Predicting Accounting Fraud: Evidence from Japan*
MINGZI SONGNAOTO OSHIROAKINOBU SHUTO†FINANCIAL TECHNOLOGYRESEARCH INSTITUTE INC., TOKYOFINANCIAL TECHNOLOGYRESEARCH INSTITUTE INC., TOKYO

Graduate School of Economics
THE UNIVERSITY OF TOKYOABSTRACTThis study develops a prediction model for identifying accounting fraud by analyzing
the accounting information for Japanese firms. In particular, we (1) explore thecharacteristics of accounting fraud firms by analyzing financial information obtained
from annual reports (yukashoken-houkokusho in Japanese) and (2) develop a model for
predicting accounting fraud based on the characteristics of Japanese fraud firms. To
identify the characteristic of fraud firms, we focus on 39 variables for the eight factorsof “accruals quality,” “performance,” “nonfinancial measures,” “off-balance-sheet
activities,” “market-related incentives,” “conservatism,” “real-activities manipulation,”and “Japanese-specific factors.” Through our univariate analysis and model building
process, we find that “accrual quality,” “market-related incentives,” “real-activities
manipulation,” “conservatism” and “Japanese-specific factors” are generally useful for
detecting accounting fraud. We also conduct several analyses that test the predictive
ability of our models, including (1) the detection rates of fraud firms, (2) Type I andType II error rates, (3) marginal effect analysis on independent variables, and (4)robustness tests on time periods and industry clustering. We find that our models havegenerally higher predictive power in detecting accounting fraud. We expect that ourmodels can be used widely in various accounting and finance practices.
JEL Classification: M41
Keywords: Earnings Quality; Accounting Fraud; Accrual Quality; F-Score; Japan*

Acknowledgements: The authors appreciate the helpful comments and suggestions received from Masahiro Enomoto(editor), Hidetoshi Yamaji (editor), anonymous referee, Shota Otomasa, and the participants of the workshops
conducted at The University of Tokyo. All errors are the responsibility of the author.
†
Corresponding Author. Address Graduate School of Economics, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku,
Tokyo 113-0033, JAPAN. Telephone +81-3-5841-5523 E-mail shuto@e.u-tokyo.ac.jp
Received August 13, 2016; accepted September 21, 2016; available online November 19, 2016 (Advance publications by
J-STAGE)
DOI: 10.11640/tjar.6.2016.01
Copyright©2016 Research Institute for Economics & Business Administration – Kobe University.

18

The Japanese Accounting Review, 6 (2016), 17-63

1. Introduction
Recent accounting scandals such as Toshiba and Olympus Corporation in Japan havesignificantly damaged the reliability of accounting information and Japanese capital markets.
Exploring the cause and consequence of accounting fraud is of critical importance to the efﬁcient
functioning of capital markets (Dechow et al. 2011). In particular, determining how to detect
earnings manipulation has been one of the major concerns for both accounting research andbusiness practice (Beneish 1999a; Lee et al. 1999; Ettredge et al. 2006; Brazel et al. 2009; Dechow
et al. 2011). This study develops a prediction model for detecting accounting fraud among
Japanese firms. Specifically, we explore the characteristics of accounting fraud firms by analyzing
financial information obtained from the annual reports (yukashoken-houkokusho in Japanese) ofJapanese firms and then develop a model for predicting accounting frauds based on thecharacteristics of Japanese fraud firms. In developing the model, we focus on the quality ofearnings such as earnings management and accounting conservatism.
We use two data sauces to identify accounting fraud firms in Japan. First, we collect firmsthat have been accused or had administrative monetary penalties imposed by the Securities andExchange Surveillance Commission (SESC) for misstatements of financial reports on material
issues (SESC firms). Second, we focus on firms that have reported accounting fraud via the Timely
Disclosure Rules of the stock exchange (TDR firms). As no prior studies have examinedaccounting fraud in Japanese firms, our subsidiary purpose is to construct a database ofaccounting fraud.
Beneish (1999a) examines the relationship between financial statement data and earnings
manipulation. He provides a probit model to detect earnings manipulation by analyzing eight
financial ratios. Prior studies also reveal that some information is useful for detecting accounting
fraud, such as accounting accruals (Lee et al., 1999), deferred tax (Ettredge et al., 2006) andnonﬁnancial measures (Brazel et al., 2009).
Dechow et al. (2011) comprehensively examine the relationship between accounting
information and accounting misstatement. They focus on five factors—accruals quality,
performance, nonfinancial measures, off-balance-sheet activities, and market-related
incentives—for use in identifying misstatements and develop a model for predicting
misstatements by analyzing 28 financial characteristics variables. They reveal that the output of
their prediction model, F-Score, has a strong predictive ability for misstatements.
Although we generally follow the analysis procedure employed by Dechow et al. (2011), we
extend their study in several ways. First, we conduct a detailed analysis of accruals quality.
Dechow et al. (2011) do not use discretionary accruals to construct their prediction models; we do
use them, based on various estimation models. The discretionary accruals are generally used tocapture earnings management behavior (Dechow et al. 2010). The research suggests that
discretionary accruals are strongly associated with the incidence of accounting fraud (Dechow etal. 1996). In addition to the level of discretionary accruals, we use the absolute value and standard
deviation of discretionary accruals in order to capture the effect of accruals reversal.
Second, while earnings management has generally been classified into accrual-based and realearnings management, Dechow et al. (2011) focus only on accrual-based earnings management.
They note that an important avenue for future research is seeking a better understanding of therole of real transaction or cash-flow management (Dechow et al. 2011, p. 77). Accordingly, we use
real-activities manipulation variables based on Roychowdhury (2006).

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

19

Third, we also use the degree of accounting conservatism since recent studies have revealed
that accounting conservatism can increase the efficiency of contracts and reduce information
asymmetry among investors (Watts 2003). We predict that the use of accounting conservatism isnegatively correlated with accounting fraud.
Finally, as most prior studies, including Dechow et al. (2011), examine accounting fraud or
misstatement among US firms, we add variables that capture the specific features of Japanesefirms. Specifically, we use ownership by financial institution to capture the effect of main banks.
We also use ownership by business corporation to proxy for cross-shareholdings.
Thus, in addition to the five factors used by Dechow et al. (2011)—accruals quality,
performance, nonﬁnancial measures, off-balance-sheet activities, and market-related
incentives—we focus on three additional factors: real-activities manipulation, accounting
conservatism, and Japanese-specific factors. We calculate 38 variables based on the above eightfactors and examine the relationship between the variables and accounting fraud.
Our sample consists of 241 SESC firm-year observations and 620 TDR firm-year
observations. The key results for the SESC firms, our main concern, are as follows. First, we
conduct a univariate analysis to compare variables between fraud and non-fraud firms. The results
indicate that most measures of accrual quality are unusually high in fraud years relative to thebroad population of firms. We also find that the measures for market-related incentives,
real-activities manipulation, accounting conservatism, and Japanese-specific factors are
significantly higher for fraud firms than for non-fraud firms.
Second, by extracting the variables that display significant differences in the univariate
analysis, we develop a prediction model for detecting accounting fraud. Model 1 includes
variables obtained from the annual reports. Model 2 adds variables for market-related incentives.
The output of these models is a scaled logistic probability for each firm-year, which we term the
F-Score following Dechow et al. (2011). We use a backward elimination technique to identify theincremental benefit for predicting misstatement from including information beyond the financial
statements. After performing the backward elimination, we retain the following variables inModel 1: soft assets, absolute value of discretionary accruals, new financing, accounting
conservatism, real-activities manipulation, and ownership by business corporation. For Model 2,
we retain soft assets, absolute value of discretionary accruals, new financing, real earnings
management, ownership by business corporation, and book-to-market ratio. The results indicatethat many of the variables added to the model of Dechow et al. (2011) are employed in theprediction models, suggesting that our models have generally higher predictive power in detecting
accounting fraud.
Finally, we conduct several analyses to test the predictive ability of our models, including (1)the detection rates of fraud firms, (2) Type I and II error rates, (3) marginal effect analysis on
independent variables, and (4) robustness tests on time periods and industry clustering. We findthat our models have generally higher predictive power to detect accounting fraud. For example,
we rank firm-years into five portfolios based on the magnitude of their F-Score and report thefrequency with which fraud and non-fraud firms fall into each quintile. If our models have better
predictive ability in identifying fraud firms, we expect the fraud firms to be clustered in the fifth
portfolio (Dechow et al. 2011). Our results indicate that 59.39% of fraud firms are in Quintile 5for Model 1 and that 90.83% of fraud firms are in Quintile 5 for Model 2. These results arehigher than our expected level of 20% and the results obtained in Dechow et al. (2011).
This study significantly contributes to the literature and has implications for accounting

20

The Japanese Accounting Review, 6 (2016), 17-63

practice. First, it contributes to the accounting fraud literature in its detailed analysis of how
earnings quality could improve the explanatory power of a prediction model. While many studies
examine the relationship between accruals quality and accounting misstatements (Beneish, 1999a;
Lee et al., 1999; Dechow et al., 2011), fewer studies investigate the effect of real earnings
management and accounting conservatism on accounting fraud. Our results suggest that a deeper
analysis of earnings quality from various dimensions could increase the predictive power ofmodels for detecting accounting fraud.
Second, our results suggest that the institutional features of a country could be importantfactors in the characteristics of accounting fraud. We examine the Japanese institutional featuresof ownership by financial institutions and business corporations, and find that corporate
ownership has a significant effect on accounting fraud. Xu and Zhang (2009) develop a model for
predicting bankruptcy in the Japanese market, finding that incorporating the unique Japaneseinstitutional features of main banks and business groups into their model improves its ability to
predict the bankruptcy of Japanese listed companies, consistent with our results.
Our results also have several implications for stakeholders such as auditors, regulators
investors, and other financial statement users. For instance, auditors and regulators could improvethe efficiency of their decision making by estimating the probability of accounting fraud, andinvestors might consider an investment strategy based on the reliability of the available
accounting information (i.e., F-Score).
The remainder of this paper is organized as follows. Section 2 reviews the research on this
topic. Section 3 explains the variable measurements used in this study. Section 4 outlines the
sample selection process and the descriptive statistics. Section 5 reports the empirical results on
the characteristics of fraud firms and develops a prediction model for identifying accounting fraud.
Section 6 summarizes the results of the additional analysis. Finally, section 7 concludes the study
with a summary.

2. Literature Review
In examining accounting fraud among US firms, most studies focus on firms that have been
subject to enforcement actions by the Securities and Exchange Commission (SEC) for allegedly
misstating their financial statements. Detailed information on such misstatements is reported in
the Accounting and Auditing Enforcement Releases (AAERs) issued by the SEC.
Beneish (1999a) is a pioneering study that develops a model for identifying fraud firms byanalyzing financial statement information. He identifies 74 AAER firms operating from 1982
and 1992 and matches the sample to 2,332 Compustat non-fraud samples using two-digit SIC
industry and year codes. Using the eight financial statement ratios, he develops a probit model toestimate a likelihood of an earnings overstatement. Beneish (1999a) indicates that five financial
ratios have relatively high explanatory power for accounting manipulation: the day’s sales inreceivables index, gross margin index, asset quality index, sales growth index, and accruals.
Lee et al. (1999) show that the difference between earnings and operating cash flow (i.e.,
accounting accruals) is larger for fraud firms than for non-fraud firms based on a sample of 56
fraud cases covering 1978 to 1991. Ettredge et al. (2006) reveal that deferred taxes can be usefulfor predicting misstatements after controlling for other factors for 169 AAER firms. Brazel et al.
(2009) investigate whether publicly available nonfinancial measures can be used to assess thelikelihood of accounting fraud, showing that non-financial measures such as number of patents,

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan21

employees, and products can be effectively used to assess fraud risk in 50 AAER firms.
Okumura (2014) comprehensively investigates misstatements by Japanese firms throughanalyses on the features of misstatement firms in Japan, the relationship between misstatements
and stock price, and the effect of corporate governance on misstatements. Okumura (2014) isclosely related to our study in that, in Appendix A, he examines the predictive ability of
discretionary accruals for detecting misstatement, and finds a significant relationship between
discretionary accruals based on various types of Jones models and misstatements. The results
suggest that discretionary accruals are useful for detecting financial misstatements among
Japanese firms.1
Finally, our research design basically follows the outline of Dechow et al. (2011), the main
purpose of which is to analyze the financial characteristics of misstating firms and to develop amodel for predicting misstatements. They analyze 494 AAER firms and use 28 financial
characteristics variables associated with “accruals quality,” “performance,” “nonfinancial measures,”“off-balance-sheet activities,” and “market-related incentives.”
First, they conduct a univariate analysis to clarify the financial characteristics of misstating
firms. Specifically, they compare variables between misstatement and non-misstatement firms.
They consider most important the comparison between 494 AAER firms and other firms(COMPUSTAT), which reveals that several factors—such as accruals, the extent of leasing, thenumber of employees, financing, and prior stock price performance—show significant differences.
Second, using the variables displaying significant effects in the univariate analysis, they present
models for predicting misstatements. To test their predictive ability, they conduct various analyses.
The results generally show that their measure of the likelihood of manipulation (the F-Score) is a
useful tool for identifying misstatement firms.

3. Research Design
3.1 Identification of accounting fraud firms
We form two groups of firms to identify those most likely to engage in accounting fraud:firms that have (1) been accused or had administrative monetary penalties imposed by the
Securities and Exchange Surveillance Commission and (2) announced accounting fraud at theirtimely disclosures.
For (1), we obtain information from the website of the Financial Services Agency(http://www.fsa.go.jp/policy/kachoukin/index.html)2 and denote as the year of fraud the year inwhich the firms suffered administrative monetary penalties or the equivalent (SESC firms),
excluding incidents such as misstatements of the register of shareholders, unfair financing, or
insider trading, as these types of fraud are not associated with accounting fraud.
For (2), we identify firms that have announced improper accounting via the timely disclosure
required by the stock exchange (TDR firms). We searched for these firms using keywords
expressing suspicion of accounting fraud in timely disclosures, available from TDnet (a timely
1

Our study extends the analyses in Okumura (2014) as follows. First, while Okumura (2014) focuses exclusively on
discretionary accruals for detecting accounting fraud, we develop a better prediction model based on all the variables
available from annual reports. Second, our sample includes, in addition to restatement firms, firms that have beenaccused, or have had administrative monetary penalties imposed by, the Securities and Exchange SurveillanceCommission (SESC), thereby constituting a more reliable sample for detecting accounting fraud.
2
The last access date on this web site was July 21, 2015.

