Market reactions to auditor switches under regulatory consent and market driven regimes

Accepted Manuscript Market Reactions to Auditor Switches under Regulatory Consent and Market Driven Regimes Andrew Ferguson, Peter Lam, Nelson Ma PII: DOI: Reference:

S1815-5669(18)30057-2 https://doi.org/10.1016/j.jcae.2018.05.001 JCAE 128

To appear in:

Journal of Contemporary Accounting & Economics

Received Date: Accepted Date:

20 February 2018 1 March 2018

Please cite this article as: Ferguson, A., Lam, P., Ma, N., Market Reactions to Auditor Switches under Regulatory Consent and Market Driven Regimes, Journal of Contemporary Accounting & Economics (2018), doi: https:// doi.org/10.1016/j.jcae.2018.05.001

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Market Reactions to Auditor Switches under Regulatory Consent and Market Driven Regimes* Andrew Ferguson**, Peter Lam, Nelson Ma University of Technology Sydney

Abstract We examine market reactions to announcements of auditor switches by Australian-listed companies during the ‘regulatory consent’ period (2000−2011) under which auditor resignations require consent by the corporate regulator before taking effect at annual general meetings. Overall, we find no clear evidence of significant market responses to firms announcing auditor switches, consistent with a lack of information content or potential information leakage argument. However, examination of a more recent sample in the ‘partial deregulation’ period (2015−2017), whereby timing and consent provisions have been relaxed under a more market-driven regime, uncovers univariate evidence of market reactions directionally consistent with the audit quality interpretation. Overall, these results provide support for the regulator’s recent initiative to deregulate the auditor resignation process in Australia to become more disclosure driven as in other jurisdictions.

Keywords: Auditor switch; Market reactions; Resignation; Dismissal; Litigation risk.

* We thank Dan Simunic, Ferdi Gul and seminar participants at Deakin University and University of Technology Sydney Summer Symposium for comments and suggestions. This project was partly funded by an AFAANZ 2013 research grant. ** Corresponding author: Accounting Discipline Group, UTS, P.O. Box 123, Broadway, NSW 2007, Australia. Tel: +61 2 9514 3565; Fax: +61 2 9514 3669; Email: [email protected]

1. Introduction Regulatory interventions in audit markets have been steadily increasing worldwide (Mohrmann et al., 2017). Where regulatory policies differ across settings, an important empirical question is which framework provides greater benefits? In many parts of the world, the voluntary termination and appointment of external auditors (hereafter auditor switches) is highly regulated. The primary reasons are to ensure (1) external auditors perform assurance work professionally while maintaining independence, and (2) shareholders and market participants remain continuously informed regarding any such changes. Across jurisdictions, there are various approaches to the regulation of auditor removal and appointment, ranging from a market-oriented, disclosure-based approach (e.g., the US) to a process-driven, regulatory consent regime (e.g., Australia). This study examines whether the informativeness of publicly disclosed announcements of auditor switches differs under a regulatory consent regime as opposed to a disclosure-based regime. Our study is motivated by arguments in DeFond and Zhang (2014), who suggest researchers examine the relative benefits of regulation and further consider which policy setting is “best”.1 Despite numerous studies on auditor switches, there is limited evidence considering regulatory aspects. The focus on the information content of auditor switches stems from ongoing interest by both the US Securities and Exchange Commission (SEC) and Australian Securities and Investments Commission (ASIC) (Griffin and Lont, 2010; ASIC, 2013). Accordingly, we consider the benefits of regulation from the perspective of information efficiency and employ an event study methodology to examine whether the market reacts to the announcement of auditor switches.2

1

For instance, the debate on whether the adoption of mandatory audit firm rotation (MFR) is beneficial in the US relies on evidence from jurisdictions with MFR requirements (e.g., Italy, Korea and Spain). 2 Carter and Soo (1999) argue that a significant market reaction (or a lack thereof) to regulatory filings reflects whether the disclosure is informative to market participants. They find that only timely announcements are associated with a significant market reaction. This methodology provides a reasonable test of whether auditor switch announcements in Australia are sufficiently timely, which is of interest to regulators.

2

Differing approaches to regulating auditor switches yield advantages and drawbacks. Under a consent-based regime in Australia, regulators play a central role by requiring ASIC approval as a necessary condition before mandatory public disclosures of switches are made. These disclosures are subject to stringent timing constraints, with switches required to take place following the completion of the most recent audit and only at the forthcoming annual general meeting (AGM). This approach has the advantage of allowing ASIC to consent only to appropriately justified switches and ensuring uniformity in information dissemination. The disadvantage is that market disclosures may occur after the switch event date due to the fixed timing requirements and delays in the consent process, meaning these announcements lack timeliness. In contrast, under a market-based regime (such as the US), regulators play a limited role and only require regulatory filings following the event date. The advantage of this approach is market participants can access more timely market disclosures regarding the switches. However, low regulation may result in switches being undertaken for opportunistic reasons and public disclosures might not always be available, requiring market participants to rely on delayed regulatory filings. This study provides out-of-US sample evidence under a consent regime. Australia is a unique setting for studying auditor switches for two reasons. First, the date and time of auditor switch announcements can be precisely identified. In the US, ambiguity exists as to when the market becomes aware of switches, with Klock (1994) concluding ‘it is not always clear what date this is’ (p.342).3 Unlike the US, auditor switches in Australia are

3

Chang et al. (2010) argue that the best way in isolating the market reaction is to employ an event window (i.e., –1, +1) around the first public announcement of the auditor switch. However, they find that only 8% of firms in their sample filed a public press release prior to the 8-K filing. Instead, they employ a 5-day window starting from the date of the 8-k filing. Other recent US studies have dealt with the uncertainties associated with the exact timing of the event in different ways, with Knechel et al. (2007) using a 3-day event window.

3

accompanied by disclosures to the market regarding the event. 4 Second, Australia has recently experienced a shift from a consent to a more disclosure-oriented approach to regulating auditor switches after ASIC held a public consultation process with practitioners and professional bodies (ASIC, 2013; CPA Australia & ICAA, 2013; Deloitte, 2013; Ernst & Young, 2013; Governance Institute of Australia (GIA), 2013; KPMG, 2013). This new approach enables auditor switches to take place with a market announcement at any time of the year, rather than delayed until the following AGM. This regulatory change allows for timelier announcements, consistent with ASIC’s objective of fulfilling “…the need for transparency of the change in auditor and maintaining audit quality” (ASIC 2015b, p.9). Accordingly, we conduct an analysis of market reactions to auditor switches occurring under the prior regulatory consent regime (2000–2011) and a more recent period (2015–2017) after the regulatory change (partial deregulation regime).5 Analysing these samples provides a unique opportunity to tease out the differences in the informativeness of auditor switches under different regulatory settings. It further provides evidence to inform policy makers on whether ASIC’s expectations of facilitating a more informed market have been achieved. Using a sample of 713 announcements of auditor switches during the regulatory consent period, we find no evidence of a significant market reaction over a 3-day event window. In a multivariate regression context, we find only weak evidence supporting an auditor quality argument in that switches from a specialist to a non-specialist auditor are associated with a negative market reaction while changes from a non-Big 4 to a Big 4 specialist auditor attract a positive market response. This ‘no-result’ finding continues to hold in our long-run returnearnings analysis and after we control for potential confounding factors. Overall, our evidence

4

Under the pre-approval system, auditor switches in Australia are typically disclosed as one of the agenda items in the notice of AGM and may therefore be subject to greater noise. The consent approach might also result in greater information leakage due to the time between ASIC consent and the announcement in the notice of AGM. 5 We refer to this period as ‘partial deregulation’ as ASIC loosened some requirements, but stopped short of adopting a full disclosure-oriented approach as implemented in the European Union (EU), UK and US. See discussions in Section 2 on regulatory background.

4

is consistent with the lack of timeliness and/or information leakage under the regulatory consent regime. These results contrast more recent US evidence under a market-based regime, showing the market distinguishes between gradations of auditor (Knechel et al., 2007; Chang et al., 2010). When extending the analysis to the more recent sample of switches during the partial deregulation period (2015-2017), we uncover modest univariate evidence of differential market reactions between Big 4 and non-Big 4 auditors, consistent with the audit quality story.6 This suggests the move towards a market-based approach enhances more timeliness, better transparency and price discovery, supporting ASICs reform initiatives and directives (ASIC, 2015b).7 The remainder of the paper is organised as follows. Section 2 discusses the Australian regulatory setting on auditor switches. Section 3 reviews the relevant literature and develops the hypotheses. Section 4 discusses the research design. Section 5 presents the primary results and section 6 reports additional analyses. Section 7 concludes. 2. Regulatory background on auditor switches 2.1 Regulatory consent regime In Australia, the process of termination and replacement of auditors of public companies is stipulated under the Corporations Act 2001. Prior to June 2015, section 329 of the Act

6

This paper contributes to the literature on market reactions to auditor switches where US studies have yielded mixed findings (Klock, 1994; Knechel et al., 2007; Chang et al., 2010). In particular, Knechel et al. (2007) and Chang et al. (2010) show different results, with Chang et al. (2010) reporting a positive market reaction to auditor switches to a third-tier (non-Big 4) from a Big 4. They attribute their differences from Knechel et al. (2007) to the introduction of SOX/PCAOB inspections in the US and/or a decline in perceived differences in Big 4 and non-Big 4 auditors (i.e., from the collapse of Arthur Andersen). As we find results more similar to Knechel et al. (2007) in Australia in the partial deregulation period, this suggests their results are driven by direct regulatory interventions rather than a change in reputational differences between Big 4 and non-Big 4 auditors. 7 These changes have also attracted support from practitioners and professional bodies. For example, Deloitte (2013) supports the alignment with other international jurisdictions that “…do not consider regulator involvement necessary to effective market operation and instead require appropriate disclosure to the market and regulators as part of the change in auditor framework” (p.1) (ASIC, 2015a, 2015b; CPA Australia & ICAA, 2013; Deloitte Touche Tomatsu, 2013; Ernst & Young, 2013; GIA, 2013; KPMG, 2013). Also see KPMG (2013) stating in a submission responding to these changes: “We support a reduction in the complexity and administration involved in the resignation and removal process. KPMG believes this can be achieved without negative impact on ASIC’s strategic objective of confident and informed investors and financial consumers” (p.1).

5

required all removals of auditors be subject to shareholder approval at an AGM and all resignations of auditors be subject to ASIC consent before taking effect. Section 327 requires all appointments of auditors be subject to a shareholder resolution passed at an AGM. To facilitate the process, ASIC released RG 26 in 1992, setting out consent and timing requirements for auditor resignations in Australia (ASIC, 2007).8 Under RG 26.11, ASIC will only consent to the resignation of the outgoing auditor if it is satisfied that (i) the reasons for the auditor’s resignation are acceptable, (ii) all reportable matters have been reported by the auditor, (iii) there are no disputes between the auditor and the company’s management, and (iv) there are no other circumstances in relation to the resignation needing to be brought to ASIC’s attention. 9 Regarding the timing of resignations, RG 26 stipulates that unless there are exceptional circumstances, resignations of auditors should normally take place in conjunction with a company’s AGM, where shareholder approval for the replacement auditor can be sought (RG 26.10 and 26.13).10 Thus, ASIC’s consent to a resignation is conditional on a replacement auditor being identified by the client. Once ASIC consent is received, the first public announcement of a change of auditor is typically disclosed as part of agenda items for voting at the forthcoming AGM. The auditor resignation and appointment will take effect at the AGM once the appointment is ratified by shareholders (RG 26.11−26.13).11

8

The rationale underpinning ASIC’s policy framework is to promote auditor independence and to ensure the effective and timely completion of an audit by the incoming auditor is not jeopardised and the statutory right of shareholders to appoint new auditors is protected. 9 Examples of circumstances that need to be brought to ASIC’s attention include auditor independence not being preserved, or evidence of opinion shopping (RG 26.17). 10 The AGM for ASX listed companies takes place about one month after the release of the annual report. The intention of ASIC requiring the resignation to take place at the AGM is to ensure the auditor remains the same throughout the year covered by the audited financial report. This is to mitigate any potential delays in the completion of the audit if the incoming auditor is appointed prior to the balance sheet date. 11 Where exceptional circumstances are present, the public announcement and resignation can take place at any time of the year, conditional on ASIC approval (RG 26.14). These exceptional circumstances, as set out in RG 26.16, include (a) failing health of the auditor, (b) loss of independence of the auditor, (c) the company is not audited by the auditor of its parent entity, and (d) relocation of the company’s or auditor’s principal place of business, making the audit impractical. Under these circumstances, the consent requirements stipulate clients nominate a replacement auditor ready to accept the engagement before ASIC approves the resignation of the outgoing auditor (26.14(c)).

