Hypothesis testing of H1a – effect of AASB 1018 amendment on classification shifting

H1a examines whether classification shifting using AI, which was presumed by many commentators to be prevalent in the pre-2001/02 period, reduced after the 2001/02 amendments. To examine H1a, unexpected core earnings are regressed against AI and control variables. I use AI as identified by the Morningstar analyst assigned to the firm. These items identified by Morningstar as abnormal may have been reported by firms in a variety of forms (e.g. as 'abnormal', 'significant', 'unusual', or ‘non-recurring’).75 A positive association between unexpected core earnings and AI is expected if firms use opportunistic classification shifting to influence core earnings. If an observed positive association between AI and unexpected core earnings in year t derives from opportunistic classification shifting rather than genuine economic events, the high unexpected level of core earnings associated with AI in year t should reverse in year

t+1. Thus, the unexpected change in core earnings in year t+1 is expected to be negatively associated with AI in year t. Therefore, classification shifting firms are expected to have both (i) a high unexpected level of core earnings in year t, and (ii) a low unexpected change in core earnings in year t+1 (McVay 2006). To test whether the extent of classification shifting reduces after the 2002 amendment, I estimate the following regressions, using various samples comprising observations from 1995 to 2005:

UE_CEt = α0 + α1%AIt+ α2POSTt + α3%AIt*POSTt + α4SIZEt+ α5ROAt + α6CFOt

+ α7LEVt + α8LOSSt + α9AUDITORt + εt (3a)

75 _{Reconciliation of all the abnormal items identified by Morningstar against the firms’ annual reports} show that abnormal items recognised by Morningstar in the pre-2001/02 amendments period (1995–2000) are consistent with the abnormal items reported on the face of the income statement. In the post-2001/02 amendments period (2002–2009), the items identified by analysts as abnormal may have been reported by firms as: (a) items recognised as ‘abnormal items’ on the face of the income statement; (b) items reported under one of these headings: ‘significant items’, ‘unusual items’ and ‘non-recurring items’ either on the face of the income statement or in the notes; (c) single items of revenue or expense reported as line items (above and/or below profit from ordinary activities) on the income statement but under none of the specific headings in (b); or (d) items that are disclosed in the notes but under no specific heading. The specific reporting choices are discussed in Chapter Six.

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UE_∆CEt+1 = η0 + η1%AIt + η2POSTt + η3%AIt*POSTt + η4SIZEt + η5ROAt + η6CFOt

+ η7LEVt + η8LOSSt + η9AUDITORt + vt+1 (3b)

Where variables are as defined below:

UE_CEt = The difference between reported and predicted core earnings,

where the predicted value is calculated using the coefficients from core earnings estimated in Model (1)

UE_∆CEt+1 = The difference between reported and predicted change in core earnings, where the predicted value is calculated using the coefficients from change in core earnings estimated in Model (2)

%AIt = (Abnormal items (#86) * -1)/Sales, both in year t, when abnormal items are income decreasing (i.e. negative), 0 otherwise. Income-increasing AI and firm-year observations with zero or no AI reported are set to zero.

POSTt = An indicator variable = 1 for financial periods ending 30 June

2002 to 30 December 2005, 0 otherwise. %AIt*POSTt = The interaction of %AIt and POSTt

SIZEt = Natural log of total assets (#5090) in year t.

ROAt = Net profit or earnings before extraordinary items scaled by

average total assets: (EBEI) (#8036)/[(TAt(#5090) + TAt-1)/2]. CFOt = Cash from operations (#9100)/ Total assets (#5090), both in

year t.

LEVt = Total debt (#6000 + #6020)/Average total assets (#5090),

both in year t.

LOSSt = Indicator variable = 1 if firm reports negative EBEI (#8036),

0 otherwise.

AUDITORt = Indicator variable = 1 if the firm’s external auditor is a big N

firm, 0 otherwise. 95

All models also include untabulated industry fixed effects, using the classifications provided by the Centre for Research in Finance (CRIF). The general form of these models is similar to that used in Barua et al. (2010) when testing the impact of SFAS No. 144.76 %AIt measures the level of income-decreasing AI in year t, and is interacted

with a dummy variable indicating observations of AI occurring after the 2001/02 amendments (%AIt*POSTt).77 If firms use classification shifting to increase core

earnings in the pre-2001/02 period, then the main effect (%AIt), which measures the pre-

2001/02 associations between unexpected core earnings (UE_CEt) and AI, is expected

to be positively associated with UE_CEt. The interaction term (%AIt*POSTt) measures

the extent to which the association between UE_CEt and %AIt differs in the post-

2001/02 amendments period,78 relative to the pre-2001/02 period. Model (3b) is estimated to ascertain whether a higher than expected level of core earnings estimated in Model (3a) reflects classification shifting rather than fundamental economic improvement. If a higher than expected level of core earnings in the pre-2001/02 period is due to classification shifting, the coefficient for %AIt in Model (3b) is expected to be

negative. That is, firms that engage in classification shifting in year t should have negative unexpected change in core earnings in year t+1.

