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Faculty of Economics and Business
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Does Auditor Industry Specialization Matter?!
An Empirical Research on the Effect of Industry
Specialization on Auditor Litigation Risk!
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Wytse Dijkstra!
6138012!
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Master’s in Accountancy & Control
Specialization: Accountancy
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Supervisor: Bo Qin, Ph.D.!
Co-assessor: Alexandros Sikalidis, Ph.D.!
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Final version - 23rd of June 2014!
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Abstract!
This thesis aims to conduct research on the effect of auditor industry specialization on litigation risk. Hogan & Jeter (1999) mention that auditors who specialize in certain industries can expect returns for their industry specific knowledge. This thesis looks at litigation risk for auditors as one of these possible returns and aims to research to what extent auditor industry specialization alter litigation risk for auditors.. I hypothesize that industry specialization will lower litigation risk for auditors because specialized auditors are deemed to provide higher quality audits. Next to this I hypothesize that big four auditors are less prone to litigation as compared with non big four auditors. Finally, I expect that the hypothesized negative relationship between industry
specialization and litigation risk will be stronger for big four auditors. Using a probit regression model and providing additional analysis with propensity score matching, I conclude that auditor industry industry specialization will lower litigation risk for auditors. However I am unable to draw a direct conclusion on the effect of big four auditors on litigation risk and the mediating effect of big four auditors on the relationship between industry specialization and litigation risk. My results however might suggest that only big four auditors will be able to become industry specialists. I add to prior literature by not only looking at the direct effect of industry specialization on auditor
litigation risk, but also exploring the mediating effect of big four auditor classification. Next to this, this thesis adds to the general understanding of returns of industry specialization by auditors and can have practical implications regarding risk management for auditors and financial reporting reporting quality.!
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Keywords:! ! Auditor industry specialization, litigation risk, big four!
Data availability:! The data used in this thesis are publicly available from the sources indicated!
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Table of contents!!
Acknowledgement! ! ! ! ! ! ! ! ! ! IV!!
1. Introduction!! ! ! ! ! ! ! ! ! ! 5!!
2. Theory and hypothesis development! ! ! ! ! ! ! 6!
! 2.1 Litigation risk and industry specialization!! ! ! ! ! 6! ! ! 2.1.1 Defining litigation risk and industry specialization! ! ! 6!
! ! 2.1.2 Prior literature! ! ! ! ! ! ! ! 7!
! ! ! 2.1.2.1 Litigation risk for audit firms! ! ! ! ! 7!
‘! ! ! 2.1.2.2 Auditor industry specialization! ! ! ! 9!
! ! ! 2.1.2.3 Auditor industry specialization and litigation risk! ! 11!
! 2.2 Hypothesis development! ! ! ! ! ! ! ! 12!
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3. Sample selection, research methodology and descriptive statistics! ! ! 14!
! 3.1 Sample selection! ! ! ! ! ! ! ! ! 14!
! 3.2 Research methodology! ! ! ! ! ! ! ! 16!
! 3.3 Descriptive statistics and Correlation matrix! ! ! ! ! 18!
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4. Empirical Results! ! ! ! ! ! ! ! ! ! 20!
! 4.1 Probit regression results! ! ! ! ! ! ! ! 20!
! 4.2 Propensity score matching results! ! ! ! ! ! 20!
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5. Discussion! ! ! ! ! ! ! ! ! ! ! 24!!
6. Conclusion! ! ! ! ! ! ! ! ! ! ! 26!!
References! ! ! ! ! ! ! ! ! ! ! 27!!
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Acknowledgement!
Foremost I would like to express my sincere gratitude to my supervisor Bo Qin, Ph.D. for his continuous support, suggestions and quick feedback. His help has contributed a great deal in completing this Master’s thesis in time.!
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Beside my supervisor, I would also like to thank mr. Pascal Braak from the Law Library, for explaining the use of the WestLaw database, dr. Jeroen van Raak for his suggestions on data collection and Alexandros Sikalidis, Ph.D. for co-assessing this thesis.!
