earnings management using income classification shifting ... · managers engage in classification...

1

Earnings Management Using Income Classification Shifting

– Evidence from the Korean IFRS Adoption Period

Minyoung Noh*

Doocheol Moon**

Andres Guiral***

Laura Parte Esteban****

May 2014

_______________________

*Ph.D. Candidate, School of Business, Yonsei University

50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Korea

E-mail: [email protected]

**Professor, School of Business, Yonsei University


E-mail: [email protected]; Phone: 82-2-2123-5458; Fax: 82-2-2123-8639

***Associate Professor, School of Business, Yonsei University


E-mail: [email protected]; Phone: 82-2-2123-6567; Fax: 82-2-2123-8639

**** Associate Professor of Accounting, Universidad Nacional de Educacion a Distancia (UNED), Spain

E-mail:l [email protected], Phone:3491 -398- 8966

mailto:[email protected]




2

Earnings Management Using Income Classification Shifting

– Evidence from the Korean IFRS Adoption Period

Abstract

This study provides evidence of earnings management using income classification

shifting in the year of International Financial Reporting Standards (IFRS) transition. While

the extant literature focuses on classification shifting of expense items, we investigate whether

managers engage in classification shifting using revenue as well as expense items. In the

Korean IFRS adoption period, managers are inadvertently allowed broad discretion to shift

other income and special expense items since Korean IFRS does not regulate the individual

items to be included in operating profits before the amendment in 2012. Using a methodology

similar to prior studies, we find that companies shift other income to other operating income

to increase operating profits. On the other hand, managers engage in classification shifting

using special expenses just to meet or beat earnings benchmarks. We conclude that managers

opportunistically used other income in a general shifting practice but special expense items in

special cases to improve their operating performance in the Korean IFRS adoption period.

Our findings have implications in an international context that the introduction of IFRS

affects the strategic use of revenue and expenses to manage core earnings.

Keywords: Core earnings; Classification shifting; Special expense items; Other

operating income; Earnings benchmarks

JEL classification: M40, M41

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I. Introduction

The existing body of empirical evidence, supportive of classification shifting, centers on

expense items, such as extraordinary items (Ronen and Sadan 1975; Barnea et al. 1976),

income-decreasing special items (McVay 2006; Fan et al. 2010; Haw et al. 2011), and

expenses in discontinued operations (Barua et al. 2010) which should be classified as

operating expenses. Despite the prevalence of this form of classification management in the

United States, we know little about its use in other countries. Some exception is the studies of

Athanasakou et al. (2010) that focus on UK companies and Haw et al. (2011) that analyze a

set of East Asian countries. As Korean IFRS (hereafter IFRS) does not regulate the individual

components of the operating profits (losses) in 2011, companies disclose operating profits

based on their own standards, providing an exceptional setting to analyze the classificatory

shifting.

Operating profit numbers have been considered more important in Korea to gauge the

intrinsic viability of an entity.1 For instance, companies reporting operating losses for four

years in a row are designated as issues for administration2 and those for five consecutive

years are delisted according to securities listing regulation in the KOSDAQ (a Korea version

1Since managers have commonly disclosed different earnings figures, such as pro-forma earnings and

operating profits, in their financial information or earnings announcement, a wide number of studies have

investigated whether the use of these different earnings metrics is motivated by informative or opportunistic

reasons. Research shows that adjusted earnings measures better portray sustainable “core” performance relative

to GAAP earnings, which typically contain transitory or one-time items, and those measures are generally more

value relevant than GAAP earnings measures (e.g., Bhattacharya et al. 2003; Doyle et al. 2003; Choi et al. 2007;

Brown et al. 2012). However, these studies also provide evidence that some managers opportunistically report

different earnings measures to meet or beat earnings benchmarks and influence investor perceptions of future

firm performance (Bhattacharya et al. 2003; Lougee and Marquardt 2004). That is the evidence that shows the

importance of alternative earnings metrics (EBIT, core earnings, etc.).

2Designation as an administrative issue has been adopted to protect investors by making them know the risk

of the company before delisting. The reasons for the designation includes sales less than KRW 3 billion in the

latest fiscal year, consecutive operating losses in four recent fiscal years, or loss from continuing operations

before taxes more than 50% of equity capital (and KRW 1 billion) for two of three most recent fiscal years. If

the company designated as an administrative issue cannot resolve the reasons for the designation for certain

period, it will be delisted in the market. Yum and Sohn (2013) show that firms with a higher propensity of being

delisted (including issues for administration) tend to manipulate earnings.

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of the NASDAQ) market. Even in the IFRS adoption year when managers have broad

discretion to compose core earnings, the stock market seems to respond to operating profits

regardless of their composition.3 As a result, credit rating agencies or other experts initiate to

disclose Korean GAAP (hereafter GAAP) based operating profits to provide consistent

information for investors using the footnote disclosure (The DongA News 2011.05.03; The

Financial News 2011.05.17).

In this study, we argue that managers wishing to inflate reported operating profits and

thus influence the market‟s perceptions in the IFRS adoption period use non-operating

income as well as special expense items for classification shifting. As an anecdotal evidence,

KSS shipping company marks turnaround to operating profits through classifying 14.5 billion

Korean won of gains on disposition of property, plant, and equipment (PPE) as other

operating income in the first quarter of 2011 (The Korea Economic Daily 2011.06.02). Also,

Sunkwang records operating profits of 77.8 billion Korean won through classifying huge

amount of equity income on investment as other operating income in the same period (Maeil

Business Newspaper 2011.05.17.). Gains on disposition of PPE and equity income on

investment should not be included in operating profits according to the GAAP before IFRS

adoption.

Using a research methodology similar to prior studies (McVay 2006; Fan et al. 2010) and

a sample of 1,230 Korean companies in 2011, we decompose operating profits into its

expected and unexpected components. We find a positive association between income-

increasing other operating income and unexpected operating profits, demonstrating that

classification shifting using other operating income occurs in the IFRS adoption year. On the

other hand, the results show a negative association between income-decreasing special

3For example, stock price for Samsungcard goes up with gains on sales of securities investment in 2011,

which is one-time transaction and should not be included in operating profits under Korean GAAP.

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expense items and unexpected operating profits, suggesting that performance-induced effect

is greater than classificatory shifting effect of special expense items.4 Therefore, companies

shift other income items rather than special expense items to increase operating profits in the

IFRS adoption year. Instead of using special items scrutinized by auditors and regulators,

managers seem to use other income as a common shifting practice. When managers consider

the possibility that they keep using the same classification scheme over other operating

income items for future financial reporting, they may have higher motivations for using this

new form of classification shifting.

To provide additional support of classification shifting, we conduct several tests

following Fan et al. (2010). First, we document that classificatory shifting of other income to

other operating income is more pervasive when managers are constrained in their ability to

manipulate current-period accruals because of prior upward accruals manipulation. Second,

we investigate the incentives to manage earnings through classification shifting, finding that

classification shifting using special expense items occurs just to meet or beat earnings

benchmarks (zero earnings, earnings in the prior year, and analysts‟ forecasts) whereas

misclassification of other income occurs in general and does not take place more to match

earnings expectations. Our supplementary tests also reveal that auditor characteristics and

firm characteristics are important dimensions to understand the pervasiveness of classification

shifting. Specifically, managers less engage in classification shifting using other income when

their auditor is a large accounting firm and as auditor tenure increases. Interestingly,

companies belonging to Chaebol (large business group) and companies with incentives to

4The overall relation between unexpected core earnings and special expense items includes both

classification shifting and a performance-driven effect (Fan et al. 2010). Performance-induced effect occurs

because the magnitude and frequency of income-decreasing special items are markedly higher among firms

experiencing poor performance (McVay 2006).

