5.1 . Univariate analysis
Table 4 reports basic statistics for the variables examined in this study. Panel A shows the details behind Table 2 (the non-rotation required regime, NR-regime hereafter), and Panel B those behind Table 3 (the rotation required regime, RR-regime hereafter). The link between the simplified classification used in Table 4 and the original classification of Table 2 is shown in Panel E of Tables 2 and 3.
[Insert Table 4]
As explained above, the actual results suggest a simplification of the variables of the original classification; consequently, Table 4 reports four variables, Both, Firm Only, Auditor Only, and None, in Columns (1), (2), (3), and (4), respectively. The rightmost three columns of table 4 report the pairwise differences; however, due to space limitations, we show only the differences between each of the three cases where a leader is involved (Both, Firm Only, and Auditor Only) and the cases where no leader is involved (None).
Panel A, Table 4, reports descriptive statistics for the non-rotation-required regime. There are three important results for each of the three dependent variables of interest, giving a total of nine key results for each. Of the nine differences for our three variables (|AbnLLP|, AbnLLP+, and
- 20 -
AbnLLP), two are significantly consistent with predictions: for |AbnLPP|, the mean and median of the difference between the values of Both and None (0.003 and 0.002); and for AbnLPP+, those of the differences between Both and None (0.003 and 0.002). The remaining seven results are insignificant. This univariate analysis implies that earnings quality can increase only if both audit-partner and audit-firm are experts. In columns (5), (6), and (7) of Panel B, however, none of the differences are significant; thus, experts have no significant effect on earnings quality when rotation is required. Thus, we provide evidence that, in the non-rotation regime, concurrent audit-partner expertise and audit-firm expertise enhance the earnings quality of banks.
However, neither partner expertise nor firm expertise alone can enhance earnings quality. In a rotation-required regime, however, auditors with expertise, as measured by market share, will be rotated out, thus affecting the variable ExpertBoth, one element of which is ExpertPartner-Only
. Thus, mandatory rotation may add noise to a previously significant measure, reducing its usefulness as information. The findings detailed in Panel B, which will be discussed shortly, support this hypothesis.
Of the other variables for the non-rotation-required regime given in Panel A, PT is insignificant in the mean and median test in columns (5)~(7). However, FT is significant in both the mean and the median of the difference between Both and None (3.783 and 4.500, respectively), and the median difference between Firm Only and None (5.500). In columns (5)~(7), all results for Listed and Loss are insignificant. In addition, BigN is significant in both the mean and the median of the difference between Both and None, in those of the difference between Firm Only, and in those of the difference between Auditor Only and None. Banks audited by experts tend to show greater size (Size) in the Auditor Only None column, greater sales growth (Growth) in the Firm Only None column, better operating performance (EBEL) in
- 21 -
the Both None and the Firm Only None columns, and better capital adequacy ratio (CADQR) in the Both None column.
Next is the discussion of the basic statistics of Panel B. Like the comparisons of PT in Panel A, the pairwise comparisons between PT are insignificant. As in Panel A, the values for FT in Column 5 (the difference of the means = 6.011) and Column 6 (the difference of the means = 3.620) in Panel B reveal that expert auditors have longer firm tenure. In addition, Listed, Growth, and Loss are insignificant in Columns (4)~(6). BigN, as in panel A, is significant in the differences both between the means and between the medians of Both and None, as it is for Firm Only and None, and for Auditor Only and None. Size is significant in the differences both between the means and between the medians of Both and None, and of Auditor Only and None.
Finally, the EBEL is significant in the difference between the means of Both and None, and in the difference between the medians of Auditor and None. As in panel A, CADQR is significant in the differences both between the means and between the medians of Both and None.
5.2. Regression results
Table 4 provides univariate results and we provide multivariate regression results reported in Table 5. Consistent with the assumed role of industry expertise, the estimated coefficients on ExpertBoth are significant, and show the predicted signs in the analyses of |AbnLLP| (0.0026, p
<0.05), AbnLLP+ (0.0028, p < 0.01), and AbnLLP (0.0037, p < 0.10). However, none of the estimated coefficients of ExpertFirm-Only or ExpertAuditor-Only
is significant. Therefore, in the non-rotation regime, a bank audited by an expert audit-firm, with an expert audit-partner actually performing the audit, is more likely to have better earnings quality.