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I also thank my family for their support over all these years which has led to the completion of this thesis. !
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Last but not least my thanks go out to all my dear friends, who had to miss me over the last few months. In particular I would like to thank my fellow student Ahmad Tariq for all his moral support and the great times we had writing our thesis in the University Library.!
1. Introduction!
Over a longer period of time, specialization of auditors has been the subject of research within the academic literature. Hogan & Jeter (1999) describe the evolvement of this phenomenon from 1976 until 1993. They conclude that auditors can specialize in certain industries, and can also expect returns from investing in industry specialization. As it can be quite costly to gain specialized knowledge in industries, it is important to take a look at some of the potential benefits/returns for auditors. This thesis will focus on litigation risk as one of the potential returns. Litigation against auditors can not only be costly, but also harm the reputation of auditors. Therefore I will conduct research on the relationship between auditor industry specialization and litigation risk. The main research objective of this thesis is to investigate to what extent auditor industry specialization will alter litigation risk for auditors, where lower litigation risk, can be seen as one of the potential returns for industry specialization by auditors. !
I add to prior literature by giving more insight on returns for specialization, but also extending prior research by not only looking at the direct relationship between auditor industry specialization and litigation risk alone, but also including the mediating effect of big four auditors on this relationship. The results can have implication from both an auditor and clients perspective. As auditor engage in risk management when making clients acceptance decisions (Johnstone & Bedard, 2004), litigation risk is one of the factors that auditors take in consideration. For clients, lower litigation could act as a proxy for higher financial reporting quality, therefore financial reporting quality might increase for clients who engage with an industry specialist auditor.!
! To give answer to the question above, I have constructed a sample of 252 observations including 12 litigation cases against auditors. Using the modified cross-sectional model from Stice (1991) I will analyze probit regression results, and also provide additional tests by conducting a propensity score matching analysis. Results show that industry specialization lowers litigation risk for auditors, however I am unable to draw any direct conclusion on the mediating effect of big four auditors on the relationship between auditor industry specialization and litigation risk for auditors.! ! The remainder of this thesis will be as follows: Section 2 will provide background literature on the terms of auditor industry specialization and litigation risk. Next to this it will also describe prior literature that is more specifically related to the subject of this thesis, the relationship between auditor industry specialization and litigation risk. The final part of section 2 will provide hypothesis development based on the prior literature as described. Section 3 will elaborate on the sample selection, research methodology and main descriptive statistics, while section 4 will show the main results of the probit regression and the results of the propensity score matching anaylis. Section 5 will put the results into past and recent academic literature, provide discussion and will end with some suggestions for future research. Finally section 6 presents some brief concluding remarks.!
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2. Theory and hypothesis development
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2.1 Auditor industry specialization and litigation risk
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2.1.1 Defining litigation risk and industry specialization!
Before discussing prior literature and developing a hypothesis, it is important to understand what we mean by litigation risk and auditor industry specialization. First I will give a definition of litigation risk, while after that I will elaborate on auditor industry specialization. Not only by providing a definition of industry specialization, but also discussing the evolvement of auditor industry specialization.
! If we look for the term ‚’litigation’ in the online oxford dictionary, the following definition is given: ’the process of taking legal action’. Legal actions can be rather broad in this context, as they could include (non exhaustive), lawsuits, legal cases and trials. This thesis will also use this broad definition of litigation, as audit firms might be involved in with legal cases from several parties, such as their clients, investors and the SEC. We can now define litigation risk for auditors in this thesis as: the risk of having legal actions taken against audit firms. There could be many reasons for third parties to take legal actions against audit firms. One of the most appealing reasons would be that an auditor faces legal actions from investors when they, for instance, fail to detect fraudulent reporting by clients. Stice (1999) provides us with other factors that are associated with litigation risk for auditors. These factors include, for example, the ratio of accounts receivable to total assets from client, the change in sales for client and whether the audit firm is a big four firm
(EY,PwC,KPMG,Deloitte). Most of the factors from Stice (1999) will be included in the research model of this thesis (see section 3.2).
