quality of financial statement of indian largecorporates

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www.indiaratings.co.in 23 July 2014

Corporates

Quality of Financial Reporting of Large Corporates - IA Quantitative Approach to Flag off Possible Reporting Issues

Special Report

Doubts on Earnings Number: The underlying financial health of at least some BSE 500

corporates (excluding banking and financial services) is not reflected through their key reported

financial numbers such as EBITDA, PBT and PAT, tentatively concludes India Ratings &

Research (Ind-Ra). The agency has used Benfords Law (Appendix 1) for analysing the quality

of financial reporting of large Indian corporates for the period between FY02-FY13 for the

purpose of this study.

Caveats on Statistical Results:The results are based on statistical confidence intervals and

present a likelihood of discrepancy in financial reporting. However, a caveat may be providedfor all statistical output. Ind-Ra does not rule out the chances of both false positives and false

negatives, despite taking ample caution regarding sample size, test design and caveat

modelling for any statistical output.

While the study does not imply fraudulent reporting, it endeavours to highlight to investors that

a disproportionately higher focus on margins and earnings than on cash margins could lead to

a very limited understanding of the health and performance of the corporates.

Large Caps Not Always Better than MidCaps: There is a significant likelihood of

discrepancies in financial reporting of earnings measure such as PAT and PBT even in top 100

corporates (by market capitalisation). A lot of such corporates as perceived externally have

world class corporate governance, however the presence of bad apples at least statisticallycannot be ruled out. The study also suggests that certain corporates within midcaps have

better financial reporting than large caps.

Bad Years Breed Reporting Issues: The highest number of financial entries (in annual

reports), with possible discrepancies, were witnessed in FY09 (10) followed by FY13 (nine).

However, the least number of discrepant financial entries were witnessed in FY07 and FY08.

The number of financial entries, in company reports, which were statistically flagged off

reduced in FY10 (seven) and FY11 (six). However, there is a strong likelihood of discrepancy in

reporting PAT for both these years. A limited analysis on P&L variables for FY14 suggests

improvements in the quality of earnings numbers.

Promoter Holding Flagged Off:Securities and Exchange Board of Indias (SEBI) endeavoursto restrict promoter holding may be an appropriate step to enhance corporate governance and

transparency. According to the study, the number of financial entries which were flagged off for

a possible discrepancy increases as the proportion of promoter holding in a company

increases. The discrepancy of variables is highest in companies where promoter holding is

above 50%. The outcome does not change whether the promoter is Indian or a foreign entity.

Vigil of Institutional Scrutiny:Corporates with higher involvement of institutional stakeholders

(equity or debt) have better quality of financial reporting than corporates with lower institutional

ownership and higher promoter holding.

Sectors to Watch Out:Sectors such as FMCG, pharmaceuticals, automobiles with significant

control over supply chain partners and ability to push channel sales manage their perceivedperformance. These are also sectors which along with fertiliser, telecom and oil & gas have the

highest number of financial entries, with a statistical possibility of poor quality reporting.

Analysts

Deep N Mukherjee

+91 22 [email protected]

Sankalp Baid+91 22 [email protected]


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No Statistical Difference between Global and Home Grown Auditors:The statistical tests

suggest that only some of the financial statements audited by the domestic arms of major

global audit players had negligible discrepancy. However, a few home-grown Indian audit firms

also reported financial statements with negligible discrepancy.

Implication for Stakeholders:The study suggests that cash-based metrics such as cash flow

from operations (CFO) and fund flow from operations (FFO) as calculated by the agency are

less likely to be managed. Investors and lenders evaluating corporates with a focus on

unadjusted financial variables as well as accrual-based earning measures such EBITDA, PBT

and PAT are more likely to be misled into overestimating the performance of corporates.

Scope of the Report

The purpose of this report is to statistically assess the likelihood of discrepancies in financial

statements for large Indian corporates who are part of BSE500. The agency has applied the

Benford law to flag off financial entries in company financial reports which may be unduly

influenced by the management. This creates a case for greater scrutiny of such variables and

exercising caution and conservatism while making decisions driven by such variables. The flag

off on any variable does not necessarily imply fraud but indicates a statistical possibility, notcertainty, that it may have been biased or managed willingly or coincidently.