22

The Japanese Accounting Review, 6 (2016), 17-63

disclosure information browsing service) on the Tokyo Stock Exchange homepage and identified
the year when the accounting fraud occurred.3
We use sets of four keywords (in Japanese) for this search. First, we use teisei (“correction”),
tekisetsu (“appropriate”), fusei-kaikei (“accounting fraud”). These keywords are expected to extract
all firms that have corrected their yukashoken-houkokusho (“annual report”) or kessan-tanshin(“earnings briefing”). Then, we clarify the reason for the correction by reviewing each report andidentifying the firms that have corrected an earnings briefing due to accounting fraud. We exclude
firms that announced a correction as being due to a simple error. Second, we use chien (“delay”),
enki (“postpone”), and kanri (“supervision”) as keywords to find firms that delayed theirsubmission of financial statements or earnings briefings. These words are used to identify thefirms that delayed submission due to accounting fraud or improper sales. Third, we use iinkai(“committee”), cyousa (“investigation”), and setchi (“installation”/”establishment”) as keywords toextract the firms for which third-party committees were established due to alleged accounting
fraud. Most of the firms that have carried out accounting fraud are identified through these
keywords. Finally, we use kadai (“excessive”), fusei (“fraud”), and syori (“process”) to extract thefirms against which allegations have been made with respect to the adequacy of their accounting
or cheating by employees.
For the firms extracted using the groups of four keywords, we impose further screening under
the following conditions. First, we select the firms for which earnings are manipulated upward
through accounting fraud with respect to accounts on the balance sheet or income statements. We
focus on net assets in the balance sheet and net income in the income statements. Second, we
exclude cases such as mistakes of the unit on account, modifications of explanatory materials,
revisions of management earnings forecasts, and modifications of segment information, as they
are likely to have little association with accounting fraud.
While the first selection criterion (i.e., SESC firms) is more important, both selection criteria
have unique features. The advantage of using SESC firms is that they may provide more reliablefraud samples, as they were fully investigated by the committee. One disadvantage is that the
SESC may not be able to investigate all fraud firms because such investigations are expensive.
Furthermore, a sample selection bias may occur due to some particular tendency in their researchpolicy; for example, the committee might tend to investigate larger firms more often than smaller
ones.4The benefit of the second selection criteria (i.e., TDR firms) is that it can collect a larger
number of fraud samples than the SESC firm criterion can. However, TDR firms, extracted
through restatement announcements by timely disclosure, might include corrections of simple,
unintentional, and minor errors. Thus, we identify the firms that are more likely to commit
accounting fraud by sorting TDR firms according to the magnitude of the restatement (thedetails are provided below).
We use the abovementioned sample selection criteria so that one could complement theother and compensate for its shortcomings. Though the samples selected by the first criterion are
more reliable, they may not include latent fraud firms. Thus, we also use the second criterion to
select a larger number of fraud firms. We are also interested in seeing how the results differ
3

When we cannot identify the exact year of the accounting fraud, we assume that it occurred in the latest fiscal year,
and all prior years are omitted as non-discriminable.
4
Dechow et al. (2011) also discuss the same limitation, whose research handles samples alleged by SEC.

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

23

TABLE 1 TYPES OF DEPENDENT VARIABLE
Type
Misstate1
Misstate2
Misstate3
Misstate4
Misstate5
Misstate6
Misstate7
Misstate8

Definition
Firms that have alleged or imposed administrative monetary penalty from the
Securities and Exchange Surveillance Commission
Firms that have announced their accounting fraud at the timely disclosures
Firms with correction ratio of net income ≧ 0.05 and correction ratio of net
assets ≧ 0.01 in Misstate2
Firms with correction ratio of net income ≧ 0.10 and correction ratio of net
assets ≧ 0.02 in Misstate2
Firms with correction ratio of net income ≧ 0.15 and correction ratio of net
assets ≧ 0.03 in Misstate2
Firms with correction ratio of net income ≧ 0.20 and correction ratio of net
assets ≧ 0.03 in Misstate2
Firms with correction ratio of net income ≧ 0.25 and correction ratio of net
assets ≧ 0.04 in Misstate2
Firms with correction ratio of net income ≧ 0.30 and correction ratio of net
assets ≧ 0.04 in Misstate2

241

Total
Number
241

620
99

861
340

72

313

62

303

58

299

43

284

42

283

NumberNote: Misstate1 includes firms that have been accused or had administrative monetary penalties
imposed by the SESC. Misstate2 includes firms identified by performing a plurality of keyword
searches related to accounting fraud at the timely disclosure. Misstate3 to Misstate8 calculate
the correction ratio by the accounting fraud of net income and net assets and are defined bycorrection ratio increases through Misstate3 to Misstate8. For more information on variable
definitions, please refer to Section 3.1.

among the subsamples based on the two criteria. Specifically, we use the variables described below
as a proxy for accounting fraud.
Misstate1 comprises SESC firms, our primary concern. Table 1 provides details on thevariables for accounting fraud firms. Misstate1 has 241 firm-year observations. Misstate2
comprises firms that announce accounting fraud by timely disclosure (i.e., TDR firms). Misstate2
contains 620 firm-year observations.5 We set variables through Misstate3 to Misstate8 in orderto gradually capture the degree of accounting fraud. To measure the variables, we focus on the
total amount of correction in net earnings (net assets) 6 through restatements of financial
statements and calculate the ratio of misstatements to reported earnings (reported net assets).7
We find that the number of subsamples decreases from Misstate3 through to Misstate8,
5

We included several firms that announced their improper accounting through their timely disclosure in Misstate1
since they are strongly suspected of conducting accounting fraud. The firms are defined as having gone bankrupt or
being delisted from the stock market before the submission of restatements. These observations total sevenfirm-years.
6
Specifically, we include the corrections of the net assets, caused by manipulation of assets and/or liabilities.
7
We calculate two ratios, (pre-correction of net income – post-correction of net income)/5-year average ofpre-correction of net income) and (pre-correction of net assets – post-correction of net assets)/pre-correction of net
assets. The former is called a “correction ratio of net income,” and the latter is called a “correction ratio of net assets.”
We classify the sample into six subsamples (Misstate3 to Missate8) with 0.05 and 0.01 intervals of net income andnet assets, respectively.

24

The Japanese Accounting Review, 6 (2016), 17-63

suggesting that the number decreases as the impact of accounting fraud increases. In the later
analyses, the misstatement variables are defined as the sum of observations in Misstate1 and from
Misstate2 to Misstate8. For example, the Misstate8 variables include a total of 283 observations,the sum of Misstate1’s 241 and Misstate8’s 42.

3.2 Measurement of explanatory variables
This section describes the variables used to identify accounting fraud firms. The variables are
based on those used in Dechow et al. (2011), including the factors of accruals quality, performance,
nonfinancial measures, off-balance-sheet activities, and market-related incentives.8 We use two
additional factors, conservatism and real-activities manipulation, in order to capture the quality ofearnings more comprehensively. We also use discretional accruals based on various estimation
models since they are a major proxy for earnings management behaviors. Furthermore, because
our sample consists of Japanese firms, we add Japanese-specific factors as variables. Details on thedefinition of each variable are provided in Table 2.9(1) Accrual quality-related variables
Accruals and discretionary accruals are traditional variables used to capture earnings
management behaviors (Dechow et al. 2010; Shuto 2010). Dechow et al. (1996) find that firms
alleged by the SEC to have committed GAAP violations via earnings manipulation also engaged
in earnings management within GAAP in the form of accruals management. These results
suggest a relationship between accrual quality and accounting fraud.
First, we employ working capital accruals (WC accruals) as a proxy for an accruals variable.
WC accruals reflect short-term accruals relating to working capital (Teoh et al. 1998; Allen et al.
2013). Next, we use RSST accruals as an extended definition of WC accruals to include changes inlong-term operating assets and long-term operating liabilities (Richardson et al. 2005). We also
examine two accruals components, Change in receivables and Change in inventory. These variables
are expected to have higher flexibility for management among accruals-related accounts(Richardson et al. 2005), and greater variable values are likely to reflect a higher probability ofaccounting fraud.
We also examine %Soft assets, defined as the percentage of assets on the balance sheet that areneither cash nor P&E (Dechow et al. 2011). Barton and Simko (2002) show that firms withgreater net operating assets have more accounting flexibility with which to manage earnings. We
assume that %Soft assets has a positive relationship with the probability of accounting fraud since a
firm with higher %Soft assets has greater accounting flexibility with which to meet short-term
earnings goals.
Finally, we examine discretionary accruals on the basis of three estimation models: the8

The definition of the variables used in this study basically follows that of Dechow et al. (2011) to ensurecomparability between the two studies.
9
Although the variables used in this study basically follow those used in Dechow et al. (2011), we do not use thefollowing variables: (1) earnings quality, as in Dechow and Dichev (2002), (2) differed tax expense, and (3) pension
plan assets. Variables (2) and (3) were excluded because our database does not contain these data. Variable (1) requires
information in the next year (t+1) in order to measure the earnings quality in the current year (t) and therefore cannot
be used, since we are building a prediction model. Dechow et al. (2011) also excluded this variable for the same
reason.

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

25

TABLE 2 VARIABLE DEFINITIONS
Variable

Pred sign

Misstate
Accruals quality related variables
WC accruals+
RSST accruals+

Change in receivables
Change in inventory% Soft assets
MJ discretionary accruals
MJ discretionary accruals
SD
MJ discretionary accruals
AB
PM discretionary accruals
PM discretionary accruals
SD
PM discretionary accruals
AB
CFO discretionary
accruals
CFO discretionary
accruals SD
CFO discretionary
accruals AB
Performance variables
Change in cash sales

+
+
+
+
+
+
+
+
+
+
+
+

-

Change in cash margin
Change in free cash flows
Nonfinancial variables
Abnormalchangein
employees
Abnormal change in orderbacklog-Calculation
Indicator variable equal to 1 for accounting fraud firm-years and zerootherwise(( ΔCurrent Assets - ΔCash and Short-term Investments ) ( ΔCurrent Liabilities-ΔDebt in Current Liabilities - ΔTaxes Payable ))/ Average total assets
RSST = ( ΔWC + ΔNCO + ΔFIN ) / Average total assets
WC = ( Current Assets - Cash and Short-term Investments ) ( Current Liabilities - Debt in Current Liabilities )，NCO =(Total assets - Current Assets – Investments and Advances ) ( Total Liabilities – Current Liabilities – Long-term Debt )，FIN = ( Short-term Investments + Long-term Investments ) ( Long-term Debt + Debt in Current Liabilities )ΔAccounts Receivable / Average total assets
ΔInventory / Average total assets( Total Assets - PP&E - Cash and Short-term Investments ) / Total
Assets × 100
Discretionary accruals following Dechow et al. (1995)
For more information on the estimation methods, see Appendix A
The standard deviation of the past five years of MJ discretionary
accruals
The absolute value of the MJ discretionary accruals
Discretionary accruals following Kothari et al. (2005)
For more information on the estimation methods, see Appendix A
The standard deviation of the past five years of PM discretionary
accruals
The absolute value of the PM discretionary accruals
Discretionary accruals following Kasznik (1999)
For more information on the estimation methods, see Appendix A
The standard deviation of the past five years of CFO discretionary
accruals
The absolute value of the CFO discretionary accruals

Change in cash sales = ( cash salest / cash salest-1 -1 ) × 100
cash sales = Sales – ΔAccounts Receivable
Change in cash margin = ( cash margint / cash margint-1 -1 ) × 100
cash margin = 1 - ( Cost of Good Sold – ΔInventory + ΔAccounts
Payable ) / ( Sales – ΔAccounts Receivable )Change in free cash flows = (free cash flowst - free cash flowst-1) /Average total assets
free cash flows = Earnings - RSST
( Number of employeest / Number of employeest-1 - 1 ) × 100 - ( Total
Assetst / Total Assetst-1 - 1 ) × 100( Order backlogt / Order backlogt-1 - 1 ) × 100 - ( salest / salest-1 -1 ) ×
100

26

The Japanese Accounting Review, 6 (2016), 17-63

TABLE 2 (CONTINUED)
Variable
Off-balance-sheet variables
Existence of operating leases
Change in operating leaseactivity
Market-related incentives
Ex ante financing need
Actual issuancePred sign
+
+

+
+

CFF
+
Leverage+Market adjusted stock-return+
Lagged market adjusted+
stock-returnBook-to-market
Earnings-to-price
Accounting conservatism variables
C score RR+
C score RRSD
C score AR+++
C score ARSD
Real activities manipulation variables
AB cash flow
Discretionary expense
AB product cost+

Calculation
Indicator variable equal to one when future operating lease obligations
are greater than zero, and zero otherwise.
(( future operating lease obligationst / Average total assetst ) / ( future
operating lease obligationst-1 / Average total assetst-1) - 1 ) × 100
Indicator variable equal to one when (( CFO - past three years average
capital expenditures ) / ( Current Assets )) < -0.5, zero otherwise.
Indicator variable equal one when ( issuance of stock ) > 0 or
( commercial paper + issuance of bond ) > 0, zero otherwise.
Financing activities net cash flows / Average total assets
Long-term Debt / Total assets( Returnt - Returnt-1) / Returnt-1 - (TOPIXt - TOPIXt-1）/ TOPIX t-1
Market adjusted stock-return in the previous year
Equity / Market Value
Earnings / Market Value
Cscore following Khan and watts(2009), estimated by use of the raw
return. See Appendix B for details of estimation method,
The standard deviation of the past five years C score RR
Cscore following Khan and watts(2009), by used of the abnormal
return. See Appendix B for details of estimation method.
The standard deviation of C score AR in the past five years
Abnormal cash flows following Roychowdhury(2006)
See Appendix C for details of estimation method.
Abnormal Discretionary expense following Roychowdhury(2006)
See Appendix C for details of estimation method.
Abnormal product cost following Roychowdhury(2006)
See Appendix C for details of estimation method.