! Defining industry specialization, does not require looking into dictionaries or other sources, as intuitively we can say that companies in general specialize to differentiate themselves from competitors. If we look at the specific case of audit firms, Dunn & Mayhew (2004) argue that audit clients all have a unique set of characteristics which audit firms will need to adjust for. This gives an incentive for audit firms to specialize themselves, to meet the specific needs from clients in a way competitors can’t. One of the first thorough examinations of auditor industry specialization was conducted by Hogan & Jeter (1999). In their paper they discuss the evolvement of auditor industry specialization from 1976 to 1993. They find that concentration levels of audit firms have increased over time and especially that market leaders tend to increase market share even further in future years. Hence they suggest that investing in industry specialization, provides audit firms with returns. Looking at the possible returns that Hogan & Jeter (1999) mention, Mayhew & Wilkins (2003) distinguish two specific advantages from audit firms specialization. First they argue that large market shares will lead to the spreading of industry-specific training costs over more clients,
that audit firms with industry specialism have developed more industry-specific knowledge and expertise. It is argued that therefore, these audit firms do a better job in providing high quality services then non-specialist audit firms. Section 3.2 will elaborate on how to measure auditor industry specialization, but prior literature (Hogan & Jeter, 1999) suggest that market shares are indeed a good measure for auditor industry specialization.!
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2.1.2 Prior literature!
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2.1.2.1 Litigation risk for audit firms!
As mentioned in section 2.1.1, there can be several factors that influence the litigation risk for audit firms (Stice, 1991). Complementing the study from Stice (1991), Lys & Watts (1994) also conducted research on the possible variables that influence litigation risk for auditors. In their research they distinguish two different type of characteristics, which are client firm characteristics and and auditor characteristics. Their variables include (Lys & Watts, 1994, pp. 69-73):!
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Clients firms characteristics:! - Market value of equity
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- Probability of bankruptcy!
- Stock returns!
- Management change and appointment of outsiders
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- Probability of acquisition
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- Accruals/Total assets ratio!
- Receivables/Total assets and Inventory/Total assets ratio
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- Auditor change
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- Log of total assets
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Auditor characteristics:! - Qualified opinion
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- Audit structure
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- Percentage of industry Audited and Largest audit share in Industry (relates to this thesis)
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- Variance of client size!
- Number of clients and Sales dollars audited
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- Number of years client audited and Proportion of revenues from client
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Lys & Watts (1994), find that a client’s stock performance and size, audit structure, qualified reports and proportion of audit revenues are all associated with factors that influence litigation for audit firms. They fail to find evidence that shows an association between auditor size and the likelihood of litigation. Though the authors mention that this could be due to the fact that their sample mainly consisted of big-eight firms. Lys & Watts (1994) also do not find evidence that industry specialization has any significant influence on litigation risk. I will elaborate more on this last result in section 2.1.2.3.!
! Other major studies concerning litigation risk, mostly involve variables such the comparison between big audit and smaller audit firms (Palmrose, 1988; Khurana & Raman, 2004) and the relationship between litigation risk and audit fees charged to clients (Seetharaman et al., 2002). Palmrose (1988) argues that big-eight audit firms, face less litigation risk as compared to non-big-eight audit firms (sample: 1960 - 1985). She concludes that these findings are in line with the believe that large audit firms deliver higher quality audit services. Complementing the study from Palmrose (1988), Khurana & Raman (2004) find that a big four audit is associated with lower ex ante cost of equity capital for auditees in the U.S. (higher litigation risk) as compared with Australia, Canada or the U.K. (lower litigation risk). This enhances the believe again that big four audit firms are perceived to deliver higher quality audit services because of litigation exposure (Khurana & Raman, 2004). Audit firms can deal with litigation risk in more then one way, depending of course on firm preferences. Evidence suggest that audit firms will charge higher audit fees when faced with higher litigation risk (Seetharaman et al., 2002). This finding is consistent with Bell et al. (2000) who argue that audit firms will bill additional hours to companies with high business risk. To compensate for the higher possibility of legal actions, audit firms will spend more time auditing companies with a relative high business risk, as opposed to companies with a relative low business risk.