The Benford law based test was applied on a total of 39 financial entries (alternately referred as

variables in this study) selected with adequate representation from balance sheet (10), profit &

loss statement (20) as well as cash flow statement (nine).

The analysis was performed on 421 corporates in BSE 500. For these companies, where

available, the analysis was extended on last 12 years of data, subject to availability of financial

numbers in public domain. This ensured that a large sample was available, which made it

amenable to a battery of statistical tests to validate results at a high degree of confidence

interval.

Background

Earning Management Study in India

The use of Benford law to analyse possible discrepancies in reported financial numbers has lot

of precedents. It has often been globally used to evaluate the quality of financial reporting of

corporates, particularly from an earnings management perspective. However, the use of

Benford law in Indian context to quantitatively assess the extent of earnings management is

more recent, with the first such effort being undertaken by Jaiswal & Banerjee* in 2012.

Various studies on Indian corporates suggest that the problem of earnings management exists

even in India. The most prominent research on this was published by SEBI DRG, in 2013**.

Difference of Ind-Ras EffortMajority of the work done thus far typically focuses on over 2,000 corporates. Thus while it

identifies the existence of earnings management problem, it does not comment on earnings

management and overall quality of financial reporting in the largest among the Indian

corporates. A detailed evaluation of BSE 500 corporates, arguably the largest among Indian

corporates, has been conspicuous by its absence.

The current study extends the scope of the analysis to beyond earnings and profitability margin

related variables, to a much larger set of 39 financial entries. Possible suboptimal quality of

reporting with respect to these other variables would cause stakeholders to draw inaccurate

conclusions with respect to the overall financial health, comparative performance and

operational efficiency of the concerned corporate.

*Study of the State of CorporateGovernance in India, Manju Jaiswaland Ashok Banerjee

**Earnings Management in India, byAjit, Malik and Verma, published in2013 under the auspices of SEBIDRG.


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The typical credit metrics include leverage and coverage besides variables such as EBITDA,

FFO, and CFO. The agency during corporate evaluation places significant focus on cash-based

metrics.Besides various aspects of a working capital cycle, investments, loan and advances

are evaluated. One may not rule out the theoretical possibility of a corporate tentatively

manipulating some of these other variables to understate effective leverage or overstate

operational efficiency.

Financial Entries Dependent on Management Discretion

Generally acceptable accounting principles in any jurisdiction implicitly acknowledge the issue

that not all financial entries which form part of financial statements can be observed, measured

and counted with precision. To capture economic realities, represented by such variables, in

the form of financial entries, a reasonable amount of discretion is provided to the management

under the accounting principles. Sound corporate governance demands that this discretion is

used by a firms management with bonafide intentions so as to reflect the appropriate financial

health of the firm to other stakeholders notably lenders and minority shareholders.

Among the variables that can be influenced by management discretion are revenue particularly

under percentage completion method, selling & distribution expense; working capitalcomponents specially debtors, bad debts; provisions, goodwill, revaluation and capital reserve;

loans & advances, investments and their respective write-downs. These represent a sample of

financial entries in company reports that are influenced by managements discretion.

Conversely, cash-related variables have lower dependence on managerial discretion.

Motivation for Distorting Financial Entries

The possible incentive of management/promoters to manage these earnings could be to meet

market expectations. One may be cautious of companies, where a significant portion of the

promoter holding is pledged for promoter loans. Additionally, some of these earnings measures

form components of credit metrics and any deterioration of metrics could trigger a covenant

breach.

Problems May Exist in Financial Reporting

The agency analysed 12 years of data was taken (subject to availability) on all 39 variables for

BSE 500 corporates to study discrepancies in financial reporting, if any. For each of the

variables, there were about 4,000 observations i.e., 4,000 company-years of data. Given the

sample size, one would like to accept the results which are in most instances statistically

significant.