Japanese specific factors-related variables
FIN

-

CORP

+

Number of shares held by financial institutions / Number of shares
outstanding × 100
Number of shares held by the other corporation / Number of shares
outstanding × 100

Note: Predicted sign shows the expected sign of the relationship between the occurrence ofaccounting fraud and each independent variable.

modified Jones model (MJ discretionary accruals) of Dechow et al. (1995); theperformance-matched discretionary accruals model (PM discretionary accruals) of Kothari et al.
(1999); and the CFO modified Jones model (CFO discretionary accruals) of Kasznik (1999). We
estimate the parameters of each model using cross-sectional regression by industry-year. Details
on the estimation methods for the models are elaborated in Appendix A. Earnings management
research generally assumes that, while positive discretionary accruals imply income-increasing
earnings management, negative discretionary accruals imply income-decreasing earnings

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan27

management.
We also examine the standard deviation of the discretionary accruals (discretionary accrualsSD) and the absolute value of the discretionary accruals (discretionary accruals AB). Managers arelikely to use a large amount of discretionary earnings when they have a strong incentive to
conduct aggressive earnings management. Such earnings management causes a reversal of accruals
in later years and amplifies the standard deviation of discretionary accruals and earnings.
Therefore, we examine the absolute values and their standard deviation over the last five years inorder to capture the effect of such accruals reversals.10 We expect that the variables related todiscretionary accruals have a positive relationship with the probability of accounting fraud.
(2) Performance variables
We focus on a set of variables concerning a firm’s financial performance to examine whether
managers engage in accounting fraud to mask their deteriorating performance (Dechow et al.
1996; Dechow et al. 2011; Beneish 1997, 1999b). The first variable is change in cash sales (Change
in cash sales) and change in cash margin (Change in cash margin). Change in cash sales is defined as
sales amount after accruals-based sales are excluded. Change in cash margin is equal to cash sales
less cash cost of goods sold; the influence of accruals such as receivables and inventory are
excluded from this variable. We assume that, when these performance variables decline, managers
are more likely to inflate earnings by boosting accruals.
A change in ROA (Change in return on assets) is an important variable for managers who areconscious about growth of earnings (Graham et al 2005). Therefore, increased ROA during an
accounting fraud period might be caused by a manager’s earnings management. Change in free cash
flows is a more fundamental measure than earnings because this variable is not affected by
accruals. We posit that managers have an incentive to increase earnings when free cash flow isdecreasing.
(3) Nonfinancial variables
We examine two nonfinancial measures. First, we use number of employees because
managers attempting to mask deteriorating financial performance will reduce employees in order
to boost earnings (Brazel et al. 2009). We assume that the change in the number of employees(Abnormal change in employees) has a negative relationship with the probability of accounting
fraud.
The second variable is the order backlog (Abnormal change in order backlog). Decreasing theorder backlog is indicative of lower future sales and earnings (Rajgopal et al. 2003). Therefore, we
assume that managers facing a decreasing backlog have an incentive to engage in accounting
fraud.
(4) Off-balance-sheet variables

10

In particular, our assumption is as follows. Panel C in Table 3 indicates that fraud firms tend to conduct accounting
fraud over multiple years. Because firms are not able to continue to increase earnings by managing accruals, fraud
firms are likely to report large negative discretionary accruals because of accruals reversal during the fraud period.
Further, firm managers might conduct illegal accounting fraud after implementing income-increasing earnings
management within GAAP, resulting in large negative discretionary accruals in fraud years. This suggests that thedispersion of discretionary accruals among fraud firms will be greater around the fraud year.

28

The Japanese Accounting Review, 6 (2016), 17-63

The most important off-balance-sheet financing variable is operating lease (Dechow et al.
2011). Accounting for operating leases allows firms to record lower expenses in the early period of
the lease term. Thus, managers who are excessively conscious of window-dressing are more likely
to increase operating lease activity. Further, we expect that the use of an operating lease increases
during accounting fraud. Consequently, we examine two variables: the use of the operating lease(Existence of operating lease) and its change (Change in operating lease activity).
(5) Market-related incentive variables
One of the major incentives for earnings management is maintaining a high stock price(Dechow et al. 2011). We investigate two motivations relating to stock price (Dechow et al. 2011).
The first motivation is to keep a high stock price in order to reduce the cost of raising new equity.
We use four variables to capture this kind of motivation. First, we use an indicator variable
identifying whether the firm has issued new equity or debt (Actual issuance). The second variable
is the net amount of new financing raised (CFF). The third variable is a need of ex ante financing(Ex ante financing need), as some firms may have wished to raise new capital but could not
because they were unable to secure favorable terms. The fourth variable is financing leverage(Leverage). Firms with higher leverage will have incentives to boost financial performance to bothsatisfy financial covenants in existing debt contracts and raise new debt on more favorable terms.
The second managerial motivation to maintain a high stock price is managementcompensation tied to stock price performance. Managers might also be forced to retire whenstock prices decrease. Because managers are conscious of stock-based performance, they are likely
to have an incentive to increase earnings. Here, we examine three variables: market adjusted stock
return (Market adjusted stock-return), book-to-market (Book-to-market), and PER(Earnings-to-price). Thus, we expect that while Market adjusted stock-return has positive sign,
Book-to-market and Earnings-to-price have negative signs.
(6) Accounting conservatism variables
The variables described so far basically follow the variables employed by Dechow et al.
(2011). Although Dechow et al. (2011) focused on variables relating to accrual qualities as proxies
for earnings quality, we also focus on other quality measures: “conservatism” and “real activities
manipulation.” Conservatism reflects accountants’ tendency to require a higher degree ofverification to recognize good news than to recognize bad news in financial statements (Basu1997). Higher conservatism is thus identified when the degree of verification needed to recognize
good news (positive earnings) is higher than what is needed to recognize bad news (negative
earnings).
Watts (2003) argues that adopting accounting conservatism, which tends to produce
conservatively measured earnings and net assets, could facilitate contract efficiency and provide
reliable information to the securities market. Thus, we expect that a higher degree of conservatism
improves earnings quality and restricts occurrences of accounting fraud.
We employ the method of Khan and Watts (2009) to measure the degree of conservatism.
Their model is an extended version of Basu (1997) with cross-sectional regression, allowing it tomeasure conservatism for an individual firm. In the model, stock price returns are used as a proxyfor economic loss. We measure two types of conservatism on the basis of raw stock return (C score
RR) and market-adjusted stock return (C score AR). Similar to the research method fordiscretionary accruals, we also examine the standard deviation (C score RRSD) and absolute value

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

29(C score ARSD) of these variables. Details on the calculation of conservatism are provided inAppendix B.
(7) Real activities manipulation variables
Earnings management is divided into two types: accrual-based earnings management andreal earnings management. Accrual-based earnings management constitutes the discretionary
behaviors that occur through the discretion of recognition and estimates in accrual accounting,such as fictional accounting adjustments without a change in cash flow. We have already discussed
the variables of accrual-based earnings management in subsection (1).
Real earnings management constitutes discretionary behaviors performed to manage
earnings by altering a firm’s real economic activities, such as a reduction in R&D activities. We
employ methods proposed by Roychowdhury (2006) to comprehensively capture a manager’s realearnings management.
Roychowdhury (2006) classifies a firm’s real earnings management into three categories: 1)
sales manipulation, 2) overproduction, and 3) reduction of discretionary expenditures. The firstmanipulation increases annual sales temporarily by discounting or altering sales term, causing anabnormally low cash flow over sales amount. The second manipulation boosts earnings byreducing total cost of production through excessive overproduction. In the third manipulation,
managers discretionally adjust a certain amount of accrued costs (such as for R&D or advertising),
resulting in abnormally high discretionary expenditures over sales amounts.
To capture these influences of real earnings management, we examine an abnormal operating
cash flow (AB cash flow), abnormal discretionary expense (Discretionary expense), and abnormal
product cost (AB product cost). Details on these variables are provided in Appendix C.11
(8) Japanese-specific factors-related variables
We examine Japanese-specific factors related to occurrences of accounting fraud. Xu andZhang (2009) developed a bankruptcy prediction model for Japanese firms and found that themodel’s performance improved when they added shareholdings by financial institutions such as
main banks and a cross-shareholding among business corporations. We thus expect that thesefactors are useful for detecting accounting fraud since anecdotal evidence suggests that firms
facing bankruptcy tend to commit it.
Shuto (2010) also finds that, while firms with higher ownership by financial institutions are
not likely to conduct earnings management to avoid earnings reductions, firms with higher
cross-shareholding tend to engage in earnings management to attain short-earnings targets.
These results suggest that shareholdings by financial institutions restrict managers’ opportunistic
behaviors through shareholders’ strict monitoring and that cross-shareholdings do not function as
a mutual monitoring system and do not prevent earnings management.
Thus, we analyze ownership by financial institutions (FIN) and cross-shareholdings (CORP)
as Japanese-specific factors. We assume that FIN (CORP) has a negative (positive) relationshipwith the probability of accounting fraud.

11

Our real earnings management variables are calculated using un-restated (i.e., manipulated) data, as described infootnote 10. Thus, we cannot deny the possibility that the variables might not reflect the firm’s real economic
activity.

30

The Japanese Accounting Review, 6 (2016), 17-63

4. Sample selection and statistics
4.1 Sample selection
We obtained our initial samples from all the listed companies on the Japanese stock market
for the period from 2000 to 2014, and eliminated the following kinds of firms:(1) financial services companies,(2) companies adopting an accounting standard other than J-GAAP,(3) companies without sufficient data to compute the F-Score.
The sample selection procedure leaves us with 65,440 firm-year observations. As shown in
Table 1, the SESC firms (i.e., Misstate1) provide 241 observations for 83 firms. The TDR firms,
who announced possible improper disclosures on their financial statements, provide 620
observations for 89 firms. As described in section 3, Misstate2 includes 861 observations for 172
firms.
The data used in this analysis are obtained from the Nikkei NEEDS financial statements
database and the Nikkei NEEDS market database. Special treatment is needed when analyzing
the financial statements of fraud firms. The financial statements included in the database wereimmediately overwritten retroactively once restatement data became available from the firm.
Since the purpose of this research is to build a prediction model, these overwritten data are notsuitable for our use. We thus restored all the restated data to the original statements.12 We use
these restored datasets for all analyses of the fraud firms.

4.2 Features of the fraud samples
As a preliminary analysis, we first observe the features of the fraud firms. Table 3 summarizes
the Misstate1 sample. Panel A shows the distribution of samples sorted by year. Most fraud
samples occur between 2003 and 2012, around the middle of our sample period. As one of thereasons for this biased distribution, we might point out that no SESC investigations took place
before 2000 since the framework of the SESC’s administrative monetary penalty was establishedin 2005. Moreover, it is possible that there are fewer samples after 2013 because few SESC
investigations have occurred recently; the number of fraud firms may increase in the coming
years.
Panel B indicates the distribution sorted by industry. The panel shows that there are more
fraud firms in Service, Trading, Electronics, and Construction. We also find that the percentage offraud firms is high in Warehouse, Power generation, and Precision machinery. Panel C presents thenumber of restatements caused by fraud statements. A total of 32.53% of fraud firms haverestated their financial statements once, and 67.47% restated them more than twice. This result
suggests that most of the fraud firms have restated their financial statements.
Table 4 summarizes the contents of the misstatements—thus, the methods of accounting
fraud. The table shows that the overstatement of assets is the most popular kind, including
accounting receivables, inventory, and work-in-process. The magnitude of the misstatement
relating to these assets is 47.15% of total revenue on average, and the number of misstatements

12

We first collected the original financial statements as pre-restated statements, and restored all restated figures to theoriginal ones in the database. By contrast, Dechow et al. (2011) do not restore their database (Compustat) even for
a company with an amended 10-K because they find via a random sampling test that these misstatement companies
are less likely to file amended financial statements.

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

31

TABLE 3 FREQUENCY OF POPULATION BY CALENDAR YEAR, INDUSTRY, AND ACCOUNTINGFRAUD FIRM-YEAR
Panel A: Frequency of the population by calendar year
Percentage(Total firm-years)

Percentage(Accounting fraud
firm-years)

4,604
4,719
4,789
4,800
4,716
4,688
4,648
4,602
4,345
4,204
4,085
3,960
3,841
3,760
3,679
65,440

7.04%
7.21%
7.32%
7.33%
7.21%
7.16%
7.10%
7.03%
6.64%
6.42%
6.24%
6.05%
5.87%
5.75%
5.62%
100.00%

0.02%
0.04%
0.13%
0.27%
0.40%
0.64%
0.77%
0.63%
0.69%
0.59%
0.42%
0.28%
0.36%
0.16%
0.05%
0.37%

Non-accounting
fraud firm-years

Total
firm-years

Percentage(Totalfirm-years)

Percentage(Accounting
fraud firm-years)

2,485
994
473
3,445
1,016
191
345
1,184
886
2,327
4,146
4,813
100
1,371

2,492
998
473
3,445
1,016
191
345
1,184
886
2,329
4,159
4,837
100
1,379

year

Accounting fraud
firm-years

Non-accounting
fraud firm-years

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Total

1
2
6
13
19
30
36
29
30
25
17
11
14
6
2
241

4,603
4,717
4,783
4,787
4,697
4,658
4,612
4,573
4,315
4,179
4,068
3,949
3,827
3,754
3,677
65,199Total
firm-years

Panel B: Frequency of the population by industry
Industry
Foods
Textiles and Apparels
Pulp and Paper
Chemicals
Pharmaceutical
Oil
Rubber Products
Ceramics
Iron and Steel
Nonferrous Metals
Machinery
Electronics
Shipbuilding
Automotive

Accounting
fraud
firm-years
7(3)
4(1)

2(1)
13(5)
24(6)
8(1)

3.81%
1.53%
0.72%
5.26%
1.55%
0.29%
0.53%
1.81%
1.35%
3.56%
6.36%
7.39%
0.15%
2.11%

0.28%
0.40%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.09%
0.31%
0.50%
0.00%
0.58%
(Continued)

32

The Japanese Accounting Review, 6 (2016), 17-63(-Continued)

Transportation
Equipment
Precision Machinery
Other Products
Fishery
MiningConstruction
Trading
RetailReal Estate
Rail and bus
Land Transportation
Marine Transportation
Air Transportation
Warehouse
Telecommunications
Power Generation
Gas
Services
Total

8(2)

24(7)
29(12)
14(5)
4(3)

10(2)
8(1)
4(1)
82(33)
241(83)

237

237

0.36%

0.00%

947
2,069
160
182
3,404
6,807
4,716
2,960
1,194
639
379
157
806
1,313
203
314
14,936
65,199

955
2,069
160
182
3,428
6,836
4,730
2,964
1,194
639
379
157
816
1,321
207
314
15,01865,440

1.46%
3.16%
0.24%
0.28%
5.24%
10.45%
7.23%
4.53%
1.82%
0.98%
0.58%
0.24%
1.25%
2.02%
0.32%
0.48%
22.95%
100.00%

0.84%
0.00%
0.00%
0.00%
0.70%
0.42%
0.30%
0.13%
0.00%
0.00%
0.00%
0.00%
1.23%
0.61%
1.93%
0.00%
0.55%
0.37%

Panel C: Frequency of accounting fraud firm-years
accounting fraud firm-years
1
2
3
4
5
6
8
Total

Number of firms

Percentage of firms
27
15
9
11
17
2
2
83

32.53%
18.07%
10.84%
13.25%
20.48%
2.41%
2.41%
100.00%Note: Brackets in the "Accounting fraud firm-years" in panel B represent number of firms.

and their amounts are also large. Fictional and inflated sales also seem to be popular methods.