6

On 30 May 2013, ASIC released Consultation Paper 209—Resignation, Removal and Replacement of Auditors: Update to RG 26, seeking public comment on potential changes to processes of removing and replacing auditors in RG 26. The primary purpose was to obtain feedback on whether changes to the regulation of auditor resignation and removal in Australia are required to be more consistent with international practice. The three main areas concerning ASIC are timing, disclosure and replacement auditor requirements. Part B of CP 209 discusses issues surrounding the timing and disclosure of resignations and solicits comment on whether ASIC should allow resignations at any time of the year. ASIC admits its current approach may create potential independence issues as the outgoing auditor still has to complete the current audit even when client disagreements might have occurred (ASIC, 2013).12 One alternative suggested by ASIC is to allow audit committees and directors more discretion in controlling the timing of auditor resignation as they are responsible for representing shareholder interests in ensuring audit quality is maintained (ASIC, 2013, p.10). Further, as ASIC requires resignations to normally occur at the AGM, the only disclosure to the market is the related appointment of the replacement auditor, which appears as an AGM agenda item. If ASIC were to deregulate the timing of resignations, enhanced disclosure requirements, similar to US practice, would be necessary. Greater disclosure improves market transparency, enabling market forces to ensure ‘auditors are not replaced in inappropriate circumstances or inappropriate times’ (ASIC, 2013, p.11). CP 209 (Part B3) discusses the need for a replacement auditor to be identified before ASIC consent to a resignation can be given. This ensures the continued timely delivery of independently audited financial reports to the market (ASIC, 2007). However, if ASIC consent is not granted due to a replacement auditor unable to be identified, the incumbent may

12

Cameran et al. (2014) find evidence of shirking by incumbent auditors in the final year of an engagement when facing mandatory firm rotation in the Italian setting.

7

be required to continue in office even if this results in conflict.13 ASIC acknowledges under an enhanced disclosure regime, ‘the inability of an entity to obtain a replacement auditor would provide important information to the market about the company’ (ASIC, 2013, p.12). Such disclosure arrangements exist in the US, where companies are required to disclose auditor termination, irrespective of the ability of the company to appoint a successor. In summary, the prior regulation approach to auditor resignations in Australia saw ASIC playing a central role, which is inconsistent with practice in other countries (e.g., Canada, EU, UK, US), where consent is not required and resignations can take place at any time of the year. Further, ASIC’s time-consuming consent process effectively filters out any market relevant information.14 These issues were recently addressed by ASIC through public consultation, culminating in important changes to how auditor resignations are regulated. 2.2 Partial deregulation regime After a two-year period of consultation and deliberation, ASIC issued an updated version of RG 26 on 18 June 2015 (hereafter, the revised RG 26), incorporating a number of important changes to the consent process. These changes include allowing resignations to occur at any time of the year and mandatory disclosure to the market at the time of the event.15 Under the partial deregulation approach, ASIC consent can be sought ‘at any time of the year’ (RG 26.4), with discretion afforded to company directors and audit committees to determine an appropriate date for the switch that maintains auditor independence and objectivity (RG 26.46). Nevertheless, auditor termination will continue to occur at the AGM and require shareholder ratification, but no longer require ASIC consent (RG 26.65).

13

Conflicts may arise in relation to the acceptance and continuance of audit engagements under auditing standards and in situations where there is a disagreement with management or limited prospects of recovering audit fees (ASIC, 2013, p.12). 14 These concerns were echoed by practitioners and professional bodies in their responses to CP 209 (Deloitte, 2013; CPA and ICCA, 2013; Ernst & Young, 2013; GIA, 2013; KPMG, 2013). 15 A discussion detailing the changes adopted in RG 26 and the accompanying response to submissions on CP 209 is provided by ASIC in Report 437 (ASIC, 2015b).

8

With the relaxation in the timing of consent, public disclosures to the market can now take place at the time of the event (RG 26.59-60), and are no longer delayed until the upcoming AGM.16 To make auditor resignations more market oriented, ASIC consent now takes effect upon the date of the public announcement (RG 26.59-61), enabling more timely dissemination of information to the market.17 To further improve the timeliness of auditor resignations, ASIC (2015b) has reduced the red tape around the consent process by no longer mandating the lodgement of outstanding audited financial reports before consent can be obtained. 18 Despite the suggested benefits of allowing auditors to resign irrespective of whether a successor has been appointed, ASIC has retained this requirement in the revised guide (RG 26.32).19 3. Prior research and hypotheses Market reactions to companies switching auditors have long been studied in the audit quality literature. The perception that Big N auditors produce higher quality financial statements is based on the notion of increasing audit quality with auditor size (DeAngelo, 1981). However, prior US studies yield mixed results, which have been attributed to imprecision in determining the exact event date of the switch (Klock, 1994). Klock (1994) identifies three key dates pertaining to US switch disclosures: (1) the engagement termination (resignation) date on the 8-K filing, (2) the 8-K filing date, and (3) the 8-K on-file date where the filing is made publicly available. Nichols and Smith (1983) examine reputational differences between Big N and non-Big N auditors and find directionally consistent but insignificant results using an 816

Auditor switches can still be announced at the AGM if the client initiates a dismissal requiring shareholder approval to remove the incumbent auditor. As discussed, resignations could be misleading in that many take place at the client’s request. Consistent with evidence from manually inspecting announcements, Ernst and Young (2013) estimates that over 90% of resignations are client, rather than auditor, initiated. 17 The move towards market-based disclosure was not universally supported (see, for example, the Ernst and Young response to CP 209, which suggests that market disclosures are unnecessary). 18 ASIC (2015a) also relaxed the condition that ‘no disagreements’ be present between the auditor and client to provide consent, opting to consider the ‘nature and extent of any disagreements’ instead. 19 This requirement was retained despite opposition, with KPMG (2013) arguing a replacement auditor as a condition for consent results in the incumbent auditor continuing the engagement under unfavourable circumstances.

9

week event window surrounding the 8-K filing date. This finding is similar to Klock (1994), who uses a 60-day window surrounding the 8-K on-file date in addition to the 8-K filing date. Using long event windows, both Fried and Schiff (1981) and Johnson and Lys (1990) report negative market reactions to the 8-K filing. While the former results (using a 21-week window) are significant only for all switch types (but not switches to (from) Big N from (to) non-Big N auditors), the latter results hold only over a 12-month period preceding the 8-K filing but not in a 3-month window. In contrast, Eichenseher et al. (1989) find that market reactions are more positive for switches to Big N firms using a 5-week window ending on the first week of the 8-K filing. Dunn et al. (1999) also report resignations of a Big N auditor attracts a more negative market reaction in a 40-day window around the date specified in auditor resignation letters in the UK.20 Recent studies of market reactions to auditor switches have focused on finer gradations of auditor quality and shorter event windows. For example, Knechel et al. (2007) examine how auditor industry specialisation influences market reactions to auditor switches. They argue industry specialists are less likely to be associated with fraudulent financial reporting, resulting in (un)favourable market valuations upon the (removal) appointment of an industry specialist auditor. Using a 3-day event window around the 8-K filing, they find significantly positive (negative) market reactions when companies switch to (from) auditor industry specialists using a US sample over the period 2000−2003. Chang et al. (2010) consider the impact of SOX in the US and argue that smaller ‘third-tier’ non-Big 4 auditors provide equivalent (if not better) audit service compared to their Big 4 counterparts while charging a lower fee.21 Using a 2000−2006 sample of auditor switches and a 5-day event window ending on the 8-K filing date, they find that switching to a ‘third-tier’ auditor from a Big 4 is associated with a positive stock price reaction. 20

Dunn et al. (1999) state that the resignation letter, similar to the 8-K filing, specifies any circumstances underlying the switch and is made publicly available. 21 Chang et al. (2010) define ‘third-tier’ auditors as non-Big 4 firms excluding Grant Thornton and BDO.

10

Event date ambiguity aside (Klock, 1994), the proximity of such announcements remains an issue since information related to the switch is disseminated at both the public announcement and 8-K filing date. Further, the tightening by the SEC (in August 2004) of the 8-K filing deadline from five to four business days has led to changes in the time lag between the public announcement and 8-K filing date. Variations in the lag between these announcements has resulted in the adoption of differing event windows in various studies. For instance, Knechel et al. (2007) rely on an average filing lag of 4.93 days (as reported by Carter and Soo, 1999) while Chang et al. (2010) report a 1.47 day lag, resulting in a 3-day and 5-day event windows used.22 Differences in the event date and windows used may contribute to inconsistent results reported in these studies, despite overlapping sample periods. Other information reported in the 8-K filing may also impact findings observed in prior US studies.23 Prior research has found the circumstances underlying the switch (e.g., initiating party and stated reasons) reported in the 8-K as providing relevant, albeit untimely, information to the market (Carter and Soo, 1999; Whisenant et al., 2003).24 We re-examine the market response to auditor switches, testing both Big 4 and industry specialist effects. The premise of Big 4 and industry specialist auditors providing better quality audit is based on arguments that reputational incentives and investment in industry knowledge are conveyed to engagements (DeAngelo, 1981; Craswell et al., 1995; Ferguson et al., 2003). Accordingly, US, Australian, and international evidence all shows Big 4 and industry specialist auditors are perceived to provide differentiated services associated with higher quality financial statements and improved market perceptions of earnings quality

22

Knechel et al. (2007) use a 3-day (–1,+1) event window around the actual date of the auditor change to avoid the effect of the 8-K filing while Chang et al. (2010) use a 5-day (–4,0) event window ending on the filing date to capture the information in the 8-K filing. 23 A complete list of the mandated disclosures required in the 8-K filing can be found on SEC Regulation S-K, Item 304: Changes in and Disagreements within Accountants and Financial Disclosure. 24 Carter and Soo (1999) show 8-K filings provide untimely information as the market reacts to the information the day prior to public disclosure, with little reaction on the announcement date.

