Earnings Management using Classification Shifting: Relation between Core Earnings and Special Items

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Bachelor Degree of Business Autumn 2009

2010-01-07

Earnings Management using Classification Shifting:

Relation between Core Earnings and Special Items

Authors: Michael Bondegård & David La Advisor: Katerina Hellström

Abstract: In this paper we examine if managers of Swedish firms listed on OMX Large Cap engage in earnings management by shifting core expenses to income decreasing special items in order to increase core earnings, with other words classification shifting. To measure classification shifting we use regression models by McVay (2006) to measure unexpected core earnings, this is the difference between the reported and expected core earnings, and unexpected change of core earnings. By examining the relation between unexpected core earnings, unexpected change of core earnings and special items we find no significance evidence of classification shifting in year 2004. We also decompose the special items into two groups, those that are unsusceptible and those that are susceptible, and test these against unexpected core earnings and unexpected change of core earnings. Based on our sample on large firms in 2004, we find no significant relation.

Keywords: Earnings Management, Classification shifting, Core Earnings, Special Items

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I. INTRODUCTION

Evidence from prior research in the U.S. (Graham et al., 2005) and in the U.K. (Choi et al., 2006) show that meeting the expectations from the analysts is a significant earnings target.

Negative earnings surprises lead to a penalty in the stock market while the positive earnings surprises lead to a market reward (Bartov et al., 2002; Kasznik and McNichols, 2002). From this point of view management might have the incentives to use their judgment over reported earnings to meet the expectations (Athanasakou et al., 2009). Since the analysts and investors focus more on core earnings (net sales or revenues less core expenses), pro forma earnings and “street earnings” than on bottom-line income, the management therefore takes advantage of this focus and might have the incentive to misclassify some core expenses within the income statement as special items (Bhattacharya et al., 2004; Gu and Chen, 2004). McVay (2006) develops regression models to test if management in U.S. firms engages in earnings management by classifying parts of core expenses (defined as costs of goods sold, and selling, general and administrative expenses) as special items in order to increase core earnings. By shifting parts of core expenses to income-decreasing special items, the firm experience increased core earnings while bottom-line income remains unaffected. In other words, as income-decreasing special items increase, the firm reported core earnings tend to be higher than expected core earnings and unexpected core earnings arise. She finds evidence to support her hypothesis. Athanasakou et al., 2009, use the same regression models to test if management in U.K. firms engages in classification shifting, and their findings are not consistent with the results in of McVay (2006). Since these regression models are relatively new and applied to few markets, we therefore want to test them on firms from of Sweden’s OMX Large Cap. We also decompose special items into income-decreasing special items that are susceptible and those that are not and test these two subsets on unexpected core earnings and unexpected change of core earnings.

The data we use to examine the regression models are from Thomson Reuters DATASTREAM Sweden OMX Large Cap, 200911. The original data contains 79 observations. After removing those firms with missing data, our final sample contains of 38 firms. We find no evidence of classification shifting in Swedish firms in 2004.

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The paper proceeds as follows. In the next section, we review in prior research and develop our hypotheses. In section III we describe in detail the measurement of unexpected core earnings and the test design. In section IV, we discuss the data, sample, and descriptive statistics and in Section V we present the results. Section VI, conclude and discuss the paper.

II. PRIOR RESEARCH AND HYPOTHESES

In their paper Graham et al. (2005) conclude that analyst expectations and prior earnings from firms are managers’ two most essential benchmarks. By reporting small negative earnings surprises can strongly affect the stock market negatively and give the investors a view that the firm is not well-managed. On the other hand reporting zero or positive earnings surprises give the investors the opposite view, the firm’s underlying performance is better than predicted. By meeting the analyst expectations the firms earn a market reward and by missing the expectations the market punish the firms (Bartov et al., 2002; Kasznik and McNichols, 2002).

Since analysts and investors typically focus rather on pro forma earnings¹ and “street”

earnings² than the reported earnings (Bhattacharya et al. 2004), Brown and Caylor (2005) therefore, find evidence that from the mid-1990s managers rather report losses or earnings decreases than earnings surprises. They strengthen this conclusion by reporting evidence of a considerably higher reward/penalty for meeting/missing the analyst expectations than for achieving/missing the other earnings targets. The managers therefore have the incentives to use different methods to meet the expectations, and one of these is by earnings management³

1Pro forma earnings are a non-GAAP measure of performance, and there is no official definition of this expression. This particular way of reporting earnings exploded in the late 1990s, pro forma earnings may exclude expenses that are non-recurring, non-cash, and a variety of other miscellaneous charges but also e.g.