! Other then charging higher audit fees, audit firms can also apply risk assessment in their client portfolio management. Johnstone & Bedard (2004) find evidence that litigation risk is one of the factors that audit firms asses, when making decisions regarding accepting and rejecting clients. They argue that this is consistent with the risk-avoidance theory for audit firms portfolio management, which suggests that audit firms will try to take preventive actions against possible risks, through management of their client portfolio (Bockus et al., 1998; Johnstone & Bedard, 2004). Strengthening this assumption, Krishnan & Krishnan (2007) and Zhan Shu (2000) find that litigation risk is also associated with the resignations from audit firms. Audit firms will resign from clients when faced with higher litigation risk.!
! Following Palmrose (1988), we need emphasize another important factor that can influence litigation risk for auditors. Palmrose (1988), argues that big-eight firms are associated with higher audit quality to deal with higher litigation risk. The main theory that underlies this assumption of
greater litigation risk for big auditors is the‚’deep pocket’ hypothesis. The deep pocket hypothesis suggests that auditors will suffer higher litigation risk when considered wealthy. Clients are deemed to engage in legal actions faster, when they believe that an auditor has enough money (deep pockets) to settle potential lawsuits. This assumption would mean that especially big four audit firms face higher litigation risk as opposed to small and medium sized audit firms. To compensate for this higher litigation risk, Dye (1993) argues that wealthy auditors will put more effort in creating high quality audits. Indeed Lennox (1999) finds that large auditors were more susceptible to litigation then small and medium sized auditors, therefore confirming the deep pocket hypothesis. While more susceptible to litigation, the larger auditors did not suffer the loss of clients as compared to auditors that faced less litigation. Lennox (1999) therefore argues that the deep pocket hypothesis and the quality argument from Dye (1993) holds.!
! On a final note, Stice (1991) already mention that there are a lot of factors that can influence litigation risk for audit firms (see section 2.1.1). On that matter, Hogan & Jeter (1999) added a variable for litigation, in their study on auditor industry specialization. They argue that some industries are more prone to litigation then others. Therefore Hogan & Jeter (1999) add a control variable (LITRISK), to account for this effect. Based on research by Bohn & Choi (1996) and O’brien & Hodges (1993), Hogan & Jeter (1999) identify the following industries as high litigation risk industries (the corresponding 2-digit SIC codes are between brackets):!
- Chemicals and Allied products (28)
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- Industrial and commercial machinery and computer equipment (35)
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- Electronic and other electric equipment, excluding computer entertainment (36)
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- Measuring, analyzing and controlling instruments (38)!
- Depository institutions (60)
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- Holding and other investment offices (67)
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- Business services (73)
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To account for this effect, my empirical research model has also included a variable for industry specific litigation risk (INDLIT, see section 3.2).!
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2.1.2.2 Auditor industry specialization!
Hogan & Jeter (1999) provide the first solid research concerning auditor industry specialization. Their research setting provides a good overview of the evolvement in auditor industry specialization from 1976 to 1993. They find evidence that audit firms indeed try to specialize in certain industries to a bigger extent then other industries. Mostly by expanding their market share, in industries where they had been identified as market leader. Hogan & Jeter (1999) therefore suggest that audit firms benefit from returns when investing in industry specialization. !
In a search for these possible returns, Mayhew & Wilkins (2003) find two possible advantages (also see section 2.1.1):!