11

(Francis, 1984; Craswell et al., 1995; Balsam et al., 2003; Ferguson et al., 2003; Geiger and Rama, 2006; Kwon et al., 2007; Francis and Wang, 2008; Carson et al., 2012). In the Australian context, because of ASIC’s requirement for a replacement auditor to be identified before a resignation can take place, any auditor change announcements by companies would typically encapsulate both the resignation/dismissal of the incumbent auditor and the appointment of the successor. However, as all auditor resignations in Australia in the regulatory consent period have to be pre-approved by ASIC and there is no disclosure requirement for the circumstances leading to the resignation, we argue the market will focus more on the relative quality of the replacement auditor than if the switch is caused by the resignation or dismissal of the incumbent. Following Knechel et al. (2007) and others, we expect a more positive market reaction when firms switch to a more reputable auditor (i.e., Big 4 and/or industry specialist), and vice versa (audit quality hypothesis). We specify our hypotheses as follows. H1: Companies switching to (from) a Big 4 auditor from (to) a non-Big 4 auditor experience a positive (negative) abnormal stock return around the date of announcement. H2: Companies switching to (from) an industry specialist auditor from (to) a non-industry specialist auditor experience a positive (negative) abnormal stock return around the date of announcement. 4. Research method 4.1 Sample and data The initial sample consists of 1,179 voluntary auditor switches announced by companies listed on the Australian Securities Exchange (ASX) during the period 2000−2011. The sample is identified through a keyword search of all company announcements on ASX’s Signal G platform, followed by manual verification. Switches involving Arthur Andersen (n=150) are 12

removed (Asthana et al., 2010), along with 38 observations without requisite firm-year audit and financial statement data for the initial engagement year of the successor auditor and final engagement year of the predecessor. A further 90 observations, where switches are caused by partners defecting to a rival auditor or the merger of the outgoing and incoming audit firm, are dropped.25 Requirements for the exact date and time of the auditor switch announcement to be identified and stock price data covering a 12-month estimation window and a 3-day event window result in the removal of a further 188 observations. 26 We obtain announcement details and financial statement data from Morningstar’s DatAnalaysis database, stock price data from SIRCA’s CRD database, and audit data hand collected from annual reports. The above selection procedures result in a final sample of 713 auditor switches. A breakdown of the sample selection criteria is shown in Table 1, Panel A. 4.2 Construction of variables To assess market reactions to auditor switches, a 3-day event window (−1,+1) spanning the day prior to the auditor switch announcement, the day of the announcement, and the day following the announcement, is employed. The 3-day window helps minimise noise from other nearby announcements, with day −1 capturing any potential information leakage occurring prior to the announcement. The event date (day 0) is the date on which the announcement was made through the Signal G platform of the ASX.27 We use market modeladjusted returns (prediction errors) accumulated over the 3-day event window as our measure of market reaction or CAR(−1,+1). Market model parameters are computed over a 250-day estimation window (–261,–12) ending 10 days prior to the event window, with returns on the 25

We checked individual announcements and audit data for any association between the successor and predecessor auditor to remove switches not considered to be a ‘clean transition’. A good example is where clients follow partners moving between the successor and predecessor auditor (clients following partners to new audit firms). This switch type conveys additional information regarding the audit partner effect, which the literature has shown to influence both market valuations (Azizkhani et al., 2012) and accounting information quality (Carey and Simnett, 2006). 26 We require that over the 12-month estimation window, firms must have at least 120 days of stock price data of which a minimum of 60 days are of non-stale prices (i.e., prices with trading). 27 For auditor switches announced after market close, we use the following trading day as the event date.

13

All Ordinaries Index that proxies for market returns (

).28 To ascertain if the market reacts

to firms switching auditors, we perform univariate analysis, testing if the mean CAR(−1,+1) is significantly different from zero for the whole sample and by different switch types. 29 We employ two measures of auditor reputation based on the extant literature in our analysis. First, the market reaction to firms switching between the Big 4 and from (to) Big 4 to (from) non-Big 4 auditors is examined (DeAngelo, 1981; Francis, 1984). Second, industry specialist measures are constructed, utilising all available firm-years with requisite data (n=19,523) over the period 1999−2010.30 Industries are based on the 4-digit Global Industry Classification Standard (GICS) codes (24 unique industries).31 Industry specialists are defined in year t as auditors commanding the largest market share within a given GICS industry based on audit fees paid in that given year.32 4.3 Model specification We use the cross-sectional model in Equation (1) to test the effect that auditor quality has on market reactions to auditor switches. Specifically: CAR(−1,+1)i = βo + β1UPSPi + β2DNSPi + β3NB_B4i + β4B4_NBi + β5NB_B4*UPSPi + β6B4_NB*DNSPi + β7RESIGNi + β8LTAi + β9TENUREi + β10TIMINGi + β11OPINIONi + β12ZSCOREi + β13PAGESi + β14AGMi + β15ACCOMPi + ei,

(1)

28

In sensitivity tests, we conduct analysis using market-adjusted returns and mean-adjusted returns as alternative measures of abnormal returns (not reported). A correlation coefficient of 0.98 between these measures of abnormal return is observed with results remaining similar (see MacKinlay, 1997). 29 We re-run analysis winsorising variables at the 1% and 99% levels with results remaining similar. 30 To be included in the estimation of industry specialisation, observations must have an industry classification code, audit firm name, audit office and audit fees paid to the auditor. Data for 1999 are required for capturing the predecessor auditor for switches announced in 2000. 31 In sensitivity tests, we require industries to have at least 30 companies present during the sample period with the results remaining unchanged. 32 Only Big 4 auditors are eligible to be industry specialists. In sensitivity analysis, we relax this restriction, resulting in some non-Big 4 auditors being classified as industry specialists in GICS 3030 (K N Bromley in 1999–2001, Bentleys in 2004–2005, RSM Bird Cameron in 2007–2010) and GICS 4530 (Grant Thornton in 2006–2010). Again, the primary results remain unchanged.

14

where the dependent variable, CAR(−1,+1), is the cumulative abnormal return around the auditor switch announcement. In terms of test variables, NB_B4 (B4_NB) is an indicator variable taking a value of 1 for a switch to (from) a Big 4 auditor from (to) a non-Big 4 auditor, 0 otherwise. UPSP (DNSP) is an indicator variable taking a value of 1 for a switch from (to) a non-specialist to (from) a specialist auditor, 0 otherwise. NB_B4*UPSP is an interaction term between NB_B4 and UPSP, taking a value of 1 for a switch from a nonspecialist non-Big 4 to a specialist Big 4 auditor, 0 otherwise. B4_NB*DNSP is an interaction term between B4_NB and DNSP, taking a value of 1 for a switch from a specialist Big 4 to a non-specialist non-Big 4 auditor, 0 otherwise. These interaction terms capture the incremental difference in market reaction for firms switching between non-specialist non-Big 4 and specialist Big 4 auditors, considered by Knechel et al. (2007) as a ‘double jump’ in the level of audit quality. A positive (negative) coefficient for NB_B4 (B4_NB), UPSP (DNSP) and NB_B4*UPSP (B4_NB*DNSP) is consistent with a positive (negative) market reaction to expected improvement (deterioration) in audit quality resulting from the switch. We include variables to control for other potential factors influencing the market reaction to auditor switches. RESIGN is an indicator variable taking a value of 1 if the auditor switch announcement is caused by a resignation of the incumbent auditor, 0 otherwise.33 LTA is the natural logarithm of the client firm’s lagged total assets. TENURE is an indicator variable with a value of 1 if the incumbent has audited the client for more than five years, 0 otherwise. TIMING is an indicator variable with a value of 1 if the switch occurs in the last quarter of the financial year, 0 otherwise. OPINION is an indicator variable with a value of 1 if a goingconcern opinion was issued by the incumbent auditor prior to the switch, 0 otherwise. ZSCORE is the Zmijewski Z-score for the client firm in the year prior to the auditor change. PAGES is measured as the number of pages in the auditor switch announcement document, 33

Consistent with ASIC’s classification, all auditor terminations requiring ASIC’s approval are treated as resignations.

15

controlling for the amount of information in the announcement. AGM is an indicator variable with a value of 1 if the switch announcement is made as part of the annual general meeting (AGM) agenda items, 0 otherwise. This control is necessary as AGM announcements typically contain more information unrelated to the auditor switch than standalone, non-AGM announcements. ACCOMP is an indicator variable with a value of 1 if other announcements occur within the event window (–1,+1), capturing any accompanying information unrelated to the auditor switch announcement, 0 otherwise. The cross-sectional model in Equation (1) is estimated using OLS regressions. As the same client firm may have experienced more than one auditor switch in the sample, t-statistics for the estimated coefficients are reported based on robust standard errors clustered by unique firms. 5. Results 5.1 Descriptive analysis Table 1, Panel B reports the sample breakdown by various auditor switch characteristics. There are 365 (51.2%) lateral switches (within auditor size band) and 348 (48.8%) non-lateral switches across size band. Among lateral switches, switching between two non-Big 4 auditors is more common than switching between two Big 4 auditors (229 vs. 136 switches). For switches across size bands (to/from Big 4), it is more likely that client firms switch from a small to a big auditor than in the opposite direction (196 vs. 152 switches). In terms of specialists, 166 cases (23.4%) involved switching between specialist and non-specialists, with 84 and 82 cases of switching to a specialist and non-specialist auditor, respectively. In terms of announcement type, 581 (81.5%) of auditor switches are announced as an AGM agenda item, with the remaining 132 (18.5%) disclosed through standalone announcements. Using ASIC’s classification of auditor termination type, there are 426 (59.7%) resignations and 287 (40.3%) dismissals (removals in ASIC’s terminology).

16

Table 2 presents the distribution of specialist auditors by industry and year. The results indicate the identity of specialist auditors is unstable over longer periods of time, suggesting auditors re-aligning their specialisation over time (Ferguson and Stokes, 2002). In 14 out of the 24 GICS industries considered, a particular specialist holds the position in nine or more years over the sample period.34 Among the Big 4, PricewaterhouseCoopers (PwC) is a clear leader claiming industry specialisation in a total of 110 industry-years, followed by Ernst & Young (EY) (84), KPMG (70) and Deloitte Touche Tomatsu (Deloitte) (17). Table 3 reports the breakdown of auditor switches by year (Panel A) and industry group (Panel B). Switching to/from Big 4 and industry specialist auditors is consistently distributed across the sample years (Panel A). In terms of industry, Panel B shows the greatest sample representation is from energy (GICS 1010) and materials (GICS 1510) firms, consistent with the prior Australian literature (Ferguson and Stokes, 2002; Ferguson et al., 2003; Carson et al., 2012; Goodwin and Wu, 2014). Table 4 presents univariate results for the market reactions to auditor switch announcements. In Panel A, there is little evidence of a significant CAR(−1,+1), suggesting the market does not react to companies announcing auditor switches. When partitioning the sample by announcement type, there is again no significant market reaction for either the AGM or standalone subsamples although the standalone subsample has the highest mean CAR of 1.4% (but the t-statistic falls short of significance) while the median CAR for the AGM subsample (–0.4%) is marginally significant (Z=−1.727). The breakdown between resignations and dismissals paints a similar picture with insignificant market reactions documented, except for the median CAR (Z=−2.002) for the resignations subsample. To test Hypotheses 1 and 2, the switch sample is partitioned across auditor size (Panel B) and industry specialisation (Panel C). Again, there is little evidence of market reaction to 34

Specialist Big 4 dominance by industry can be seen as follows: GICS 2530, 3020, 4020, 4510 and 4530 (PwC); GICS 1010, 2540, 2550, 3010, 3520 and 5010 (Ernst & Young); GICS 2010, 2030 and 3510 (KPMG).