R&D costs and write-offs, restructuring charges, asset impairment charges, losses on the sale of businesses and assets, and goodwill amortization (Doyle et al., 2003; Dechow and Schrand, 2004).

2Street earnings are the ”actual earnings reported by I/B/E/S. It is this earnings number that analysts are trying to forecast (e.g. earnings before depreciation and amortization) (Dechow and Schrand, 2004).

3 Earnings management is a meaningful intervention in the external financial reporting process, with the purpose of achieving some private gain (as opposed to, say, merely facilitating the neutral operation of the process (Schipper, 1989). Earnings management tends to occur when management use their opinions in financial

reporting and in structuring transactions to change financial reports to either mislead some stakeholders about the firm’s underlying economic performance or to affect contractual outcomes that depend on reported accounting numbers (Healy and Wahlen, 1999).

: classification shifting of core expenses to special items.

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According to former studies there are two main methods of earnings management: real activities management and accrual management (Schipper, 1989). When using real activities manipulation managers can e.g. provide price discounts to temporarily boost the sales, cut expenditures such as Research & Development, advertising and structuring costs, and other transactions (Baber et al., 1991; Bushee, 1998). Such actions have major impacts on earnings quality⁴ and devastating effects on the firm’s future performance (Dechow and Schrand 2004). Real activities management increase net income in the short term, but also have real costs. For example cutting R&D may result in the loss of future income related to forgone R&D opportunities. Since these activities are not GAAP⁵

McVay (2006) investigates a third form of earnings management. She tests whether U.S.

firms manipulate core earnings which are defined as sales minus core expenses (costs of goods sold and selling, general and administrative expenses). To examine this statement she predicts that managers use classification shifting by classifying core expenses as special items

violation, they bear a relative lower cost of detection (Dechow and Sloan, 1991). The latter earnings method of manipulation, accrual management means that a manager can borrow earnings from future periods, through the acceleration of revenues or deceleration of expenses, in purpose to improve current earnings. Like the real activities management this process also bears a cost, since this action borrows earnings from the future period it will reduce with the same amount borrowed in the next period (Healy, 1985; Jones, 1991; McNichols and Wilson, 1988; Dechow and Schrand, 2004).

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4 A high-quality earnings number is one that correctly replicates the firm’s current operating performance, and is a good sign of future operating performance. In other words, earnings are considered to be of high quality is when the earnings number accurately annuitizes the intrinsic value of the firm and another way to think about high quality numbers are when return on equity is a good measure of the internal rate of return on the firm’s current portfolio of project. Therefore when management decides to engage in earnings management then they decrease the earnings quality (Dechow and Schrand, 2004).

5Generally Accepted Accounting Principles (GAAP) which are a widely accepted collection of regulations, principles, conventions, standards, and procedures for reporting financial information (fasb.org, 2009;

investorwords.org, 2009).

6Special items are a set of many unusual or infrequent items, which contain (-)special charge, (-)special liability accruals, (±) nonrecurring items, (-) asset write-downs, (±) changes in estimates, (-) start-up costs expensed, (±) profits and losses from asset sales, (-) restructuring costs, (±) profits and losses from discontinued operations, (±) extraordinary operating items, (±) accounting charges, (±) unrealized gains and losses on equity investments, (±) gains from share issues in subsidiaries, (±) currency gains and losses, (±) derivative gains and losses (operations) (Penman, H. S., 2007).

within the income statement. Since analysts and investors typically focus rather on core earnings (pro forma earnings) and “street” earnings than bottom-line income (reported

