7.1 Introduction
This thesis investigates two central issues: whether regulatory reforms affecting the scope for opportunistic classification shifting impacted actual classification shifting behaviour, and whether these reforms affected firms’ use of alternative earnings management methods such as discretionary accruals and real transactions. This chapter reports and discusses the findings of tests derived from the first research issue - the impact of regulation on classification shifting behaviour using abnormal items (H1a and H1b) and discontinued operations (H1c). H1a predicts that the 2001/02 amendments to AASB 1018, proscribing the disclosure of profits ‘before or after abnormal items’ decreased classification shifting using abnormal items (AI). The amendment to AASB101 (upon IFRS adoption in 2005) for firms with financial period ending on or after 1 January 2005, relaxed the restrictions on the reporting of non-recurring items by removing prohibitions on the placement and the prominence given to additional line items, andfirms were required to disclose the nature and amount of material income or expense items separately on the income statement (AASB 101 para. 97). These amendments may have encouraged firms to report additional line items opportunistically on the income statement. Thus, H1b predicts that firms engaged in significantly greater classification shifting using AI in the post-IFRS period (31 December 2005 – 2009) relative to the pre-IFRS period (2002 – 30 December 2005). Finally, AASB 5 (also introduced with the adoption of IFRS) broadened the definition of discontinued operations (DO) by defining it as a component of an entity that has either been ‘disposed of’ or is classified as ‘held for sale’, and assets ‘held for sale’ were not part of the definition under AASB 1042. This broader definition increases the scope and propensity for firms to opportunistically manipulate core earnings through classification shifting. H1c therefore predicts that following the introduction of AASB 5, firms engaged in significantly greater classification shifting using DO.
The balance of this chapter is structured as follows. Section 7.2 reports and analyses the findings for the main test of H1a, followed by the robustness test findings in Section 7.3. Next, the findings for the main test of H1b are reported and analysed in Section 7.4, followed by the robustness test results in Section 7.5. Section 7.6 then presents and evaluates the results for the main test of H1c, followed by the robustness test results in Section 7.7. Finally, Section 7.8 provides a summary of the Chapter. Tests of hypotheses concerning possible substitution between earnings management mechanisms are reported and analysed in the next chapter.
7.2 Results for the main test of H1a
H1a predicts that classification shifting using income-decreasing AI decreased as a result of the 2001/02 amendments to AASB 1018, which proscribed the disclosure of results ‘before and after abnormal items’ on the income statement. The main test of H1a comprises two regressions of the following general form:
UE_CEt = α0 + α1%AIt + α2POSTt + α3%AIt*POSTt + Controls (3a)
UE_∆CEt+1 = η0+ η1%AIt+ η2POSTt+ η3%AIt*POSTt + Controls (3b) Where UE_CEt and UE_ΔCEt+1 are unexpected core earnings and unexpected change in
core earnings generated by the modified McVay (2006) models (1) and (2) (see Chapter 4). %AIt is income-decreasing AI as a percentage of sales. POSTt is an indicator variable
that equals 1 for observations in the period 2002 to 30 December 2005 (the post- 2001/02 amendments period), and 0 otherwise. %AIt*POSTt is the interaction of %AIt
and POSTt which measures whether there is greater (or lesser) classification shifting in
the pre-IFRS period. Control variables are as defined in Chapter Four.
The coefficient for the main effect (%AIt) captures the association between AI reported
in the pre-2001/02 period and unexpected core earnings (unexpected change in core earnings). The coefficient for POSTt measures the average difference in the levels of
each dependent variable in the post-2001/02 amendments period relative to pre-2001/02 period, while the interaction (%AIt*POSTt) captures the incremental association
between reported AI and unexpected core earnings (unexpected change in core earnings) for the post-2001/02 period. H1a implies that a significant degree of classification shifting occurred in the pre-2001/02 amendments period. If this is the case, income-decreasing AI (%AIt) should be positively associated with unexpected core
earnings (UE_CEt) in the pre-2001/02 period (1995 – 2000) regressions based on
Equation (3a). Any such positive association is, however, not a sufficient condition to suggest that firms engage in classification shifting in the pre-2001/02 period to improve core earnings. To ascertain that the unexpectedly high core earnings is due to opportunistic classification shifting rather than an economic improvement associated with AI (e.g. through restructuring), I expect this improvement in core earnings (i.e. positive association between UE_CEt and %AIt) to reverse in the subsequent period.