17

announcements of switches between Big 4 and non-Big 4 and between specialist and nonspecialist auditors. Thus, both Hypotheses 1 and 2 are not supported by the univariate analysis. These results are consistent with prior studies reporting an insignificant market reaction to auditor switches (Fried and Schiff, 1981; Nichols and Smith, 1983; Johnson and Lys, 1990; Klock, 1994; Chang, et al., 2010). 5.2 Cross-sectional regression results Though little evidence of a significant market reaction is found, it is still possible market reactions may vary with attributes of the firm and the switch in the cross-section. We employ multivariate regression analysis (Equation 4) to gauge whether the market reacts differently to switch announcements signifying differential audit quality. Table 5 reports descriptive statistics of the independent variables used in the model. The mean level of total assets is much higher than the median ($1.4 billion vs. $1.67 million). This is due to a large number of small mining firms (GICS 1510) dominating the left tail of the distribution, and several large multinational firms in the right tail, driving up the mean. Relative to Knechel et al. (2007), fewer instances of auditor switches (8.7 %) occur in the fourth quarter of the financial year (TIMING) in Australia. This is driven by ASIC’s requirement (RG 26) for firms to announce switches at AGMs (81.6 %), which are commonly held in the first quarter of the following financial year. Given AGMs typically include other agenda items, it may induce noise in the announcements. We consider the potential impact of this in additional analyses (see next section). Further, 55.7% of switches involve other announcements occurring within the 3-day event window (controlled for by ACCOMP). We report the cross-sectional regression results for the full sample in columns 1 and 2 of Table 6. Results in column 1 show, without the auditor size-specialist interaction terms, all the test variables representing switches between different gradations of auditor quality (size and specialist) are not significant in explaining the cross-sectional variation in the event CAR. 18

Of the control variables included, financial distress (ZSCORE) is significant at the 5% level, suggesting less distressed firms are associated with a higher CAR. AGM is marginally significant at the 10% level, implying switches announced as part of AGM agenda items experience a negative market reaction. When the auditor quality interaction terms are included (column 2), the coefficients for DNSP and NB_B4*UPSP become significant at the 10% level, indicating a switch from a specialist (non-specialist non-Big 4) to a non-specialist (specialist Big 4) auditor is met with a negative (positive) market reaction. Table 6 also presents subsample results based on auditor termination type (columns 3 and 4). With the reduced sample size, not all the significant variables observed from the full sample analysis continue to hold across the subsamples. For instance, in the dismissal subsample (column 4), DNSP is no longer significant while NB_B4*UPSP remains positive and significant (at the 5% level). However, the variable NB_B4 (indicating switches from a non-Big 4 to a Big 4), is significant (at the 10% level) but carries the wrong sign (negative, instead of positive). We attribute these inconsistent results to the confounding noise associated with other AGM agenda items based on the analysis of the standalone announcements given below. None of the independent variables is significant in the ‘resignation’ subsample in column 3. To control for noise associated with other AGM agenda items, we split the sample into standalone announcements and AGM announcements and re-run our analysis separately. Results in column 6 (standalone subsample) show the variable NB_B4*UPSP loads significantly (at the 5% level) with a larger coefficient of 0.093 (as compared with 0.035 for the full sample), despite the smaller sample size (132 switches). On the other hand, the AGM subsample (column 5) shows a significantly negative coefficient (at the 5% level) for the variable DNSP only.

19

Overall, we find only weak evidence of any audit quality argument in explaining the crosssectional variation in market reactions to auditor switches. Firms switching from a specialist to a non-specialist auditor tend to experience a negative market reaction while firms making a ‘double jump’ in auditor quality (NB_B4*UPSP) record a positive stock price response. A further caveat is that the models themselves are not statistically significant, as judged by the low adjusted R2 and F-statistic obtained. 6. Additional analysis 6.1 Long-run return-earnings analysis We consider whether gradations of audit quality following an auditor switch have any effects on the informativeness of earnings. We expect earnings informativeness improves upon appointing a higher quality auditor if the market perceives a reputation change from the auditor switch. The underlying assumption is the market recognises earnings information as more credible when audited by a larger/specialist auditor, leading to a stronger association between stock returns and reported earnings. 35 However, if litigation risk, instead of audit quality, is the primary driver of perceived financial reporting credibility, we would not expect a significant difference in the return-earnings association across gradations of auditor quality in a low litigation risk environment like Australia (Khurana and Raman 2004). We examine this prediction by analysing long-run stock returns leading to the first earnings announcement following the auditor switch. This allows us to capture the incremental effect industry specialists and Big 4 auditors have on the credibility of earnings information perceived by the

35

This is based on the argument that larger/specialist auditors help reduce omissions and errors in financial statements, improving the accuracy of the information (DeAngelo 1981; Craswell et al. 1995).

20

market.36 We employ the approach used in Warfield et al. (1995) and Gul et al. (2002) and specify the following model: 37 BHRi = βo + β1EP*UPSPi + β EP*DNSPi + β3EP*NB_B4i + β4EP*B4_NBi + β5EP*NB_B4*UPSPi + β6B4_NB*DNSPi + β7EPi + β8EP*SIZEi + β9EP*BETAi + β10EP*DEi + β11EP*MTBi + β12EP*DIRSHi + β13EP*YEARENDi + β14EP*EVARi + β15EP*EPERSi + β16FINi + β17MINi + β18YEAR8i + ei,

(2)

where BHR is 12-month holding-period return over a window spanning nine months before to three months after the balance sheet. We control for the potential drivers of stock returns in the period leading to the earnings announcement, including firm profitability (EP), earnings variability (EVAR), earnings persistence (EPERS), valuation (MTB), leverage (DE), size (SIZE), systematic risk (BETA), announcement timing (YEAREND), director shareholding (DIRSH) and industry effects (FIN, MIN).38 If market participants associate greater (reduced) credibility in the earnings announcement where a higher (lower) quality auditor (Big 4 or industry specialist) is appointed, a positive (negative) coefficient would be observed for the interaction terms EP*UPSP, EP*NB_B4 and EP*NB_B4*UPSP (EP*DNSP, EP*B4_NB and B4_NB*DNSP). Tables 7 and 8 present descriptive statistics and multivariate results for the long-run analysis. In Table 7, the statistics on BHR and earnings per share show a similar trend with both reporting a positive mean but negative median value. The data restriction that requires a total of four years of historical earnings for EVAR and EPERS results in a reduced sample of 493 observations. To ensure results do not differ when samples of different sizes are used, we

36

This would capture additional information not already realised in the market reaction to the announcement of the auditor switch. 37 The following variables are winsorised at the 1 st and 99th percentile: BHR, EP, BETA, DE, MTB, EVAR, and EPERS. Descriptive statistics of unwinsorised variables are reported in Table 7. 38 All independent variables are defined in Appendix A.

21

run the analysis using this reduced sample (data restricted sample) incorporating EVAR and EPERS and the full sample without these controls. We present multivariate results of the long-run analysis in Table 8 . Regression results across the full and reduced samples show, despite the coefficients on EP*UPSP and EP*DNSP are directionally consistent, there is no significant association between holdingperiod returns and differential quality of auditors. This suggests the market does not perceive improved earnings informativeness from higher audit quality. 39 Overall, these findings largely support our primary results that there is no differential impact of auditor quality on stock returns, consistent a litigation risk explanation.40 6.2 Confounding announcement noise While our event window is more precisely captured relative to prior US studies, this setting also

features the presence of confounding

information contained within

AGM

announcements. To control for these noise effects, we employed Perceptive Search to provide keyword counts of terms unrelated to the switch appearing in the announcement documents, similar to the approach adopted by Chang et al. (2010).41

39

The control variables are largely consistent with expectations with both earnings levels and persistence (EP, EP*EPERS) significantly associated with holding-period returns (BHR) (Gul et al. 2002). Similarly, the impact of the GFC on stock returns is observed in the negative coefficient on YEAR8. The models are also statistically significant with reasonable explanatory power. We find results do not differ when including an indicator for Big 4 audit firms or splitting the sample by Big 4 and non-Big 4 firms. 40 A minor caveat on our findings is that the return-earnings association models are typically run on firms reporting positive net income. However, only 203 observations in our sample reported positive net income. When running models using this ‘positive net income’ sample, we find that EP*DNSP is negative and statistically significant. This is consistent with the market reaction findings in Table 7, suggesting the market reacts negatively if the firm removes a specialist auditor. This result is based on a sample involving 23 firms switching away from a specialist auditor. 41 This software requires documents in machine-readable form. Any announcements not in machine-readable form were manually typed. See Appendix B for a list of the keywords used.

22

hese noise variables are then included as additional controls in the multivariate regression model of Equation (1). Table 9 presents regression results controlling for AGM-related noise. Column (1) shows when the natural logarithm of the aggregate AGM noise control Ln(AGM_NOISE) is included, only the estimated coefficient for DNSP continues to be significant (at the 5% level) but not the coefficient for NB_B4*UPSP. Similar results are observed when the aggregate noise measure is replaced by the individual noise variables in column (2). Overall, this suggests the negative market reaction previously found for a down-specialist (DNSP) switch continues to hold but the positive reaction found for the ‘double-jump’ (NB_B4*UPSP) switch might have been driven by the presence of noise related to other AGM agenda items bundled with the switch announcement. 6.3 Voluntary disclosure of reasons for the auditor switch We next consider the effect of voluntary disclosure of the reasons underlying the auditor switch on the market reaction (Sankaraguruswamy and Whisenant, 2004). We find (results not tabulated) 211 switches with accompanying reason(s) and group them into the following categories: cost and tender (57%), relocation of operations (16%), growth in operations (8%), independence issues (6%), mandatory partner rotation requirements (6%), shareholder nomination (4%), and other (2%). When re-running the primary results with the inclusion of an indicator variable capturing whether a reason was voluntarily disclosed, we find a negative and significant coefficient (at the 10% level, unreported) for this variable. This suggests the market views this information negatively, or these switches themselves (accompanied by voluntarily disclosed reasons) are believed to result in adverse shareholder outcomes leading management to disclose the information. When the single indicator variable is replaced by 23

separate indicator variables representing different categories of reasons (unreported), we find the negative and significant coefficient (at the 5% level) is largely driven by ‘shareholder nomination’ reasons. The lack of significance on the indicator variable for ‘cost and tender’ reasons is inconsistent with Sankaraguruswamy and Whisenant (2004).42 We note that adding variables for reasons underlying the switch does not qualitatively change the coefficients on the test variables 6.4 Other analysis and sensitivity tests Further sensitivity analysis includes examining whether multivariate results are sensitive to observations from particular industries, especially mining (GICS 15) and financial (GICS 40) firms, and particular years (e.g., 2008 and 2009 affected by the global financial crisis (GFC)).43 Untabulated results show when excluding mining firms, only DNSP maintains its significance (at the 10% level), but not NB_B4*UPSP. When financial firms are removed, both DNSP and NB_B4*UPSP are not significant at conventional levels. In addition, when only mining firms are used, results (unreported) indicate a positive and significant coefficient (at the 10% level) for the variable NB_B4*UPSP, consistent with the primary analysis. When GFC observations are excluded, results (unreported) show both DNSP and NB_B4*UPSP remain significant (at the 10% level). Overall, this subsample evidence suggests full sample results are sensitive to the exclusion of financial firms but not mining firms nor GFC years. We next consider whether individual auditors within the Big 4 may impact results. When re-running primary analysis dropping each individual brand name auditor, one at a time, we find that coefficients for DNSP and NB_B4*UPSP are not significant when PwC is used. This

42

39.6% of all switches disclose a cost-related reason but report significantly higher fee residuals in both the switch year and the year prior, suggesting these firms are unsuccessful in lowering their fees. 43 Mining firms constitute the largest proportion of switches (30%), while financial firms have financial statement data subject to different regulation (Francis, 1984; Craswell et al., 1995).