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earnings), because they consider them to be more value-relevant, managers might have the incentive to misclassify some core expenses within the income statement as special items (Bhattacharya et al. 2004; Gu and Chen 2004). Bradshaw and Sloan (2002) also find evidence that the market responds to street earnings more than to bottom-line earnings. McVay (2006) finds evidence to support her hypothesis and remarks that classification shifting bears a relatively low cost compared to the other two earnings management actions. There is no accrual that later reverses (illustrated in Figure 1), or forgone revenues or income by manipulating real activities. Furthermore the detection by auditors is fairly low. As the bottom-line income does not change, they might spend less energy on the identification or adjustments of these accounts. And to measure unexpected core earnings she uses a core earnings expectation model, similar to the accrual expectation model (Jones, 1991). The expected core earnings are measured by the relation between reported core earnings and a number of firm performance measures (e.g. core earnings for prior period, asset turnover ratio for the fiscal year, accruals for the fiscal and prior year, and change in sales for the fiscal year) for all other firms in the same industry. The unexpected core earnings are then calculated as the difference between the reported core earnings and the expected core earnings. If unexpected core earnings increase within overall in as the negative special items, this is a sign of classification shifting, since the management shift core expenses to negative items so that reported core earnings are higher than the expected. If management does not engage in classification shifting then the reported core earnings should be equal to the expected core earnings. McVay (2006) makes clear that the unexpected core earnings are increasing in special items in the fiscal year, t and for this increase to reverse in the next year, t+1. This is to assure that it is due to classification shifting and not to an economic improvement associated with the income-decreasing special items.

Athanasakou et al., (2009) use the same methods as McVay (2006) on firms in the U.K. to examine the relation between core earnings, unexpected core earnings and special items.

Their conclusion is opposite to the findings by McVay (2006). There is no significant relation between unexpected core earnings and income-decreasing special items. Since classification shifting has been examined only on U.S. Data (McVay, 2006) and U.K. data (Athanasakou et al., 2009), we find it interesting to examine if managers in of Swedish firms engage in the third form of earnings management, classification shifting. This leads to our first hypothesis:

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H1: Managers classify core expenses as special items

McVay (2006) admits that by using COMPUSTAT special items, it contains many types of special items. These special items include those that are not susceptible to classification shifting such as asset write-downs, and other items that are more open to classification shifting, e.g. restructuring expenses other than asset write-downs, or merger related costs.

Therefore, she hand-collects data for a sub-sample of firms, which have income-decreasing special items of at least 5% of sales in 1996-1997 and have transitory charges. Then she divides them into two subsets of special items, those that are open to classification shifting and those that are not. She considers Property, Plant & Equipment write-offs, goodwill write- offs, and losses on asset sales to be unsusceptible and all other are considered as acceptable for expense shifting. If the PP&E write-offs, goodwill write-offs, or losses on asset sales are not clearly broken out from susceptible charges, then she classifies the entire charge as susceptible. And by testing unsusceptible and susceptible special items with unexpected core earnings and unexpected change of core earnings she concludes that susceptibility is consistent with classification shifting. As McVay (2006), Athanasakou et al., (2009) examine these two subsets of income-decreasing special items on U.K. firms. Instead of using income- decreasing unsusceptible and susceptible special items, they sort the special items as non- operating exceptional items and other non-recurring items. By categorizing parts of special items differently to McVay (2006) they find no evidence of a significant correlation between unexpected core earnings and income-decreasing other non-recurring items for the entire sample. When analyzing the relation between unexpected core earnings and non-operating exceptional items, they find no significant evidence for any subset of the firms. But since our focus is on McVay (2006) we use the same categorization as McVay and apply it to Swedish firms. This lead us to our second hypothesis:

H2: There is a significant relation between unexpected core

earnings and susceptible special items

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III. RESEARCH DESIGN

We replicate McVay (2006) two stage regression model. By following this model we create two phases, phase one to measure unexpected core earnings and phase two to examine if there is a relation between unexpected core earnings and special items.

PHASE ONE

Our first regression, regression (1) measures expected core earnings. The dependent variable core earnings (CEt) is scaled by sales. Following McVay (2006), the first variable in the regression is lagged core earnings (CEt-1) and it is included because prior research finds that core earnings tend to be persistent⁷. The asset turnover ratio (ATO_t) tends to have an opposite effect on core earnings (Nissim and Penman, 2001) and therefore is included in the model⁸. Sloan (1996) suggests that accruals can be explained, if holding earnings constant, accruals can be used to predict future performance. For that reason, in line with McVay (2006), we include both lagged accruals (ACCRUALS_t-1) and accruals (ACCRUALS_t) for current year.