Statistically relevant conclusions may be drawn for 21 variables of the 39 financial entries

(variables) considered.
http://indiaratings.co.in/upload/CriteriaReport/Cash%20Flow%20Measures%20in%20Corporate%20Analysis.pdfhttp://indiaratings.co.in/upload/CriteriaReport/Cash%20Flow%20Measures%20in%20Corporate%20Analysis.pdfhttp://indiaratings.co.in/upload/CriteriaReport/Cash%20Flow%20Measures%20in%20Corporate%20Analysis.pdfhttp://indiaratings.co.in/upload/CriteriaReport/Cash%20Flow%20Measures%20in%20Corporate%20Analysis.pdfhttp://indiaratings.co.in/upload/CriteriaReport/Cash%20Flow%20Measures%20in%20Corporate%20Analysis.pdfhttp://indiaratings.co.in/upload/CriteriaReport/Cash%20Flow%20Measures%20in%20Corporate%20Analysis.pdf


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Figure 1Problem Variable Identified on 12-Year Data

95% Confidence Intervala 90% Confidence Interval

a

Balance Sheet VariablesDebtors XNet fixed assets XLoans and advances X

Cash and bank XDeferred taxes XProvisions XOther current liabilities X

Profit and Lo ss VariablesOther income XBad debt XEBITDA XPBT XPAT XFringe benefit tax XCurrent tax XDepreciation XMat credit entitlement XSelling & distribution expenses X

Cash Flow VariablesCash flow from investing activities XCash flow from financing activities XCFO according to Ind-Ra XaHigher the confidence interval, higher is the statistical likelihood that there may be a discrepancy in the variable.Source: Ind-Ras analysis, Company reports

Earnings Management Possibly Prevalent

Key measures of earnings such as EBITDA, PBT and PAT may have some discrepancy

regarding their reporting. The other two papers (Jaiswal & Banerjee and Ajit, Malik & Verma)

have drawn a similar conclusion.

Other P&L VariablesFinancial entries relating to bad debt and sales & distribution expenses also raise a statistical

flag, calling for higher scrutiny by stakeholders. According to this analysis, there is a high

statistical likelihood of a discrepancy in almost all tax related entries.

Balance Sheet Variables

Other current liabilities and provisions also show a high statistical likelihood of being managed.

By simple extrapolation, it also points out at the possible earnings management in profit and

loss variables.

Goodwill, revaluation and capital reserve also showed a high probability of discrepancy.

However, Ind-Ra has not focussed on these parameters for the current study, considering

limited changes in these numbers on a yearly basis.

Cash-Based Measures Less Susceptible

Among the financial entries, cash-based variables are less susceptible to managements

discretion. The likelihood of distortion in a variable such as CFO is lesser than in popular

variables such as EBITDA, PBT and PAT.

The agency has in its reportBalance Sheet Strength of BSE 500 Corporates,published on 17

July 2013, highlighted that the extent of deterioration in financial health, leading to a surge in

the number of distressed corporates, of Indian corporates would have been more accurately

reflected by the CFO margin than by EBITDA margin. Thus, evaluation methodologies which

lend a higher focus on earnings measure and EBITDA based credit metrics may underreport

the extent of deterioration in a downturn than CFO or FFO based credit metrics.
http://indiaratings.co.in/upload/research/specialReports/2013/7/17/indra17BalanceSheetStrengthBSE500.pdfhttp://indiaratings.co.in/upload/research/specialReports/2013/7/17/indra17BalanceSheetStrengthBSE500.pdfhttp://indiaratings.co.in/upload/research/specialReports/2013/7/17/indra17BalanceSheetStrengthBSE500.pdfhttp://indiaratings.co.in/upload/research/specialReports/2013/7/17/indra17BalanceSheetStrengthBSE500.pdf


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Quality of Reporting Varies by the Year

The study observes that in years with an unexpected or intense slowdown, the number of

financial entries which are statistically flagged off for a possible discrepancy shoots up as

opposed to years with a more benign economic environment.

