest financial statement manipulation using
timing differences in recording transactions.
Disproportionate increases might suggest hidden costs
related to fraudulent activity.
When LVGI is greater than 1, it indicates new or
increased debt loading, creating higher risk elements
related to financial statement misrepresentation in
order to meet debt covenants.
TATA is also typically stable, and positive
calculations indicate a higher level of accruals and
therefore less cash, creating a higher risk for financial
statement misrepresentation. When this calculation is
positive, advanced accrual analysis calculations will
determine whether the accruals represent future cash
flows.
The chart below shows the calculations for the Beneish M-
Score model for a wholesale supply company.
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES The calculations show that both Year 1 and Year 4
indicate the possibility of financial statement manipulation
because the amounts are greater than a -2.22. This
company had issues with missing inventory.
Many times, it is very beneficial to create a chart of
several components of the Beneish M- Score Model to
compare the relationships within the financial statement
accounts. The following chart is an example of analyzing
the relationships associated with inventory from a
manufacturing company.
The chart poses several questions concerning Year 2 and
Year 3. In Year 2 there is a significant increase in SGI,
indicating accelerated sales growth, but inventory did not
increase, suggesting the use of inventoried items covering
some of the sales. Secondly, the GMI calculation indicates
that gross margin had decreased from the prior year,
suggesting the manipulation of inventory.
In Year 3, sales decreased substantially over the prior
year. There was a decrease in GMI and a decrease in TITA
compared to the prior year, with a more substantial
decrease in TITA compared to GMI. Usually, as sales
decrease, inventory increases, therefore TITA will
increase. Year 4 and Year 5 show this relationship. The
0
0.5
1
1.5
2
2.5
3
YR 2 YR 3 YR 4 YR 5
1.17
0.98 0.92
1.02
2.57
1.43
0.960.78
1.17
0.93
1.01 1.15
GMI SGI TITA
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES CFO of this company misappropriated cash and generated
fictitious invoices for raw materials to cover the theft of
cash. The CFO also took small inventory items from the
company and sold them below cost to the company’s
customers through a company set up by the CFO for this
purpose. Year 4 and Year 5 calculations represent normal
operations and activity since the company terminated the
CFO in the latter part of Year 3.
The Accruals
There are three different analytics used to measure
accruals recorded in the financial statements: Dechow-
Dichev Accrual Quality, Sloan’s Accruals, and Jones
Nondiscretionary Accruals. Each model performs a
specific task concerning accruals. Dechow-Dichev
Accrual Quality measures the stability and the quality of
the accruals in terms of generating future cash flows. Poor
quality indicates poor generation of future cash flows. The
Dechow-Dichev implied earnings calculation measures the
influence of accruals to net income. Sloan’s Accruals
calculates the implied cash component of earnings and is
used not only for monthly, quarterly, or annual financial
information but also for specific time periods outside of
normal financial statement preparation, such as six weeks,
nine weeks, etc. While Jones Nondiscretionary Accruals
actually measure these types of accruals, Jones determined
that by measuring nondiscretionary accruals, discretionary
accruals are measured indirectly.
The important feature to remember for these models is the
term accruals. Accruals mean all financial statement
accounts that are not cash only. For these models,
accounts receivables, accounts payables, accrued payroll
and other types of accrual accounts are measured to
determine the quality of the accruals, as well as the
implied future earnings and cash flows. Examples of
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES discretionary accruals are the allowances and reserve
accounts that depend upon management’s assertions and
representations. As nondiscretionary accruals decrease,
discretionary accruals increase.
The best method to analyze both the Dechow-Dichev
Accrual Quality and Earnings calculations is to compare
the calculations to net income. The important factor to
remember in analyzing these calculations is that the
Dechow-Dichev calculations should follow the same
movement as net income. If they move in opposite
directions, then there are unusual variations in the
financial statement information that must be examined.
There are certain conditions that affect the Dechow-
Dichev Accrual Quality calculation that the forensic
accountant needs to consider in evaluating the results.
These conditions include a longer operating cycle, the size
of the company, stability of sales, continued losses, and
the amount of accruals are just some of these factors.
The slides in the presentation represent these calculations
and comparisons to net income for a financial institution
in the midst of two different types of fraudulent activity—
embezzlement and financial statement fraud. The chart on
the following page clearly indicates opposite movement
between net income and the Dechow-Dichev Accrual
Quality for Year 3 and Year 4.
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES
The best method for analyzing Sloan’s Accruals
calculations is to compare net income, the implied cash
component, and the accrual component of the calculations.
Higher levels of the accrual component compared to net
income and the implied cash component is a key factor.
The implied cash calculation represents future anticipated
cash flow. The chart on the following page from the
presentation showing the calculations for a manufacturing
company with embezzlement issues clearly defines the
higher accrual component for Year 2 and Year 5. The
most notable observation for Year 5 is that net income and
the accrual component calculations are almost equal, while
the implied cash component is negative.
(0.03)
(0.02)
(0.01)
-
0.01
0.02
0.03
0.04
0.05
0.06
$0
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
$160,000
$180,000
YR 2 YR 3 YR 4 YR 5Net Income Dechow-Dichev
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES
The important point to remember in analyzing Jones
Nondiscretionary Accruals is that as nondiscretionary
accruals decrease, discretionary accruals increase, thus
creating more influence in the financial statements. Since
discretionary accruals represent accounts in the financial
statements that are based primarily on management’s
assertions and representations, these accounts represent a
higher risk for potential fraudulent activity. The chart in
the presentation shows a significant decline in
nondiscretionary accruals beginning in Year 3 through
Year 5. In Year 5, the calculations indicate that the
discretionary accruals are the only accruals affecting the
financial statements. These calculations are from a
manufacturing company that has the following
discretionary accounts: allowance for bad debts, warranty
reserves, and inventory reserves. Both the warranty
reserves and the inventory reserves were understated in
Year 3 through Year 5 in an attempt to show more profit
to its investors compared to the actual losses the company
was incurring.