If the 2001/02 amendments were effective in curbing classification shifting, I expect at least one of the following 2 conditions to hold:

1) the coefficient for %AIt*POSTt in the unexpected core earnings model (3a) is

significantly negative, and/or

2) the coefficient for %AIt*POSTt is significantly positive in the unexpected change

in core earnings model (3b).

That is, even if tests of the levels model (3a) suggest that there is no reduction in the association between AI and unexpected core earnings after the 2001/02 amendments, if the extent to which this association reverses in t+1 weakens significantly, then there is evidence of a reduction in classification shifting. I also test the total effect size (sum of the main effect and interaction terms) and estimate regression restricted to the pre- 2001/02 (post-) amendments period to provide confirmatory evidence regarding the impact of the reforms.

76 _{I use leverage in preference to book-to-market (used by Barua et al. 2010) as this results in superior} model fit when applied to Australian firms.

77 _{Income-increasing abnormal items are set to zero.}

78 _{Throughout the remainder of this thesis, I use the terms post-2001/02 amendments, post-2001/02} reforms and post-2001/02 interchangeably to refer to the period after the 2001/02 reforms and before the adoption of IFRS in 2005 (i.e. 2002-30 December 2005).

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Control variables are included in both regressions to account for the effect of firm characteristics that are associated with earnings management and firm performance, and are broadly consistent with those used in Barua et al. (2010), the study closest in nature to mine. SIZEt is included to control for the effect of firm size on earnings management

where size has been found to be negatively associated with earnings management (Watts and Zimmerman 1978; Bartov, Gul and Tsui 2000; Davidson et al. 2005). Specifically, large firms have less flexibility and weaker incentives to overstate earnings because they are subject to more scrutiny from regulators and market participants (Watts and Zimmerman 1978, Bartov et al. 2000; Davidson et al. 2005; Lobo and Zhou 2006). In this case, a negative association is expected between UE_CEt and SIZEt.

However, large firms have diverse and complex operations that may also allow them more opportunity to manipulate earnings that external users may find difficult to detect (Lobo and Zhou 2006). To that end, UE_CEt is expected to be positively correlated with

SIZEt.

Return on assets (ROAt) is included to control for the possibility that unexpectedly good

performers may improve earnings through earnings manipulation (Brown 2001; Brown and Higgins 2001; Barua, Legoria and Moffitt 2006). Barua et al. (2006) document that strong performers use income-increasing abnormal accruals to just meet or beat analyst forecasts. Moreover, Brown (2001) and Brown and Higgins (2001) document that strong performers are more likely to report small positive earnings surprises. LOSSt is

included to control for possible differences in earnings management incentives for loss and profit firms (Brown 1997, 1998; Brown and Higgins 2001; Beaver, McNichols and Nelson2007). Beaver et al. (2007) find that on average, loss firms recognise more special items overall, and report more negative special items than profit firms. Also, loss firms attempt to avoid extreme negative surprises while profit firms are more likely to report small positive earnings surprises (e.g. Brown 2001; Brown and Higgins 2001).

CFOt is cash from operation scaled by total assets, both in year t, and controls for

performance level effects not captured by LOSSt where changes in firms’ CFO are likely

to be associated with change in core earnings (Wilson and Wu 2011).

High leverage firms are more likely to manage earnings than other firms in order to avoid debt covenant violations (Beasley and Salterio 2001; Klein 2002; Davidson et al. 2005). This effect is controlled for by LEVt. Finally, prior studies provide evidence that

big N audit firms are more likely to constrain earnings management (e.g. DeFond and Subramanyam 1998; Bartov et al. 2000; Lee and Mande 2003; Cahan and Zhang 2006;

Francis and Wang 2008). More directly, Haw et al. (2011) find that big N auditors effectively reduce classification shifting in East Asian countries with strong legal institutions. Therefore, auditor size (AUDITORt), is included in the regressions.

4.3.4 Hypothesis testing of H1b – classification shifting following the adoption of