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avoid delisting use classificatory shifting of special expense items to increase operating

profits.

Our study makes several contributions to the literature. First, this study extends the

classification shifting literature by providing evidence that revenue in addition to expense line

items are used to manage earnings. Most studies in the classification shifting literature have

focused on expense items. However, this paper focuses on other income items which are

allowed to be classified as other operating income items under IFRS, supporting the

Securities and Exchange Commission (SEC)‟s concern of firms‟ classification shifting

behavior (Alfonso et al. 2012) using revenue as well as expense items.5 Second, given the

debate whether the SEC should mandate the use of IFRS in the US market, our study

provides timely evidence that shows a cost of IFRS adoption.6 In particular, we find evidence

that autonomy on composition of operating profits under IFRS affords the discretion to use

classificatory shifting of other income. Finally, this study contributes to the literature on pro

forma that documents that managers exercise discretion for opportunistic reasons

(Bhattacharya et al. 2003; Doyle et al. 2003; Choi et al. 2007; Brown et al. 2012). We provide

clear evidence on the motivations for the use of non-recurring items to influence investors‟

perceptions about core earnings.

Section II discusses institutional background of operating profits. Section III develops

hypotheses and section IV specifies research design. Section V describes the data collection,

and section VI reports results. Section VII concludes.

5The SEC states that they are concerned about account misclassification of financial statement line items

including improperly reflecting interest or investment income as product or service revenue. The SEC further

note that gains and losses on disposals of assets should be reported and disclosed separately in the financial

statement, consistent with SEC Staff Accounting Bulletin (SAB) No. 101 and SAB Topic 5B, and in MD&A

(SEC 2000).

6While De George et al. (2013) quantify the directly observable cost of IFRS compliance by examining

audit fees incurred by firms for the statutory audit of their financial statements at the time of transition using a

comprehensive dataset of all publicly traded Australian companies, this study shows the direct effect of IFRS

adoption on earnings management, especially through classification shifting.

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II. Institutional Background of Operating Profits

Operating profits under GAAP

Conceptual framework of GAAP does not define operating profits (losses) directly;

however, GAAP No. 21, Presentation of Financial Statements, requires that income

statements should comprise of the following line items; sales, cost of goods sold, gross profits,

selling, general and administrative expenses (SG&A expenses), operating profits (losses),

non-operating income, non-operating expenses, earnings before taxes for continuing operation,

tax expenses for continuing operation, earnings for continuing operation (after tax),

discontinued operations (after tax), net income (loss), and earnings per share (Paragraph 60),

as shown in Table 1, Panel A. Also, GAAP No. 21 regulates that operating profits should be

calculated by subtracting SG&A expenses from gross profits (Paragraph 70) and shows the

examples of accounts which are subject to SG&A expenses, non-operating income and non-

operating expenses.

As GAAP regulates the individual items which should be included in operating profits

(losses), non-operating income and non-operating expenses, all companies classify the same

accounts in the same manner. Standardized classification may not reflect economic reality

well as ordinary course of business could be different depending on companies‟ operating

activities. As several income and expense accounts such as gains (losses) on disposition of

tangible assets may incur in the ordinary course of business, it would be questionable that

operating profits (losses) excluding those items in accordance with GAAP represent the

companies‟ operating activities. However, standardized classification in presentation of

income statements leads to better comparability that enables information users to identify and

understand similarities or differences among the different companies or different periods at

the expense of true and fair view.

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Operating profits under IFRS7

All listed companies in Korea are required to adopt the new internationally accepted

accounting standard, IFRS, from 2011 and early adoption is allowed from 2009. As IFRS No.

1001, Presentation of Financial Statements, does not mandate the disclosure of operating

profits (losses) in 2009, early adopters of IFRS do or do not disclose operating profits (losses)

and the way they calculate the operating profits (losses) is different even for the companies

which disclose operating profits (losses). Therefore, disclosure of this information brings

more confusion to investors (Moon 2013).

In 2010, IFRS mandates the disclosure of operating profits (losses) on statement of

comprehensive income or footnote as shown in Table 1, Panel B. In addition, all listed

companies should disclose items included in operating profits on footnote if the items are

different compared with those under GAAP (Paragraph 138.2). Although disclosure of

operating profits is required, comparability issue still remains as the companies could choose

the items included in operating profits.

7IFRS does not prescribe a detailed format for the presentation of the income statement, although many

national standards that are previously applied does provide formats. IAS 1, Presentation of Financial Statements,

omits the requirement in the 1997 version to disclose the results of operating activities as a line item in the

income statements (BC55), and only requires that the income statement contains the following line items;

revenue, financial costs, share of the profit or loss of associates and joint ventures accounted for using the equity

method, tax expense, a single amount for the total of discontinued operation, and profit or loss (Paragraph 82).

Conceptual framework of IFRS mentions that income and expenses may be presented in the income

statement in different ways so as to provide information that is relevant for economic decision-making. It is

common practice to distinguish between those items of income and expenses that arise in the course of the

ordinary activities of the entity and those that do not. This distinction is made on the basis that the source of an

item is relevant in evaluating the ability of the entity to generate cash and cash equivalents in the future. For

example, incidental activities such as the disposal of a long-term investment are unlikely to recur on a regular

basis. When distinguishing between items in this way consideration needs to be given to the nature of the entity

and its operations. Items that arise from the ordinary activities of one entity may be unusual in respect of another

(Paragraph 4.27). IFRS places autonomy on presentation of line items in income statements supporting

accounting reporting flexibility; however, IFRS does not mandate the disclosure of operating profits (losses).

It has been argued that the adoption of IFRS as a domestic accounting standard around the world is one of

the most significant regulatory changes in accounting history (e.g., Daske et al. 2008; Byard et al. 2011). The

introduction of a uniform set of accounting standards is expected to ensure greater comparability and

transparency of financial reporting. To date the evidence on mandatory IFRS application suggests that their

impact on value relevance, persistence and earnings quality depends on local business environments, institutional

frameworks and incentives for transparent financial reporting (e.g., Daske et al. 2008; Byard et al. 2011; Ahmed

et al. 2013).

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As all listed companies adopt IFRS in 2011, comparability issue receives attention again

from information users and regulators. IFRS No. 1001 is amended to disclose operating

profits on statement of comprehensive income, not on footnote. However, managers still have

discretion on how they compose the operating profits. In 2012, IFRS mandates the scope of

operating profits so that all listed companies follow the same criteria in calculating operating

profit numbers as they do under GAAP as shown in Table 1, Panel C. However, IFRS No.

1001 allows them to disclose adjusted operating profits on footnote if they could reflect the

companies‟ business performance better by adding the other operating income and expenses

to adjusted operating profits (Paragraph 138.4), which affords them to manage adjusted

operating profits.

We take the advantage of unique institutional setting to conduct the research in order to

provide insights into the issue of international convergence of accounting standards. This

paper focuses on this specific period when discretion is given to managers in composing

operating profits, and applies classification shifting used by prior studies (McVay 2006; Fan

et al. 2010) to other operating income as well as special expense items. The evidence sheds

some light on managers‟ choice preference on classification shifting in the IFRS adoption

period. This question is a matter of considerable interest and importance to the financial

reporting community.