24

is consistent with prior studies documenting a differential brand effect associated with PwC (Simunic, 1980; Hay et al., 2006; Carson, 2009; Hay, 2012; Ferguson and Scott, 2014).44 The potential effect of ownership concentration is considered by including the percentage shareholding of the top 20 shareholders (TOP20) as an additional control variable in the model. We find the results (untabulated) remain unchanged, with the coefficient for TOP20 insignificant across all model specifications. In addition, we conduct further robustness tests, including computing CAR using both market-adjusted and mean-adjusted abnormal returns, allowing non-Big 4 auditors to be classified as industry specialists, and winsorising all the continuous variables (at 1% and 99% levels), with results (untabulated) remaining qualitatively the same. 6.5 Market reactions in the partial deregulation period (2015–2017) With the release of the revised RG 26 in June 2015, the expectation is auditor switches under the ‘partial deregulation period’ will exhibit characteristics of enhanced transparency and timelier information flow to the market. To ascertain if the revised RG 26 has resulted in a fundamental shift in the timing of and market reaction to auditor switches, a sample of 142 announcements of auditor switches between 18 June 2015 and 30 June 2017 are hand collected.45 Cumulative market-adjusted returns (CMAR) around a 3-day event window to proxy for market reactions are computed.46 Table 10 presents descriptive statistics and results of univariate tests for the CMAR. In Panel A under the revised RG 26 regime, we first note a major shift in the timing of auditor 44

We interact all switches involving an incoming Big 4 auditor (i.e., both NB_B4 and B4_B4) with each individual Big 4 brand and find no significant difference in market reactions across Big 4 auditors. 45 Starting with 152 first announcements of auditor switches where the incoming and outgoing auditor are clearly identified, 10 observations lacking stock price data in the 3 days surrounding the announcement and one more observation (occurring in June 2015) for a company which did not adopt the revised RG 26 immediately are excluded with no changes to the results. The use of recent data (up to June 2017) prevented multivariate analysis including auditor industry specialist effects owing to the absence of financial and auditor information for individual auditor switches, and market-wide audit data.. 46 Given the inability to obtain a large enough sample of auditor switches in the partial deregulation period, we do not use the market model prediction error to measure the market reaction, which would result in a high sample attrition rate from requiring a 12-month estimation window.

25

switches, with 72% (102 observations) of all switches being disclosed as non-AGM standalone announcements. This is a significant increase from 19% disclosed as standalone announcements in the regulatory consent period. The revised RG 26.4 allows announcements to take place at any time of the year in order to improve the timeliness of disclosure (ASIC, 2013). Upon closer inspection, auditor switches are observed evenly across the year, with some clustering (32%) of standalone announcements in January and February. 47 The clustering of auditor switches around mid-financial year is consistent with GIA (2013), which claims that clients will usually appoint auditors outside busy reporting periods (i.e., 30 June) to avoid delays in financial reporting. 48 Thus, the evidence seems to suggest the revised RG 26 has fundamentally altered the timing of auditor switch announcements to take place upon termination of the auditor-client relationship, consistent with ASIC seeking to improve the timeliness of disclosures to market participants (ASIC, 2015b). Focusing on market reactions, we find that CMAR for the full sample in Panel A is relatively muted (mean=–0.0007, median=–0.0018), suggesting the market does not systematically view auditor switches as positive or negative news. The same pattern persists across the standalone (mean=–0.0005, median=–0.0018) and AGM (mean=–0.0014, median =–0.0021) subsamples. Univariate tests also confirm CMAR is not significantly different from zero for the full, standalone and AGM subsamples. Further partitioning of the full sample based on the differential quality of incoming and outgoing auditors reveals half (50%) of the announcements involve a switch between non-Big 4 audit firms, followed by switches from Big 4 to non-Big 4 (25%), non-Big 4 to Big 4 (15%) and Big 4 to Big 4 (10%) auditors. Both the mean and median CMAR are negative for all types of switches, with the exception of switches from non-Big 4 to Big 4 auditors

47

A cluster of auditor switches (25%) is also found occurring in May and June, possibly reflecting auditor-client disagreements resulting in the switch (Schwartz and Soo, 1996; Pacheco-Paredes et al., 2017). 48 In comparison, some 81.5% of auditor switch announcements take place at the AGM occurring in September, October or November, following the June 30 fiscal year-end during the prior regulatory consent period.

26

(mean=0.0382, median=0.0053) of which 64% have a positive market reaction. 49 Withinsample univariate tests of CMAR indicate switches from non-Big 4 to Big 4 audit firms are significantly more positive than other switches for both the mean and median (p<0.10). This is consistent with the market reacting positively when a higher quality Big 4 auditor is appointed in place of a non-Big 4 auditor. Conducting additional subsample analysis on standalone announcements indicates switches from non-Big 4 to Big 4 auditors elicit a significant, positive CMAR (mean=0.0408, p<0.10 and median=0.0279, p<0.10). In contrast, we find evidence of a significantly negative CMAR (mean=–0.0126, p<0.10) for switches from Big 4 to non-Big 4 audit firms.50 Overall, evidence of a positive (negative) market reaction to switches to (from) Big 4 auditors suggests the market views announcements in the partial deregulation period (revised RG 26 regime) as providing more timely and informative disclosures to enable investors to differentiate between changes in audit quality (Knechel et al., 2007). The results in Panel B (Table 10) comparing market reactions between the regulatory consent and partial deregulation periods reveals a similar pattern. Switches from non-Big 4 to Big 4 auditors (increase in quality) in the partial deregulation period are associated with a significantly more positive (p<0.10) CMAR than in the regulatory consent period for both the full sample and standalone subsample. This evidence is consistent with more timely and informative disclosures in the partial deregulation period. In addition, switches from Big 4 to non-Big 4 auditors (decrease in quality) in the standalone subsample exhibit a significantly more negative (p<0.10) CMAR after the revised RG 26 is implemented. Given the timing of standalone announcements should be sufficiently close to the event, the relatively stronger

49

The mean market reaction of 3.8% is broadly comparable with 3.4% reported in Knechel et al. (2007). The mean market reaction is not significantly different from zero when analysing the full sample and standalone sample of auditor switches from Big 4 to third-tier auditors (non-Big 4 firms excluding BDO and Grant Thornton) based on Chang et al. (2010). 50

27

evidence observed for the standalone subsample in support of the audit quality hypothesis suggests ASIC consent is provided on a timelier basis after the policy change. 7. Conclusions This study examines market reactions to announcements of auditor switches by Australianlisted companies. Using a sample of 713 publicly announced auditor switches in the regulatory consent period between 2000 and 2011, limited evidence of a significant market reaction to firms announcing auditor switches is found. This lack of response may indicate any relevant information is filtered (or leaked) out due to delays caused by the lengthy and complicated ASIC consent process Long-run return-earnings analysis also yields no evidence of a distinction between various gradations of audit quality resulting from auditor switches. This no-result finding continues to hold even after controlling for noise accompanying announcements, voluntary disclosure of reasons, individual auditor brand effect, ownership concentration and other robustness tests. In contrast, using a sample of switch announcements in the partial deregulation period following the release of the revised RG 26 (2015–2017), we uncover univariate evidence of quality signals in market reactions around auditor switches, consistent with the prior literature. We conclude that ASIC’s prior highly-regulated, process-driven consent approach is not only costly, but also denies the market of relevant and timely information arising from auditor switches. The partial deregulation towards a disclosure-based approach has uncovered stronger capital market effects. Further steps to free up the process may have the potential to cut compliance costs and enhance market signalling and price discovery. Nevertheless, it is important to recognize the potential limitations of these results. Most notably, the limited sample in the partial deregulation period prevented comprehensive multivariate analysis for this latter period. This may provide an interesting avenue for future research.

28

References Australian Securities and Investments Commission (2007), Resignation of Auditors, Regulatory Guide 26, Sydney. Australian Securities and Investments Commission (2013), Resignation, Removal and Replacement of Auditors: Update to RG 26, Consultation Paper 209, Sydney. Australian Securities and Investments Commission (2015a), Resignation, Removal and Replacement of Auditors, Regulatory Guide 26, Sydney. Australian Securities and Investments Commission (2015b), Response to submissions on CP 209 Resignation, removal and replacement of auditors: Update to RG 26, Report 437, Sydney. Azizkhani, M., G.S. Monroe and G. Shailer (2012), ‘Audit Partner Tenure and Cost of Equity Capital’, Auditing: A Journal of Practice & Theory, Vol. 32, No. 1, pp. 183−202. Asthana, S.C., S. Balsam and J. Krishnan (2010), ‘Corporate Governance, Audit Firm Reputation, Auditor Switches, and Client Stock Price Reactions: The Andersen Experience’, International Journal of Auditing, Vol. 14, No. 3, pp. 274−293. Balsam, S., J. Krishnan and J.S. Yang (2003), ‘Auditor Industry Specialization and Earnings Quality’, Auditing: A Journal of Practice & Theory, Vol. 22, No. 2, pp. 71−97. Beattie, V. and S. Fearnley (1995), ‘The Importance of Audit Firm Characteristics and the Drivers of Auditor Change in UK Listed Companies’, Accounting and Business Research, Vol. 25, No. 100, pp. 227−239.

29

Beattie, V. and S. Fearnley (1998), ‘Audit Market Competition: Auditor Changes and the Impact of Tendering’, British Accounting Review, Vol. 30, No. 3, pp. 261−289. Cameran, M., J.R. Francis, A. Marra and A.K. Pettinicchio (2014), ‘Are There Adverse Consequences of Mandatory Auditor

Rotation? Evidence

from the

Italian

Experience’, Auditing: A Journal of Practice and Theory, Vol. 34, No. 1, pp. 1−24. Carey, P. and R. Simnett (2006). ‘Audit Partner Tenure and Audit Quality’, The Accounting Review, Vol. 81, No. 3, pp. 653−676. Carson, E. (2009), ‘Industry Specialization by Global Audit Firm Networks’, The Accounting Review, Vol. 84, No. 2, pp. 355−382. Carson, E., R. Simnett and B.S. Soo (2012), ‘Changes in Audit Market Competition and the Big N Premium’, Auditing: A Journal of Practice & Theory, Vol. 31, No. 3, pp. 47−73. Carter, M.E. and B.S. Soo (1999), ‘The Relevance of Form 8-K Reports’, Journal of Accounting Research, Vol. 37, No. 1, pp. 119−132. Catanach, A., J.H. Irving, S.P. Williams and P.L. Walker (2011), ‘An Ex Post Examination of Auditor Resignations’, Accounting Horizons, Vol. 25, No. 2 pp. 267−283. Cenker, W.J. and A.L. Nagy (2008), ‘Auditor Resignations and Auditor Industry Specialization’, Accounting Horizons, Vol. 22, No. 3, pp. 279−295. Chang, H., A.C.S. Cheng and K.J. Reichelt (2010), ‘Market Reaction to Auditor Switching from Big 4 to Third-tier Small Accounting Firms’, Auditing: A Journal of Practice & Theory, Vol. 29, No. 2, pp. 83−114. CPA Australia & ICCA (2013), Consultation Paper 209: Resignation, removal and replacement of auditors: Update to RG 26, Sydney. Craswell, A.T., J.R. Francis and S.L. Taylor (1995), ‘Auditor Brand Name Reputations and Industry Specializations’, Journal of Accounting and Economics, Vol. 20, No. 3, pp. 297−322.

30

DeAngelo, L.E. (1981), ‘Auditor Size and Audit Quality’, Journal of Accounting and Economics, Vol. 3, No. 3, pp. 183−199. DeFond, M. and J. Zhang (2014), ‘A Review of Archival Auditing Research’, Journal of Accounting and Economics, Vol. 58, No. 2−3, pp. 275−326. Deloitte (2013), Response to Consultation Paper 209 (CP 209): Resignation, removal and replacement of auditors, Sydney. Dunn, J., D. Hillier and A.P. Marshall (1999), ‘The Market Reaction to Auditor Resignations’, Accounting and Business Research, Vol. 29, No. 2, pp. 95−108. Eichenseher, J., M. Hagigi and D. Shields (1989), ‘Market Reaction to Auditor Changes by OTC Companies’, Auditing: A Journal of Practice & Theory, Vol. 9, No. 1, pp. 29−40. Ernst and Young (2013), Comments regarding Consultation Paper 209: Resignation, removal and replacement of auditors: Update to RG 26, Melbourne. Ettredge, M.L., S. Scholz and C. Li (2007), ‘Audit Fees and Auditor Dismissals in the Sarbanes-Oxley Era’, Accounting Horizons, Vol. 21, No. 4, pp. 371−386. Ferguson, A., J.R. Francis and D.J. Stokes (2003), ‘The Effects of Firm-wide and Office-level Industry Expertise on Audit Pricing’, The Accounting Review, Vol. 78, No. 2, pp. 429−448. Ferguson, A. and T. Scott (2014), ‘What If There Were Three? Audit Pricing within the Big 4 and the PricewaterhouseCoopers’ Premium in the Australian Audit Market’, International Journal of Auditing, Vol. 18, No. 1, pp. 57−67. Ferguson, A. and D.J. Stokes (2002), ‘Brand Name Audit Pricing, Industry Specialization, and Leadership Premiums Post‐Big 8 and Big 6 Mergers’, Contemporary Accounting Research, Vol. 19, No. 1, pp. 77−110. Francis, J.R. (1984), ‘The Effect of Audit Firm Size on Audit Prices: A Study of the Australian Market’, Journal of Accounting and Economics, Vol. 6, No. 2, pp. 133−151.