Following McVay (2006) we finally include sales growth (ΔSALESt) and negative sales growth (NEG_ΔSALESt). The regression (1) is shown below⁹:

Regression (1)

In regression (2) we use change in core earnings scaled by sales (ΔCEt) as a dependent variable. We include, following McVay (2006), lagged accruals, current year accruals, sales growth, negative sales growth, change in asset turnover ratio (ΔATOt) and add change in core earnings from prior year. (ΔCEt-1). The regression (2) shown is below¹⁰

7 The Spearman correlation in McVay (2006) is 0.798 between core earnings and lagged core earnings compared to our Spearman correlation which is 0.936 in APPENDIX A.

8 The Spearman correlation in McVay (2006) is 0.366 between core earnings and ATO compared to our Spearman correlation which is 0.400 in APPENDIX A.

9 See Table 1 – Panel B for our definition for the variables

:

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Regression (2)

Both regressions are estimated by fiscal year and we have ignored an industry classification since our sample is too small. We have make two tests on regression 1 and regression 2. The first test consists of data for one year and the second test is a control if our sample is valid even if we double the observations.

PHASE TWO

To test H1 we follow McVay (2006) unexpected core earnings for current year (UE_CE_t) and unexpected change in core earnings for the next year (UE_ΔCEt+1). We calculate coefficients¹¹ for each observation from regression (1) respectively regression (2). Further, we use reported core earnings (reported change in core earnings) and subtract obtained coefficients to calculate unexpected core earnings (unexpected change in core earnings). Next we include Special items (SIt) and define it as income-decreasing special items¹². We compose special items in a slightly different way than McVay (2006) because of missing data¹³. We obtain special items in two parts, in the first part we collect data from DATASTREAM and in the second part we collect data manually from annual reports. The data from DATASTREAM contains extraordinary charges¹⁴, extraordinary items and gain/loss from sales of assets¹⁵. The annual report data contains goodwill write-offs and losses from discontinued operations ¹⁶. We combine these two sets of variables to create our definition for special items. The regression (3) and regression (4) are shown below¹⁷

11 We acquire unstandardized coefficients from SPSS

13 Further discussion in the result section

14 Extraordinary charges are pretax extraordinary charge which is infrequent or unusual included in the net income of a company. For non-U.S. companies it is as defined by the company. It includes but not restricted to for example force majeure costs, expropriation of assets by forgeign government and write-down or write-off of inventories or other write-downs that are normally a part of operations if they exceed 25% of earnings.

(DATASTREAM, 2009-12-01)

15Extraordinary items & gain/losses of assets represents gains and losses resulting from nonrecurring or unusual events (DATASTREAM, 2009-12-01)

16 We manually collect goodwill write-offs and losses from discontinued operations compared to McVay (2006) which collect Goodwill write-offs, losses on asset sale and additionally PP&E (Property, plant and equipment)

:

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Regression (3)

Regression (4)

The last step in phase two is to form two subsets of special items for testing our second hypothesis. We define special items that are susceptible to classification shifting as SHIFTABLEt and special items that are not susceptible to classification shifting as NOT_SHIFTABLEt. In line with McVay (2006) we move goodwill write-offs, PP&E (property, plant and equipment) write-offs or losses of assets sales to NOT_SHIFTABLEt if it is clearly broken out in the annual report. Since we find all goodwill write-off items in the annual reports we classify all goodwill items as NOT_SHIFTABLE_t. The remaining parts of our composed special items are defined as SHIFTABLE_t.

The regression (5) and regression (6) is shown below¹⁸:

Regression (5)

Regression (6)

IV. PRESENTATION OF DATA & DESCRIPTIVE STATISTICS

PRESENTATION OF DATA

We gather data for all Swedish listed companies on the OMX Large-Cap from Thomson Reuters DATASTREAM for the period from 2003 to 2005. Our original data contains 79 observations where we exclude firms which report missing data and we also remove the least traded share if the same company issued A and B shares. From this sample we also eliminate,

18 See Table 3 for our definition for the variables

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consistent with McVay (2006) all firms which report less than 7.03 MSEK¹⁹

DESCRIPTIVE STATISTICS

in net sales to avoid outliers as sales are used as a component for most of the variables. The final sample contains a total of 38 firms in different industries. We have chosen to not classify our sample into smaller subgroups such as type of industry and amount of income decreasing special items of sales due to the extremely small sample.