III. Hypothesis Development

Classification shifting has recently received a greater degree of attention (e.g., McVay

2006; Fan et al. 2010; Haw et al. 2011). This earnings management tool simply moves certain

revenues, expenses, gains and losses to different line items on the income statement (Ronen

and Sadan 1975; Barnea et al. 1976; McVay 2006; Fan et al. 2010; Barua et al. 2010). McVay

(2006) provides the first study providing evidence that managers opportunistically engage in

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classificatory shifting. Specifically, McVay (2006) investigates whether managers engage in

classification shifting by reclassifying ordinary operating expenses as special expense items

and concludes that managers opportunistically shift core expenses to inflate current core

earnings, resulting in a positive relation between unexpected core earnings and income-

decreasing special expenses. Fan et al. (2010) provide broad support for McVay (2006)‟s

conclusion that managers engage in classification shifting using quarterly data, as opposed to

annual data in McVay (2006). They find that classification shifting using income-decreasing

special items is more prevalent in the fourth quarter than in other quarters. They also find

evidence of classification shifting for companies that just meet or beat analysts‟ forecasts,

one-year-ago same-quarter earnings, and zero earnings. Barua et al. (2010) investigate

whether managers use classification shifting to manage earnings when reporting discontinued

operations. Using a methodology similar to McVay (2006), they find evidence consistent with

the hypothesis that companies shift operating expenses to income-decreasing discontinued

operations to increase core earnings.

The existing body of empirical evidence centers around expense items: special expense

items or expenses in discontinued operations.8 However, this study investigates whether

other operating income in addition to special expenses is used for classification shifting in the

IFRS adoption year. Our study is motivated by prior research suggesting that managers are

opportunistic when reporting operating profits under IFRS. Cheon (2011) examines company

characteristics that are likely to affect companies‟ decision on the disclosure of operating

income using early adopters of IFRS in 2009 and 2010 and finds that companies tend to

include all items except investment revenue and financial costs in operating income when

GAAP operating income is low and other expenses are small.

8Expenses in discontinued operation are not used in our analyses because only about 2% of companies (8%

in Barua et al. 2010) report discontinued operations in our sample, resulting from the limited sample period.

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Shifting other income to other operating income increases core earnings, operating

profits, which are widely believed to be good indicators of company performance and to be of

primary interest to investors. This type of classification shifting is not expected to raise red

flags by outside monitors as IFRS does not regulate the individual items of operating profits

until 2012‟s amendment in IFRS. In addition, other operating income is reported on the

income statement in an aggregated form and detailed components of other operating income

are disclosed in footnotes. Because of these several reasons, we predict a shifting of other

income as well as special items. Considering that special expense items have been scrutinized

for classification shifting, we expect that managers are more likely to abuse their discretion

by classifying other income as other operating income to ensure the benefits of retaining this

shifting for future financial reporting. This leads to the following hypothesis stated in

alternative form:

Hypothesis 1: Managers engage in classification shifting using other operating income and

special expenses to increase operating profits in the IFRS adoption period.

Fan et al. (2010) show that classification shifting using special expense items provides

the better alternative when the ability to manage earnings using accruals upward is

constrained. They measure companies‟ previous earnings management as the beginning level

of net operating assets based on Barton and Simko (2002), who find that companies with

higher net operating assets are less likely to report earnings that just meet or beat analysts‟

forecasts. Therefore, classification shifting using other operating income and special expenses

is more likely to occur in the IFRS adoption year when managers are constrained in their

ability to manipulate current-period accruals because of prior upward accrual manipulation.

This leads to our second hypothesis as follows:


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special expenses more when the ability to manipulate accruals is constrained.

Prior research provides evidence of companies managing earnings to meet or beat three

earnings benchmarks: zero earnings, prior year‟s earnings, and analysts‟ forecasts. For

example, prior studies document a discontinuity in the earnings distribution, with more

companies reporting income just above these benchmarks than companies reporting income

below these benchmarks (Burgstahler and Dichev 1997; Degeorge et al. 1999; Song et al.

2004). The evidence in McVay (2006) suggests that the strategic classification of special

items is most pronounced when companies are close to earnings benchmarks. Fan et al. (2010)

also show the results which support classification shifting for companies that just meet or

beat earnings benchmark using quarterly data. Consequently, managers in the IFRS adoption

period might shift other income and special items more to meet or beat earnings benchmarks.

Therefore, the next hypothesis is as follows:


special expenses more when reported earnings meet or narrowly beat earnings

benchmarks.

IV. Research Design

Measuring Unexpected Operating Income

The empirical analyses test whether companies increase operating profits by shifting

other income and special expenses. We employ Fan et al. (2010)‟s model, which extends

McVay (2006)‟s methodology to measure expected and unexpected operating profits by

dropping current-period accruals and including additional controls for performance, stock

returns for current and prior periods. Fan et al. (2010) use current year‟s returns to control for

current performance and prior period returns as the market may detect deteriorating

performance and decrease its expectations of core earnings prior to it being reported in the

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current year. To estimate unexpected operating profits, we use the following expectation

model:

OI = β0 + β1OIt-1 + β2ATO + β3ACCRUALSt-1 + β4△SALES + β5NEG_△SALES +

β6RETURNS + β7RETURNSt-1 + ε (1)

where:

OI = operating profits disclosed in financial statements under IFRS, scaled by sales.

ATO = asset turnover ratio, defined as Sales/((NOA+NOAt-1)/2)

NOA = net operating assets

= operating assets – operating liabilities

= (total assets–cash and short-term investments) – (total assets–total debts-

book value of common and preferred equity)

ACCRUALSt-1 = operating accruals, calculated as (net incomet-1–cash flowst-1)/Salest-1

△SALES = percentage change in sales, calculated as (sales-salest-1)/(salest-1)

NEG_△SALES = percentage change in sales (△SALES) if △SALES is negative, and 0 otherwise

RETURNS =12-month market-adjusted returns corresponding to the fiscal year

We measure the expected value of operating profits for company i using the predicted

value from Equation (1). We estimate this equation by industry, excluding company i from

the estimation. Unexpected operating profits are the difference between the actual and

predicted value of Equation (1). IFRS is enacted for all public companies from 2011 in Korea;

however, the individual components of operating profits are regulated from 2012. Therefore,

only data for 2011 are available for our analyses.

Classification Shifting Using Other Operating Income and Special Expenses

We follow McVay (2006) in designing our regression models to test whether companies

increase operating profits by using classification shifting of other income and special

expenses when IFRS is adopted in 2011. We modify her equation by adding a variable, other

operating income (%OOI), as shown in the following equation:

UEOI = β0 + β1%OOI + β2%SI + ε (2)

UEOI is unexpected operating profits, which are calculated as the difference between

reported and expected operating profits from Equation (1). The variable of interest, %OOI, is

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other operating income scaled by sales. %SI is non-operating expenses scaled by sales.9

Under IFRS, special items are not disclosed separately; instead, non-operating income, non-

operating expenses, and financial costs are disclosed as separate line items. We use non-

operating expenses comparable to special items in US, specifically income-decreasing special

items used by McVay (2006)10

, to maintain comparability with prior studies (McVay 2006;

Fan et al. 2010; Haw et al. 2011). To be consistent with Hypothesis 1, we predict a positive

association between unexpected operating profits and other operating income (i.e., β1 > 0). To

test whether managers engage in classification shifting using special items, we again predict a

positive relation between unexpected operating profits and special items (i.e., β2 > 0).