67

3

2

1

14

2

1

2

7

2

164

6

9

3

9

18

53

15

6

45

Total

197.59%7.23%

10.84%

3.61%

10.84%

21.69%

63.86%

18.07%

7.23%

54.22%

Percentage(Total / 83)

120.53%82.05%

1108.16%

15.32%

36.14%

9.44%

47.15%

51.90%

6.68%

123.15%

Average percentage(max(modified net
income or modified
sales) / sales)

Note: ”Method-1” represents main method of accounting fraud. ”Method-2” and ”Medhod-3”
represent the reasons for cases of multiple techniques. Therefore, the total percentage exceeds
100%.

83

3

Others (Consolidated Remove, etc.)

Total

5

Trading
impersonation,transactions,fictitious capital increase

circulation

2

Embezzlement, misappropriation

4

6

10
4

39

7

Misstated stock

4

2

6

9

4

Misstated Cost of Goods Sold

Misstated expense or loss (impairment loss,
goodwill, etc.)
Misstated assets (Accounts receivable,
work-in-process, inventory, fixed assets, etc.)
Misstated liabilities (Bad debt allowance,
short-term Debt, etc.)

39

Method-1 Method-2 Method-3

Misstated sales

Type of accounting fraud

TABLE 4 TYPES OF ACCOUNTING FRAUD

18.38%

45.54%

2.64%

8.79%

35.30%

12.94%

42.92%

14.26%

9.86%

16.53%

Average percentage(max(modified net
income or modified
sales) / total assets)Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan33

34

The Japanese Accounting Review, 6 (2016), 17-63

5. Results
5.1 Univariate analysis(1) Analysis of Misstate1
We describe the statistical features of the accounting fraud firms by comparing samples
between the 241 fraud observations identified as Misstate1 and the 65,199 non-fraud
observations. Table 5 summarizes the results of the univariate analyses, providing the mean andmedian for fraud and non-fraud firms, as well as the results of a t-test between them. The
shadows in the table indicate the significant mean differences with the expected sign.
The table shows that many variables of accruals quality show significant differences between
fraud and non-fraud firms. First, soft asset ratio (% Soft assets) is strongly significant, which
implies that firms with higher soft asset ratios have more flexibility in earnings manipulation.
Second, the values of almost all the discretionary accruals variables of the fraud firms are higher
than are those of the non-fraud firms, as expected. This indicates that the discretionary accruals
are useful for detecting accounting fraud. Furthermore, their derivatives, such as the standard
deviation of discretionary accruals (discretionary accruals SD) and their absolute value (discretionary
accruals AB) indicate more significant differences on the basis of t-statistics. This result suggeststhat a consideration of accrual reversal may improve accounting fraud prediction.
None of the variables of performance, nonfinancial, or off-balance-sheet is significant. The
non-significance of the performance variables is consistent with the result in Dechow et al. (2011).
On the other hand, most of variables of market show significant differences. We find that thebook-to-market (Book-to-market) has strong significant differences, with a t-value of 25.3, thehighest value among the results for all the variables. The firms with higher financial needs (Actualissuance), higher leverage company (Leverage), and lower PER (Earnings-to-price) are more likely
to conduct accounting fraud.
We also find that all variables of accounting conservatism have significant differences. The
variables calculated by using the market adjusted return (C score AR, and C score ARSD) show
more significant results than do those calculated by using the raw return. The results suggest thatthe firms with higher conservatism are less likely to be accounting fraud firms.
The table reveals that the abnormal operating cash flow (AB cash flow) and the abnormal
production cost (AB product cost) among the variables of real activities manipulation are statisticallysignificant, with the expected sign, indicating that firms involved in real earnings management suchas the manipulation of sales or production costs tend to engage in accounting fraud.
The ratio of business corporation ownership (CORP) of the Japanese-specific factors has
significant differences, although ownership by financial institution (FIN) has no significant results.
Cross-shareholdings among business companies may reduce the mutual monitoring function ofshareholders and increase the opportunity for accounting fraud.
Finally, Table 6 presents the results of Wilcoxon rank sum tests as a non-parametric measure.
The results are similar to those of the t-test reported in Table 5, except for a few variables of
performance. Some variables show significant differences between fraud and non-fraud firms.13
13

In addition to the main analyses, Dechow et al. (2011) conducted other comparative analyses: (1) between the fraud
year and the other years for fraud companies and (2) between the fraud year and the previous years for fraudcompanies. Although we conduct the same analyses, we omit the results because of space constraints. The results are
generally consistent with those of the main analyses. However, for (1), we observed a declining tendency in the
explanatory power of the accruals variables.

Accruals quality related variables
WC accruals
RSST accruals
Change in receivables
Change in inventory% Soft assets
MJ discretionary accruals
MJ discretionary accruals SD
MJ discretionary accruals AB
PM discretionary accruals
PM discretionary accruals SD
PM discretionary accruals AB
CFO discretionary accruals
CFO discretionary accruals SD
CFO discretionary accruals AB
Performance variables
Change in cash sales
Change in cash margin
Change in return on assets
Change in free cash flows
Nonfinancial variables
Abnormal change in employees
Abnormal change in order backlog
Off-balance-sheet variables
Existence of operating leases
Change in operating lease activity

Variable
N

0.180
0.016
0.010
0.006
74.261
0.041
0.137
0.173
0.063
0.172
0.199
0.013
0.052
0.063
8.069
0.172
-0.0230.004-5.20422.6840.259
27.460

239
239
239
239
241
239
193
239
239
193
239
238
153
238
238
238
238
238
239
107
239
56

Mean

0.000
-17.200-1.050-1.731

0.724-13.650-0.006
0.001

0.167
0.017
0.000
0.000
75.535
0.021
0.084
0.117
0.027
0.080
0.120
0.006
0.046
0.048

MedianAccounting fraud firm-years

63,80918,63363,376
21,651

63,071
63,05563,09863,09864,19164,19164,19164,191
65,19964,191
43,21564,19164,191
43,21564,19157,091
36,65457,091N

0.325
66.390-1.188
6.677

5.151-3.219
0.000
0.005

0.146
0.014
0.006
0.003
65.173
0.014
0.113
0.141
0.001
0.094
0.124
0.001
0.034
0.033

Mean

0.000
-12.936-0.695-1.1412.135-1.406
0.001
0.003

0.140
0.012
0.001
0.000
67.025-0.0070.045
0.081-0.008
0.044
0.079
0.001
0.025
0.022

MedianNon-accounting fraud firm-years

+
+

-

+
-

+
+
+
+
+
+
+
+
+
+
+
+
+
+

Predicted
sign-0.066-38.930-4.01616.0072.918
3.391-0.024-0.001

0.034
0.003
0.004
0.002
9.088
0.027
0.024
0.032
0.062
0.077
0.075
0.012
0.018
0.029

2.309
1.627

1.615
-1.104-1.224-0.1542.458
0.135-2.689-0.244-0.587-0.431-10.873-1.608-1.891-2.800-2.903-3.939-4.007-1.788-6.324-8.384(Continued)

0.022
0.109

0.108
0.272

0.222
0.878
0.015
0.893

0.008
0.808
0.558
0.667
<.0001
0.109
0.060
0.006
0.004
<.0001<.00010.075<.0001<.0001

Accounting fraud – Non-Acc fraud
Diff.
t-statistics
p-valuein mean

TABLE 5 DESCRIPTIVE STATISTICS ON ACCOUNTING FRAUD FIRM-YEARS VERSUS
NON-ACCOUNTING FRAUD FIRM-YEARS: T-TEST WITH MISSTATE1

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan
35

Variable
N

0.124
0.345
0.270
0.354
-0.060
0.013
0.051
1.091
7.931

233
149
233
149
239
237
238
225
236

0.064
0.197

-0.039-0.007
0.059

0.022
0.276
0.236
0.183

0.000
1.000
0.020
0.103
-0.068-0.0900.000
0.007

Median56,54559,39861,96563,92863,09851,252
33,67851,252
33,67851,451
62,35561,966
65,19949,677
48,32751,252
51,252

N

0.427
2.417

0.001
0.000
0.000

0.020
0.187
0.099
0.135

0.374
0.256
-0.0010.105-0.009-0.0171.245-0.007

Mean

0.035
0.058

0.001
-0.0140.019-0.031
0.121
0.072
0.089

0.000
0.000
-0.0140.060-0.009-0.0181.061
0.046

MedianNon-accounting fraud firm-years

+

+

+
+
+
+

+
+
+
+
+
+
-

Predicted
sign

0.664
5.515

-0.061
0.013
0.052

0.104
0.158
0.172
0.220

-0.106
0.302
0.088
0.017
-0.074-0.078-1.052-0.185-4.811-7.002

6.189-1.350-3.022-5.633-3.591-10.700-5.141

3.599-9.395-7.232-2.272
1.799
1.904
25.3327.106<.0001<.0001<.00010.178
0.003<.0001<.0001<.0001<.0001<.0001<.0001<.0001
0.024
0.073
0.058
<.0001<.0001

Accounting fraud – Non-Acc fraud
Diff.
t-statistics
p-valuein meanNote: All variables are defined in Table 2. Shaded variables are those with p-values less than 10%
and with the correct sign.

0.268
0.558
0.087
0.122
-0.082-0.0950.193-0.191

228
240
238
241
221
206
233
233

Mean

Accounting fraud firm-years

Market-related incentives
Ex ante financing need
Actual issuance
CFF
LeverageMarket adjusted stock-return
Lagged market adjusted stock-returnBook-to-market
Earnings-to-price
Accounting conservatism variables
C score RR
C score RRSD
C score AR
C score ARSD
Real activities manipulation variables
AB cash flow
Discretionary expense
AB product cost
Japanese specific factors-related variables
FIN
CORP

(-Continued)

36
The Japanese Accounting Review, 6 (2016), 17-63

Accruals quality related variables
WC accruals
RSST accruals
Change in receivables
Change in inventory% Soft assets
MJ discretionary accruals
MJ discretionary accruals SD
MJ discretionary accruals AB
PM discretionary accruals
PM discretionary accruals SD
PM discretionary accruals AB
CFO discretionary accruals
CFO discretionary accruals SD
CFO discretionary accruals AB
Performance variables
Change in cash sales
Change in cash margin
Change in return on assets
Change in free cash flows
Nonfinancial variables
Abnormal change in employees
Abnormal change in order backlog
Off-balance-sheet variables
Existence of operating leases
Change in operating lease activity

Variable

0.180
0.016
0.010
0.006
74.261
0.041
0.137
0.173
0.063
0.172
0.199
0.013
0.052
0.063
8.069
0.172
-0.0230.004-5.20422.6840.259
27.460

239
239
239
239
241
239
193
239
239
193
239
238
153
238
238
238
238
238
239
107
239
56

0.000
-17.200-1.050-1.731

0.724-13.650-0.006
0.001

0.167
0.017
0.000
0.000
75.535
0.021
0.084
0.117
0.027
0.080
0.120
0.006
0.046
0.048

Accounting fraud firm-years
N
Mean
Median63,80918,63363,376
21,651

63,071
63,05563,09863,09864,19164,19164,19164,191
65,19964,191
43,21564,19164,191
43,21564,19157,091
36,65457,091

0.325
66.390-1.188
6.677

5.151-3.219
0.000
0.005

0.146
0.014
0.006
0.003
65.173
0.014
0.113
0.141
0.001
0.094
0.124
0.001
0.034
0.033

0.000
-12.936-0.695-1.1412.135-1.406
0.001
0.003

0.140
0.012
0.001
0.000
67.025-0.0070.045
0.081-0.008
0.044
0.079
0.001
0.025
0.022

Non-accounting fraud firm-years
N
Mean
Median

+
+

-

+
-

+
+
+
+
+
+
+
+
+
+
+
+
+
+

Predicted
sign-2.166-0.4390.174-0.423-1.737-2.250-4.168-1.5272.523
0.593-1.242-1.950
9.427
2.292
7.768
5.410
3.541
8.508
5.875
1.894
6.991
9.368

（Continued)

0.015
0.330

0.431
0.336

0.041
0.012
<.0001
0.063

0.006
0.276
0.107
0.026
<.00010.011<.0001<.0001<.0001<.0001<.00010.029<.0001<.0001

Wilcoxon rank sum test
z-statistics
p-value

TABLE 6 DESCRIPTIVE STATISTICS ON ACCOUNTING FRAUD FIRM-YEARS VERSUS NON-ACCOUNTINGFRAUD FIRM-YEARS: WILCOXON RANK SUM TEST WITH MISSTATE1

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan37

Variable

0.022
0.276
0.236
0.183
-0.039-0.007
0.059
0.064
0.197

0.124
0.345
0.270
0.354
-0.060
0.013
0.051
1.091
7.931

56,54559,39861,96563,92863,09851,252
33,67851,252
33,67851,451
62,35561,966
65,19949,677
48,32751,252
51,252

0.427
2.417

0.001
0.000
0.000

0.020
0.187
0.099
0.135

0.374
0.256
-0.0010.105-0.009-0.0171.245-0.007

0.035
0.058

0.001
-0.0140.019-0.031
0.121
0.072
0.089

0.000
0.000
-0.0140.060-0.009-0.0181.061
0.046

Non-accounting fraud firm-years
N
Mean
Median

+

+

+
+
+
+

+
+
+
+
+
+
-

Predicted
sign

Note: All variables are defined in Table 2. Shaded variables are those with p-values less than 10%
and with the correct sign.