Table 1 – panel A presents descriptive statistics. The reported values for the variables in the sample are comparable to some extent to McVay (2006). The mean sales growth (ΔSalest) is approximately 27.9% and is about 6% higher in our full sample compared to McVay (2006).

The mean Core Earnings (CE_t) in our full sample is 20.3% and is also larger than McVay (2006) which is 7%. The rest of the variables seem approximately identical to our results in terms of mean is income decreasing special items (SI_t) 2.9%, Accruals (ACCRUALS_t) -7.9%

and Asset Turnover Ratio (ATO_t) 2.72 compared to McVay (2006) which is SI_t 2.7%, Accrualst -10.4% and ATOt 2.82. The mean Change Core Earnings (ΔCEt-1, t), Unexpected Core Earnings (UE_CEt) and Unexpected Change Core Earnings (UE_ΔCEt+1) are nearly identical to our result. Table 1 – panel B explains the variables and how we calculate the data.

Table 1 – Panel A

Descriptive statistic for full sample (N=38)

Variables: Mean Median Standard

Deviation Percentile 25 Percentile 75

ΔSALESt 0,279482 0,080242 0,884038 0,014211 0,162326

CE_t 0,203 0,160 0,181 0,109 0,209

ΔCEt-1, t 0,011 -0,002 0,061 -0,008 0,012

ΔCEt, t+1 0,017 0,007 0,073 -0,001 0,019

UE_CEt 0,000000 0,000352 0,026142 -0,013869 0,005865

UE_ΔCEt+1 0,000000 -0,005469 0,041064 -0,017809 0,023586

SI_t -0,029271 -0,012385 0,041348 -0,038353 -0,004612

ACCRUALSt -0,079 -0,035 0,384 -0,071 -0,024

19 McVay (2006) excludes firms that report net sales of less than 1 million dollar. We estimate one million as 7.03 million Swedish krona based on forex exchange rate (2009-12-03)

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ATO_t 2,722162 1,838748 3,009967 1,177453 3,502305

Table 1 – Panel B

Variables Definitions and [DATASTREAM CODES]

SALESt Company net sales [X(WC01001)]

ΔSALES t The amount in percent of change in sales (Sales growth), calculated as (Salest-Salest-1)/Salest-1

CE_t Core Earnings_t = Core Earnings scaled by sales = (Sales_t - Cost of Goods Sold_t - Selling, Gen. & Adm. Costs_t)/Sales_t [(X(WC01001) - X(WC01051) - X(WC01101)) / X(WC01001)]

ΔCEt+1 Change in Core Earnings, CEt+1 - CEt

UE_CEt Unexpected Core Earnings is calculated as the difference between Core Earnings (CEt) and predicted value of Core Earnings.

Predicted values collected from Regression of CEt where t = 2004 UE_ΔCEt Unexpected change in Core Earnings is calculated as the difference

between change Core Earnings (ΔCEt+1) and the predicted value of Core Earnings. Predicted values collected from Regression of ΔCEt+1

NEG_ ΔSALESt If ΔSalest is less than 0 then ΔSalest else 0

ACCRUALSt Accrualst = Operating Accruals = (Net Income before Extraordinary Itemst - cash flow from operationst)/Salest

[(X(WC01551) - X(WC04860)) / X(WC01001)]

NOAt Net Operating Assetst = Operating Asstst - Operating Liabilitiest

[(X(WC02999) - X(WC02001)) - (X(WC02999) - X(WC03999) - X(WC03255) + X(WC03351))]

ATOt Asset Turnover Ratiot = ATOt = Salest /((NOAt + NOAt-1)/2) ΔATOt Change in Assets Turnover Ratio, ATO_t – ATO_t-1

SIt Income decreasing special items scaled by sales calculated as if negative (goodwill write-off_t + Extraordinary charges_t + Losses

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from discontinued operationst) * (-1) / Salest [Goodwill write-offs collected by hand + X(WC01254) + Losses from discontinued operations collected by hand / X(WC01001)]

SHIFTABLE_t Income decreasing special items that are susceptible to classification shifting scaled by sales calculated as if negative (Extraordinary charges_t + Losses from discontinued operations_t) * (-1) / Sales_t [X(WC01254) + Losses from discontinued operations collected by hand / X(WC01001)]

NOT_SHIFTABLEt Income decreasing special items that are not susceptible to classification shifting scaled by sales calculated as if negative (goodwill write-offt) * (-1) / Salest [Goodwill write-offs collected by hand / X(WC01001)]

t represents the year; for all cases if not stated differently: t = year 2004

V. RESULTS

We begin to explain our results in phase one and phase two.