Our second hypothesis is that managers engage in classification shifting more when the

ability to manipulate accruals is constrained. Following Fan et al. (2010), we measure

managers‟ accrual manipulation constraint using net operating assets (NOA) at the beginning

of the year scaled by sales. Fan et al. (2010) find that companies with higher NOA use

classificatory shifting of special items more because these companies are more constrained in

their ability to manipulate accruals. We classify companies into four groups with respect to

their NOA for the industry they belong to. To test whether the classification shifting behavior

is more pervasive for companies with high NOA, we add an indicator variable for being in

the highest NOA quartile, HighNOA, and interactions of HighNOA with %OOI and %SI, as

shown in the following equation:

UEOI = β0 + β1%OOI + β2%OOIHighNOA + β3%SI + β4%SI HighNOA + β5HighNOA + ε (3)

9Special items in the US literature are not formally GAAP-specified line items in the income statement;

instead, they are Compustat-defined items consisting of certain non-recurring items identified from the income

statement and the accompanying notes (Chen and Wang 2004). The scope of non-operating expenses examined

in this study is similar to special items in the US literature in that those expenses do not occur from the

companies‟ ordinary operating activities.

10

McVay (2006) uses income-decreasing special items (income-increasing special items are set to zero) to

test classification shifting of special expense items. Therefore, we use non-operating expenses only.

15

To be consistent with Hypothesis 2, we predict that the coefficients on %OOIHighNOA

and %SIHighNOA are positive (i.e., β2 > 0 and β4 > 0).

Hypothesis 3 predicts that classification shifting in the IFRS adoption year occurs more

when the company has greater net benefits from classification shifting; specifically, when the

classification shifting allows managers to meet or narrowly beat earnings benchmarks. To test

this hypothesis, we group companies based on reported operating profits relative to three

targets: positive operating profits, last year‟s operating profits, and analysts‟ forecasts. Just

meeting or narrowly beating earnings are defined as indicator variables which equal to one if

(1) reported operating profits scaled by total assets are between 0% and 2% (JustMBZ), (2)

changes in operating profits divided by total assets are between 0% and 2% (JustMBP)11

, or

(3) the difference between reported annual operating profits and the median analyst forecasts

for six months as fiscal year-end, deflated by total assets, is between 0% and 0.05%

(JustMBF). If these conditions are not met, the indicator variables are set to zero. The

observations meeting these criteria include approximately 11.95 percent and 21.38 percent of

our sample companies for zero-profit and last year‟s earnings benchmarks, and 11.03 percent

of our sample companies with analyst forecast data for analysts‟ forecast benchmark.12

To

test whether classification shifting in the IFRS adoption period occurs more to meet or beat

earnings targets, we estimate the following regression:

UEOI = β0 + β1%OOI + β2%OOIJustMBZ(JustMBP/JustMBF) + β3%SI + β4%SI

JustMBZ(JustMBP/JustMBF) + β5JustMBZ(JustMBP/JustMBF) + ε (4)

To be consistent with Hypothesis 3, we expect β2 and β4 to be positive.

11

Prior studies suggest that earnings management to meet or beat earnings benchmarks occurs in broader

interval in Korea than that in US (Song et al. 2004; Kim et al. 2008; Park and Yoon 2008). Therefore, we use the

interval between 0% and 2% to define just meeting or beating zero earnings and last year‟s earnings.

12

Fan et al. (2010) consider companies that report an operating income per share between $0.00 and $0.04,

a change in reported operating income per share between $0.00 and $0.03, and earnings forecast error between

$0.00 and $0.01 as just meeting or beating earnings benchmarks; those subsamples constitute approximately 9%,

17% and 26% of quarterly observations, respectively.

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V. Data

We obtain financial data and auditor information from the KIS-value13

of Korean

Information Services (KIS) and collect analysts‟ forecast data from FnConsensus of FnGuide.

To determine whether the companies are included in Chaebol or not, we obtain the annual list

of Chaebols and the member companies from the Korea Fair Trade Commission (KFTC). To

estimate unexpected operating profits as shown in Equation (1), comparable data based on

IFRS for both adoption and previous years are needed. Therefore, we use the numbers

disclosed in 2011 financial statements in comparison form for the previous year‟s data. We

eliminate banks and financial institutions to enhance comparability. Finally, we require a

minimum of 15 observations per industry14

to ensure a data pool that is sufficiently large to

estimate the expectation model in Equation (1). Thus, a total of 1,230 observations for the

year 2011 are used in the analyses.

Table 2 provides descriptive statistics for the variables used in the analyses. Sales are

skewed with a mean (median) value of KRW 729.897 (112.849) billion. The mean (median)

of unexpected operating profits deflated by sales (UEOI) is 0.1% (0.3%). The mean (median)

of operating profits scaled by sales (%OI) is 4.9% (4.9%), substantially larger than the 0.07%

(0.11%) in McVay (2006). The mean (median) of other operating income as a percentage of

sales (%OOI) is 2.7% (1.0%). The mean (median) of special expense items as a percentage of

sales (%SI) is 5.5% (2.3%), which is again larger than 2.7% (0.0%) reported in McVay (2006).

Table 3 provides Pearson correlation matrix between SALES, UEOI, %OI, %OOI,

and %SI for the full sample. The primary relation of interest is for the variables included in

13

The KIS-value database in Korea provides both financial data and auditor information for companies

listed on the Korea Stock Exchange (KSE) and KOSDAQ markets.

14

Companies are classified into 17 industry groups based on KSIC-9 (Korea Standard Industry Code) from

KIS-value.

17

Equation (2). UEOI is positively correlated with %OOI (0.067), but negatively correlated

with %SI (-0.063), indicating that unexpected operating income is associated with larger

(smaller) other operating income items (special expense items). The correlation

between %OOI and %SI is low at 0.200.

[Insert Tables 2 and 3 here]

VI. Results

Test of Hypothesis 1

Table 4 reports regression results of unexpected operating profits on other operating

income (%OOI) and special expenses (%SI). When we include only %OOI in Model (1), the

coefficient on %OOI is positive and significant (0.111, t=2.38). This result suggests that

classification shifting takes place in the IFRS adoption year as companies report more other

operating income in operating profits. Economically, classification shifting using other

operating income appears to be a viable method of misrepresenting corporate performance.

Other things remaining constant, a typical company with a mean sales of KRW 729.897

billion grows its unexpected operating profits by KRW 1.62 billion (=0.111 0.020 KRW

729.897 billion) as %OOI increases from the first quartile to the third quartile. When we

include only %SI in Model (2), the coefficient on %SI is negative and significant (-0.063, t=-

2.21). Our results indicate that, when income-decreasing special expenses increase,

unexpected operating income actually decreases. That is, firm performance rather than

classification shifting is the dominating effect for special items. This is consistent with

evidence in Fan et al. (2010). They contend that a performance-driven relation between core

earnings and income-decreasing special items is in accordance with prior research findings

that firms incurring large write-offs or corporate restructuring charges tend to be poor

performers (Elliott and Shaw 1988; DeAngelo et al. 1994; Carter 2000).

18

When we include both %OOI and %SI in the same regression, the coefficient on %OOI

remains positive and significant (0.137, t=2.89), while the coefficient on %SI also remains

negative and significant (-0.079, t=-2.75). Overall, our findings in Table 4 indicate that

companies in the IFRS adoption period use other operating income items rather than special

expense items in general shifting practice to inflate core earnings. When managers have the

discretion to compose operating profits, they may consider the benefits of using other

operating income for classification shifting. These income items are largely ignored by

auditors and regulators and so relatively easy to justify this first account reclassification being

more closely related to underlying economic activities. Furthermore, managers might

contemplate the possibility of keeping the same classification scheme over operating income

items to calculate operating profits for future financial reporting.