Figure 2Yearly Distribution of Variables HighlightedFY13 FY12 FY11 FY10 FY09 FY08 FY07 FY06 FY05 FY04 FY03 FY02

Balance Sheet ItemsCWIP X X X XProvisions X X X XDebtors X X XTrade payables X X XDeferred tax X X XOther current liabilities X X X

Cash Flow MeasuresFree cash flow X X X XCash flow frominvesting activities

X X X

Profit & Los s MeasuresFringe benefit tax X X X X X X XRaw material consumed X X X X X XMinimum alternate tax(MAT) credit entitlement

X X X X X X

Total provision for tax X X X XSelling & distributionexpenses

X X X X

Other income X X XEBITDA X X XPAT X X XInterest cost X X XTotal 9a 8 6 7 10 6 5 9 8 9 8 5aIn FY13, statistically three other financial entries were flagged off.bIn FY09, statistically four other variables were flagged off.Source: Company Financials, Ind-Ras analysis

The highest number of variables flagged off was for FY09. The number of flagged variables has

crept up to almost the same level for FY13. FY07 and FY08 were arguably better years in

terms of economic activity and also the years where the lowest number of financial variables

was tagged.

The agency notes that the quality of financial reporting may have improved directionally since

2007. Before 2007 a large number of variables were statistically flagged for possible distortion.

A limited analysis of P&L variables of FY14 suggests that the quality of reporting particularly

the earnings variable may have improved to the extent the statistical test suggests no

discrepancy.

High Promoter Holding an IssueA majority of Indian firms are owned by promoters who also run the operations of the company

in managerial capacity. Thus, the principal agent problem does not arise. The theoretical

possibility of such promoter-managers occasionally having a conflict of interest with minority

shareholders and lenders may not be ruled out.

The statistical tests suggest that the discrepancy in reporting financial numbers increases as

the promoter holding in the company increases. Promoter-managers may at times present a

less than accurate representation of the health of a company to minority shareholders, stock

markets and lenders. Information with respect to below-par performance of a company may

affect its share price, which directly affects promoter-managers many of whom have pledged

their shares to lenders.

In a developed market, studies havecited Principal Agent problem as oneof the important causes of earningsmanagement. The motivation behindsuch behaviour is usually the fact thatthe pay of executives is impactedsignificantly with the financialperformance of the company. Thus,non-promoter managers resort toearnings management. The issue maybe somewhat different in India.


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Figure 3

Corporates with foreign promoter holding also exhibit faster deterioration in the quality of

financial reporting with increased level of promoter holding than with the comparable level of

holding by Indian promoters. The tentative explanation for a relatively lesser deterioration in the

quality of financial reporting in companies with a high level of Indian promoter holding is thepresence of several groups which anecdotally have a higher level of corporate governance.

Figure 4

Institutional Stakeholders Keep a Check

Institutional Equity Investors

Till March 2014, steady institutional equity holding in majority of companies was below 25%

and the number of companies with institutional holding above 25% was somewhat limited.

However, in all instances (except domestic institutional investor holding in 25%-50% bucket)

the sample size in company-years is statistically large i.e., above 200.

Figure 5Institutional Equity Holding Usually Limited

0% to 25% 25% to 50% 50% to 75% >75% No holding

Foreign Institutional Investor (FII) 350 60 1 - 10Domestic institutional (DII) 395 14 - - 12

Source: BSE, Ind-Ras analysis

The study suggests that with an increase in institutional holding, the quality of financial

reporting is likely to increase. As such the number of variables, with a possible discrepancy,

reduces as institutional holding increases.

0

2

4

6

8

10

0%-25% 25%-50% 50%-75% >75%

Higher the Promoter Holding, Greater the Earnings ManagementForeign promoters are not better than Indian promoters

(Nos)

a Most of the companies are in this shareholding bracketSource: Ind-Ra's analysis, company reports

02

4

6

8

10

12

0%-25% 25%-50% 50%-75% >75% No holding

Ind promoters Foreign promoters

Foreign Promoters are Worse Off than Indian Promoters

(Nos)

Most of the companies are in this shareholding bracketSource: Ind-Ra's analysis, company reports


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Figure 6Number of Financial Entries with Discrepancy

0% to 25% 25% to 50% 50% to 75% >75% No holding

FII 9 3 4 n.a. 10DII 13 3 n.a. n.a. 4

Source: BSE, Ind-Ras analysis

However, it is difficult to conclude from the current study whether FII and DII invested in

companies knowing a priori that these corporates had better reporting standards or the

reporting standards improved significantly post continuous investment interest by institutions.