PHASE ONE – MEASURE EXPECTED EARNINGS

In Table 2 – panel A we see that almost all results are consistent in term of sign prediction and significance level with McVay (2006)²⁰, for example CE_t-1, ACCRUALS_t-1 and ΔSALESt. The predicted sign of ATO_t²¹ and ACCRUALS_t²²

20 McVay (2006) uses a different approach to display significance level. We run one regression on all firms in the sample, compared to McVay (2006) who runs several regressions based on industries and therefore displays a percentage of all runs when expressing significance level.

21 ATOt in our results (Table 2 – panel A) is 0.001 and not significant which can be compared to McVay (2006):

-0.003 and not significant.

22 ACCRUALS_t in our result (Table 2 – panel A) is -0,031 and significant which can be compared to McVay (2006): 0.220 and significant.

is on the other hand not consistent with McVay (2006). ATO_t however is not significant. To solve ACCRUALS_t, we run a second test by increasing our sample by adding a second year shown in Table 2 – panel B. We find that ACCRUALSt is not significant anymore in our sample. Our adjusted R² for the mean

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coefficient is 97.5% for Table 2 – panel A and 93% for Table 2 – panel B compared with McVay (2006) which is 75.5%.

Dependent Variable: CE_t Observations: 38

Year t = 2004

Mean

Coefficent t Sig.

Collinearity Statistics

Toleranc

e VIF

Independent Variable

(Constant) 0,012 1,043 0,305

CE_t-1 0,897 31,955 0,000 0,612 1,634

ATOt 0,001 0,409 0,686 0,709 1,411

ACCRUALSt-1 -0,108 -9,595 0,000 0,694 1,441

ACCRUALSt -0,031 -2,167 0,038 0,725 1,380

ΔSALESt 0,041 6,903 0,000 0,805 1,242

NEG_ ΔSALESt 0,260 0,864 0,394 0,914 1,094

Adjusted R² 97,50%

Dependent Variable: CE_t Observations: 76 (Pooled)

Year t = 2004/2005

Mean

Tolerance VIF

(Constant) 0,019 1,322 0,190

CEt-1 0,900 26,967 0,000 0,671 1,491

ATOt 0,000 -0,228 0,820 0,784 1,276

ACCRUALS_t-1 -0,061 -4,311 0,000 0,735 1,360

ACCRUALS_t -0,019 -1,027 0,308 0,707 1,414

ΔSALESt 0,042 5,520 0,000 0,863 1,159

NEG_ ΔSALESt 0,064 ,559 0,578 0,890 1,124

Dummy Variable 0,001 ,074 0,941 0,949 1,053

Adjusted R² 93,00%

In Table 3 – Panel A we find the results to match McVay (2006), both sign prediction and significance level except of ACCRUALS_t. We perform a second test to find out if the

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regression is valid for two years shown in Table 3 – Panel B. We find no change to prediction sign values and significance compared to McVay (2006). Our adjusted R² for the mean coefficient is still high for Table 3 – Panel A but decreases in Table 3 – Panel B. Our adjusted R² for Table 3 – Panel A is 85.4% and for Table 3 – Panel B is 51% compared to McVay which is 51.7%.

Dependent Variable: ΔCEt

Observations: 38 Year t = 2004

Mean

(Constant) 0,008 0,912 0,369

CE_t-1 -0,092 -3,356 0,002 0,620 1,614

ΔCEt-1 -0,026 -0,282 0,780 0,696 1,337

ΔATOt 0,012 1,569 0,127 0,767 1,303

ACCRUALSt-1 -0,108 -9,686 0,000 0,680 1,470

ACCRUALS_t -0,034 -2,443 0,021 0,761 1,313

ΔSALESt 0,041 7,453 0,000 0,889 1,125

NEG_ ΔSALESt 0,372 1,229 0,229 0,869 1,151

Adjusted R² 85,40%

Dependent Variable: ΔCEt

Observations: 76 (Pooled) Year t = 2004/2005

Mean

(Constant) 0,015 1,364 0,177

CEt-1 -0,095 3,138 0,003 0,789 1,268

ΔCEt-1 -0,148 1,663 0,101 0,800 1,250

ΔATOt 0,003 -0,383 0,703 0,877 1,140

ACCRUALSt-1 -0,059 4,298 0,000 0,767 1,304

ACCRUALSt -0,023 -1,261 0,212 0,700 1,428

ΔSALESt 0,047 6,111 0,000 0,835 1,198

NEG_ ΔSALESt -0,001 -0,010 0,578 0,776 1,288

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Dummy Variable 0,003 0,248 0,941 0,892 1,121