[Insert Table 4 here]


Table 5 reports regression results for Equation (3) that examines classification shifting,

conditional on the ability to manipulate accruals. HighNOA represents accruals manipulation

in prior year and is an indicator variable for companies whose net operating assets scaled by

sales at the beginning of the year are in the highest quartile for the industry. When we include

only other operating income in Model (1), the coefficient on %OOIHighNOA is positive and

significant (0.229, t=2.26), consistent with more pervasive classification shifting in companies

with high net operating assets. However, when we include only special items in Model (2), the

coefficient on %SIHighNOA is positive but insignificant (0.045, t=0.69), indicating that

managers‟ constraints on accruals management do not affect the relation between unexpected

operating income and special expense items.

When we include both other operating income and special expense items in the same

19

regression, the coefficient on %OOIHighNOA remains positive and significant (0.227,

t=2.23) while that on %SIHighNOA also remains positive and insignificant (0.025, t=0.38).

In short, the results suggest that when managers‟ ability to manipulate accruals is constrained

classification shifting using other operating income occurs more than using special expense

items.



In Table 6, we present tests whether misclassification is more prevalent when it allows

companies just to meet or beat earnings benchmarks. For the zero or last year‟s targets, we use

the full sample of 1,230 observations. The sample for analyst forecasts is reduced due to the

availability of analyst forecasts data and consists of 281 observations.

In Panel A with the zero-profit benchmark, the coefficient on %OOI in Model (1) is

positively significant (0.115, t=2.19). However, the coefficient on %OOIJustMBZ is not

significant (-0.029, t=-0.25), demonstrating that classificatory shifting using other operating

income does not occur more for companies with just avoiding losses. On the other hand, the

coefficient on %SI in Model (2) is negative and significant (-0.107, t=-3.38), while the

coefficient on %SIJustMBZ is positive and significant (0.206, t=2.88). The results suggest

that classification shifting using special expense items occurs for companies that just meet or

beat the zero earnings threshold, while performance-driven relation (i.e., negative relation

between UEOI and %SI) is still dominant for other companies. When we include both other

operating income and special expense items in Model (3), the coefficient on %OOIJustMBZ

is still insignificant (-0.103, t=-0.87) whereas the coefficient on %SIJustMBZ remains

positive and significant (0.215, t=2.93). Therefore, different from the findings in Tables 4 and

5 that other operating income items are used as a general shifting tool, managers appear to

20

shift core expenses to special items just to avoid losses.

Similar to the results using zero earnings threshold in Panel A, Panel B using last year‟s

earnings benchmark shows the negative but insignificant coefficient on %OOI X JustMBP

and the positive and significant coefficient on %SI X JustMBP. For instance, in Model (3), the

coefficient on %OOIJustMBP is -0.088 (t=-0.73) while the coefficient on %SIJustMBP is

0.163 (t=2.46). Again, our results suggest that classification shifting using special expense

items occurs to avoid the decrease in operating profits, while classificatory shifting using

other operating income occurs in general but not just for meeting or beating last year‟s

earnings. We also find similar results using analysts‟ forecasts benchmark in Panel C. When

included in the same regression, the coefficient on %OOIJustMBF is not significant (0.235,

t=0.30) whereas the coefficient on %SIJustMBF is positive and significant (0.835, t=2.42).

The results show the effect of special expense items on just meeting or beating analysts‟

forecasts benchmark.

Furthermore, we consider three earnings benchmarks jointly following Fan et al. (2010).

That is, we test for classification shifting of other operating income and special expense items

when companies just meet or beat any of the three earnings thresholds. The sample size for

this test reduces to 281 observations as we need data for all three earnings benchmarks.

Results in Panel D are consistent with those in Panels A, B, and C. JustMB is an indicator

variable that equals one if any of JustMBZ, JustMBP, and JustMBF is equal to one. In Model

(3) of Panel D, the coefficient on %OOIJustMB is not significant (-0.340, t=-0.80), while the

coefficient on %SIJustMB is positive and significant (0.690, t=2.36). In summary, overall

results from Table 6 show that classification shifting using special items occurs for just

meeting or beating earnings benchmarks, whereas misclassification of other operating income

occurs as a common practice to inflate core earnings. Special expense items have been

21

scrutinized by auditors and regulators and this increased scrutiny may reduce the benefits of

classification shifting using these items (Barua et al. 2010). That is why managers tend to use

special items in special cases including meeting or narrowly beating earnings targets.


Sensitivity Analyses

Auditor Characteristics (BIG 4, Auditor Tenure)

Prior studies show that earnings management activities are negatively associated with the

choice of quality auditors since external auditors with high quality perform a corporate

governance role (Fan and Wong 2005; Francis and Wang 2008; Haw et al. 2011). Regarding

classificatory shifting in East Asian countries, Haw et al. (2011) show that the

misclassification decreases when a company hires a BIG 4 auditor, demonstrating that BIG 4

auditors play an effective monitoring role in reducing classification shifting. Further, this role

would be more effective with longer auditor tenure as auditors with short tenure are

associated with lower earnings quality because of the lack of client-specific knowledge and/or

low balling. Consistent with this view, Myers et al. (2003) report that longer auditor tenure is

associated with a lower dispersion in the distributions of discretionary and current accruals.

Therefore, we examine whether the auditor characteristics (i.e., BIG 4 and auditor tenure)

affect the extent to which the classification shifting occurs.

Panel A in Table 7 shows the regression results to examine whether the earnings

management via classification shifting occurs less when companies have BIG 4 auditors.

When we include only other operating income in Model (1), the coefficient on %OOI is

positive and significant (0.215, t=2.93). However, the coefficient on %OOIBIG 4 is negative

and significant (-0.170, t=-1.78), demonstrating that companies with BIG 4 auditors less

engage in classificatory shifting using other operating income. On the other hand, when we

22

include only special expense items in Model (2), the coefficient on %SI is negative and

significant (-0.087, t=-2.45), while the coefficient on %SIBIG4 is not significant (0.057,

t=0.97). When we include both other operating income and special expense items in Model

(3), the results remain the same. The coefficient on %OOIBIG4 and %SIBIG4 are -0.179

(t=-1.84) and 0.057 (t=0.93), respectively. These findings indicate that clients generally

engage in classificatory shifting using other operating income, and BIG 4 auditors seem to

restrain this kind of earnings management more than non-BIG 4 auditors.

Panel B in Table 7 reports the regression results to test whether managers more or less

engage in classification shifting as the auditor tenure gets longer. When we include only other

operating income in Model (1), the coefficient on %OOI is positive and significant (0.269,

t=3.17), while the coefficient on %OOITenure is negative and significant (-0.181, t=-2.28),

suggesting that companies having the relationship with their auditors for longer period less

engage in classification shifting using other operating income. However, when we include

only special expense items in Model (2), the coefficient on %SI is negative and significant (-

0.102, t=-1.78), and the coefficient on %SITenure is not significant (0.036, t=0.72). The

results remain the same when we include both other operating income and special expense

items in Model (3). The overall results indicate that high quality auditors with longer tenure

limit classification shifting using other operating income. Therefore, our findings in Table 7

support our earlier argument that special items have been scrutinized by auditors and this

increased scrutiny leads managers in the IFRS adoption period to use this form of

classification shifting only in specific situations; however, the alternative form of

classification shifting through other operating income is used in the common practice of

earnings management, but the extent of this shifting behavior is affected by the auditor

characteristics.