Additional Scrutiny of Debt Investors

Companies which have a higher level of debt have relatively lesser discrepancy than

companies with limited amount of debt.

Figure 7Additional Scrutiny By Lenders Improve Financial Reporting

Top 100

Borrowers

Corporates Ranked

101-200 by Amount of Debt

Corporates Ranked

201-300 by Amount of Debt Least DebtEBITDA X X XInterest X XNet sales X XOther income XPATPBT X X

X- indicates that there is a strong likelihood of discrepancy in reported numbers at the 95% confidence interval.Source: BSE, Ind-Ras analysis.

Among the possible reasons for this observation is that companies which borrow face added

scrutiny from borrowers and rating agencies.

Among the corporates which borrowed even during the recent period, only a few actually

defaulted or were in financial distress. Thankfully, an overwhelming majority of corporates

despite weakened credit metrics are not in distress.

This analysis does not comment on the reporting quality of corporates which historically have

high levels of leverage and have subsequently gone into financial distress.

Figure 8Companies with Moderate Debt Levels Exhibit Better Quality Numbers

Leverage

Below 1x 1x to 3x 3x to 5x Above 5x

Inventory X XDebtors

Trade payables XNet sales X XEBITDA X XPAT X XCFO XCFO INDRA

X Indicates that there is a strong likelihood of discrepancy in reported numbers at the 95% confidence interval.Source: BSE, Ind-Ras analysis

Highly leveraged corporates could be motivated to avoid breaching covenants as well as

hoodwink credit evaluators who focus on EBITDA-based (and other accrual based) measures

of leverage and coverage.

On the other hand, corporates with a low level of debt experience a lower level of institutional

scrutiny. Minority shareholders may be more circumspect of financial numbers in such

corporates, particularly if promoter holding is high, because there is an absence of any

institutional scrutiny.

Ind-Ra while evaluating the financialprofiles puts significant emphasis oncash flow measures. This is because amajority of the statistical tests show thatmeasures such as CFO are lessinfluenced by managements discretion

in interpreting financial numbers.As goes the age old saying, earnings isa matter of opinion, while cash is a fact.


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Financial Reporting Quality by Sectors

The variables which were flagged off most in any industry include depreciation, interest cost,

employee cost, selling & distribution expenses, provision for taxation and net sales. Majority of

sectors exhibit a significant discrepancy with respect to financial entries related to taxes.

However, if a significant number of variables are flagged off in some of these sectors, it is not

implied that all corporates in the sector would have an issue with financial reporting. Each of

the sectors may have several corporates with high quality of financial reporting and disclosures.

This reflects the corporate governance practice of those specific corporates.

Sectors with Possibly Higher Discrepancy

Sectors with the highest number of discrepancies in financial entries include defensive sectors

such as FMCG and pharmaceuticals. Sectors such as fertiliser, pharmaceuticals, telecom and

automotive also exhibit a high statistical likelihood of discrepancy with respect to their earnings

variable

Figure 9

The specific variables which are found to have a strong likelihood of statistical discrepancy are

also the ones which are directly affected when a questionable operational practice is followed

by a corporate.

Weak Reporting Reflects issues with Operational Practises

Arguably, a lot of established players in FMCG, automobiles and pharmaceuticals have

significant control on their supply chains, which essentially constitute of weak players. In an

economic downturn, these weak players may be made to bear the brunt of the slowdown so as

to insulate the earnings of large players to the extent possible. Additionally, instances of

channel pushing, which is supplying finished goods to distributors so as to book higher

revenue during year or quarter end, may not be ruled out.

Sectors with Possibly Lower Discrepancy

While the total number of financial entries with possible discrepancies may be lower in power,

infrastructure and construction and textiles sectors, it may be difficult to give a clean chit

straight away.

An example being the agricultural commodity sector and cement where some of the earnings

variables are flagged off for a possible discrepancy.