Adjusted R² 51,00%

We compare the results in Table 2 – Panel A and Panel B respectively Table 3 Panel A and Panel B and we can conclude that there are no or small differences between one year data and pooled data. We also find that our dummy variable in both cases display no significance, for this reason we decide to continue our further phase two analysis based on one year data since there should be no impact.

PHASE TWO – TESTING THE HYPOTHESES

We report results from unexpected core earnings on special items and unexpected change in core earnings on special items, shown in Table 4 and Table 5. We see that our result for regression (3) in Table 4 differs from McVay (2006). We find a positive sign but no evidence of a significant relationship between UE_CEt and SIt. McVay (2006) finds on the contrary a significant relationship between UE_CEt and SIt23

. However, our results are consistent with Athanasakou et al. (2009) who find no evidence of significance between unexpected core earnings and special items²⁴. Our adjusted R² is 1.00% compared to McVay (2006) which is 0.03% and compared to Athanasakou et al., (2009) which is 0.01%.

Next, our result for regression (4) is shown in Table 5 and illustrates a negative predicted sign in line with McVay (2006) but there is still no significance between UE_ΔCEt+1 and SIt.

Table 4

Dependent Variable: UE_CE_t Observations: 38

Year t = 2004

Mean Coefficent t Sig.

Independent Variable (Constant) 0,004 0,679 0,502

SIt 0,120 -1,164 0,252

Adjusted R² 1,00%

23 McVay (2006) t-statistics is 4.61

24 The t-statistics in Athanasakou et al., (2009) is 1.08

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Table 5

Dependent Variable: UE_ΔCEt+1

(Constant) 0,007 0,862 0,394

SI_t -0,238 -1,479 0,148

Adjusted R² 3,1%

We can conclude that in our case we find no evidence that H1 can be true, large Swedish firms in our sample are probably not using classification shifting.

To test hypothesis 2 we perform regression (5) and regression (6). The results of regression (5) are shown in Table 6. Our results show in line with McVay (2006) in both cases that SHIFTABLEt are more significant than NOT_SHIFTABLEt but overall both our results are still insignificant while McVay (2006) have significant t-statistics for SHIFTABLEt 25

.

The results of regression (6) are provided in Table 7. Our results show no significant values compared to McVay where both values are significant. If we compare Table 6 and Table 7 with the results in McVay (2006), we see that despite the fact that we received no significant values they are following the same pattern.

Table 6

Dependent Variable: UE_CEt+1

(Constant) 0,004 0,687 0,496

SHIFTABLE 0,199 1,126 0,268

NOT_SHIFTABLE 0,071 0,605 0,549

R² 4,10%

25 McVay (2006) t-statistics for SHIFTABLE_t is 4.43

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Table 7

Dependent Variable: UE_ΔCE t+1

(Constant) -0,007 -0,913 0,367

SHIFTABLE -0,350 -1,280 0,209

NOT_SHIFTABLE -0,188 -1,035 0,308

Adjusted R² 1,20%

We conclude, based on the none-existing significance on both variables in relationship to both depended variables that there is no evidence of a relation between unexpected core earnings and special items that are susceptible to classification shifting in large Swedish firms.

VI. CONCLUSION AND DISCUSSION

Based on our sample on large firms in Sweden, we find no evidence of classification shifting.

These results must be taken with caution since it is a very small sample and hence we ignored a classification into industries and also a classification into income decreasing special items subgroups. The small sample could also influence the results in phase one, as we see both regression 1 and regression 2 containing no significant accruals for current period which is opposed to McVay (2006) findings.