23


Firm Characteristics (Chaebol, Delisting)

The business groups in Korea called Chaebol, multinational conglomerates of public and

private companies from a broad range of industries and largely controlled by wealthy

founding families, can exercise more control rights than their cash flow rights, which might

generate the agency problem between majority owners and minority owners. Prior studies find

that managers of Chaebol companies might not be free to make decisions against majority

shareholders and might be more involved in manipulating reported earnings to cover majority

owners‟ expropriation (Ahn 2004; Kim and Yi 2006; Jung et al. 2009; Haw et al. 2011).

Specifically for classification shifting, Haw et al. (2011) show that misclassification increases

with the degree of control divergence (i.e., the disparity between control and cash flow rights)

in East Asia‟s family-controlled businesses. Therefore, we examine whether the classification

shifting using other operating income and special items occurs more for companies included

in these large business groups.15

Panel A in Table 8 presents the tests that examine whether the misclassification takes

place more for Chaebol companies. When we include only other operating income in Model

(1), the coefficient on %OOI is positive and significant (0.118, t=2.50), while that

on %OOIChaebol is not significant (-0.276, t=-0.85), indicating that classificatory shifting

using other operating income does not occur more for Chaebol companies. On the other hand,

when we include only special items in Model (2), the coefficient on %SI is negative and

significant (-0.079, t=-2.73), but the coefficient on %SIChaebol is positive and significant

(0.402, t=2.82). The results suggest that Chaebol companies engage in classification shifting

using special expense items. When we include both other operating income and special

15

We define Chaebol companies as in the largest four business groups, Samsung, Hyundai, LG, and SK, and

the Chaebol companies comprise 6.17 percent of all sample companies.

24

expense items in Model (3), the coefficient on %OOIChaebol is still insignificant (-0.260,

t=-0.80) whereas that on %SIChaebol is positive and significant (0.418, t=2.93). Taken

together, Chaebol companies with the bigger disparity between ownership and control seem to

use both other operating income and special expense items to inflate core earnings in the IFRS

adoption year, while non-Chaebol companies use other operating income alone for

classification shifting.

According to securities listing regulation in the KOSDAQ market, companies reporting

operating losses for four years in a row are designated as issues for administration and those

for five consecutive years are delisted. Therefore, companies having consecutive operating

losses have strong incentives to turn into operating profits to avoid administrative issue or

liquidation. Therefore, we test whether the misclassification takes place more for companies

that have consecutive operating losses for the previous four consecutive years and operating

profits in the current year. After only including the companies listed on the KOSDAQ market,

which officially requires administrative issue or delisting upon operating losses, the sample

size reduces to 708 observations.

Panel B in Table 8 reports the regression results for testing whether classificatory shifting

occurs more for companies to be liquidated if they have operating losses in the current year.

ADELIST is an indicator variable that equals one if a company has operating losses for four

consecutive years just prior to the current year but has operating profits in the current year and

zero otherwise. When we include only other operating income in Model (1), the coefficient

on %OOI is positive and significant (0.199, t=2.84). However, the coefficient

on %OOIADELIST is not significant (0.025, t=0.16), suggesting that classificatory shifting

using other operating income does not occur more for companies having incentives to avoid

25

delisting (constituting approximately 4.1 percent of our sample companies).16

On the other

hand, when we include only special items in Model (2), the coefficient on %SI is negative and

significant (-0.203, t=-5.14), whereas the coefficient on %SIADELIST is positive and

significant (0.375, t=3.35). The results suggest that classification shifting using special

expense items occurs for companies with strong incentives to report operating profits while

performance-driven effect is prevalent in other companies. When we include both other

operating income and special expense items in Model (3), the coefficient on %OOIADELIST

is still insignificant (-0.087, t=-0.53) while the coefficient on %SIADELIST remains positive

and significant (0.339, t=2.79).


High Other Operating Income and Special Expenses

Companies with high other operating income or special expenses have more opportunities

to classification shift. To examine whether the classification shifting behavior depends on the

amount of other operating income and special expenses, we define indicator variables, OOIH

and SIH, for other operating income and special expenses greater than 5% of sales,

respectively. Consistent with McVay (2006) who uses 5% of sales in identifying companies

with high income-decreasing special items, we also use 5% criteria when we define both

indicator variables. Then we include the main and interaction variables (e.g., OOIH,

SIH, %OOI OOIH and %SI SIH) into Equation (2).

Untabulated results show that the coefficient on %OOIOOIH is positive and significant

(0.649, t=2.36), and that on %SISIH is positive but insignificant (0.117, t=0.45). The results

indicate that classification shifting using other operating income is more pervasive in

16

Approximately 5.5 percent of our sample companies have operating losses for the previous three

consecutive years and operating profits in the current year and the results using these alternative sample

companies that have incentives to avoid administrative issue remain the same.

26

companies with high other operating income, while the relationship between unexpected

operating income and special expense items does not vary with the amount of special

expenses.

Early IFRS Adopting Companies

The country-level enforcement, the incentives to engage in earnings management, the

culture of the country, and the period of adoption (early adopters vs. late adopters) play a

relatively greater role in shaping the IFRS outcomes.17

Especially, voluntary IFRS adoption

is not random; therefore, self-selection bias may occur when we include both early and late

adopters in our sample.18

To eliminate the effects of early adopters of IFRS, we conduct the

same analyses only for first-time IFRS adopters in 2011.

Excluding early adopters of 54 companies (i.e., adopting IFRS in 2009 or 2010), the

sample consists of 1,176 observations. Untabulated results show that when we include both

other operating income and special items in the same regression, the coefficient on %OOI

remains positive and significant (0.113, t=2.42) while that on %SI becomes insignificant (-

0.020, t=-0.82). These results indicate that classification shifting using other operating income

reported in earlier tables is unaffected by the omission of early IFRS adoption companies.

No Other Operating Income

17

Prior studies investigate the characteristics of companies voluntarily adopting IFRS earlier than others.

For instance, Barth et al. (2008) use a sample of early IFRS adopting companies to investigate whether early

adoption of IFRS improves accounting quality by deterring opportunistic earnings management and show that

early IFRS adopting companies exhibit less earnings smoothing, higher frequency of large losses, and slightly

lower (but insignificant) frequency of small positive earnings in the post-adoption period. However, subsequent

studies document that companies‟ incentives to voluntarily adopt IFRS, the local business environment, and

institutional frameworks can induce confounding effects when the adoption of IFRS is examined (Daske et al.

2008; Byard et al. 2011; Ahmed et al. 2013).

18

Selection occurs when observations are non-randomly sorted into discrete groups, resulting in the

potential for coefficient bias in estimation procedures such as ordinary least squares (OLS) (Maddala 1991;

Lennox et al. 2012). As only a few companies adopt IFRS earlier (approximately 4.4% of our sample) before

mandatory adoption of IFRS, it is not appropriate to implement the selection model to control for self-selection

bias in this study.

27

Companies can be classified into two groups depending upon how they calculate

operating profits under IFRS. One group includes other operating income in operating profits.

The other group calculates operating profits in the same manner as GAAP by subtracting the

sum of cost of goods sold and SG&A expenses from sales revenues (Cheon and Ha 2011).

While our analyses are conducted for the full sample including both groups, we also test

whether companies in the latter group engage in classification shifting using special expense

items in circumstances with no other operating income.