31

Francis, J.R. and D. Wang (2008), ‘The Joint Effect of Investor Protection and Big 4 Audits on Earnings Quality Around the World’, Contemporary Accounting Research, Vol. 25, No. 1, pp. 157−191. Fried, D. and F. Schiff (1981), ‘CPA Switches and Associated Market Reactions’, The Accounting Review, Vol. 56, No. 2, pp. 326−341. Geiger, M.A. and D.V. Rama (2006), ‘Audit Firm Size and Going-concern Reporting Accuracy’, Accounting Horizons, Vol. 20, No. 1, pp. 1−17. Goodwin, J. and D. Wu (2014), ‘Is the Effect of Industry Expertise on Audit Pricing an Office-level or a Partner-level Phenomenon?’, Review of Accounting Studies, Vol. 19, No. 4, pp. 1−47. Governance Institute of Australia (2013), Consultation Paper 209: Resignation, removal and replacement of auditors: Update to RG 26, Sydney. Griffin, P.A. and D.H. Lont (2010), ‘Do Investors Care about Auditor Dismissals and Resignations? What Drives the Response?’, Auditing: A Journal of Practice & Theory, Vol. 29, No. 2, pp. 189−214. Gul, F.A., S.G. Lynn and J.S. Tsui (2002), ‘Audit Quality, Management Ownership, and the Informativeness of Accounting Earnings’, Journal of Accounting, Auditing & Finance, Vol. 17, No.1, pp. 25-49. Hay, D.C. (2012), ‘Further Evidence from Meta-analysis of Audit Fee Research’, International Journal of Auditing, Vol. 17, No. 2, pp. 162−176. Hay, D.C., W.R. Knechel and N. Wong (2006), ‘Audit Fees: A Meta-analysis of the Effect of Supply and Demand Attributes’, Contemporary Accounting Research, Vol. 23, No. 1, pp. 141−191.

32

Huang, Y. and S. Scholz (2012), ‘Evidence on the Association Between Financial Restatements and Auditor Resignations’, Accounting Horizons, Vol. 26, No. 3, pp. 439−464. Johnson, W.B. and T. Lys (1990), ‘The Market for Audit Services: Evidence from Voluntary Auditor Changes’, Journal of Accounting and Economics, Vol. 12, No.1, pp. 281−308. Khurana, I.K. and K.K. Raman (2004), ‘Litigation Risk and the Financial Reporting Credibility of Big 4 versus Non-Big 4 Audits: Evidence from Anglo-American Countries’, The Accounting Review, Vol. 79, No. 2, pp. 473-495. Klock, M. (1994), ‘The Stock Market Reaction to a Change in Certifying Accountant’, Journal of Accounting, Auditing & Finance, Vol. 9, No. 2, pp. 339−347. Knechel, W.R., V. Naiker and G. Pacheco (2007), ‘Does Auditor Industry Specialization Matter? Evidence from Market Reaction to Auditor Switches’, Auditing: A Journal of Practice & Theory, Vol. 26, No. 1, pp. 19−45. KPMG (2013), Submission – Consultation Paper 209: Resignation, removal and replacement of auditors: Update to RG 26 Resignation of auditors, Sydney. Kwon, S.Y., Y.L. Chee and P.M. Tan (2007), ‘Legal Systems and Earnings Quality: The Role of Auditor Industry Specialization’, Auditing: A Journal of Practice & Theory, Vol. 26, No. 2, pp. 25−55. MacKinlay, A.C. (1997), ‘Event Studies in Economics and Finance’, Journal of Economic Literature, Vol. 35, No. 1, pp. 13−39. Mohrmann, U., Stefani, U. and Hess, B., (2017) Audit Market Regulation around the World: Descriptive Evidence, Market Structure, and Audit Quality. Available at SSRN: https://ssrn.com/abstract=2434444 or http://dx.doi.org/10.2139/ssrn.2434444 Nichols, D.R. and D.B. Smith (1983), ‘Auditor Credibility and Auditor Changes’, Journal of Accounting Research, Vol. 21, No. 2, pp. 534−544.

33

Pacheco-Paredes A.A., D.V. Rama, and C.M. Wheatley (2017) The Timing of Auditor Hiring: Determinants and Consequences. Accounting Horizons, Vol. 31, No. 3, pp. 85103. Raghunandan, K. and D.V. Rama (1999), ‘Auditor Resignations and the Market for Audit Services’, Auditing: A Journal of Practice & Theory, Vol. 18, No. 1, pp. 124−134. Sankaraguruswamy, S. and J.S. Whisenant (2004), ‘An Empirical Analysis of Voluntarily Supplied Client-auditor Realignment Reasons’, Auditing: A Journal of Practice & Theory, Vol. 23, No. 1, pp.107−121. Schwartz, K. B., and B. S. Soo, (1996). The association between auditor changes and reporting lags. Contemporary Accounting Research, Vol. 13, No.1, pp. 353-370. Securities and Exchange Commission (1989), Accelerations of the Time for Filing Forms 8-K Relating to Changes in Accountants and Resignations of Directors: Amendments to Regulation S-K Regarding Changes in Accountants, Financial Reporting Release No. 34, Washington, D.C., Government Printing Office. Shu, S.Z. (2000), ‘Auditor Resignations: Clientele Effects and Legal Liability’, Journal of Accounting and Economics, Vol. 29, No. 2, pp. 173−205. Simunic, D. (1980), ‘The Pricing of Audit Services: Theory and Evidence’, Journal of Accounting Research, Vol. 18, No. 1, pp. 161−190. Smith, D.B. (1988), ‘An Investigation of Securities and Exchange Commission Regulation of Auditor Change Disclosures: The Case of Accounting Series Release No. 165’, Journal of Accounting Research, Vol. 26, No. 1, pp. 134−145. Smith, D.B. and D. Nichols (1982), ‘A Market Test of Investor Reactions to Disagreements’, Journal of Accounting and Economics, Vol. 4, No. 2, pp. 109−120.

34

Warfield, T.D., J.J. Wild and K.L. Wild (1995), ‘Managerial Ownership, Accounting Choices, and Informativeness of Earnings’, Journal of Accounting and Economics, Vol. 20, No. 1, pp. 61−91. Wells, D.W. and M.L. Loudder (1997), ‘The Market Effects of Auditor Resignations’, Auditing, Vol. 16, No. 1, pp. 138−144. Whisenant, J., S.S. Sankaraguruswamy and K. Raghunandan (2003), ‘Market Reactions to Disclosure of Reportable Events’, Auditing: A Journal of Practice & Theory, Vol. 22, No. 1, pp. 181−194.

35

Appendix A. Definition of variables Variable CAR

Definition Cumulative abnormal return in the 3-day window (–1, +1) surrounding the announcement of auditor switch

UPSP

Indicator variable with a value of 1 if the switch is from a nonspecialist auditor to a specialist auditor, 0 otherwise

DNSP

Indicator variable with a value of 1 if the switch is from a specialist auditor to a non-specialist auditor, 0 otherwise

NB_B4

Indicator variable with a value of 1 if the switch is from a nonBig 4 auditor to a Big 4 auditor, 0 otherwise

B4_NB

Indicator variable with a value of 1 if the switch is from a Big 4 auditor to a non-Big 4 auditor, 0 otherwise

NB_B4*UPSP

An interaction term between NB_B4 and USP, taking a value of 1 if the switch is from a non-specialist non-Big 4 auditor to a specialist Big 4 auditor, and 0 otherwise

B4_NB*DNSP

An interaction term between B4_NB and DSP, taking a value of 1 if the switch is from a specialist Big 4 auditor to a nonspecialist non-Big 4 auditor, and 0 otherwise

RESIGN

RESIGN is an indicator variable taking a value of 1 if the auditor switch announcement is caused by a resignation (based on ASIC’s classification) of the predecessor auditor, 0 otherwise

LTA

Natural logarithm of the firm’s prior year total assets

TENURE

Indicator variable with a value of 1 if the current auditor has audited the client for more than five years, 0 otherwise

TIMING

Indicator variable with a value of 1 where the auditor switch occurs in the last quarter of the financial year, 0 otherwise

OPINION

Indicator variable with a value of 1 if a going-concern opinion was issued by the current auditor prior to the auditor switch, 0 otherwise

ZSCORE

Zmijewski Z-score for the client firm measured in the year prior to the year of the auditor change

PAGES

Number of pages in the announcement document

AGM

Indicator variable with a value of 1 if the auditor switch is announced as part of the annual general meeting agenda items, 0 otherwise

ACCOMP

Indicator variable with a value of 1 if other announcements occur within the announcement window (–1, +1), capturing any accompanying information unrelated to the auditor switch, 0 otherwise

BHR

Holding-period returns for a window spanning nine months before to three months after the balance sheet date

36

EP

Earnings per share scaled by closing share price nine months prior to the balance sheet date

SIZE

Fourth root of the market capitalisation of the firm (in millions) at the balance sheet date

BETA

Market model beta estimated using weekly stock returns in the two years preceding the balance sheet date

DE

Ratio of long-term debt to total assets at the balance sheet date

MTB

Ratio of market to book equity at the balance sheet date

DIRSH

Proportion of shares held by directors in the company scaled by total shares outstanding

YEAREND

A binary variable taking the value of 1 if the firm has a non-June 30 balance sheet date, 0 otherwise

EVAR

Time-series standard deviation of reported annual earnings over the preceding four years scaled by the current year reported earnings

EPERS

First-order autocorrelation of reported annual earnings over the preceding four years

FIN

A binary variable taking the value of 1 if the firm operates in the mining industry (GICS 10 and 15), 0 otherwise

MIN

A binary variable taking the value of 1 if the firm operates in the finance industry (GICS 40), 0 otherwise

YEAR8

A binary variable taking the value of 1 if the firm’s balance sheet date falls in the year 2008, 0 otherwise

LN(AGM_NOISE)

Natural log of the frequency of all AGM-related noise terms appearing in the announcement

ASSET

Frequency of all terms consistent with asset acquisition or disposals appearing in the announcement scaled by the aggregate noise counts of all search terms in the same announcement

DIRECTOR

Frequency of all terms consistent with director appointments used in the announcement scaled by the aggregate noise counts of all search terms in the same announcement

EQUITY

Frequency of all terms consistent with approvals for equity issuance sought at AGM appearing in the announcement scaled by the aggregate noise counts of all search terms in the same announcement

BORROWING

Frequency of all terms consistent with approvals for debt issuance sought at AGM appearing in the announcement scaled by the aggregate noise counts of all search terms in the same announcement

CONSTITUTION

Frequency of all terms consistent with amendments to company constitution appearing in the announcement scaled by the aggregate noise counts of all search terms in the same 37

announcement REMUNERATION

Frequency of all terms consistent with approvals of remuneration report appearing in the announcement scaled by the aggregate noise counts of all search terms in the same announcement