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- spreading industry-specific trainings, hence benefiting from economies of scale
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- developing more industry-specific knowledge and expertise
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! Next to these indirect non-financial advantages, Carson (2009) argues that global specialists auditors, charge an audit fee premium for their specialism. This study focused on audit firms with global networks, which are used to gain valuable information on industries, hence making it easier to specialize. Her findings imply that audit firms can charge higher audit fees to their clients, when a firm is deemed to be an industry specialist. Therefore monetary gains can provide audit firms with incentives for industry specialization. Adding to this assumption, Castrella et al. (2004) find that industry specialists in the U.S. audit market charge higher fees relative to non-specialist auditors. Though they do mention, that bigger clients are deemed to have bigger bargaining power, therefore paying less then smaller clients which seem to have smaller bargain power over audit fees. These findings (Carson, 2009; Castrella et al. 2004) can well be explained by the two possible advantages mentioned earlier (Mayhew & Wilkinks, 2003). Due their ability to differentiate themselves from others, specialist auditors are able to charge a premium on their audit fee.
! After looking at the auditor side of industry specialization (above), we now have to look whether other parties could also value auditor industry specialization. Knechel et al. (2007) argue that companies switching from a big four non-specialist audit firm to big four specialist audit firm, experience significantly positive abnormal returns. While companies who switch from a big four specialist audit firm, experience significantly negative abnormal returns. This evidence suggests that the market does indeed value auditor industry specialization. Next to this, the market reactions are likely to be caused by the higher perceived audit quality, rather then the costs that are associated with auditor specialists (Knech et al., 2007).!
! Contradicting with the findings from Carson (2009) and Castrella et al. (2004), Dunn & Mayhew (2004) argue that, the benefits from economies of scale by industry specialization, are shared by audit firms with their clients (Dunn & Mayhew, p.38, 2004). Therefore clients can potentially benefit from lower audit fees with specialists as opposed to non-specialists. Secondly Dunn & Mayhew (2004) argue that companies can use a specialist auditor as signaling mechanism. Evidence suggest that companies associated with a specialist auditor, deliver higher quality earnings then those companies without a specialist auditor (Balsam et al., 2003). By hiring a specialist auditor, companies could signal to investors that they intend to provide higher quality disclosure. I will elaborate more on auditor industry specialization and its relation with earnings
quality in the section 2.2 (hypothesis development). Finally Dunn & Mayhew (2004) suggest that, by using their industry-specific knowledge, specialist auditors will assist companies in delivering enhanced disclosure. !
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2.1.2.3 Auditor industry specialization and litigation risk!
In this section I will elaborate on relevant literature that is concerned with the main topic of this thesis, the relationship between auditor industry specialization and litigation risk. Over time there has been little research that involves the investigation of this relation, and the most recent article was written in 2008. This thesis aims to add to the academic literature, by looking at auditor industry specialization and litigation risk from a different perspective then prior literature. Prior research suggests that audit firms will take litigation risk in account, when deciding on accepting and rejecting clients, but also resignations (Krishnan & Krishnan, 1997). Audit firms use various dimensions to assess the probability of litigation such as (non exhaustive), financial distress, auditor independence and variance of abnormal returns.
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resignation and auditor industry specialization. Their research involved industry specialists on both the national level and local level. They find a negative relationship between joint specialists (i.e. specialist at local as well as national level) and auditor resignations. There was no significant relationship between local specialists and auditor resignation, though additional analysis using different variables for industry specialization did show significance between local specialists and auditor resignations. They argue that the effect of local specialists on resignation is therefore inconclusive at this moment. Prior research suggest that the main reasons for auditor to resign are (Zhan Shu, 2000; Johnstone & Bedard, 2004, see also section 2.1.2.1):!
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- Clientele mismatch
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- Litigation risk!
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Given these prior assumptions and their own findings, Cenker & Nagy (2008) conclude that auditor industry specialization minimizes clientele mismatches and lowers litigation risk for auditors. Other then Cenker & Nagy (2008), Lys & and Watts (1994), do not find significant evidence that industry specialization is positively associated with litigation risk for auditors.!