0.000
1.000
0.020
0.103
-0.068-0.090
0.000
0.007

0.268
0.558
0.087
0.122
-0.082-0.0950.193-0.191

Accounting fraud firm-years
N
Mean
MedianMarket-related incentives
Ex ante financing need
228
Actual issuance
240
238
CFF
241
LeverageMarket adjusted stock-return
221
206
Lagged market adjusted stock-return233
Book-to-market
Earnings-to-price233
Accounting conservatism variables
C score RR
233
C score RRSD
149
C score AR
233
149
C score ARSD
Real activities manipulation variables
AB cash flow
239
Discretionary expense
237
AB product cost
238
Japanese specific factors-related variables
FIN
225
CORP
236

(-Continued)

5.178
5.770

-9.300
1.486
3.465

5.851
9.910
12.231
11.947-3.303
10.6917.937
3.841-2.935-2.345-22.454-10.156<.0001<.0001<.00010.069<.0001<.0001<.0001<.0001<.0001<.0001<.0001<.0001<.00010.002
0.010<.0001<.0001

Wilcoxon rank sum test
z-statistics
p-value38
The Japanese Accounting Review, 6 (2016), 17-63

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan39(2) Analysis of Misstate 2
Table 7 shows the results for Misstate2. The results are generally consistent with the resultsfor Misstate 1 seen in Table 5, except that the variable of operating lease (Existence of operatingleases) is significant with the expected sign, whereas leverage (Leverage) is not. The results of a
Wilcoxon rank sum test for Misstate2 are summarized in Table 8. The results are also similar to
those of Table 6, except that the variables of off-balance-sheet are more significant.
Table 9 shows the AR (accuracy ratio) of all variables for Misstate1 and Misstate2. The AR
is a popular indicator, especially for corporate failure prediction models. In this performanceindicator, AR reaches 1 when the model’s prediction performance becomes perfect.14 The results
show that 1) the variables with higher AR values are common to both Misstate1 and Missate2,
and 2) the AR values for Misstate1 are higher than are those for Misstate2. This suggests that thediscrimination performance of accounting fraud deteriorated for Misstate2; we infer that thisoccurred because Misstate2 contains more indefinite samples with less significant incidents (i.e., asmall amount of fraud).

5.2 Parameter estimation of fraud detection model(1) Method of estimation
In this section, we develop a prediction model to identify accounting fraud on the basis of theresults of the univariate analysis in the previous section. We present the model based on Misstate1
as the main result since, in univariate analysis, the explanatory powers (the AR value) of thevariables of Misstate 1 are generally higher than are those of Misstate 2. The results based on
Misstate 2 to Misstate 8 are presented as additional analyses.
We performed variable selections and parameter estimations for each model following theprocedures of Dechow et al. (2011). Specifically, (1) candidate variables for use in the prediction
model are selected among those in Table 5 or Table 6 with a significance level of 10% or higher
with the correct expected sign; (2) a dependent variable is equal to 1 for firm-years involving
Misstate1 and 0 otherwise; and (3) a logistic regression is employed to determine a battery ofvariables and their parameters. Our logistic regression is based on a backward method with arestriction of the correct parameter sign by eliminating a variable step-by-step from the highest
p-value.15
Dechow et al. (2011) build three models with different sets of parameters. Model 1 includes
only financial-statement variables as predictors. Model 2 adds nonfinancial and off-balance-sheet
variables to Model 1, and Model 3 adds market-based measures to Model 2 (aside from thevariables used in Model 1).
Our research follows the procedures of Dechow et al. (2001) but extend it in the following
ways. First, we employ variables relating to discretionary accruals as significant variables, which
Dechow et al. (2011) do not use. Although the level and absolute value of discretionary accruals
have significant differences in the univariate analysis, both variables should be used in theestimation procedure. However, we use only the absolute value of discretionary accruals in the1415

Please see Appendix D for details on the construct of AR and its method of calculation.
Other than the abovementioned method of backward elimination with the restriction of the correct parameter sign,
we employed other methods of variable selection, such as standard backward elimination, forward elimination, andstepwise elimination. The backward elimination with sign restriction provided the best results in terms of correct
signs and higher AR.

Accruals quality related variables
WC accruals
RSST accruals
Change in receivables
Change in inventory% Soft assets
MJ discretionary accruals
MJ discretionary accruals SD
MJ discretionary accruals AB
PM discretionary accruals
PM discretionary accruals SD
PM discretionary accruals AB
CFO discretionary accruals
CFO discretionary accruals SD
CFO discretionary accruals AB
Performance variables
Change in cash sales
Change in cash margin
Change in return on assets
Change in free cash flows
Nonfinancial variables
Abnormal change in employees
Abnormal change in order backlog
Off-balance-sheet variables
Existence of operating leases
Change in operating lease activity

Variable
N

0.175
0.004
0.006
0.001
70.289
0.027
0.132
0.172
0.041
0.116
0.154
0.006
0.041
0.042
5.377
-1.226-0.0100.004-1.9447.444
0.393
96.763

859
859
859
859
861
859
783
859
859
783
859
854
709
854
856
856
856
856
858
373
858
320

Mean

0.000
-9.337-0.1340.372

1.800-3.270-0.001
0.002

0.163
0.009
0.001
0.000
70.938
0.010
0.059
0.088
0.009
0.055
0.089
0.003
0.035
0.029

MedianAccounting fraud firm-years

63,19018,36962,757
21,38562,45362,43762,48062,48063,571
63,571
63,571
63,57164,57963,571
42,62563,571
63,571
42,62563,571
56,47536,09856,475N

0.324
65.742-1.1936.744

5.159-3.233
0.000
0.005

0.146
0.014
0.006
0.003
65.139
0.014
0.113
0.140
0.000
0.094
0.124
0.001
0.034
0.033

Mean

0.000
-12.988-0.699-1.1602.134-1.400
0.001
0.003

0.139
0.012
0.001
0.000
67.009-0.0070.045
0.081-0.008
0.043
0.079
0.001
0.025
0.022

MedianNon-accounting fraud firm-years

FRAUD FIRM-YEARS: T-TEST WITH MISSTATE2

+
+

-

+
-

+
+
+
+
+
+
+
+
+
+
+
+
+
+

Predicted
sign

0.069
31.021-0.751
0.700

0.218
2.007
-0.010-0.0020.029-0.0090.000-0.003
5.150
0.013
0.019
0.032
0.041
0.022
0.030
0.006
0.007
0.009

-4.098-0.8440.814-0.147-0.232-0.2342.665
0.459-5.089
2.202
0.154
1.347
-11.122-0.947-1.760-2.275-4.451-3.089-3.831-2.245-6.561-6.223（Continued)<.0001
0.399

0.416
0.883

0.816
0.815
0.008
0.646

<.0001
0.028
0.878
0.178
<.0001
0.344
0.079
0.023
<.00010.002<.00010.025<.0001<.0001

Accounting fraud – Non-Acc fraud
Diff.
t-statistics
p-valuein mean

TABLE 7 DESCRIPTIVE STATISTICS ON ACCOUNTING FRAUD FIRM-YEARS VERSUS NON-ACCOUNTING40
The Japanese Accounting Review, 6 (2016), 17-63

Variable
N

0.218
1.598
0.403
1.538
-0.031
0.009
0.019
0.652
4.479

841
685
841
685
857
857
856
830
845

0.038
0.078

-0.019-0.0050.022-0.031
0.165
0.125
0.116

0.000
0.000
-0.0060.082-0.044-0.054
0.655
0.033Median55,94058,78961,34763,30862,48050,64433,14250,64433,14250,843
61,737
61,34864,57949,07647,73950,64450,644

N

0.426
2.409

0.001
0.000
0.000

0.017
0.158
0.095
0.107

0.375
0.256
-0.0010.105-0.008-0.0171.247-0.006

Mean

0.035
0.058

0.001
-0.0140.019-0.031
0.121
0.072
0.089

0.000
0.000
-0.0140.060-0.009-0.017
1.061
0.046

MedianNon-accounting fraud firm-years

+

+

+
+
+
+

+
+
+
+
+
+
-

Predicted
sign

0.226
2.069

-0.031
0.009
0.019

0.201
1.439
0.309
1.431

-0.094
0.113
0.030
0.003
-0.031-0.037-0.419-0.060-3.592-6.1708.414-2.072-2.668-1.982-3.116-2.242-3.373

5.955-6.795-6.529-0.693
1.867
2.190
13.0925.637<.0001<.0001<.0001
0.039
0.008

0.048
0.002
0.025
0.001

<.0001<.0001<.0001
0.488
0.062
0.029
<.0001<.0001

Accounting fraud – Non-Acc fraud
Diff.
t-statistics
p-valuein meanNote: All variables are defined in Table 2. Shaded variables are those with p-values less than 10%
and with the correct sign.

0.281
0.368
0.029
0.107
-0.039-0.0530.828-0.067

836
858
856
861
822
794
841
841

Mean

Accounting fraud firm-years

Market-related incentives
Ex ante financing need
Actual issuance
CFF
LeverageMarket adjusted stock-return
Lagged market adjusted stock-returnBook-to-market
Earnings-to-price
Accounting conservatism variables
C score RR
C score RRSD
C score AR
C score ARSD
Real activities manipulation variables
AB cash flow
Discretionary expense
AB product cost
Japanese specific factors-related variables
FIN
CORP

(-Continued)

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan41

Accruals quality related variables
WC accruals
RSST accruals
Change in receivables
Change in inventory% Soft assets
MJ discretionary accruals
MJ discretionary accruals SD
MJ discretionary accruals AB
PM discretionary accruals
PM discretionary accruals SD
PM discretionary accruals AB
CFO discretionary accruals
CFO discretionary accruals SD
CFO discretionary accruals AB
Performance variables
Change in cash sales
Change in cash margin
Change in return on assets
Change in free cash flows
Nonfinancial variables
Abnormal change in employees
Abnormal change in order backlog
Off-balance-sheet variables
Existence of operating leases
Change in operating lease activity

Variable

0.175
0.004
0.006
0.001
70.289
0.027
0.132
0.172
0.041
0.116
0.154
0.006
0.041
0.042
5.377
-1.226-0.0100.004-1.9447.444
0.393
96.763

859
859
859
859
861
859
783
859
859
783
859
854
709
854
856
856
856
856
858
373
858
320

0.000
-9.337-0.1340.372

1.800-3.270-0.001
0.002

0.163
0.009
0.001
0.000
70.938
0.010
0.059
0.088
0.009
0.055
0.089
0.003
0.035
0.029

Accounting fraud firm-years
N
Mean
Median63,19018,36962,757
21,38562,45362,43762,48062,48063,571
63,571
63,571
63,57164,57963,571
42,62563,571
63,571
42,62563,571
56,47536,09856,4750.324
65.742-1.1936.744

5.159-3.233
0.000
0.005

0.146
0.014
0.006
0.003
65.139
0.014
0.113
0.140
0.000
0.094
0.124
0.001
0.034
0.033

0.000
-12.988-0.699-1.1602.134-1.400
0.001
0.003

0.139
0.012
0.001
0.000
67.009-0.0070.045
0.081-0.008
0.043
0.079
0.001
0.025
0.022

Non-accounting fraud firm-years
N
Mean
Median

+
+

-

+
-

+
+
+
+
+
+
+
+
+
+
+
+
+
+

Predicted
sign

4.273
1.635

1.758
0.346

-2.024-1.206-3.405-2.126

5.118-2.869-1.747-2.368
8.816
3.529
8.477
3.636
4.712
8.717
3.522
2.410
8.473
6.446

（Continued)<.0001
0.051

0.039
0.365

0.021
0.114
<.00010.017<.0001
0.002
0.040
0.009
<.0001<.0001<.0001<.0001<.0001<.0001<.00010.008<.0001<.0001

Wilcoxon rank sum test
z-statistics
p-value

TABLE 8 DESCRIPTIVE STATISTICS ON ACCOUNTING FRAUD FIRM-YEARS VERSUS NON-ACCOUNTINGFRAUD FIRM-YEARS: WILCOXON RANK SUM TEST WITH MISSTATE2

42
The Japanese Accounting Review, 6 (2016), 17-63

Variable-0.031
0.165
0.125
0.116
-0.019-0.005
0.022
0.038
0.078

0.218
1.598
0.403
1.538
-0.031
0.009
0.019
0.652
4.479

55,94058,78961,34763,30862,48050,64433,14250,64433,14250,843
61,737
61,34864,57949,07647,73950,64450,644

0.426
2.409

0.001
0.000
0.000

0.017
0.158
0.095
0.107

0.375
0.256
-0.0010.105-0.008-0.0171.247-0.006

0.035
0.058

0.001
-0.0140.019-0.031
0.121
0.072
0.089

0.000
0.000
-0.0140.060-0.009-0.017
1.061
0.046

Non-accounting fraud firm-years
N
Mean
Median

+

+

+
+
+
+

+
+
+
+
+
+
-

Predicted
sign

Note: All variables are defined in Table 2. Shaded variables are those with p-values less than 10%
and with the correct sign.

0.000
0.000
-0.0060.082-0.044-0.054
0.655
0.033

0.281
0.368
0.029
0.107
-0.039-0.0530.828-0.067

Accounting fraud firm-years
N
Mean
MedianMarket-related incentives
Ex ante financing need
836
Actual issuance
858
856
CFF
Leverage
861
Market adjusted stock-return822
794
Lagged market adjusted stock-returnBook-to-market
841
Earnings-to-price841
Accounting conservatism variables
C score RR
841
C score RRSD
685
C score AR
841
685
C score ARSD
Real activities manipulation variables
AB cash flow
857
Discretionary expense
857
AB product cost856
Japanese specific factors-related variables
FIN
830
CORP
845

(-Continued)

3.127
5.508

-10.843
3.905
1.129

1.064
11.056
11.29713.731-5.546
7.492
7.702
3.264
-2.530-2.614-16.267-5.2830.001<.0001<.0001<.00010.129

0.144<.0001<.0001<.0001<.0001<.0001<.0001
0.001
0.006
0.005
<.0001<.0001

Wilcoxon rank sum test
z-statistics
p-valueSong,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan43

44

The Japanese Accounting Review, 6 (2016), 17-63

TABLE 9 ACCURACY RATIO (AR) FOR EACH INDEPENDENT VARIABLE
Variable
Accruals quality related variables
WC accruals
RSST accruals
Change in receivables
Change in inventory% Soft assets
MJ discretionary accruals
MJ discretionary accruals SD
MJ discretionary accruals AB
PM discretionary accruals
PM discretionary accruals SD
PM discretionary accruals AB
CFO discretionary accruals
CFO discretionary accruals SD
CFO discretionary accruals AB
Performance variables
Change in cash sales
Change in cash margin
Change in return on assets
Change in free cash flows
Nonfinancial variables
Abnormal change in employees
Abnormal change in order backlog
Off-balance-sheet variables
Existence of operating leases
Change in operating lease activity
Market-related incentives
Ex ante financing need
Actual issuance
CFF
LeverageMarket adjusted stock-return
Lagged market adjusted stock-returnBook-to-market
Earnings-to-price
Accounting conservatism variables
C score RR
C score RRSD
C score AR
C score ARSD
Real activities manipulation variables
AB cash flow
Discretionary expense
AB product cost
Japanese specific factors-related variables
FIN
CORP

AR whit Misstate1
Value
rank

AR whit Misstate2
Value
rank

0.094
0.022
-0.046-0.073
0.351
0.086
0.324
0.202
0.132
0.354
0.220
0.071
0.327
0.351

21
28
31
34
8
22
11
17
19
6
15
24
10
7

0.101
-0.057-0.035-0.047
0.175
0.070
0.177
0.072
0.093
0.182
0.070
0.048
0.186
0.128

15
35
30
32
9
18
8
17
16
7
19
23
6
11

0.065
0.084
-0.156
0.057

25
23
38
26

0.040
0.024
-0.068
0.042

25
26
37
24

-0.007
0.024

29
27

-0.035-0.01031
29-0.066-0.034

33
30

0.069
0.053

20
22

-0.106
0.302
0.298
0.142
-0.114-0.095
0.851
0.385

36
12
13
18
37
35
1
5

-0.094
0.113
0.153
0.064
-0.051-0.054
0.327
0.106

39
12
10
21
33
34
1
14

0.222
0.470
0.464
0.566

14
3
4
2

0.021
0.246
0.227
0.306

28
3
4
2

0.348
-0.056
0.130

9
32
20

0.215
-0.078
0.022

5
38
27

-0.200
0.217

39
16

-0.063
0.110

36
13

Note: All variables are defined in Table 2.