Appendix B. Search terms for AGM noise variables Variable

Search Terms

ASSET DIRECTOR

Acquisition Director Election

EQUITY

Option Share

BORROWING

Loan Security Convertible

CONSITUITON

Constitution

REMUNERATION

Remuneration

38

Table 1 This table provides a summary of the sample selection process (Panel A) and a breakdown of the sample by various auditor switch, announcement and termination types (Panel B). N

Panel A: Sample selection All switches identified via ASX announcements from 2000 to 2011 Less: Switches involving Arthur Andersen Less: Firms without required audit and financial statement data Less: Switches involving partner jumps and audit firm mergers Less: Firms without sufficient stock price data Final Sample

1,179 150 38 90 188 713

Panel B: Distribution of sample by auditor switch, announcement and termination types N

%

By auditor size type: non-Big 4 to non-Big 4 Big 4 to Big 4 Total switches within size band

229 136 365

32.1 19.1 51.2

non-Big 4 to Big 4 Big 4 to non-Big 4 Total switches across size band

196 152 348

27.5 21.3 48.8

Total By industry specialist type: non-Specialist Big 4 to Specialist Big 4 non-Specialist non-Big 4 to Specialist Big 4 Total switches from non-Specialist to Specialist

713

100.0

40 44 84

5.6 6.2 11.8

Specialist Big 4 to non-Specialist Big 4 Specialist Big 4 to non-Specialist non-Big 4 Total switches from Specialist to non-Specialist

41 41 82

5.8 5.8 11.6

547 713

76.6 100.0

581 132 713

81.5 18.5 100

426 287 713

59.7 40.3 100.0

No switch to/from Specialist Total By announcement type: AGM announcements Standalone announcements Total By termination type: Resignations Dismissals Total

39

Table 2 This table presents the distribution of industry specialist auditor by GICS 4-digit industry groups and by year over the period 2000–2011. Industry specialist auditors are defined as Big 4 audit firms having the largest market share in a given industry and year as measured by audit fees. PwC = PricewaterhouseCoopers, DTT = Deloitte Touche Tomatsu, KPMG = KPMG, EY = Ernst and Young, AA = Arthur Andersen. See Table 3 (Panel B) for the classification of the GICS industry groups. GICS / Year 1010 1510 2010 2020 2030 2510 2520 2530 2540 2550 3010 3020 3030 3510 3520 4010 4020 4030 4040 4510 4520 4530 5010 5510

2000 KPMG PwC DTT DTT KPMG KPMG EY PwC AA PwC EY PwC PwC KPMG AA KPMG PwC EY KPMG AA PwC PwC EY DTT

2001 KPMG PwC KPMG DTT KPMG KPMG PwC PwC AA PwC EY PwC PwC KPMG AA KPMG PwC EY KPMG AA PwC PwC EY AA

2002 KPMG PWC KPMG PwC PwC KPMG EY PwC EY EY EY PwC PwC KPMG EY KPMG PwC EY KPMG PwC PwC PwC EY DTT

2003 EY KPMG KPMG PwC PwC KPMG PwC PwC EY EY EY PwC PwC KPMG EY KPMG PwC EY KPMG PwC EY PwC EY DTT

2004 EY KPMG KPMG PwC KPMG DTT PwC PwC EY EY EY PwC PwC KPMG EY KPMG PwC PwC KPMG PwC PwC PwC EY KPMG

2005 EY KPMG KPMG PwC PwC KPMG PwC EY PwC EY PwC PwC PwC KPMG EY EY PwC PwC KPMG PwC DTT PwC EY EY

2006 EY KPMG KPMG DTT KPMG KPMG PwC PwC EY EY PwC PwC KPMG KPMG EY EY PwC PwC KPMG PwC PwC PwC EY DTT

2007 EY PwC KPMG DTT KPMG PwC PwC PwC EY EY PwC PwC KPMG KPMG EY EY PwC PwC EY EY DTT PwC EY DTT

2008 EY PwC KPMG PwC KPMG EY PwC PwC EY EY EY PwC KPMG KPMG EY PwC PwC PwC PwC PwC DTT PwC EY DTT

2009 EY PwC KPMG PwC KPMG EY PwC PwC EY EY EY EY KPMG KPMG EY PwC PwC PwC EY PwC KPMG PwC EY DTT

2010 EY PwC KPMG DTT KPMG EY PwC PwC EY EY EY EY KPMG KPMG EY PwC PwC KPMG EY PwC KPMG PwC EY PwC

2011 EY KPMG KPMG PwC KPMG EY PwC PwC EY EY EY PwC KPMG KPMG EY PwC EY KPMG EY PwC KPMG PwC EY EY

Table 3 This table provides a breakdown of the auditor switch sample by year (fiscal year) and industry (GICS 4-digit codes). Panel A: Distribution of switches by year Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Total

All switches N % 31 27 40 46 50 49 77 82 87 74 98 52 713

4.3 3.8 5.6 6.5 7.0 6.9 10.8 11.5 12.2 10.4 13.7 7.3 100.0

To/from Big 4 N % 20 17 18 25 29 27 38 34 30 32 49 29 348

5.7 4.9 5.2 7.2 8.3 7.8 10.9 9.8 8.6 9.2 14.1 8.3 100.0

To/from specialist N % 10 9 6 10 13 16 21 16 21 17 17 10 166

6.0 5.4 3.6 6.0 7.8 9.6 12.7 9.6 12.7 10.2 10.2 6.0 100.0

Panel B: Distribution of switches by industry GICS Industry Group 1010 Energy 1510 Materials 2010 Capital Goods 2020 Commercial & Professional Services 2030 Transportation 2510 Automobiles & Components 2520 Consumer Durables & Apparel 2530 Hotels Restaurants & Leisure 2540 Media 2550 Retailing 3010 Food & Staples Retailing 3020 Food, Beverage & Tobacco 3030 Household & Personal Products 3510 Health Care Equipment & Services 3520 Pharmaceuticals & Biotechnology 4010 Banks 4020 Diversified Financials 4030 Insurance 4040 Real Estate 4510 Software & Services 4520 Technology Hardware & Equipment 4530 Semiconductors 5010 Telecommunication Services 5510 Utilities Total

All switches N % 78 211 47 32 6 5 8 16 20 15 2 18 1 31 39 4 56 0 37 35 19 0 23 10 713

10.9 29.6 6.6 4.5 0.8 0.7 1.1 2.2 2.8 2.1 0.3 2.5 0.1 4.3 5.5 0.6 7.9 0 5.2 4.9 2.7 0 3.2 1.4 100.0

To/from Big 4 N % 35 101 19 20 1 2 4 8 13 8 1 9 0 17 25 2 19 0 12 23 12 0 12 5 348

10.1 29.0 5.5 5.7 0.3 0.6 1.1 2.3 3.7 2.3 0.3 2.6 0 4.9 7.2 0.6 5.5 0 3.4 6.6 3.4 0 3.4 1.4 100.0

To/from specialist N % 14 44 11 10 2 3 1 3 4 5 0 2 1 5 15 3 17 0 15 4 3 0 2 2 166

8.4 26.5 6.6 6.0 1.2 1.8 0.6 1.8 2.4 3.0 0 1.2 0.6 3.0 9.0 1.8 10.2 0 9.0 2.4 1.8 0 1.2 1.2 100.0

Table 4 This table reports results of univariate analysis of market reaction to auditor switches. Market reaction is the cumulative abnormal return, measured as market model prediction error, in the 3-day event window (–1,+1) surrounding the announcement of auditor switches. * and ** indicate significance at the 10% and 5% level, respectively. Measure

N

Panel A: Overall market reaction to auditor switches All switches CAR 713 AGM announcements CAR 581 Standalone announcements CAR 132 Resignations CAR 426 Dismissals CAR 287 Panel B: Market reaction to switches to/from Big 4 auditors Predecessor: Successor: Non-Big 4 Big 4 CAR 196 Big 4 Non-Big 4 CAR 152 Panel C: Market reaction to switches to/from industry specialist and Big 4 auditors Predecessor: Successor: Non-Specialist Big 4 Specialist Big 4 CAR 40 Non-Specialist Non-Big 4 Specialist Big 4 CAR 44 Specialist Big 4 Non-Specialist Big 4 CAR 41 Specialist Big 4 Non-Specialist non-Big 4 CAR 41

Mean

Median

Std. dev.

t-stat

Z-stat

–0.0007 –0.0040 0.0140 –0.0003 –0.0011

–0.0031 –0.0039 0.0021 –0.0042 0.0012

0.1140 0.1081 0.1339 0.1216 0.0042

–0.155 –0.890 1.201 –0.063 –0.184

–1.423 –1.727* 0.450 –2.002** –0.499

–0.0055 –0.0083

0.0004 –0.0061

0.1088 0.1222

–0.707 –0.839

–0.479 –1.082

–0.0013 0.0103 –0.0120 –0.0076

0.0010 0.0040 –0.0130 –0.0087

0.0390 0.0801 0.0680 0.0810

–0.021 0.864 –1.078 –0.515

–0.672 0.432 –0.680 –1.121

Table 5 This table reports descriptive statistics on the independent variables used in the cross-sectional regression analysis. See Appendix A for the definition of variables. Continuous variables Total assets ($m) LTA PAGES ZSCORE Indicator variables UPSP DNSP NB_B4 B4_NB NB_B4*UPSP B4_NB*DNSP RESIGN TENURE TIMING OPINION AGM ACCOMP

N

Mean

Median

Std. dev.

Min.

Max.

713 713 713 713

1,400 16.94 15.04 –0.50

1.67 16.63 9.00 –2.81

22,200 2.10 21.04 3.64

0.02 10.09 1 –12.38

425,000 26.78 224 10.99

Frequency=1

%

84 82 196 152 44 41 428 195 62 112 582 397

11.8 11.5 27.5 21.3 6.2 5.8 60.0 27.3 8.7 15.7 81.6 55.7

N 713 713 713 713 713 713 713 713 713 713 713 713

43

Table 6 This table reports cross-sectional regression results for the model specified in Equation (4) for the full sample and various subsamples. The dependent variable is the 3-day cumulative abnormal return, CAR(–1, +1), around the event window based on market model prediction errors. See Appendix A for the definition of the independent variables. Results reported in columns (1) and (2) are based on the full sample. Columns (3) and (4) are the subsample results corresponding to auditor resignations and dismissals, respectively, based on ASIC’s classification. Columns (5) and (6) report results for standalone announcements and AGM announcements samples, respectively. Robust t-statistics are in parentheses. * and ** indicate significance at the 10% and 5% level, respectively. VARIABLES

CONSTANT UPSP DNSP NB_B4 B4_NB NB_B4*UPSP B4_NB*DNSP RESIGN LTA TENURE TIMING OPINION ZSCORE PAGES AGM ACCOMP

Observations Adjusted R2 F-statistic

(1) Full sample

(2) Full sample

0.014 (0.33) 0.005 (0.62) –0.011 (–0.99) –0.012 (–1.22) –0.005 (–0.42)

0.005 (0.10) –0.014 (–1.26) –0.025* (–1.78) –0.019 (–1.57) –0.011 (–0.77) 0.035* (1.71) 0.025 (1.12) –0.001 –0.002 (–0.11) (–0.19) 0.001 0.001 (0.24) (0.59) –0.008 –0.007 (–0.89) (–0.81) –0.010 –0.010 (–0.78) (–0.80) 0.001 –0.000 (0.05) (–0.02) 0.002** 0.002** (2.08) (2.05) 0.000 0.000 (1.25) (1.24) –0.030* –0.031* (–1.70) (–1.75) 0.015 0.014 (1.17) (1.15) 713 0.000 1.134

713 0.000 1.047

(3) (4) Resignation Dismissal sample sample –0.002 (–0.02) –0.009 (–0.53) –0.022 (–1.54) –0.009 (–0.52) –0.020 (–0.96) 0.009 (0.31) 0.027 (0.93)