(1,500,000)
(1,000,000)
(500,000)
-
500,000
1,000,000
1,500,000
2,000,000
2,500,000
YR 2 YR 3 YR 4 YR 5
Net Income Accrual Component Implied Cash Component
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES Benford’s Law is an analytical technique that measures the
reasonableness of financial information. It is well known
as a great tool for large data sets; it can be used on smaller
data sets as long as the data set meets the following
requirements:
The mean is greater than the median.
The skew is positive.
Most do not consider this technique in analyzing financial
statements, but it is useful as a “reasonableness” test when
using a 95 percent confidence level indicating that there is
a 5 percent chance of error in the conclusions reached
from the testing. By using the first two digits test instead
of the first digit test, there is a smaller chance for “false-
positives” requiring additional analysis that is not
necessary. When combined with the 95 percent confidence
level, the chance for false-positives decreases even more.
The most important aspect of Benford’s Law as it relates
to embezzlement activity is that an amount on a fictitious
invoice will not follow the rules of Benford’s Law no
matter how clever the embezzler thinks he or she is.
Z-Scores measure variability in the data and easily point to
anomalies in financial information. The formula is found
in Excel and is easily accessible. There are two statistical
rules that apply to the analysis of Z-Scores— the
Empirical Rule and the Chebyshev’s Theorem. The
Empirical Rule is for data sets that exhibit the
characteristics of normally distributed sets of data. The
easiest way to determine this is to chart the data and
compare it to the picture below. Generally, the graph looks
like a top hat.
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES
The presentation includes the rules as they apply to
distance from the mean measured by the standard
deviation. While this seems complicated, it is very easy to
remember the following as it applies to financial
information:
If the Z-Score of a number is less than -2 or more than
2, this number is occurring about 5 percent of the time
in the data, so it is unusual and possibly and outlier.
If the Z-Score of a number is less than -3 or more than
3, this number is occurring about 1 percent of the time
in the data, so it is very unusual and probably an
outlier.
For other sets of data that do not meet the characteristics
of normally distributed sets of data, the Chebyshev’s
Theorem applies. When graphed, these data sets will
definitely not have the “top-hat” look but will exhibit
other shapes and sizes as noted in the picture below.
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
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NOTES They might even have two humps within the data or be
“skewed” either to the right, as in the picture, or to the left.
No matter what the shape, Chebyshev’s Theorem is the
tool to use.
Chebyshev’s Theorem does measure the Z-score of the
number the same way as the Empirical Rule with the
following exceptions:
If the Z-Score of a number is less than -3 and more
than 3, the number occurs about 11 percent of the time
in the data set, and it is unusual and possibly an
outlier.
If the Z-Score is less than -4 or more than 4, the
number occurs less than 6 percent of the time in the
data set, and it is very unusual and probably an outlier.
All of these techniques determine areas of financial
information that require additional study to determine the
reasons for the anomalies, whether the information is truly
unusual for the company’s operations or they represent
fraudulent activity. They are applicable for all types of
companies and industries just by defining some of the
terms in the models to the applicable type of company
under review. For example, SGI for a nonprofit
organization might measure the different types of program
revenues. Do not let the terms used in the formulas of
these techniques restrict their use—be creative!
CASE STUDIES IN ADVANCED FINANCIAL STATEMENT ANALYSIS
27th Annual ACFE Global Fraud Conference ©2016 18
NOTES Sources used in the presentation and in the previous pages
include the following:
Beneish, Messod D. 1999. “The Detection of Earnings
Manipulation.” Financial Analysts Journal 55 (5): 24–36.
Dechow, Patricia M. and Ilia D. Dechev. 2002. “The
Quality of Accruals and Earnings: The Role of Accrual
Estimation Errors.” The Accounting Review 77: 35–59.
Jones, Jennifer J. 1991. “Earnings Management During
Import Relief Investigations.” Journal of Accounting
Research 29 (2).
Lev, Baruch and Ramu Thiagarajan. 1993. “Fundamental
Information Analysis.” Journal of Accounting Research 31
(2).
Mantone, Pamela. 2013. Using Analytics to Detect
Possible Fraud: Tools and Techniques. New Jersey: John
Wiley & Sons, Inc.
Nigrini, Mark J. 1999. “I’ve Got Your Number.” Journal
of Accountancy 87 (5): 79.
Nigrini, Mark J. and Linda J. Mittermaier. 1997. “The Use
of Benford’s Law as an Aid in Analytical Procedures.”
Auditing: A Journal of Practice & Theory 16 (2): 52.
Pelosi, Marilyn K. and Theresa M. Sandifer. 2002. Doing
Statistics for Business with Excel. New Jersey: John Wiley
& Sons, Inc.
Piotroski, Joseph D. 2002. “Value Investing: The Use of
Historical Financial Statement Information to Separate
Winners from Losers.” Journal of Accounting Research
38.
Sloan, Richard D. 1996. “Do Stock Prices Fully Reflect
Information in Accruals and Cash Flows About Future
Earnings?” The Accounting Review 71 (3): 289–315.