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2.2 Hypothesis development
This section aims to develop a solid hypothesis for the relationship between auditor industry specialization and litigation. Now that an introduction on industry specialization and litigation risk has been given, it is important to place this thesis into recent research framework. Figure 1 provides an overview of research conducted in these areas and places the current thesis in this framework. The framework is provided by Balsam et al. (2003, p. 72) and has been adjusted for the purpose of this thesis including all studies up until 2013.!
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As mentioned before, major studies such as Palmrose (1988) and Dye (1993) argue that brand name (big-eight audit firms) are associated with higher quality finical reporting and audit quality. Another stream of research was conducted from with audit quality being measured by auditor industry specialization. Carcello & Nagy (2004) find that industry specialists are less likely to receive negative SEC enforcement, and that clients from specialists are also less likely to engage in fraudulent financial reporting. Next to this, Dunn & Mayhew (2004) also find that disclosure quality from clients of specialist auditors is considered higher by financial analysts. They therefore argue that a specialist auditor can act as a signaling mechanism for clients (see also section 2.1.2.2.). Regarding earnings quality, Balsam et al. (2003) find that the quality of earnings is higher for clients engaged with a specialist auditor. They conclude this by looking at two different proxies for earnings quality, namely; discretionary accruals and the earnings response coefficient. In their study, they find that auditor industry specialization is positively associated with the earnings response coefficient and that clients from specialists auditors, have less discretionary accruals. Hence Balsam et al (2003) conclude that specialist auditors do a better job in decreasing earnings
management and therefore increase earnings quality. The final proxy from figure 1 is directly related to this study, and is concerned with litigation risk. This thesis aims to provide empirical evidence on the relationship between auditor industry specialization and litigation risk. The prior study of Lys & Watts (1994) fails to find a significant relationship between specialization and litigation risk for auditors. Next to this is the study from Cenker & Nagy (2008) who conclude that litigation risk for auditors is lowered for specialists, but using not litigation risk, but auditor resignations as proxy. Studies on the relationship between auditor industry specialization and litigation risk therefore seem inconclusive at the moment. Though looking at other prior research there is enough evidence to state the following hypothesis:!
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H1: There is a negative relationship between auditor industry specialization and litigation risk for
auditors!
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! As shown in the research framework in figure 1, auditor size/brandname is commonly used to measure audit quality. In fact, the deep pocket hypothesis as explained in section 2.1.2.1. would suggest that rather then industry specialization, the auditor size/brandname will have a significant effect on litigation risk for auditors. The results by Palmrose (1988), Dye (1993) and Lennox (1999) show that big auditors are deemed to provide higher quality audits to reduce litigation risk. Explicitly following the results from Palmrose (1988), who finds that big-eight auditors have lower litigation activity as opposed to non big-eight firms and the quality argument by Dye (1993), there is enough evidence to state the second hypothesis:!
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H2: There is a negative relationship between auditor size/brandname (Big 4) and litigation risk for
for auditors.! !
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! Palmrose (1988) and Dye (1993) conclude that auditor size (i.e. big eight accounting firms), are associated with higher quality financial reporting. These findings suggest auditor size/ brand name will lead to better financial reports. Thus it wil be interesting to see what the effect of auditor size/brand name will have on the relationship between auditor industry specialization and litigation risk. Moreover I will be the first (to my knowledge) to research the effect of auditor size/brandname on the relationship between auditor industry specialization and litigation risk. Figure 2 provides an overview of the relationship I aim to research with my second hypothesis.!
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Given the results from Palmrose (1988) and Dye (1993) and the second hypothesis of this thesis there is enough evidence to suggest that auditor size/brandname will lead to a stronger negative relationship between auditor industry specialization and litigation risk. This because of the joint effect of the (expected) negative relationships as hypothesized in H1 and H2. I therefore state the following hypothesis:!
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H3: The negative relationship between auditor industry specialization and litigation risk for auditor will be stronger for Big 4 Audit firms!
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3. Sample selection, Research methodology and Descriptive statistics!