0.039 (0.72) –0.014 (–0.88) –0.032 (–1.26) –0.034* (–1.82) 0.000 (0.02) 0.063** (2.25) 0.029 (0.82)

0.002 (0.45) –0.008 (–0.62) –0.025 (–1.30) 0.024 (1.09) 0.002 (1.60) 0.000 (0.12) –0.037 (–1.30) 0.025 (1.23)

–0.000 (–0.05) –0.006 (–0.48) –0.004 (–0.23) –0.042* (–1.77) 0.002 (1.44) 0.000 (1.29) –0.024 (–1.19) –0.002 (–0.17)

426 –0.003 0.624

287 0.009 0.789

(5) AGM sample

(6) Standalone sample

–0.024 (–0.46) –0.015 (–1.24) –0.034** (–1.98) –0.015 (–1.08) –0.016 (–1.06) 0.022 (0.94) 0.041 (1.61) –0.002 (–0.21) 0.002 (0.65) –0.006 (–0.70) –0.006 (–0.34) –0.017 (–1.11) 0.002** (2.27) 0.000 (1.39)

0.015 (0.17) 0.009 (0.33) 0.021 (0.68) –0.030 (–1.10) 0.037 (0.72) 0.093** (2.37) –0.057 (–1.03) 0.031 (0.81) –0.003 (–0.46) –0.000 (–0.01) –0.023 (–1.15) 0.062 (1.10) 0.001 (0.48) 0.002 (0.48)

0.004 (0.39)

0.097 (1.22)

581 –0.002 1.084

132 0.014 1.582

Table 7 This table reports descriptive statistics on the independent variables used in the long-run returnearnings analysis. See Appendix A for the definition of variables. Continuous variables BHR Earnings per share Year-end share price ($) EP Market capitalisation ($m) SIZE BETA DE MTB DIRSH EVAR EPERS Indicator variables YEAREND

N 713 713 713 713 713 713 713 713 713 713 493 493

Mean 0.06 0.05 1.27 –0.20 385 2.67 0.89 0.21 0.19 0.21 –0.56 –0.06

N 713

Frequency=1 85

Median Std. dev. –0.14 0.84 0.00 0.33 0.16 5.17 –0.05 0.53 21 286 2.14 1.64 0.82 1.26 0.02 0.45 0.03 0.39 0.12 0.25 –0.38 2.55 –0.01 0.46

Min. –0.91 –1.52 0.01 –2.38 0.12 0.59 –3.34 0.00 0.00 0.00 –14.41 –1.05

Max. 4.63 4.75 111.50 0.78 6,420 15.92 5.02 2.54 1.91 1.14 6.80 0.62

% 11.92

45

Table 8 This table reports results for for the model specified in Equation (6) for the full sample and various subsamples. The dependent variable is the 12-month holding-period return, BHR, computed for the nine months prior to and three months following the balance sheet date. Columns (1) and (3) report results for the full sample while columns (2) and (4) report results for the data restricted sample. See Appendix A for the definition of the independent variables. Robust t-statistics are in parentheses. * and ** indicate significance at the 10% and 5% level, respectively.

VARIABLES CONSTANT EP*UPSP EP*DNSP EP*NB_B4 EP*B4_NB

(1) Full sample

(2) Data restricted sample

(3) Full sample

(4) Data restricted sample

0.084* (1.96) 0.009 (0.59) –0.019 (–0.18) –0.015 (–0.96) 0.005 (0.48)

0.090* (1.83) –0.018 (–0.92) –0.003 (–0.02) 0.014 (0.73) 0.003 (0.23)

0.282* (1.90) –0.021 (–0.19) 0.034 (1.52) –0.027 (–0.11) 0.108 (0.12) –0.003 (–0.02) 0.082 (1.03)

0.056 (0.87) 0.129** (2.13) –0.518*** (–10.58)

0.101 (0.61) 0.037 (0.34) 0.009 (0.43) 0.116 (0.40) –0.677 (–0.93) –0.071 (–0.48) 0.029 (0.31) –0.172** (–2.32) 0.005 (0.05) 0.186 (1.27) 0.111 (1.63) –0.500*** (–7.76)

0.085** (1.98) 0.215 (0.38) –0.178 (–0.75) –0.014 (–0.89) 0.005 (0.46) –0.207 (–0.37) 0.223 (0.87) 0.283* (1.92) –0.021 (–0.20) 0.034 (1.52) –0.036 (–0.15) 0.063 (0.07) –0.007 (–0.06) 0.084 (1.05)

0.055 (0.86) 0.125** (2.05) –0.515*** (–10.42)

0.090* (1.84) 0.189 (0.37) –0.252 (–0.85) 0.016 (0.82) 0.003 (0.21) –0.209 (–0.41) 0.325 (1.05) 0.110 (0.67) 0.034 (0.31) 0.009 (0.41) 0.105 (0.36) –0.700 (–0.96) –0.075 (–0.51) 0.032 (0.33) –0.167** (–2.21) 0.009 (0.09) 0.187 (1.29) 0.106 (1.54) –0.497*** (–7.62)

713 0.085 14.58

492 0.070 8.486

713 0.083 12.75

492 0.068 7.626

EP*NB_B4*UPSP EP*B4_NB*DNSP EP EP*SIZE EP*BETA EP*DE EP*MTB EP*DIRSH YEAREND EP*EVAR EP*EPERS FIN MIN YEAR8

Observations Adjusted R2 F-statistic

46

Table 9 This table reports cross-sectional regression results with controls for AGM noise. The dependent variable is 3-day cumulative abnormal return, CAR (–1,+1), around the event window based on market model prediction errors. See Appendix A for the definition of the independent variables used. For the noise control variables (see Appendix B), Ln(AGM_NOISE) is the natural logarithm of one plus the aggregate noise count associated with AGM agenda items and the other individual noise control categories for ASSET, DIRECTOR, EQUITY, BORROWING, CONSTITUTION, REMUNERATION are all expressed as a proportion of AGM_NOISE. Robust t-statistics are reported in parentheses. * and ** indicate significance at the 10% and 5% level, respectively. VARIABLES CONSTANT UPSP DNSP NB_B4 B4_NB NB_B4*UPSP B4_NB*DNSP RESIGN LTA TENURE TIMING OPINION ZSCORE PAGES AGM ACCOMP LN(AGM_NOISE)

(1) Full sample

(2) Full sample

0.006 (0.13) –0.014 (–1.20) –0.025* (–1.80) –0.018 (–1.52) –0.011 (–0.77) 0.033 (1.64) 0.026 (1.14) –0.003 (–0.32) 0.001 (0.61) –0.006 (–0.68) –0.011 (–0.81) –0.001 (–0.03) 0.002** (2.07) 0.000 (1.57) –0.024 (–1.24) 0.015 (1.16) –0.003 (–0.95)

0.010 (0.21) –0.013 (–1.13) –0.024* (–1.75) –0.018 (–1.50) –0.011 (–0.72) 0.033 (1.60) 0.025 (1.08) –0.002 (–0.28) 0.001 (0.45) –0.007 (–0.77) –0.009 (–0.69) –0.001 (–0.04) 0.002** (2.03) 0.000 (1.46) –0.025 (–1.27) 0.015 (1.17) –0.003 (–0.55) 0.098 (0.45) 0.010 (0.38) –0.001 (–0.04) 0.018 (0.54) –0.016 (–0.27) 0.028 (0.32) 713 –0.007 0.921

ASSET DIRECTOR EQUITY BORROWING CONSTITUTION REMUNERATION Observations Adjusted R2 F-statistic

713 0.000 1.060

47

Table 10 This table reports descriptive statistics and results of univariate analysis of market reactions to auditor switches announced in a period after the implementation of the revised RG 26 (18 June 2015 to 30 June 2017). Market reaction is the cumulative market-adjusted return (CMAR), in the 3-day event window (–1,+1) surrounding the announcement of auditor switches. Panel A reports univariate tests of differences from zero while Panel B reports univariate tests of differences between the pre- and post-deregulation samples. * and ** indicate significance at the 10% and 5% level, respectively. Panel A: Market reactions to auditor switches announced in the post-revised RG 26 period (2015-2017) Sample Measure N % Mean Median Std. Dev. % Positive %Negative t-stat Z-stat Full Sample CMAR 142 100% –0.0007 –0.0018 0.0649 45% 55% –0.138 –0.809 Standalone CMAR 102 72% –0.0005 –0.0018 0.0616 45% 55% –0.079 –0.152 AGM CMAR 40 28% –0.0014 –0.0021 0.0734 45% 55% –0.123 –1.237 Full Sample Big 4 to Big 4 non-Big 4 to Big 4 Big 4 to non-Big 4 non-Big 4 to non-Big 4

Measure CMAR CMAR CMAR CMAR

N 14 22 35 71

% 10% 15% 25% 50%

Mean –0.0129 0.0382 –0.0066 –0.0075

Median –0.0041 0.0053 –0.0029 –0.0038

Std. Dev. 0.0353 0.0956 0.0393 0.0647

%Positive 29% 64% 37% 46%

% Negative 71% 36% 63% 54%

t-stat –1.36 1.87* –0.99 –0.98

Z-stat –1.287 1.542 –0.950 –9.510

Standalone Sample Big 4 to Big 4 non-Big 4 to Big 4 Big 4 to non-Big 4 non-Big 4 to non-Big 4

Measure CMAR CMAR CMAR CMAR

N 9 14 27 52

% 9% 14% 26% 51%

Mean 0.0041 0.0408 –0.0126 –0.0061

Median –0.0017 0.0279 –0.0047 –0.0046

Std. Dev. 0.0166 0.0649 0.0372 0.0714

% Positive 44% 79% 30% 44%

% Negative 56% 21% 70% 56%

t-stat 0.74 2.35** –1.76* –0.62

Z-stat 0.296 2.040** –1.177 –0.710

Panel B: Comparisons of market reactions to auditor switches announced in the pre- and post-revised RG 26 periods Post-revised RG 26 (2015-2017) Pre-revised RG 26 (2000-2011) Full Sample Measure N Mean Median N Mean Median All switches CMAR 142 –0.0007 –0.0018 713 –0.0010 –0.0018 Big 4 to Big 4 CMAR 14 –0.0129 –0.0041 136 –0.0087 –0.0041 non-Big 4 to Big 4 CMAR 22 0.0382 0.0053 196 –0.0087 –0.0029 Big 4 to non-Big 4 CMAR 35 –0.0066 –0.0029 152 –0.0051 0.0053 non-Big 4 to non-Big 4 CMAR 71 –0.0075 –0.0038 229 0.0120 –0.0038

Mean diff. 0.000 –0.004 0.047 –0.002 –0.020

t-stat 0.200 –0.220 1.78* –0.100 –1.210

Z-stat 0.840 –0.342 1.590 –0.243 –0.477

Mean diff. –0.015 0.012 0.041 –0.048 –0.052

t-stat –0.990 0.550 1.83* –1.93* –1.54

Z-stat –0.384 0.543 1.757* –1.182 –0.658

Standalone Sample All switches Big 4 to Big 4 non-Big 4 to Big 4 Big 4 to non-Big 4 non-Big 4 to non-Big 4

Measure CMAR CMAR CMAR CMAR CMAR

Post-revised RG 26 (2015-2017) N Mean Median 102 –0.0005 –0.0018 9 0.0041 –0.0017 14 0.0408 0.0279 27 –0.0126 –0.0047 52 –0.0061 –0.0046

Pre-revised RG 26 (2000-2011) N Mean Median 132 0.0141 0.0000 41 –0.0074 –0.0021 40 –0.0006 0.0053 14 0.0357 0.0051 37 0.0455 0.0014