6 5

03 4

5 4 5

7 9

97

7 5 66

2 0

4 32

2 2 1

0

3

6

9

12

15

18

FMCG Fertiliser Pharma Oil & gas Diversified Telecom Auto Engineering

BS variables PL variables CF variables

Industries with Highest Number of Variables HighlightedNumbers skewed against balance sheet and profit and loss variables

(No.)

a PAT flagged offb EBITDA flagged offc PBT flagged offSource: Ind-Ra's analysis, company reports

b, c , c

FMCG:Key variables with likelihood ofa statistical discrepancy are inventory,debtor, provisions, gross sales, baddebt and other income.

Pharmaceuticals:Key variables with alikelihood of statistical discrepancy aregross sales, net sales, cost of goodssold, EBITDA and PAT.

Oil & Gas:Key variables with alikelihood of statistical discrepancy arenet fixed assets, depreciation, cash &cash equivalent, capital reserve,revaluation reserve and employee cost.

Automobi les:Key variables with alikelihood of statistical discrepancy arenet fixed assets, other current liability,depreciation, inventory, total expense,PBT and PAT.


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Figure 10

Sectors with Medium Range Benford Flag Raised

Power, infrastructure and construction and textiles closely follow the industries with high

statistical flag for a possible discrepancy being raised for as many as 10 variables

Figure 11

Auditors: Sweeping Generalisations Avoidable

Ind-Ra attempted to evaluate whether the quality of financial reports, as evaluated by using

statistical methods, varied among major auditing firms. Several global auditing firms, their

Indian arms and home-grown Indian audit firms were evaluated. Most of the major firms within

each of these categories had sufficiently large samples for running statistical tests.

The key findings suggest that broad generalisation driven by the jurisdiction of parent or

origination of the audit firm may be avoidable. Only some of the major global audit players had

negligible discrepancy. However, a few home-grown Indian audit firms also reported financial

statements with negligible discrepancy.

According to these statistical tests, even corporates audited by Big X audit firms may still

require detailed scrutiny and understanding of their entire set of financial numbers, to evaluate

their financial health. Caution, irrespective of the stature of the audit firm, may be exercised for

corporates with high promoter holdings and industries more prone to earnings management.

3 34

23 3

1

3 3

3

5

5

2

2

2 2 1 10

2

2

0

3

6

9

Consumerdurable

Metals &minning

Cementmanufacturers

Real estate Shipping Automotivesuppliers

Agriculturalcommodities


Industries with Lowest Number of Variables HighlightedNumbers continue to be skewed against balance sheet and profit and loss variables

(No.)


ba, b, c

3 4 3 4

1

63 2 3

1

55 6

6

6

36

44 7

3 2 21

31 1

3 21

0

3

6

9

12

Power

Textiles

Others

Infrastructure&

construction

Media&

entertainment

Gems&

jewellery

Chemicals&

chemical

products

ITservices

Services-

other

Beverage&

tobacco


Industries with Medium Number of Variables HighlightedInfrastructure, power & textile sectors continue to have high BF flags

(No.)


cc

bb,c

b


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Deep Dive into Specific Profile

PromoterInstitutional Investors Interaction

According to the analysis, the agency would cautiously suggest that a high promoter holding

coupled with weak institutional holding gives the largest leeway to management to report

financial numbers of weaker quality.

Figure 12Number of Financial Entries with Discrepancy

PH


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Annexure 1: Benford Law

What is Benfords Law

Benford's Law, also called the First-Digit Law, refers to the frequency distribution of digits in

many (but not all) real-life sources of data. In a set of numbers which are likely to converge to

Benfords distribution, number 1 occurs as the first digit about 30% of the time, while higher

numbers occur in that position (i.e., first digit) less frequently: 9 as the first digit less is expectedto occur less than 5% of the time. Benford's law also concerns the expected distribution for

digits beyond the first digit, which approach a uniform distribution. This law has been applied to

detect accounting frauds in forensic auditing***.

Figure 15Expected Frequencies of Occurrence of Digits as per Benfords LawDigit 1

stplace 2

ndplace

0 0.119681 0.30103 0.113892 0.176091 0.198823 0.124939 0.104334 0.09691 0.100315 0.079181 0.096686 0.066947 0.093377 0.057992 0.090358 0.051153 0.087579 0.045757 0.085

History on Benfords law

Benfords law is named after a physicist Frank Benford, who independently observed Benford

distribution in several number sequences. However, the first observation is recorded history

and was made by Simon Newcomb, who in 1881 published the observation in American

Journal of Mathematics. Both these academics observed that in logarithmic tables, pages with

data on numbers starting with lower digits such as 1, 2 and 3 were more worn than pages with

data on numbers starting with higher digits such as 8 and 9. Both of them forwarded the

hypothesis that the number of numbers which start with 1 are higher than numbers starting

with other digits, with their proportion decreasing as the digit increases.