Thus, evidence of opportunistic classification shifting requires both a significant positive association between UE_CEt and %AIt (Equation (3a)) and a significant
negative association between %AIt and UE_∆CEt+1 (Equation (3b)). To test the predicted reduction in classification shifting following the 2001/02 amendments, I focus on the interaction term %AIt*POSTt which measures the incremental association
(relative to the pre-2001/02 period) between AI and unexpected core earnings and unexpected change in core earnings. I expect %AIt*POSTt to be negatively correlated
with UE_CEt in Equation (3a) (‘levels’ regression) and positively correlated with
UE_∆CEt+1 in Equation (3b) (‘changes’ regression).
Table 7.1 summarises the regression results for tests of H1a (’levels’ and ‘changes’ regressions) using the main and alternative samples. The three samples employed are: (1) all Morningstar firms with available data to test H1a, (2) only firms that report income-decreasing AI in at least one year within the sample period, and (3) a propensity score matched (PSM) sample comprising all firm-years from firms that reported income-decreasing AI in the pre-2001/02 period irrespective of their post- 2001/02 amendments behaviour (treatment firm-years), and similar firm-years from firms that did not report income-decreasing AI pre-2001/02 regardless of their post- 2001/02 amendments behaviour (control firm-years). The results are presented in the first, second, and third columns, respectively.
All models are reasonably well fitted. The adjusted R2 statistics for Equation (3a) are similar across the three samples, ranging from 15.80 percent for Samples 1 and 2 to 18.2 percent for Sample 3. These are more than twice the adjusted R2 of 7.0 (and 7.1) percent for US firms reported in Barua et al. (2010, Table 3), the study closest in nature to this thesis. These adjusted R2 statisticsare also higher than that reported for East Asian firms in Haw et al. (2011, Table 9: 3.16%) who excluded accruals from the determination of UE_CEt (as has been done in this study), but had no control variables
in their model.114 For Equation (3b), the adjusted R2 for all three samples (ranging from 0.50 percent to 1.30 percent) are similar to those of 0.75 and 0.82 percent reported in Barua et al. (2010), and 0.6 percent and 1.08 percent in Haw et al. (2011).
The coefficients for control variables are consistent with predictions (and with prior studies) except for AUDITORt. For the sample comprising all firms (Sample 1), SIZEt is
significantly negatively correlated with UE_CEt, suggesting that large firms are less
likely to report unexpectedly high core earnings. LOSSt is also significantly negative,
consistent with loss firms reporting negative core earnings. ROAt, CFOt, and LEVt are
all significantly positively correlated with UE_CEt. That is, unexpectedly strong
performers, firms with unexpectedly large cash from operations, and highly leveraged firms are significantly more likely to report unexpectedly high core earnings.
AUDITORt is insignificant, suggesting no impact of the quality of the auditor on firms’
core earnings. The results for alternative samples (Columns (2) and (3)) are substantively similar to these.
I first discuss whether there is evidence of classification shifting in the pre-2001/02 period (as implied in H1a), and then the findings for the test of H1a. Panel A of Table 7.1 reports results for the ‘levels’ models, while results for the ‘changes’ models are reported in Panel B. Column (1) of Panel A reports that %AIt is positively associated
with UE_CEt in the pre-2001/02 period as predicted (α1 = 0.552, t = 8.055). Further, Column (1) of Panel B shows that, as predicted, %AIt is negatively associated with
UE_∆CEt+1 (η1 = -0.172, t = -2.436). Thus, the positive association between %AIt and
core earnings in year t, reverses in year t+1, consistent with firms classification shifting to increase core earnings prior to the 2002 reform.
The results for the alternative sample in Column (2) are substantively similar to those for the main sample. That is, %AIt is positively associated with UE_CEt in Equation
(3a) while %AIt is negatively associated with UE_∆CEt+1 in Equation (3b). Hence, the results for the two samples are consistent with managers engaging in opportunistic 114 McVay’s (2006, Table 6) main results using regressions that included accruals in the determination of UE_CEt and UE_ΔCEt+1 but did not have control variables show very low adjusted R2 of 0.03 percent
for the sample of all Compustat firms and 0.04 percent for the sample of firms with income-decreasing special (abnormal) items. When she removed accruals from her models in determining UE_CEt and
UE_ΔCEt+1 the coefficient of %SIt in Equation (3a) became significantly positive against prediction, and
significantly negative against prediction in Equation (3b) (Table 6). However, McVay (2006) explained that when she added a control to the regressions (untabulated results), she found evidence consistent with classification shifting. To that end, she proposed that her model was weak as a result of inadequate control of performance. The results from Equations (3a) and (3b) in this study which include controls for firm characteristics and performance attest to this explanation (see Table 7.1 below).