[Insert Table 5]
- 22 -
Regarding the role of audit-partner tenure and audit-firm tenure, except for the estimated coefficients of FT and PT in the analysis of AbnLLP, none of the remaining estimated coefficients are significant. Hence, unlike Chen et al. (2008), we fail to find supporting evidence that auditor tenure, firm tenure, or partner tenure can enhance earnings quality in the banking industry. Finally, while Beatty et al. (2002) finds that private banks have better earnings quality, our empirical results do not offer evidence to support the claim that listed banks and non-listed banks have different levels of earnings quality. Other insignificant results include BigN, Size, Loss, and LagLLP. There are nevertheless some variables that show a significant correlation to earnings quality. As can be seen in the estimated coefficients of Growth in the analyses of
|AbnLLP| (0.0174, p < 0.05), AbnLLP+ (0.0154, p < 0.05), and AbnLLP (0.0192, p < 0.05), banks with a high level of growth have poor earnings quality. Banks with a better operating performance (EBEL), on the other hand, have better earnings quality, as is shown by the significantly negative estimated coefficient of EBEL (0.2067, p < 0.05) in the analyses of
|AbnLLP| and AbnLLP+ (0.1798, p < 0.01). Finally, banks with a higher capital adequacy ratio tend to show greater provision for extreme abnormal loan losses, as is shown by the significantly positive estimated coefficient of CADQR (0.0001, p < 0.01) when |AbnLLP| is examined, although the coefficients are marginally insignificant for the subsamples AbnLLP+ and AbnLLP.
Taken together, the above results suggest a particularly significant association between expertise (as measured by ExpertBoth) and audit quality in the non-rotation-required regime. In the rotation-required regime, however, the role of the expert disappears: in Table 6 none of the estimated coefficients of ExpertBoth, ExpertFirm-Only, and ExpertAuditor-Only
are significant. Therefore, although in Table 5 we find that banks in the non-rotation-required regime that are audited by expert audit-firms, with an expert audit-partner actually performing the audit, show better
- 23 -
earnings quality, this function disappears when audit-partner rotation is required. Thus, mandatory rotation problematises the use of market share as a proxy for audit-partner expertise.
That is, these findings reveal another, less direct, unintended consequence to mandatory partner rotation that complements the one detailed in Chi et al. (2009), i.e. that non-banking firms audited by newly rotated-in auditors have worse earnings quality than the same firms when audited by the rotated-out former auditors, presumably because of differences in client-specific experience between the new and former audit partners. Thus, both tenure (as shown by Chi et al.
(2009)) and expertise (as shown by this paper) lose their explanatory power in a rotation-required regime. Our findings further suggest that the function of market share as a proxy for audit-partner expertise is weakened by rotation.
[Insert Table 6]
5.3. Further checks and caution
One caveat to these findings lies in the underlying economic conditions during the two different periods of time of the non-rotation-required and the rotation-required regimes. The period of the rotation-required regime includes the years 2008 and 2009, when the financial crisis hurt the performance of banks, most noticeably in the categories of losses (LOSS in this study), and earnings before extraordinary items and loan-loss provision (EBEL). To account for these effects, we have rerun our calculations without these two years. Our untabulated results show that they are qualitatively consistent with the findings reported in Table 6.
6. Conclusion
This study finds that in a highly regulated industry such as banking, earnings quality is significantly associated with the market share-based proxy for industry expertise in a way that
- 24 -
suggests auditor industry expertise enhancing earnings quality in the voluntary audit partner rotation regime. Such a relation, however, disappears in the mandatory partner rotation regime.
Our findings suggest that mandatory audit-partner rotation decouples the link between the market share-based proxy for audit partner industry expertise and that partner’s industry expertise. This is because, without rotation, an audit partner’s market share in an industry is determined by the free choice of the market—expert partners are more likely to attract clients than non-expert partners. Consequently, an audit partner’s market share naturally flows after his or her industry expertise. Mandatory audit partner rotation, however, disrupts the free choice of the market and thus introduces noise into the market share-based proxy for industry expertise. Thus, our findings suggest that one of the unintentional consequences of mandatory partner rotation is the potential inability of an audit partner’s market share in an industry to capture that partner’s industry expertise because market share under mandatory partner rotation regime is influenced by the rotation rule. We therefore caution researchers in using audit partner market share as a proxy for industry expertise.
There are two caveats about these findings. First, our data, which makes the study possible in the first place, is from an environment with a relatively low level of litigation risk on the part of auditors, as well as less mature capital markets and regulatory regimes for the audited firms (i.e., banks). Second, we deem less extreme earnings as high quality earnings following Myers et al.
(2003). In contrast, Kanagaretnam et al. (2010a) deem smaller earnings as high quality earnings.
We encourage future studies to use different data and alternative proxies for earnings quality to examine whether mandatory audit partner rotation introduce noise into the market share-based proxy for industry expertise.