Table 9 reports the results from using 158 companies for which reported other operating

income is zero. The coefficient on %SI is negative and significant (-0.294, t=-6.79), thus

demonstrating that the companies which calculate operating profits according to old GAAP

do not engage in classification shifting using special expense items. The results indicates that

companies which do not use the discretion in composing the operating profits in the IFRS

adoption year also do not engage in classification shifting using special expense items.


VII. Conclusions

This study examines whether managers use other operating income and special expenses

for classification shifting to increase operating profits in the Korean IFRS adoption period. As

IFRS does not regulate the individual items of operating profits until the amendment of IFRS

in 2012, managers inadvertently have the discretion to compose revenue and expense items

for core earnings. Following a methodology similar to that employed by prior studies (McVay

2006; Fan et al. 2010), we find a positive association between unexpected operating profits

and other operating income and a negative relation between unexpected operating profits and

special items. We interpret these results as evidence that companies engage in classification

28

shifting using other operating income in the IFRS adoption period and performance-driven

effect is more prevalent with respect to special expense items.

We also find that classificatory shifting of other operating income is more pervasive

when managers appear to be constrained in their ability to manipulate current period accruals

because of prior upward accrual manipulation. Further, we investigate the motivations to

manage earnings using classification shifting and find that classification shifting using special

items occurs to meet or narrowly beat earnings benchmarks, whereas misclassification of

other operating income occurs as a general shifting tool but not just for meeting or beating

earnings targets.

From a number of sensitivity analyses, we find that classificatory manipulation is more

prevalent in companies with high other operating income, consistent with prior researches

examining classification shifting using income-decreasing special items and discontinued

operations (McVay 2006; Fan et al. 2010; Barua et al. 2010). We also find that high quality

auditors (i.e., BIG 4 auditors or auditors with longer tenure) restrain classification shifting

using other operating income. We further find evidence that Chaebol companies with the

bigger control divergence engage in classification shifting using both other operating income

and special expenses while non-Chaebol companies use other operating income alone. In

addition, we document that classificatory shifting using special items occurs for companies

having incentives to turn into operating profits as these companies are liquidated if they have

operating losses in the current year. Our results are also robust to a sample of companies

excluding early IFRS adopters. We further find that companies not reporting any other

operating income do not engage in classification shifting using special expense items.

Given the ongoing debate by the SEC about whether to permit adoption of IFRS for US

firms, the findings of this study imply that accounting standards-setting bodies need to pay

attention to classification shifting of revenue as well as expense items when they adopt IFRS

29

in the future. Furthermore, our study justifies the SEC‟s concern on improper classification of

line items in financial statements, especially revenue items.

Even after IFRS is amended to regulate the individual items of operating profits in 2012,

managers still have discretion in disclosing “adjusted operating profits” in footnote reflecting

their unique business environment. Considering that investors place a higher value on items of

income expected to be persistent in the future, “adjusted operating profits” disclosed in

footnote would be an important area of research in the future.

30

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Table 1

Comparison of Income Statement under GAAP and IFRS

*Those items are not mandatory. **Disclosure of operating profits (losses) on statement of comprehensive income or footnote was not mandatory in 2009 but mandatory in 2010 and 2011.

***Operating profits (losses) should be disclosed on statement of comprehensive income, not allowed on footnote, and calculated as follows: Revenue – Costs of goods sold –SG&A expenses.

****Disclosure of adjusted operating profit (losses) is allowed on footnote.

Panel A: I/S Under GAAP

Panel B: I/S Under IFRS (2011)

Panel C: I/S Under IFRS (from 2012)

Sales Revenue Revenue

Cost of goods sold Cost of goods sold* Cost of goods sold

Gross profits Gross profits* Gross profits

SG&A expenses SG&A expenses* SG&A expenses

Operating profits (losses) Other operating income* Operating profits (losses)***

Non-operating income Other operating expenses*

Non-operating expenses Operating profits (losses)**

Earnings before taxes for continuing operation Financial costs Financial costs

Tax expenses for continuing operation Share of the profit or loss of associates and joint

ventures accounted for using the equity method

Share of the profit or loss of associates and joint

ventures accounted for using the equity method

Earnings for continuing operation Non-operating incomes* Non-operating incomes*

Discontinued operation (net of taxes) Non-operating expenses* Non-operating expenses*

Net income (Loss) Tax expenses Tax expenses

Earnings per share Earnings for continuing operation Earnings for continuing operation

Single amount for the total of discontinued operation

(net of taxes)

Single amount for the total of discontinued operation

(net of taxes)

Net income (Loss) Net income (Loss)

Other comprehensive income Other comprehensive income - subsequently reclassified

to current earnings

Other comprehensive income - subsequently not

reclassified to current earnings

Comprehensive income Comprehensive income

Adjusted operating profits (losses)****

34

Table 2

Descriptive Statistics

Variables Mean 25% Median 75% Std. Dev.

SALES (KRW billions) 729.897 45.125 112.849 313.293 3,012.668

UEOI 0.001 -0.032 0.003 0.043 0.106

OI (KRW billions) 38.687 1.088 5.429 17.143 244.639

%OI 0.049 0.016 0.049 0.098 0.184

%OOI 0.027 0.003 0.010 0.023 0.065

%SI 0.055 0.010 0.023 0.050 0.106

Unexpected operating income (UEOI) is the differences between reported and predicted operating income, where the

predicted values are calculated using the coefficients from Equation (1), estimated by industry and excluding company i.

OI = β0 + β1OIt-1 + β2ATO + β3ACCRUALSt-1 + β4△SALES + β5NEG_△SALES + β6RETURNS + β7 RETURNSt-1 + εt (1)

OI is reported operating income in financial statements. ATO is the asset turnover ratio, defined as Sales/((NOA+NOAt-1)/2),

where NOA is Net Operating Assets. ACCRUALSt-1 is Operating accruals, calculated as (Net income–Cash From

Operations)/Sales. △SALES is the percentage change in sales from year t-1 to t (Sales-Salest-1)/(Salest-1). NEG_△SALES is

△SALES if △SALES is negative, and 0 otherwise. RETURNS is 12-month market-adjusted return corresponding to the fiscal

year. %OI is operating income items scaled by sales. %OOI is other operating income items scaled by sales. %SI is special

expense items scaled by sales.

35

Table 3

Pearson Correlation Matrix

Variable definitions are in Table 2. p-values are in parenthesis.

SALES UEOI %OI %OOI %SI

SALES 1.000 -0.032 0.005 -0.017 -0.052

(0.259) (0.855) (0.544) (0.066)

UEOI 1.000 0.653 0.067 -0.063

(<.001) (0.017) (0.027)

%OI 1.000 0.100 -0.123

(<.001) (<.001)

%OOI 1.000 0.200

(<.001)

%SI 1.000

36

Table 4

Regression of Unexpected Operating Income on Other Operating Income and Special Expenses

Variable definitions are in Table 2. t-statistics are shown in parentheses.

*, **, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively, for two-tailed test.