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

45

TABLE 10 LOGISTIC REGRESSIONS WITH MISSTATE1
VariableIntercept% Soft assets
CFO discretionary accruals AB
Actual issuanceC score AR
AB cash flow
CORP
Book-to-market
AR
Accounting fraud firm-years
Non-accounting fraud firm-years

Model1
Wald
CoefficientChi-square-8.913515.6260.038
55.6767.654
33.8540.945
44.4720.049
7.610-1.3378.429
0.032
51.096p-value<.0001<.0001<.0001<.00010.006
0.004<.00010.577
229
47,709-7.209
0.036
2.639
0.666

Model2Wald
Chi-square
325.390
49.701
3.396
20.194-1.6900.024-1.45513.62126.804139.081

Coefficientp-value<.0001<.00010.065<.0001<.0001<.0001<.00010.826
229
47,709Note: All variables are defined in Table 2.

analysis because the two variables have a strong correlation and might cause multicollinearity.
Furthermore, as mentioned, the use of absolute values of discretionary accruals has an advantage
in that it can capture the effect of accruals reversal. Second, we also employ the variables ofconservatism, real activities manipulation, and Japanese-specific factors. All these variables are
publicly available and easily obtained by financial statement users. These variables are added to all
three models in the settings used by Dechow et al. (2011).
In our univariate analysis, none of the variables of nonfinancial and off-balance-sheet are
significant. Therefore, we are not able to build Model 2. Thus, we build the following twomodels:16
Model 1: variables available from financial statement in the categories of accruals,
performance, market-related incentive, conservatism, real activities manipulation,
Japanese-specific factors
Model 2: variables in the categories of accruals, performance, conservatism, real activities
manipulation, Japanese-specific factors, and market-related incentive(2) Estimation results
In estimating the parameters of Models 1 and 2, we incorporate the variables of the three
types of discretionary accruals separately into the model because these variables have highcorrelations. For the same reason, the two variables in the category of conservatism are also
incorporated separately into the models. Consequently, we estimate a total of six models as a
combination of these variables.17 From among the results of the six models, we present only the
results of the model with the highest AR value.
The regression results are shown in Table 10. Six variables are selected in Model 1 by the
backward elimination method with sign restriction: % Soft assets, CFO discretionary accruals AB,
16

Therefore, the models in our research are equivalent to Models 1 and 3 in Dechow et al. (2011), with additionalvariables in the categories of conservatism, real activities manipulation, and Japanese-specific factors.
17
The variables of Ex ante financing need, CFF and Abnormal change in order backlog are excluded, as in Dechow et al.
(2011), because they are available for only a limited set of firms.

46

The Japanese Accounting Review, 6 (2016), 17-63

Actual issuance, C score AR, AB cash flow, and CORP. The four variables among those are newly
added to our model: absolute value of discretional accruals, conservatism score, abnormal
operating cash flow to capture abnormal discretional activities, and ownership by businesscorporation. The results suggest the importance of considering earnings quality andJapanese-specific factors when capturing financial fraud.
As for Model 2, the table indicates that six variables are selected: %Soft assets, CFO
discretionary accruals AB, Actual issuance, AB cash flow, CORP, and Book-to-market. These variables
are almost the same as the results of Model 1, except Book-to-market is a substitute forconservatism (C score AR). The results of the χ2 value indicate that the value of Book-to-market ishighly significant. The result suggests that managers of potential fraud firms are likely to have astrong incentive to maintain higher stock prices, consistent with our prediction.
(3) Further validation
This section reports the estimation results for Misstate2 to Misstate8. As described in section
3.1, we define the variables from Misstate3 to Misstate8 so that we can capture the degree ofaccounting fraud gradually (see Table 1). Table 11 summarizes the estimation results of Model 1.
The major findings are as follows. 1) The variables selected for Misstate2 to Misstate8 are almost
the same as those for Misstate1; 2) the discriminant power measured by AR is less capable thanfor Misstate1; and 3) the discriminant power increases as it approaches Misstate8. These results
suggest that the discriminant power increases as accounting fraud becomes more severe. Thus, inthe following analysis on the validity of our models, we will present the results of the models on
the basis of Misstate1.

5.3 Performance of the prediction model(1) Calculation of the F-Score
We discuss the validity of our prediction models, Model 1 and Model 2. The F-Score is thepredicted probability of financial statement fraud in accordance with Dechow et al. (2011) as
follows:
Probability =e  Predicted Value 
1  e  Predicted Value 

The predicted value is the intensity parameter for the likelihood of financial statement fraud.
We then derive the probability by the unconditional expectation (unconditional probability) of
Misstate1 to calculate our F-Score. The unconditional probability is a value of the number ofaccounting fraud samples divided by the total number of samples. An F-Score of 1.00 indicatesthat the firm has the same probability of accounting fraud as the unconditional expectation. An
F-Score greater than 1 indicates firms with higher probabilities of fraud. Below is an example of
how this is done for Model 1 for the company named OLYMPUS (stock code #7733) in thefinancial statements of March 2008:

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

47

TABLE 11 LOGISTIC REGRESSIONS WITH MISSTATE8 FROM MISSTATE2Model1
Variable
Misstate2
Intercept% Soft assets
CFO discretionary accruals AB
Leverage
Actual issuanceC score AR
AB cash flow
CORP
Accounting fraud firm-years
Non-accounting fraud
firm-years
Misstate3
Intercept% Soft assets
CFO discretionary accruals AB
Actual issuanceC score AR
AB cash flow
CORP
Accounting fraud firm-years
Non-accounting fraud
firm-years
Misstate4
Intercept% Soft assets
CFO discretionary accruals AB
Actual issuanceC score AR
AB cash flow
CORP
Accounting fraud firm-years
Non-accounting fraud
firm-years
Misstate5
Intercept% Soft assets
CFO discretionary accruals AB
Actual issuanceC score AR

Coefficientestimate-5.700
0.019
3.103
0.511
0.279
0.089
-1.7080.021

Wald Chi-squarep-valueAR

874.879
54.56012.5642.840
12.25734.99728.07945.137<.0001<.0001<.00010.092
0.001<.0001<.0001<.00010.316

608.735
69.961
20.22826.47839.12817.86947.146<.0001<.0001<.0001<.0001<.0001<.0001<.00010.496

580.67861.32124.48333.04739.27515.90545.020<.0001<.0001<.0001<.0001<.0001<.0001<.00010.513

564.069
56.25220.84437.60138.985<.0001<.0001<.0001<.0001<.00010.511

837
47,101-8.127
0.037
5.505
0.610
0.072
-1.8030.028
328
47,610-8.205
0.035
6.143
0.709
0.072
-1.7230.029
301
47,637-8.175
0.034
5.799
0.768
0.072

(Continued)

48

The Japanese Accounting Review, 6 (2016), 17-63(-Continued)

Model1
Variable
AB cash flow
CORP
Accounting fraud firm-years
Non-accounting fraud
firm-years
Misstate6
Intercept% Soft assets
CFO discretionary accruals AB
Actual issuanceC score AR
AB cash flow
CORP
Accounting fraud firm-years
Non-accounting fraud
firm-years
Misstate7
Intercept% Soft assets
CFO discretionary accruals AB
Actual issuanceC score AR
AB cash flow
CORP
Accounting fraud firm-years
Non-accounting fraud
firm-years
Misstate8
Intercept% Soft assets
CFO discretionary accruals AB
Actual issuanceC score AR
AB cash flow
CORP
Accounting fraud firm-years
Non-accounting fraud
firm-years

Coefficientestimate-1.7900.029

Wald Chi-squarep-valueAR

16.66845.091<.0001<.0001560.169
54.37321.59839.61338.872
16.18545.816<.0001<.0001<.0001<.0001<.0001<.0001<.00010.515

548.058
51.18824.17443.64115.45715.07248.762<.0001<.0001<.0001<.0001<.0001<.0001<.00010.527

545.64250.01823.50944.47615.41715.00449.159<.0001<.0001<.0001<.0001<.0001<.0001<.00010.525

291
47,647-8.174
0.034
5.910
0.793
0.072
-1.7670.029
287
47,651-8.251
0.033
6.291
0.855
0.055
-1.7140.030
272
47,666-8.228
0.033
6.223
0.865
0.055
-1.7150.030
271
47,667

Note: All variables are defined in Table 2.

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

49［Model1］
Predicted Value =-8.913 + 0.038 × (% soft assets) + 7.654 × (CFO discretionary accruals AB)+ 0.945 × (Actual issuance) + 0.049 × (C score AR)-1.337 × (AB cash flow) + 0.032 × (CORP)
Predicted Value =-8.913 + 0.038 × (79.341) + 7.654 × (0.032) + 0.945 × (1) + 0.049 × (-0.318)-1.337 × (-0.014) + 0.032 × (0.010)
Predicted Value = -4.705
Probability = e (-4. 705) / (1 + e (-4. 705))
e = 2.71828183
Probability = 0.00897
Unconditional probability =229 / (47,709 + 229) = 0.00478
F-Score = 0. 00897 / 0.00478 = 1.88
OLYMPUS has an F-Score of 1.88. This suggests that OLYMPUS has almost twice theprobability of engaging in financial fraud compared to a randomly selected firm from the
population.
(2) Accuracy of the prediction model
In order to test the validity of our F-Score in Models 1 and 2, we calculate the detection rate
for accounting fraud firms as follows. We 1) sort all samples into five portfolios on the basis of the
F-Score in ascending order, and 2) observe the frequency of the accounting fraud firms andnon-accounting fraud firms in each quintile. If our models do a good job of identifying theaccounting fraud firms, we expect the accounting fraud firms to be clustered in the fifth portfolio.
The results are reported in Table 12. For Model 1, 59.39% of the accounting fraud firms arein quintile 5, which is higher than the expected level of 20%. For Model 2, 90.83% of theaccounting fraud firms are in quintile 5. Since the percentages in Dechow et al. (2011) are around
50% for all models, our model demonstrated superior performance. The cutoff to be included inquintile 5 (i.e., the minimum value) is 1.205 for Model 1 and 1.293 for Model 2, both above the1.00 of the unconditional expectation.
(3) Type I and Type II error rates of the prediction model
In this section, we discuss the Type I error and Type II error of our models. A Type I error
means that the model incorrectly classifies a non-accounting fraud firm (non-FSF firm) as anaccounting fraud firm (FSF firm), and a Type II effort means the model incorrectly classifies a
FSF firm as a non-FSF firm. We classify the observations based on the following procedure. We1) define a firm with an F-Score of 1.00 or more as a predicted FSF firm (pred-FSF) and thatwith an F-Score less than 1.00 as a predicted non-FSF firm (pred-NonFSF); 2) we count the
number of FSF and non-FSF firms among pred-FSF firms, and then 3) count the number of
FSF and non-FSF firms among pred-NonFSF firms.
Table 13 shows the result of the classifications. The “correct classification” is the sum of thenumber of FSF firms among pred-FSF firms and the number of Non-FSF firms among
pred-NonFSF firms, divided by the total firms. The result for Model 1 indicates that the correct

50

The Japanese Accounting Review, 6 (2016), 17-63

TABLE 12 VALIDATION OF PREDICTION MODEL ACCURACY
Model1
Min. F-score% of total

N
Quintile1
Accounting fraud firm-years
Non-accounting fraud
firm-years
Quintile2
Accounting fraud firm-years
Non-accounting fraud
firm-years
Quintile3
Accounting fraud firm-years
Non-accounting fraud
firm-years
Quintile4
Accounting fraud firm-years
Non-accounting fraud
firm-years
Quintile5
Accounting fraud firm-years
Non-accounting fraud
firm-years

Model2
Min. F-score% of total

N

8

0.182

3.49%

4

0.008

1.75%

9,579

0.039

20.08%

9,583

0.001

20.09%

9

0.342

3.93%

5

0.161

2.18%

9,579

0.331

20.08%

9,583

0.130

20.09%

31

0.489

13.54%

1

0.361

0.44%

9,557

0.486

20.03%

9,587

0.315

20.09%

45

0.720

19.65%

11

0.637

4.80%

9,543

0.708

20.00%

9,577

0.591

20.07%

136

1.205

59.39%

208

1.293

90.83%

9,451

1.172

19.81%

9,379

1.179

19.66%TABLE 13 VERIFICATION OF TYPE I ERRORS AND TYPE II ERRORS OF
THE PREDICTION MODEL
Observed
Misstate
No-misstate
Misstate
No-misstate
Correct classification
Sensitivity
Type I errors
Type II errors

Model1 predicted
Misstate
No-misstate
152
77
11,87635,833
12,02835,91066.38%
33.62%
24.89%
75.11%
75.07% (1)66.38% (2)24.89% (3)33.62% (4)229
47,70947,9380.48%
99.52%

Model2 predicted
Misstate
No-misstate212
17
11,32636,383
11,53836,40092.58%
7.42%
23.74%
76.26%