- 25 -
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- 28 - Appendix 1
Variable definitions
Variables Definition EarnQuality earnings quality, consists of the following four LLP-related measures:
AbnLLP residuals of equation (1)
|AbnLLP| absolute value of AbnLLP AbnLLP+ positive value of AbnLLP AbnLLP negative value of AbnLLP LLPt provision for loan losses;
LLAt-1 beginning loan loss allowance;
NPLt-1 beginning nonperforming loans;
NPLt change in nonperforming loans;
LCOt net loan charge-offs;
Loant change in total loans;
Loans total loans outstanding;
ExpertBoth 1 if both audit firm and at least one audit partner are leader in market share , and 0 otherwise ExpertFirm-Only 1 if only audit firm is leader in market share while neither of the audit partners is leader, and 0
otherwise
ExpertPartner-Only 1 if at least one audit partner is leader in market share while the audit firm is not a leader, and 0 otherwise
PT number of years theauditor tenure of the longer of the two audit partners has audited the company’s financial statements
FT number of years the audit-firm has audited the company’s financial statements Listed 1 if the companies is a listed banks, and 0 otherwise
BigN 1 if auditor is a Big N audit firm, and 0 otherwise Size Natural log of total size
Growth percentage of net interest revenue growth Loss 1 if companies incurs loss, and 0 otherwise LagLLP prior period’s loan loss provision
EBEL earnings before extraordinary and loan loss provision CADQR capital adequacy ratio
- 29 - Table 1
Sample selection
Panel A - Non-rotation required regime (2001-2003) research sample
Year Listed Non-Listed Totals
Original sample size 2001 29 5 34
2002 25 9 34 2003 16 18 34
Original sample 70 32 102
Less:
audit-partner rotation* (4) (4)
missing variables (6) (9) (15)
Research sample 60 23 83
Panel B - Rotation required regime (2004-2009) research sample
Year Listed Non-Listed Totals
Original sample size 2004 16 18 34
2005 16 19 35
2006 16 19 35
2007 12 19 31
2008 11 21 32
2009 11 21 32
Original sample 82 117 199
Less:
missing variables (7) (15) (22)
Research sample 75 102 177
* We classify 2003, a transition period of partner rotation, as a non-rotation-required regime because (1) as Panel A of Table 1 reports, only four signing audit-partners of companies are switched due to required rotation; and (2) if the observations of 2003 are deleted, there will be only two years’ data in the pre-rotation regime.
- 30 - Table 2
Distribution of expertise in a non-rotation required regime
Panel A - Original sample
FAA FA F AA A None
Number 0 28 13 6 3 52
(0.00%) (27.45%) (12.75%) (5.88%) (2.94%) (50.98%)
Panel B - Research sample
FAA FA F AA A None
Number 0 23 10 6 2 42
(0.00%) (27.71%) (12.05%) (7.23%) (2.41%) (50.60%)
Panel C - Listed banks in the research sample
FAA FA F AA A None
Number 0 16 7 6 0 31
(0.00%) (26.67%) (11.67%) (10.00%) (0.00%) (51.67%)
Panel D - Non-listed banks in the research sample
FAA FA F AA A None
Number 0 7 3 0 2 11
(0.00%) (30.43%) (13.04%) (0.00%) (8.70%) (47.83%)
Panel E - Simplified classification for Table 4, Panel A, suggested by above results
FAA FA F AA A None
NA Both Firm Only Auditor Only None
Note: Since there are three signatures in an audit report, one representing the firm (F), and those of two audit-partners (A), there are six possible specifications of industry expertise: (a) all three are experts (FAA); (b) the audit firm is expert but only one of the audit partners is expert (FA); (c) the audit firm is expert while neither of the audit partners is expert (F); (d) both the audit partners are expert but their audit firm is not (AA); (e) only one of the audit partners is expert (A); and (f) none of them is expert (None). Panel E links Table 2 with Panel A of Table 4.
- 31 - Table 3
Distribution of expertise in a rotation required regime
Panel A - Original sample
FAA FA F AA A None
Number 0 34 56 0 15 94
(0.00%) (17.09%) (28.14%) (0.00%) (7.54%) (47.24%)
Panel B - Research sample
FAA FA F AA A None
Number 0 29 46 0 14 88
(0.00%) (16.38%) (25.99%) (0.00%) (7.91%) (49.72%)
Panel C- Listed banks in the research sample
FAA FA F AA A None
Number 0 14 23 0 4 34
(0.00%) (18.67%) (30.67%) (0.00%) (5.33%) (45.33%)
Panel D - Non-listed banks in the research sample
FAA FA F AA A None
Number 0 15 23 0 10 54
(0.00%) (14.71%) (22.55%) (0.00%) (9.80%) (52.94%)
Panel E - Simplified classification for Table 4, Panel A, suggested by above results
FAA FA F AA A None
NA Both Firm Only Auditor Only None
Note: Since there are three signatures in an audit report, one representing the audit-firm (F), and those of two audit-partners (A), there are six possible specifications of industry expertise: (a) all three are experts (FAA); (b) the audit firm is expert but only one of the audit partners is expert (FA); (c) the audit firm is expert while neither of the audit partners is expert (F); (d) both the audit partners are expert but their audit firm is not (AA); (e) only one of the audit partners is expert (A); and (f) none of them is expert (None). Panel E links Table 2 with Panel B of Table 4.