The next issue is the missing data to build a whole income decreasing special items (SI_t). We collect only a part of the income decreasing special items available. Given that, we recieve a different definition of income decreasing special items than McVay (2006). McVay (2006) include fully composed income decreasing special items from COMPUSTAT and hand collect three items: goodwill write-offs, PP&E write-offs and losses on asset sale while we combine parts of special items from DATASTREAM and goodwill write-offs from annual reports collected by hand. This makes our result somehow misleading, since the obtained result is just one part of decreasing special items. Despite this, we conclude that there is no evidence of classification shifting using our definition of income decreasing special items.

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However since our attempt has shown opposite results to McVay (2006) we welcome future research on this subject. Future research could consist of a larger sample of Swedish firms together with better data on special items to receive more reliable results. Another possible idea would be to examine relation between for example classification shifting and firms with a high book to market ratio or the 25% most traded firms.

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REFERENCES

LITERATURE

Penman, H. S., 2007, Financial Statement Analysis and Security Valuation (4^th ed.), New York: McGraw-Hill

Pallant, J., 2001, SPSS Survival Manual, A step by step guide to data analysis using SPSS for Windows (Version 10), Buckingham: SPSS Inc., also Available [online]

< http://mcgraw-hill.co.uk/openup/chapters/0335208908.pdf > [2009-12-10]

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Propensities and Valuation Consequences. The Accounting Review, 80 (2): 423-440

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Journal of Accounting and Economics, 38: 129-170

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Evidence from Analyst Forecast Revisions and Share Prices. Journal of Accounting Research, 40 (3): 727-759

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McVay, E. S., 2004, The Use of Special Items to Inflate Core Earnings. Dissertation, University of Michigan, 1-52

McVay, E. S., 2006, Earnings Management Using Classification Shifting: An Examination of Core Earnings and Special Items, Accounting Review, 81 (3): 501-531

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WEBSITES

Standard & Poors, 2002, Available [online]

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OTHER SOURCES

DATASTREAM, 2009-12-01 ANNUAL REPORTS

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FIGURE 1

A simple View of Accrual Management versus Expense Shifting

Accrual Management

Year 1 Year 2

Pre-managed Core Earnings 250 250

Earnings Management 25 0

Earnings Management Reversal 0 -25

Managed Core Earnings 275 225

Expense-Shifting _{Year 1} _{Year 2}

Pre-managed Core Earnings 250 250

Earnings Management 25 0

Earnings Management Reversal 0 0

Managed Core Earnings 275 250

Source: McVay (2004)

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APPENDIX A

Spearman Correlation matrix

Spearman Correlatio n matrix

SALE S _t

ΔSAL ES _t

CEt CEt+1 ΔCEt UE_C E_t

UE_ ΔCEt

SIt ACC

RUA LSt

ATOt

SALES t 1 -0,09 -0,18 -0,3 0,178 0,25 -0,08 0,106 -0,04 0,504 ΔSALES t -0,09 1 0,142 0,207 0,533 0,234 0,213 0,227 -0,01 -0,23 CEt -0,18 0,142 1 0,936 -0,23 0,238 0,424 0,174 0,006 -0,4 CE_t+1 -0,3 0,207 0,936 1 -0,17 0,187 0,492 0,175 0,026 -0,46 ΔCEt 0,178 0,533 -0,23 -0,17 1 0,638 0,188 0,38 0,051 -0,04 UE_CE_t 0,25 0,234 0,238 0,187 0,638 1 0,183 0,462 0,059 -0,01 UE_ ΔCEt -0,08 0,213 0,424 0,492 0,188 0,183 1 -0,09 0,045 -0,21 SI_t 0,106 0,227 0,174 0,175 0,38 0,462 -0,09 1 0,332 0,142 ACCRUA

LS_t

-0,04 -0,01 0,006 0,026 0,051 0,059 0,045 0,332 1 0,142 ATOt 0,504 -0,23 -0,40 -0,46 -0,04 -0,01 -0,21 0,142 0,142 1 No Correlation: 0 to 0,09 (-0,09) Small Correlation: 0.10 (-0.10) to 0.29 (-

0.29) Medium Correlation: 0.30(-0.30) to 0.49(-

0.49)

Strong Correlation: 050 (-0.50) to 1.00 (- 1.00)²⁶

26 Definitions of correlation significance gathered from Pallant, J. (2001)

Earnings Management using Classification Shifting: Relation between Core Earnings and Special Items