229
47,70947,9380.48%
99.52%

76.34%
92.58%
23.74%
7.42%(1) Correct classification = (152+35,833) / 47,938(2) Sensitivity = 152 / 229(3) Type I errors = 11,876 / 47,709(4) Type II errors = 77 / 229

classification rate is 75.07%, and the sensitivity is 66.38%.
Since the Type I error is defined as the misclassifications of Non-FSF firms as FSF firms, theSong,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

51

FIGURE 1 CUMULATIVE DISTRIBUTION OF F-SCORES
Panel A：Accounting fraud firm-years
100%
F1:66.38%

F1:45.41%

F1:33.62%

90%
80%
70%
F1:66.38%

60%
F1:54.59%

50%
40%
30%

F1:33.62%

20%
10%
0%

Fscore1(Model1, N=229)

Fscore2(Model2, N=229)

Panel B：Non-accounting fraud firm-years
100%

F1:4.98%
F1:24.89%

F1:9.90%

F1:75.11%

F1:90.10%

90%
80%
70%
60%

F1:95.02%

50%
40%
30%
20%
10%
0%

Fscore1(Model1, N=47,709)

FScore2(Model2, N=47,709)

Type I error ratio is calculated as the number of pred-FSF firms among Non-FSF firms divided
by Non-FSF firms; this is 24.89%. Furthermore, since the Type II error is defined as the
misclassifications of FSF firms as Non-FSF firms, the Type II effort is defined as the number of
pred-NonFSF firms among FSF firms divided by FSF firms; this is 33.62%.
We find that the correct classification rate of Model 2 is better. The sensitivity is greatly
improved from 66.38% to 92.58%. Type I and Type II errors decrease more than in Model 1;
Type II errors decrease drastically, from 33.62% to 7.42%.
We provide further insights into sensitivity and Type I and Type II error rates in Figure 1.
Panel A provides the cumulative distribution of the F-Score for FSF firms, and Panel B presents

52

The Japanese Accounting Review, 6 (2016), 17-63

FIGURE 2 RELATIVE COST RATIO: AN ANALYSIS OF TYPE I ERRORS AND TYPE II ERRORS(Type Ⅰerrors + sensitivity) / sensitivity

250

200

150

Fscore1(Model1)=1→（11,876+152）/152＝79.13
100

50

0

Fscore1(Model1)

Fscore2(Model2)the cumulative distribution for all Non-FSF firms. The upper part of the dotted line in Panel A
indicates the sensitivity; the lower part indicates the Type II error, and the upper part of thedotted line in Panel B indicates the Type I error. For example, for an F-Score of 2.75, Panel A
reveals that the sensitivity is 33.62% (the correct classification rate of FSF firms) and that theType II error rate (the misclassification of Non-FSF firms as pred-FSF firms) is 66.38%, whereas
the Type I error rate (the misclassification of FSF firms as pred-NonFSF firms) is 4.98% in Panel
B. In general, the Type I error rate becomes smaller and the Type II error rate become greater as
the F-Score increases. Similar results are found for Model 2, where the sensitivity increases in thehigher F-Score region, and the Type II error rate is lower than in Model 1.
Figure 1 also provides helpful insights into the cost of these errors. The costs of Type I andType II errors are not the same; the costs depend on who uses this information. For example,
from an auditor’s perspective, the cost of Type II errors seems to be far higher than that of Type I
errors, since the former is the cost of overlooking an FSF firm. When an FSF firm goes
undetected and is later revealed, the auditor is likely to be sanctioned by regulatory bodies andsuffer a loss of reputation. Meanwhile, the cost of a Type I error is also not zero. It may result inlost fees, as the auditor may choose to drop a client. Because Type II errors are more costly to theauditor, an auditor is likely to prefer an F-Score cutoff that makes more Type I errors than Type IIerrors (Dechow et al. 2011, p. 62).
Figure 2 provides a tradeoff cost between Type I and Type II errors. The figure provides aratio of the sum of the number of Type I errors (misclassification of FSF firms) and Sensitivity(correct classification of FSF and NonFSF firms) divided by the number of Sensitivity. We
assume that the cost of investigating a firm is 1 yen (Y ) and that an investigation always detects
FSF firms if they exist. At an F-Score cutoff of 1.00, the relative cost ratio is Y79.13[(11,876+152)/152] for model 1. In other words, a cost of Y12,028 is incurred to avoid the 152
FSF firms. Therefore, if a cost of missing a FSF firm is Y79.13 or more, then an F-Score cutoff of
1.00 should be used by the auditor. If the cost is over Y230, then all firms should be investigated,because the F-Score cutoff is equal to zero. If the cost is less than Y5, it is cheaper not to do theSong,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan53

investigation and to just pay the extra cost of the FSF firms as they are identified. Because therelative cost ratio is about Y20, even the F-Score cutoff is around Y5.
(4) Marginal analysis of the prediction model
In this section, we evaluate the influence of each of the variables in the models fordetermining the magnitude of the F-Score (marginal effect analysis). We 1) calculate the value ofthe F-Score when all variables are held at their mean values, 2) recalculate the F-Score after
moving one independent variable to its lower quartile value, holding all other variables at theirmean values,18 3) recalculate the F-Score, moving the independent variable to its upper quartile
value, 4) calculate the change in the F-Score across the interquartile range for that variable, and 5)
repeat steps 2) through 4) for the next independent variable.
Table 14 summarizes the results. Panel A summarizes the descriptive statistics of eachvariable. Panel B shows the marginal effect of Model 1, revealing that the top three largest effectvariables are %Soft assets, CFO discretionary accruals AB, and Actual issuance. It is noteworthy thatthe two variables relating to accruals have large marginal effects. Panel C shows the marginal
effect of Model 2. We find that the top three largest effect variables are %Soft assets, Actual issuance,
and Book-to-market. Among these, Book-to-market has an exceedingly high marginal effect. Finally,
Panel D shows a matrix of the correlations among the variables. The correlations are all
significant and are generally consistent with the results of the marginal effect analysis along withthe above analysis. For example, the correlation coefficients in the Spearman’s correlation in
Model 1 increase in the order of %Soft assets, Actual issuance, and CFO discretionary accruals AB,
and these three variables are the same as the three found in the marginal effect analysis.
(5) Robustness tests
Finally, we conduct robustness tests to investigate the sensitivity of our models to theindustry and the time period examined. In our analysis of the detection rate in Table 12, we use
the same sample both to develop our prediction model and to evaluate its effectiveness. This may
cause overestimates of the predictive ability due to the hindsight bias. To address the issue, we use
data covering 2000 to 2006 to build the prediction models and data covering 2007 to 2010 toevaluate their predictive ability. The analyses conducted in the previous section are replicatedbased on the above out sample seen in Table 15. Panels A through C show the results of theparameter estimation (the same as is shown in Table 10), prediction ability (the same as is shownin Table 12), and Type I and Type II errors (the same as is shown in Table 13), respectively. Panel
A summarizes the estimated parameters for the period from 2000 to 2006. There is little
difference between the results of Table 10 and Table 15. We find that two variables, the change ofcash flow margin (Change in cash margin) and the change of free cash flow (Change in free cash
flows), are selected in Model 1, instead of abnormal operating cash flow (AB cash flow) andconservative score (C score AR). The panel also shows that, in Model 2, the abnormal operating
cash flow is deselected.
Panel B shows the result for the predictive ability of our model for the sample from 2007 to
2010. The results show the same high capacity, whereby the fifth portfolio includes fraud firms at
18

For indicator variables such as Actual issuance, we set the upper quantile value at 1 and the lower quantile value at 0.
For variables with negative estimated parameters, we swap the values of the upper and lower quantiles to calculate
the F-Score.

% Soft assets

66.30214.40157.90067.62676.38425.16694.082% Soft assets

0.476

0.038
0.938
0.462

% Soft assets

0.261

0.036
0.541
0.279

Spearman correlation
Fscore（Model1）
Fscore（Model2）
Pearson Correlation
Fscore（Model1）
Fscore（Model2）

0.324
0.179
0.437
0.195

0.674
0.284

0.294
0.131

CFO discretionary accruals AB

0.031

Note: All variables are defined in Table 2.

% Soft assets

0.776

0.355
0.159

0.560
0.457

Actual issuance

0.298

CFO discretionary accruals AB Actual issuance
2.639
0.666
0.387
0.614
0.355
0.316

0.149

C score AR

CFO discretionary accruals AB Actual issuance C score AR
7.654
0.945
0.686
1.272
0.536
0.496

0.118

0.083

0.006

0.049
0.640
0.634

-0.362-0.162-0.302-0.071

AB cash flow

0.044

CORP

AB cash flow CORP
-1.690
0.399
0.355

0.059

AB cash flow CORP
-1.337
0.667
0.608

0.407
0.182

0.098
0.174

0.003

0.024
0.360
0.357

0.006

0.032
0.600
0.594

-0.286-0.854Book-to-market

0.724

Book-to-market-1.455
0.933
0.209

CFO discretionary accruals AB Actual issuance C score AR
AB cash flow CORP
Book-to-market
0.032
0.266
0.107
0.000
2.234
1.237
0.035
0.442
0.970
0.087
7.523
0.873
0.010
0.000
-0.007-0.034
0.024
0.613
0.021
0.000
0.075
0.001
0.059
1.054
0.042
1.000
0.175
0.036
0.317
1.645
0.000
0.000
-0.315-0.269
0.003
0.000
0.186
1.000
0.690
0.229
37.196
4.145

Panel D: Correlation between F-score and its components

Coefficient estimate
Fscore at upper quartile
Fscore at lower quartile
Interquartile marginalchange in F-score

Panel C: Marginal effect for Model2

Coefficient estimate
Fscore at upper quartile
Fscore at lower quartile
Interquartile marginalchange in F-score

Panel B: Marginal effect for Model1

Mean
Std Dev
Lower quartile
MedianUpper quartile
1%
99%

Panel A: Descriptive statistics

TABLE 14 MARGINAL EFFECT ANALYSIS

54
The Japanese Accounting Review, 6 (2016), 17-63

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

55

TABLE 15 ROBUSTNESS TESTS
Panel A: Logistic regressions

Intercept% Soft assets
CFO discretionary accruals AB
Actual issuanceC score AR
AB cash flow
CORP
Book-to-market
Change in cash margin
Change in free cash flows

Coefficientestimate-8.463
0.037
8.140
0.808

Model1Wald
Chi-square199.254
22.52217.27512.7460.030

17.046<.0001-0.001-2.261

4.511
3.991

0.034
0.046

AR
Accounting fraud firm-years
Non-accounting fraud firm-years

p-value<.0001<.0001<.0001<.0001

Coefficientestimate-6.956
0.042
3.676
0.523

Model2Wald
Chi-square
120.882
28.576
3.271
4.859

0.016
-2.4934.584
48.5920.535
93
13,775p-value<.0001<.0001
0.071
0.028

0.032
<.00010.872
93
13,822

Panel B: Validation of the prediction model accuracy
Model1
Min. F-score% of total

Model2
Min. F-score% of total

2

0.220

2.06%

2

0.000

2.06%

2,844

0.037

20.12%

2,853

0.000

20.12%

7

0.418

7.22%

0

0.000

0.00%

2,840

0.351

20.09%

2,856

0.023

20.14%

13

0.525

13.40%

1

0.192

1.03%

2,833

0.513

20.04%

2,855

0.104

20.13%

16

0.742

16.49%

1

0.504

1.03%

2,831

0.736

20.03%

2,855

0.284

20.13%

59

1.212360.82%

93

0.798

95.88%

2,787

1.199219.72%

2,762

0.767

19.48%N
Quintile1
Accounting fraud firm-years
Non-accounting fraud
firm-years
Quintile2
Accounting fraud firm-years
Non-accounting fraud
firm-years
Quintile3
Accounting fraud firm-years
Non-accounting fraud
firm-years
Quintile4
Accounting fraud firm-years
Non-accounting fraud
firm-years
Quintile5
Accounting fraud firm-years
Non-accounting fraud
firm-years

N56

The Japanese Accounting Review, 6 (2016), 17-63

Panel C: Verification of the type I errors and type II errors of the prediction model
Observed
Misstate
No-misstate
Misstate
No-misstate
Correct classification
Sensitivity
Type I errors
Type II errors

Model1 predicted
Misstate
No-misstate
66
31
3,675
10,4603,741
10,49168.04%
31.96%
26.00%
74.00%
73.96%
68.04%
26.00%
31.96%

97
14,135
14,2320.68%
99.32%

Model2 predicted
Misstate
No-misstate
92
5
2,147
12,034
2,239
12,03994.85%
5.15%
15.14%
84.86%

97
14,181
14,2780.68%
99.32%

84.93%
94.85%
15.14%
5.15%

Note: All variables are defined in Table 2. Shaded variables in panel A are not used in theprediction model.

a rate of 60.82% in Model 1 and 95.88% in Model 2. Finally, Panel C reveals the results on Type I
and Type II errors for the later period of 2007 to 2010. The Sensitivity is 68.04% for Model 1 and
94.85% for Model 2. These error rates are largely the same as the original results seen in Table 13.
Thus, the results obtained using the subperiod samples show no significant differences from ourmain results.
Finally, to test the impact of industry differences on our results, we build models withindustry indicator variables for Electronics, Construction, Trading, and Services, industries withrelatively large numbers of firms. Although some industry indicator variables are selected as
significant variables for both Model 1 and Model 2, the other selected variables are almost thesame as those in the main results, and the models’ predictive ability shows no significant
differences from the main results (in untabulated results).

6. Conclusion
In this study, we investigate the relationship between accounting fraud and accounting
information for Japanese firms. Our accounting fraud sample consists of two subsamples: 1) firmsthat have been accused or had administrative monetary penalties imposed by the SESC and 2)
firms that have announced accounting fraud at timely disclosure disclosures. Specifically, we 1)
explore the characteristics of accounting fraud firms by analyzing financial information obtained
from the annual reports of Japanese firms and 2) develop a model to predict accounting fraud
based on the characteristics of Japanese fraud firms.
To identify the characteristics of fraud firms, we focus on 38 variables for the eight factors of
accruals quality, performance, nonfinancial measures, off-balance-sheet activities, market-related
incentives, conservatism, real-activities manipulation, and Japanese-specific factors.
Through our univariate analysis and model building process, we find that accrual quality,
market-related incentive, real activities manipulation, conservatism, and Japanese-specific factors
are generally useful for detecting accounting fraud. Our analysis contributes to prior studies byclarifying the importance of focusing on the various dimensions of earnings quality andcountry-specific factors.