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classification shifting to improve core earnings in the period prior to the reforms. These findings verify the concerns raised about the opportunistic classification of AI (McCahey 1999; McLean 1999; Parker and Porter 2000), justifying the need to constrain this practice.
In Column (3), I tabulate the results of regressions estimated on a PSM sample of treatment and control firms with similar inherent likelihood to report AI. I define treatment firms as those that operate in both the pre- and post-2001/02 amendments periods and report income-decreasing AI in at least one year in the pre-2001/02 period (1995–2000) regardless of their post-2001/02 amendments behaviour. Control firms are those that exist in both test periods but do not report income-decreasing AI in the pre- 2001/02 period, irrespective of their post-2001/02 amendments behaviour. The firms are matched using first stage logit models with the following variables: firm size (SIZEt);
return on assets (ROAt); cash from operations (CFOt); leverage (LEVt); whether they
make a net loss or otherwise (LOSSt); and the size of their external auditor (AUDITORt).
Thus the PSM sample comprises all firm-years for firms that reported income- decreasing AI in at least one year in the pre-2001/02 period (treatment firm-years), matched to firm-years drawn from firms with similar characteristics but which did not report income-decreasing AI during the pre-2001/02 period (control firms-years) and all post-2001/02 reforms observations of the firms matched by this process.115
Column (3) of Table 7.1 Panel A reveals that %AIt is positively associated with
UE_CEt, (α1 = 0.654, t = 4.018) in the pre-2001/02 reforms period, consistent with the main analysis. Further, Column (3) of Panel B, shows that %AIt is significantly
negatively associated with UE_ΔCEt+1 (η1 = -0.399, t = -2.313), indicating that unexpected core earnings in year t reverse in t+1. Once more, the results are consistent with firms opportunistically classifying core expenses as AI to improve core earnings in the pre-2001/02 period.
To examine whether classification shifting is reduced following the regulatory amendment in 2002, I focus on the coefficients for the interaction term %AIt*POSTt, in
each regression. If opportunistic classification is reduced after the 2002 amendment, the following conditions must hold: %AIt*POSTt is significantly negatively correlated with
UE_CEt; and %AIt*POSTt is significantly positively correlated with UE_∆CEt+1.
Referring to Column (1) of Table 7.1 Panel A, %AIt*POSTtis insignificant (α3 = 0.091,
115 Including only the firm-years in which abnormal items were recorded during the pre-reform period leads to similar results to those discussed here.
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t = 0.977), suggesting that there is no significant reduction in the association between unexpected core earnings and AI in the post-2001/02 reforms period, relative to the pre- 2001/02 period. Further, the combined coefficient (i.e. the effect size) for AI in the post- 2001/02 reforms period, remains positive and significant (0.091 + 0.552 = 0.643, t = 6.907), consistent with the continued use of opportunistic AI disclosures to influence core earnings in the post-2001/02 reforms period. However, if the pre-2001/02 level of
TABLE 7.1
Regression of Unexpected Core Earnings and Future Unexpected Change in Core Earnings on Abnormal Items as a Percentage of Sales for the Period 1995 – 30
December 2005 (excluding 2001)
Panel A: Dependent Variable = UE_CEt
Independent Variables Predicted Sign All Morningstar Datalink Firms (1) Firms with Income- Decreasing Abnormal Items (2) Propensity Score Matched Sample (3) Intercept 0.033 (1.137) 0.017 (0.544) 0.110*** (2.765) %AIt + 0.552*** (8.055) 0.553*** (7.882) 0.654*** (4.108) %AIt*POSTt - 0.091 (0.977) 0.095 (1.003) 0.168 (0.924) POSTt 0.020*** (3.483) 0.019*** (3.233) 0.016** (2.221) SIZEt -0.003* (-1.848) -0.002 (-1.187) -0.008*** (-3.462) ROAt 0.342*** (14.502) 0.347*** (14.190) 0.419*** (11.260) CFOt 0.150*** (6.016) 0.155*** (5.932) 0.122*** (3.452) LEVt 0.069*** (4.7026) 0.064*** (4.076) 0.072*** (3.533) LOSSt -0.018* (-2.093) -0.014 (-1.556) -0.014 (-1.161) AUDITORt 0.004 (0.749) 0.002 (0.273) 0.010 (1.526)
Industry Dummies included included included
Number of observations 5,019 4,521 2,201
Adjusted R2 15.80% 15.80% 18.2%
F-stat 66.81 64.83 62.99
p-value of F-stat 0.000 0.000 0.000