- 32 - Table 4
Basic statistics
Panel A - Non-rotation required regime
(1) (2) (3) (4) (5) (6) (7)
Both Firm Only Auditor Only None Both None Firm Only None Auditor Only None
Variables Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median
|AbnLLP| 0.003 0.002 0.004 0.004 0.005 0.004 0.006 0.004 0.003** 0.002** 0.002 0.000 0.001 0.000 BigN 1.000 1.000 1.000 1.000 1.000 1.000 0.738 1.000 0.262*** 0.000*** 0.262*** 0.000*** 0.262*** 0.000***
Size 19.670 19.202 19.211 19.145 20.073 20.078 19.326 19.155 0.344 0.047 0.115 0.010 0.747* 0.923** Panel B - Rotation required regime
(1) (2) (3) (4) (5) (6) (7)
Both Firm Only Auditor Only None Both None Firm Only None Auditor Only None
Variables Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median
|AbnLLP| 0.002 0.002 0.003 0.001 0.003 0.002 0.002 0.001 0.000 0.001 0.001 0.000 0.001 0.001 AbnLLP 0.000 0.000 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.001 AbnLLP+ 0.002 0.001 0.003 0.002 0.004 0.002 0.002 0.001 0.000 0.000 0.001 0.001 0.002 0.001 AbnLLP 0.002 0.002 0.002 0.001 0.002 0.002 0.002 0.001 0.000 0.001 0.000 0.000 0.000 0.001 PT 5.034 4.000 3.239 3.000 4.429 4.000 4.432 3.000 0.602 1.000 1.193 0.000 0.003 1.000 FT 15.000 15.000 12.609 14.000 11.214 11.500 8.989 6.500 6.011*** 8.500*** 3.620*** 7.500*** 2.225 5.000*
Listed 0.483 0.000 0.500 0.500 0.286 0.000 0.386 0.000 0.097 0.000 0.114 0.500 0.100 0.000 BigN 1.000 1.000 1.000 1.000 1.000 1.000 0.909 1.000 0.091** 0.000** 0.091** 0.000** 0.091** 0.000**
Size 20.311 20.528 20.023 19.683 20.523 20.775 19.804 19.483 0.507** 1.045** 0.219 0.200 0.719** 1.292**
Growth 0.039 0.037 0.020 0.009 0.015 0.017 0.015 0.004 0.024 0.033 0.035 0.005 0.000 0.013 Loss 0.241 0.000 0.370 0.000 0.357 0.000 0.352 0.000 0.111 0.000 0.018 0.000 0.005 0.000 EBEL 0.011 0.008 0.008 0.008 0.003 0.004 0.006 0.006 0.005** 0.002 0.002 0.002 0.003 0.002*
LagLLP 0.006 0.005 0.012 0.007 0.008 0.006 0.008 0.005 0.002 0.000 0.004** 0.002* 0.000 0.001 CADQR 12.913 11.030 11.454 10.355 10.626 10.440 10.602 10.805 2.311** 0.225* 0.852 0.450 0.024 0.365 Note: The upper ***, **, and * in columns (4), (5), and (6) of Panel A, and those in Columns (5), (6), and (7) of Panel B denote the differences are significant at 1%, 5%, and 10% level, using a two-tailed t-test. Variable definitions refer to Appendix 1.
- 33 - Table 5
Regression results in the non-rotation required regime
|AbnLLP| AbnLLP+ AbnLLP
Variables Coeff. p-value Coeff. p-value Coeff. p-value Intercept 0.0062 0.364 0.0153 0.193 0.0054 0.813 ExpertBoth 0.0026** 0.016 0.0028*** 0.000 0.0037* 0.087 ExpertFirm-Only 0.0024 0.142 0.0015 0.235 0.0049 0.115 ExpertAuditor-Only 0.0010 0.541 0.0011 0.539 0.0003 0.910 PT 0.0001 0.308 0.0001 0.781 0.0002*** 0.006
Variables Coeff. p-value Coeff. p-value Coeff. p-value Intercept 0.0062 0.364 0.0153 0.193 0.0054 0.813 ExpertBoth 0.0026** 0.016 0.0028*** 0.000 0.0037* 0.087 ExpertFirm-Only 0.0024 0.142 0.0015 0.235 0.0049 0.115 ExpertAuditor-Only 0.0010 0.541 0.0011 0.539 0.0003 0.910 PT 0.0001 0.308 0.0001 0.781 0.0002*** 0.006