In this section I will provide background information on the methods used to compute my sample. Secondly, the research model to test the three hypotheses in this thesis is described including a definition of all variables used, after which I will present the descriptive statistics.!
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3.1 Sample selection!
Looking at prior research, the most common method to gather information on auditor litigation cases, is to search several databases such as LexisNexis and Wall Street Journal Index (Stice, 1991; Lys & Watts, 1994). For my thesis I was bound to only one database to gain insight on auditor litigation cases over the last 20 years. This because I was unable to get acces to other resources through my educational institution. I have used the WestLaw legal database to search for auditor litigation cases by using different terms in the ‚’search-box’ such as: KPMG,Audit, Audit failure. Then switching names for different auditors, with the list of auditors that have audited companies over the last 20 years, which was readily available through the Compustat database. Due to availability of data, I restricted my search to north-american cases. The WestLaw database research resulted in 146 registered cases against several auditors, excluding SEC enforcements. For my empirical research model (see section 3.2), information on several client characteristics was needed, therefore availability of this data was checked within the Compustat North-America
database. This resulted a drop of 116 litigation cases leaving 30 cases of which information was available. After further research on specific data needed for my research model, I was forced to drop another 18 litigation cases resulting in a final litigation sample of 12 ranging from 1999 until 2012.!
! For my final sample, I have constructed a control sample matched on the same timeframe as those of the litigation sample (1999-2013). The sample was randomly selected out of all companies within the compustat North-America database using the STATA12 statistics program. As litigation cases are not very common, I used a 1:20 ratio to construct my control sample. This means that for every litigation case, 20 other companies were randomly selected. I should notice that it is possible this sample includes litigation cases which were not reported in WestLaw. This has a resulted in a final sample size of of 252 which will be used for my empirical research model, which is described in the next section. Table 1 summarizes the overall sample selection.!
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TABLE 1Summary of Sample Selection"
" " " Lawsuits identified (WestLaw)! ! 146
! ! ! Less cases lacking compustat data 113
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! ! ! Firms with compustat data! ! ! 30
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Litgation sample! ! ! ! 12! ! !
! Plus control sample (1:20 ratio)! ! 240
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Final Sample! ! ! ! ! 252
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3.2 Research methodology!
! For my research I handled the outliers/extremes in my sample by winsorizing my continuous variables at the top and bottom 1 per cent, where after I have constructed a model which is drawn from the paper by Stice (1991) and has been adjusted for the purpose of this thesis:!
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Modified cross-sectional research model by Stice (1991)!
SUIT=f (A/R, INV, GROWTH, FC, BIG4, TENURE, MV, INDLIT, AUDSPECIAL, BIG4SPECIAL) + E!
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Where:!
SUIT = Dummy-variable to denote whether lawsuit has occurred, lawsuit(=1), no lawsuit (=0)! A/R = Ratio of accounts receivable to total assets!
INV = Ratio of inventory to total assets!
GROWTH = Change in sales for clients (in percentage)!
FC = The z-score for clients (measure for probability of bankruptcy)!
BIG4 = Quality classification of the auditor (Big four=1, non-Big four=0) (hypothesis 2)! TENURE = Number of years the auditor has worked for the clients (4<=0, 3>=1)! MV = Natural LOG of the market value of the firm!
INDLIT = 1 if industry is high litigation-risk, 0 otherwise (see section 2.1.2.1 for complete list)! AUDSPECIAL = 1 if auditor is specialist in industry, 0 otherwise (hypothesis 1)!
BIG4SPECIAL = 1 if auditor is big four and specialist in industry, 0 otherwise (hypothesis 3)!
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As compared with the original model that was constructed by Stice (1991) two variables have been dropped due to availability of data and time span of this thesis. These include a variable for auditor independence and variance of abnormal returns. In order to test the first and the third hypothesis of this thesis, I have added a variable for auditor industry specialization (AUDSPECIAL) and a variable for the interaction between auditor size and industry specialism (BIG4SPECIAL). The variable for the second hypothesis was already included in the original model by Stice (1991), which the NAME variable. Another variable that has been added as compared with the original model is INDLIT. Hogan & Jeter (1999) mention that auditors in some industries are more prone to litigation then other other industries. INDLIT is a control variable to account for this effect.!