The law was named after Benford since he provided comprehensive theoretical explanation for

the observation. Research on Benford distribution and its usage in possible detection of frauds

or discrepancies in a sequence of numbers has been quite extensive. In 1995, Hill forwarded a

proof for Benfords law as well as how the law is applicable to stock market data, census data

and some accounting data.

Application of Benfords Law to Accounting Numbers

Accounting numbers on a financial statement are an outcome of thousands of transactions the

business entity undertakes during the accounting period. These transactions are of different

sizes such as day-to-day sales, expenses, receivables and payables generated and other

corporate transactions. Per se, the amount involved in each of these transactions is unrelated

to each other. Thus, the precise numerical value which captures these transactions is random

in nature.

These thousands and sometimes millions of transactions are aggregated to create a financial

statement for the accounting period. In essence, a financial statement itself represents a

second order distribution generated from the first order distribution. The individual transaction

level data is the first order distribution, from which ultimately the financial statement is created.

This represents the second order distribution, which is expected to follow a Benford distribution,

as per Hesman.

***The effective use of Benfords lawto assist in detecting fraud inaccounting data by Durtshi, Hillisonand Pacini, published in July 2003

As Hesman stated that combining

unrelated numbers gives a distributionof distribution, a law of truerandomness that is universal.The second series of numbersgenerated out of the first series ofnumbers is likely to converge toBenford Distribution.


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This is in line with the findings of Boyle, who showed that numbers which are the results of

mathematical operations, such as additions, subtractions, divisions and multiplications, on

other set of numbers which are randomly generated tend to have a Benford Distribution.

Statistical Procedure Adopted:

In a Benfords distribution, the expected proportion of numbers which contains the digit 1 in

the first position is 30.103%. The actual proportion observed will most likely deviate from this

expected amount due to a random variation.

The expected distribution of digit frequency, based on Benfords law, is a logarithmic

distribution that appears visually like a Chi-square distribution. The standard deviation for each

digits expected proportion is:

Si= [Pi*(1-Pi)/n]1/2

Where Siis the standard deviation of each digit, 1 through 9

Piis the expected proportion of a particular digit based on Benfords law; and

n is the number of observations in the data set

A z-statistic can be used to determine whether a particular digits proportion from a set of data

is suspect. The z-statistic is calculated as follows (Refer: The use of Benfords Law as an Aid in

Analytical Procedures, Nigrini & Mittermaier, 1996)

Z= (lPo-Pel-1/(2n))/Si

Where: Pois the observed proportion in the data set

Peis the expected proportion based on Benfords law

Siis the standard deviation for a particular digital; and

n is the number of observations (the term (1/2n) is a continuity correction factor and is used

only when it is smaller than the absolute value term).

A z-statistic of 1.96 would indicate a p-value of 0.05 (95% confidence) while a z-statistc of 1.64

would suggest a p-value of .10 (90% confidence).

Caveat

Two underlying concepts should be considered for determining the effectiveness of digital

analysis based on Benfords law. First, the effectiveness of digital analysis declines as the level

of contaminated entries drops. This is to say that if the number of

contaminated/fraudulent/discrepant entries is low in the overall sample, the test may suggestthat there is a low likelihood of fraud.

Secondly, in many instances accounts identified as non-conforming do not contain a fraud.

These facts are particularly important when considering the usefulness of a statistical test. If a

set of accounting numbers do not conform to Benfords distribution, this does not imply that

there is a discrepancy or fraudulent reporting. It implies that there is a statistical possibility of

discrepancy thus requiring more in-depth understanding of the reported numbers before one

may draw an appropriate conclusion about the companys performance or health.


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Corporates

Quality of Financial Reporting of Large Corporates - I 13

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quality of financial statement of indian largecorporates

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