Variables Dependent Variable = UEOI

Model (1) Model (2) Model (3)

Intercept -0.002 0.004 0.001

(-0.71) (1.22) (0.37)

%OOI 0.111 0.137

(2.38)** (2.89)***

%SI -0.063 -0.079

(-2.21)** (-2.75)***

Observations 1,230 1,230 1,230

Adjusted R2 0.38% 0.32% 0.91%

37

Table 5

Regression of Unexpected Operating Income on Other Operating Income and Special Expenses: High

Net Operating Assets

Variables

Dependent Variable = UEOI


Intercept 0.004 0.006 0.006

(1.21) (1.73)* (1.64)

%OOI -0.015 -0.002

(-0.19) (-0.04)

%OOIHighNOA 0.229 0.227

(2.26) ** (2.23)**

%SI -0.078 -0.078

(-1.42) (-1.42)

%SIHighNOA 0.045 0.025

(0.69) (0.38)

HighNOA -0.025 -0.012 -0.021

(-3.33) *** (-1.47) (-2.41)**


Adjusted R2 1.19% 0.33% 1.35%

HighNOA is an indicator variable that equals 1 if a company has net operating assets scaled by sales at the beginning of the year being

in the highest quartile for the industry and 0 otherwise. Other variable definitions are in Table 2. t-statistics are shown in parentheses.


38

Table 6

Regression of Unexpected Operating Income on Other Operating Income and Special Expenses:

Earnings Benchmarks

Variables



Panel A: Using Zero Operating Profit Benchmark

Intercept -0.003 (-0.96) 0.005 (1.41) 0.001 (0.52)

%OOI 0.115 (2.19)** 0.152 (2.86)***

%OOIJustMBZ -0.029 (-0.25) -0.103 (-0.87)

%SI -0.107 (-3.38)*** -0.124 (-3.85) ***

%SIJustMBZ 0.206 (2.88)*** 0.215 (2.93)***

JustMBZ 0.008 (0.87) -0.005 (-0.46) -0.003 (-0.27)


Adjusted R2 0.28% 0.95% 1.46%

Panel B: Using Last Year’s Operating Profit Benchmark

Intercept -0.007 (-2.08)** 0.001 (0.38) -0.002 (-0.53)

%OOI 0.123 (2.37)** 0.157 (3.00)***

%OOIJustMBP -0.032 (-0.28) -0.088 (-0.73)

%SI -0.105 (-3.21)*** -0.123 (-3.70)***

%SIJustMBP 0.156 (2.43)** 0.163 (2.46)**

JustMBP 0.024 (3.11)*** 0.014 (1.74)* 0.016 (2.01)**


Adjusted R2 1.06% 1.44% 2.03%

Panel C: Using Analysts’ Forecasts

Intercept 0.013 (2.60)*** 0.031 (5.27)*** 0.023 (3.76)***

%OOI 0.368 (3.55)*** 0.357 (3.49)***

%OOIJustMBF 0.000 (0.00) 0.235 (0.30)

%SI -0.361 (-2.82)*** -0.344 (-2.74)***

%SIJustMBF 0.807 (2.33)** 0.835 (2.42)**

JustMBF 0.008 (0.44) -0.017 (-1.01) -0.021 (-0.93)***

Observations 281 281 281

Adjusted R2 3.48% 2.50% 6.14%

Panel D: Using Composite Measure of Earning Benchmarks

Intercept 0.014 (2.55)** 0.035 (5.38)*** 0.025 (3.73)***

%OOI 0.381 (3.57)*** 0.358 (3.38)***

%OOIJustMB -0.266 (-0.63) -0.340 (-0.80)

%SI -0.400 (-3.01)*** -0.365 (-2.78)***

%SIJustMB 0.727 (2.49)** 0.690 (2.36)**

JustMB 0.001 (0.14) -0.026 (-2.11)** -0.017 (-1.32)


Adjusted R2 3.50% 2.76% 5.97%

JustMBZ is an indicator variable that equals 1 if a company has a reported operating income in the current year between 0% and 2% of

total assets, and 0 otherwise. JustMBP is an indicator variable that equals 1 if a company has the change in operating income in the

current year between 0% and 2% of total assets, and 0 otherwise. JustMBF is an indicator variable that equals 1 if a company has an

analyst forecast error between 0% and 0.05% of total assets, and 0 otherwise. Analyst forecast error is defined as actual operating

income as reported by FnGuide less the median of analyst forecast for six months as of fiscal year-end. JustMB is an indicator variable

that equals 1 if any of JustMBZ, JustMBP, and JustMBF is equal to one, and 0 otherwise. Other variable definitions are in Table 2. t-

statistics are shown in parentheses.


39

Table 7

Regression of Unexpected Operating Income on Other Operating Income and Special Expenses:

Auditor Characteristics

Variables



Panel A: BIG 4 Auditors

Intercept -0.001 (-0.17) 0.010 (1.95)* 0.005 (1.01)

%OOI 0.215 (2.93)*** 0.242 (3.28)***

%OOIBIG4 -0.170 (-1.78)* -0.179 (-1.84)*

%SI -0.087 (-2.45)** -0.102 (-2.85)***

%SIBIG4 0.057 (0.97) 0.057 (0.93)

BIG4 -0.002 (-0.38) -0.010 (-1.55) -0.007 (-1.02)


Adjusted R2 0.58% 0.36% 1.14%

Panel B: Auditor Tenure

Intercept 0.006 (0.97) 0.021 (2.94)*** 0.014 (1.91)*

%OOI 0.269 (3.17)** 0.316 (3.65)***

%OOITenure -0.181 (-2.28)** -0.201 (-2.49)**

%SI -0.102 (-1.78)* -0.146 (-2.52)**

%SITenure 0.036 (0.72) 0.065 (1.27)

Tenure -0.008 (-1.41) -0.017 (-2.68)*** -0.012 (-1.88)*


Adjusted R2 0.28% 0.76% 1.76%

BIG4 is an indicator variable that equals 1 if a company‟s auditing firm is one of BIG 4 auditors, and 0 otherwise. Tenure is the natural

log of the number of years the auditor has been with the company. Other variable definitions are in Table 2. t-statistics are shown in

parentheses.


40

Table 8

Regression of Unexpected Operating Income on Other Operating Income and Special Expenses: Firm

Specific Characteristics



Panel A: Chaebol

Intercept -0.003 (-0.90) 0.004 (1.32) 0.001 (0.41)

%OOI 0.118 (2.50)** 0.151 (3.15)***

%OOIChaebol -0.276 (-0.85) -0.260 (-0.80)

%SI -0.079 (-2.73)*** -0.098 (-3.34)***

%SIChaebol 0.402 (2.82)*** 0.418 (2.93)***

Chaebol 0.013 (0.99) -0.010 (-0.77) -0.005 (-0.34)


Adjusted R2 0.31% 0.82% 1.47%

Panel B: Delisting

Intercept -0.004 (-1.06) 0.011 (2.42)** 0.005 (1.21)

%OOI 0.199 (2.84)** 0.245 (3.55)***

%OOIADELIST 0.025 (0.16) -0.087 (-0.53)

%SI -0.203 (-5.14)*** -0.220 (-5.57)***

%SIADELIST 0.375 (3.35)*** 0.339 (2.79)***

ADELIST 0.052 (1.90) 0.031 (1.05) 0.027 (0.91)


Adjusted R2 2.51% 4.96% 6.51%

Chaebol is an indicator variable that equals 1 if a company belongs to Chaebol, which is a multinational conglomerate of public and

private companies from a broad range of industries, and 0 otherwise. ADELIST is an indicator variable that equals 1 if a company has

operating losses for previous four years and operating profits in the current year and 0 otherwise. Other variable definitions are in

Table 2. t-statistics are shown in parentheses.


41

Table 9

Regression of Unexpected Operating Income on Special Expenses: Zero Other Operating Income

Companies


Intercept 0.025

(3.41)***

%SI -0.294

(-6.79)***

Observations 158

Adjusted R2 22.34%

Variable definitions are in Table 2. t-statistics are shown in parentheses.


earnings management using income classification shifting ... · managers engage in classification...

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