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan57

For example, the marginal effect analysis reveals that soft asset ratio, actual issuance, and theabsolute value of discretional accruals are useful when we focus on the accounting information in
annual reports (i.e., the variables in Model 1). Further, when we add stock-related variables to themodel, we find that the book-to-market has a higher marginal effect. In general, our prediction
models for identifying accounting fraud have stronger predictive ability than do those developedby previous studies. Our models can be used widely in various aspects of accounting and financepractice.
Our results have several important implications for business practice. For example, auditors
and regulatory bodies could use the model to reduce their investigation costs and improve theaccuracy of their judgments. Furthermore, capital providers such as stock investors and banks
could also improve their decision-making by estimating the probability of accounting fraud using
our prediction model. Although investment strategies on the basis of accrual quality such asaccrual anomalies have already become popular, stock investors in the Japanese market might beable to consider their investment strategies based on the F-Score. Furthermore, creditors could
also use the F-Score to evaluate the credit risk in lending or doubtful accounts receivable.
Finally, this study has several limitations. Our main accounting fraud samples are based on
firms that have been accused or had administrative monetary penalties imposed by the (SESC).
We cannot deny the possibility that the SESC has misclassified an accounting fraud firm as a
non-fraud firm because its investigatory power is limited. To address this issue, we supplemented
the observations by using timely disclosure information and conducted additional analyses basedon the extended sample. However, we cannot entirely rule out the sample selection issue.
Furthermore, we do not examine the effect of corporate governance on accounting fraud
except for two Japanese-specific factors.19 As Dechow et al. (2011) also argue, this is because theavailability of data on corporate governance are limited and costly, reducing the practicability ofour prediction models. It is important to examine the relationship between accounting fraud andcorporate governance in future research. Finally, it might be useful to focus on the information
concerning internal control such as significant deficiency and material weakness for predicting
accounting fraud.
REFERENCES
ALLEN, E., C. LARSON, AND R. SLOAN. 2013. Accrual reversals, earnings and stock returns. Journal of
Accounting and Economics 56 (1): 113-129.
BARTON, J., AND P. SIMKO. 2002. The balance sheet as an earnings management constraint. The Accounting
Review 77 (Supplement): 1–27.
BASU, S. 1997. The conservatism principle and the asymmetric timeliness of earnings. Journal of Accounting
and Economics 24 (1): 3–37.
BENEISH, M. D. 1997. Detecting GAAP violations: Implications for assessing earnings management
among firms with extreme financial performance. Journal of Accounting and Public Policy 16 (3): 271–
309.

19

For example, we can point out board independence, executive compensation contracts, and other ownership structureelements (such as ownership by foreign investors or management).

58

The Japanese Accounting Review, 6 (2016), 17-63

BENEISH, M. D. 1999a. The detection of earnings manipulation. Financial Analysts Journal 55 (5): 24–36.
BENEISH, M. D. 1999b. Incentives and penalties related to earnings overstatements that violate GAAP. The
Accounting Review 74 (4): 425–457.
BRAZEL, J. F., K. L. JONES, AND M. F. ZIMBELMAN. 2009. Using nonfinancial measures to assess fraud risk.
Journal of Accounting Research 47 (5): 1135–66.
DECHOW, P. M., R. G. SLOAN, AND A. P. SWEENEY. 1995. Detecting earnings management. The Accounting
Review 70 (2): 193–226.
DECHOW, P. M., R. G. SLOAN, AND A. P. SWEENEY. 1996. Causes and consequences of earnings
misstatement: An analysis of firms subject to enforcement actions by the SEC. Contemporary
Accounting Research 13 (1): 1–36.
DECHOW, P. M., GE, W., AND C. SCHRAND. 2010. Understanding earnings quality: A review of the proxies,
their determinants and their consequences. Journal of Accounting and Economics 50 (2-3): 344-401.
DECHOW, P. M., GE, W., LARSON, C. R. AND R.G. SLOAN. 2011. Predicting material accounting
misstatements. Contemporary Accounting Research 28: 17–82.
ETTREDGE, M., L. SUN, P. LEE, AND A. ANANDARAJAN. 2008 Is earnings fraud associated with highdeferred tax and/or book minus tax levels? A Journal of Practice and Theory 27 (1): 1–33.
GRAHAM, J., C. HARVEY, AND S. RAJGOPAL. 2005. The economic implications of corporate financial
reporting. Journal of Accounting and Economics 40 (1–3): 3–73.
GRUBBS, F. E. 1969. Procedures for detecting outlying observations in samples. Technometrics 11 (1): 13-14.
KASZNIK, R. 1999. On the association between voluntary disclosure and earnings management. Journal of
Accounting Research 37 (1):57–81.
KHAN, M.N., AND R.L. WATTS. 2009. Estimation and empirical properties of a firm-year measure ofaccounting conservatism. Journal of Accounting and Economics 48 (2-3): 132–150.
KOTHARI, S. P., A. LEONE, AND C. WASLEY. 2005. Performance-matched discretionary accrual measures.
Journal of Accounting and Economics 39 (1): 163–197.
LEE, T., INGRAM, R., AND T, HOWARD. 1999. The difference between earnings and operating cash flow as
an indicator of financial reporting fraud. Contemporary Accounting Research 16(4): 749–786
OKUMURA, M. 2014. Misstatement of Earnings Information and the Stock Market. : Chuo-Keizaisha. (inJapanese).
OSHIRO, N. 2014. Research on early detection of financial statement fraud, FSA Institute Discussion Paper
Series, DP 2014-6, Financial Research Center, Financial Services Agency Government of Japan (inJapanese).
RAJGOPAL, S., T. SHEVLIN, AND M. VENKATACHALAM. 2003. Does the stock market fully appreciate theimplications of leading indicators for future earnings? Evidence from order backlog. Review of
Accounting Studies 8 (4): 461–492.
RICHARDSON, S., R. SLOAN, M. SOLIMAN, AND I. TUNA. 2005. Accrual reliability, earnings persistence, andstock prices. Journal of Accounting and Economics 39 (3): 437–485.
ROYCHOWDHURY, S. 2006. Earnings management through real activities manipulation. Journal of
Accounting and Economics 42 (3):335–370.
SHUTO, A. 2010. Earnings Management of Japanese Firms–Theories and Practices. : Chuo-Keizaisha (inJapanese).
STEFANSKY, W. 1972. Rejecting outliers in factorial designs. Technometrics 14 (4): 469-479.

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan

59

SUDA, K. 2000. Positive Theory of Financial Accounting. : Hakuto Shobo (in Japanese).
TEOH, S. H., I. WELCH, AND T. J. WONG. 1998. Earnings management and the long-run market
performance of initial public offerings. The Journal of Finance 53 (6): 1935–1974.
WATTS, R. L. 2003. Conservatism in accounting part I: Explanations and implications. Accounting Horizons
17 (3): 207–221.
XU, M., AND C. ZHANG. 2009. Bankruptcy prediction: the case of Japanese listed companies. Review of
Accounting Studies 14 (4): 534–558.

60

The Japanese Accounting Review, 6 (2016), 17-63

APPENDIX
A. Measurement of discretionary accruals
We use three types of modified versions of Jones’ (1991) model to measure discretionary accruals.
Specifically, we use the following three models: the modified Jones model in Dechow et al. (1995;
MJ discretionary accrual), the performance-matched Jones model in Kothari et al. (2005; PM
discretionary accruals), and the CFO modified Jones model in Kasznik (1999; CFO discretionary
accruals). The methods of calculation are as follows:(1) The modified Jones model

WC accrualst    1  ΔSalest  ΔReceivablest    2 PPEt   twhere,WC accruals = working capital accruals,
 Sales = change in sales, Receivables = change in account receivables, and
PPE = net property, plant and equipment.
(2) The performance-matched Jones model

WC accrualst    1  ΔSalest  ΔReceivablest    2 PPEt   3 ROAt   twhere,ROA = return on assets; net earnings / total assets.
(3) CFO modified Jones model

Total accrualst    1  ΔSalest  ΔReceivablest    2 PPEt   3CFOt   twhere,
Total accruals = total accruals as the net current profit after tax minus cash flow from operation,
CFO = cash flow from operations.

 is a change in the value of a variable, and t stands for fiscal year. All variables are scaled bylagged total assets. The model is estimated cross-sectionally for each industry in a given fiscal year
according to the Nikkei Industry Classification Code (Nikkei gyousyu chu-bunrui). We combinedsome industries in order to include more than 10 firms at least. Using the estimated coefficientsof the model, we measured non-discretionary accruals (NDA). The difference between total
accruals and measured non-discretionary accruals is a proxy for discretionary accruals (DA).
For the performance-matched Jones model in (2), we estimate two models based on two
types of ROA, at current year (ROAt) or at previous year (ROAt-1), following Kothari et al. (2005).
Although the estimation results of the two models are similar to each other, we employed themodel with ROAt because the explanatory power (Adj.R2) of the model with ROAt is higher thanthat with ROAt-1. The definition of accruals in CFO modified Jones model is based on the moststandard definition used in empirical research on Japanese firms (Shuto 2010).
The untabulated results indicate that all independent variables of all models are consistent
with the expected signs on average. The explanatory power of the CFO modified Jones model has
the highest value among the models, consistent with the previous study (Shuto 2010).
B. Measurement of conservatism

Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan61

We estimate accounting conservatism following the method of Khan and Watts (2009). Khan andWatts (2009) extended the cross-sectional measurement model of conditional conservatismproposed by Basu (1997) in order to estimate conservatism for an individual firm. The model ofBasu (1997) is as follows:

X t  1   2 Dt   3 Rt   4 Dt Rt   t(1)where,
X = net earnings divided by the market value at beginning of fiscal year,
R = annual return,D = a dummy variable equal to 1 when R < 0 and equal to 0 otherwise,
where  3 is the good news timeliness measure, and  4 is conditional conservatism. We
assume that the timeliness and conditional conservatism are the liner functions of threefirm-specific characteristics: firms size, book-to-market, and leverage.

Gscore   3  1   2 Sizet  3 M / Bt   4 Levt
Cscore   4  1  2 Sizet  3 M / Bt  4 Levt

(2)
(3)

X t  1   2 Dt  Rt ( 1   2 Sizet   3 M / Bt   4 Levt ) Dt Rt (1  2 Sizet  3 M / Bt  4 Levt ) ( 1Sizet   2 M / Bt   3 Levt   4 Dt Sizet   5 Dt M / Bt   6 Dt Levt ) t
We substitute Eqs. (2) and (3) into regression (1), and perform annual cross-sectional regression
as follows:(4)
where,
Size = natural logarithm of market capital,
M/B = book-to-market ratio,
Lev = leverage, net interesting bearing debt divided by total asset.
In estimating the regressions, we winzorized these variables due to outliers. Specifically, four
variables, X, Size, M/B, and Lev, are winsorized at 1% and 99%. Furthermore, we adopt the
Smilnov-Grabbs test20 to detect outliers.
In our regression models, we use two types of returns: the row returns and themarket-adjusted returns. The regression results reveal that the coefficients on the independent
variables of both models to estimate the Cscore have the expected signs. We also find that the
explanatory power of the model with market-adjusted returns is higher than is that of the modelwith raw returns.
C. Measurement of real-activities manipulation
20

For more details on the Smilnov–Grabbs test, please refer to Grubbs (1969) and Stefansky (1972).

62

The Japanese Accounting Review, 6 (2016), 17-63

We measure real-activities manipulation in accordance with the method of Roychowdhury (2006).
As discussed above, we expect that managers may perform manipulations and discretionary
activities such as control of their sales, reducing discretionary costs/expenses, and overproduction.
In order to capture these manipulations, we measure three variables: (1) abnormal cash flow fromoperation (AB cash flow), (2) discretionary expenses (Discretionary expense), and (3) abnormal
production cost (AB product cost). The estimation methods for each variable are as follows:(1) Abnormal cash flow from operation

CFOt / At 1    1 1 / At 1    2 Salest / At 1    3  ΔSalest / At 1    twhere,
CFO = cash flow from operation,

A = total assets, andSales = amount of sales.

(2) Discretionary expense

DEt / At 1    1 1 / At 1    2 Salest 1 / At 1    twhere,DE = discretionary expenses: the sum of following costs/expenses, research and development,
advertisement, other costs of sales/marketing, board members’ compensation, labors
cost/welfare expense.
(3) Abnormal production cost

PDt / At 1    1 1 / At 1    2 Salest / At 1    3  ΔSalest / At 1    4  ΔSalest 1 / At 1    twhere,PD = cost of production; the sum of cost of goods sold, inventory at the end of fiscal year
minus inventory at the beginning of fiscal year.
We estimate the parameters by a cross-sectional industry regression in year and industry, the sameprocedure as for discretionary accruals. By calculating the residual in each model, we estimate
three real-activities manipulations as the difference between the actual and estimated value(residual).
D. Calculation method of AR
The AR is a variable used to measure the accuracy of prediction models. First, we explain the
CAP curve in order to understand the AR. The CAP curve is the result of a plot on the basis of
the F-Score in descending order (i.e., the F-Score becomes higher in origin), of which the X-axis is
the ratio of total (accumulated) samples, and the Y-axis is the ratio of accounting fraud samples
only (see the “CAP curve of estimated model” in the figure below).
If the prediction model can discriminate (capture) among all firms in terms of accounting
fraud (generally referred to as a “perfect model”), the CAP curve corresponds to the “CAP curvein perfect model” in the figure.
On the other hand, if the prediction model cannot capture the firms in terms of accounting fraud(generally referred to as a “random model”), the situation is the same as when we discriminate
among fraud samples randomly (i.e., when we use dice to determine the probability of accounting
fraud instead of using a prediction model). For the random model, the CAP curve is “CAP curvein random model (45 degree).”Song,Oshiro and Shuto: Predicting Accounting Fraud: Evidence from Japan63

In general, the CAP curves of the prediction models are positioned between two curves (i.e.,
the perfect model and the random model). It shows higher accuracy when the CAP curve in theestimated model swells out widely. The AR is the ratio of the area between the estimated modeland the random model (Region B) divided by the sum of the area between the perfect model andthe estimated model (Region A) and Region B as follows:AR  Region B/ ( Region A  Region B)
In the perfect model, AR becomes 1 because Region A is 0. In the random model, AR becomes 0
because Region B is 0. Therefore, the larger AR closer to 1 (i.e., closer to the perfect model)
indicates a better prediction result.

CAP curve in perfect model
100%

accounting fraud firm (%)

Capture ratio of

Region A

Region B
CAP curve in estimated model
CAP curve in random model(45 degree)0%

Bad  accumulated samples (%)  Good100%