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! To measure auditor industry specialization, I follow the definition by Dunn & Mayhew (2004), who argue that auditors with a 20 percent market share, are considered to be an industry specialist. The market share for each auditor in specific industries was calculated separately every year. This to account for the possibility that the auditor might be a non-specialist in year t and has increased its marketshare and becomes a specialist in year t+1. The basis for calculating industry specialization was the amount of clients. Balsam et al (2003, p.75) argues that it unclear at this moment, whether industry specialization arises from auditing a lot of clients in one industry, or a few large ones. This thesis will use a large amount of clients (20 per cent or more) as a proxy for industry specialization, and did not include other measures such as those based on clients sales ratios. This due to a lack of time for this thesis. The Z-score which is used to measure the probability of bankruptcy was calculated using the following formula:!
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Altman Z-score!
Z = 1.2T1 + 1.4T2 + 3.3T3 + 0.6T4 + 0.99T5! T1 = Working capital/total assets!
T2 = Retained earnings/total assets!
T3 = Earnings before interest and taxes/total assets! T4 = Market value of equity/book value of total liabilities! T5 = Sales/total assets!
Z > 2.99 -“Safe” Zones!
1.81 < Z < 2.99 -“Grey” Zones! Z < 1.81 -“Distress” Zones!
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The list to denote the INDLIT variable can be found in section 2.1.2.1 and was composed by Bohn & Choi (1996), O’brien & Hodges (1993) and Hogan & Jeter (1999). !
! In my thesis I will conduct probit regression using the model above on my final sample to test the three hypotheses. Next to this I will conduct additional analysis using the propensity score matching method (PSM) to see wether the results of my regression analysis hold, or might differ. I will elaborate on my additional analysis in section 4.2. The next paragraph will show the descriptive statistics including the correlation matrix amongst the all the variables in the research model!
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3.3 Descriptive statistics and Correlation Matrix!
Panel A in Table 2 show the main descriptive statistics, including the means of the independent variables of my research model. The results show that on average the companies in my research sample have a ratio of accounts receivable and inventory to total assets of respectively 16 per cent and 14 per cent. The average growth of the companies is around 20 per cent and the average z-score is 1.023, with a rather large standard deviation (11.753). It therefore seems that there are large differences in the probability of bankruptcy in my research sample. The means of auditor tenure and name are 0.722 and 0.849 respectively and are not surprising, as most companies are audited by big four companies (BIG4) and it is not uncommon for companies to stay with the same auditor over a longer period of time. The fact that my research sample was drawn from the North-America compustat database, which mostly consists of larger companies, could also mean that larger companies are audited by big four auditors more often. The average natural logarithm of the market value of the firm is 5.188 and around 42 per cent of the companies in my sample do business in industries that are considered more litigious. The means of audit specialization and big four specialist (BIG4SPECIAL) are exactly the same, as all auditor that were identified as industry specialist are big four auditors. These results have implications for my empirical research model, but I will elaborate more on this in section 4. Roughly 25 per cent of auditors in the research sample are identified as industry specialist.
! The correlation matrix amongst all variables of the research model is shown in Panel B of Table 2. Other then most correlation matrices, I have added an extra * for the indication of a 20 per cent significance level. This because I want to emphasize the relation between SUIT and independent variables. Panel B shows that AUDSPECIAL and BIG4 are positively and significantly correlated. This is an indication that big four auditors are more likely to be industry specialists, which is non-surprising due to their size. The final correlation I would like to pay attention to, is the correlation between SUIT and AUDSPECIAL. Although the significance level is only 20 per cent, the negative (small) negative relation is a first indication that industry specialist auditors have lower litigation risk and acts as support for my first hypothesis. The next section will include the empirical research results, of both the probit regression and propensity score matching.!
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TABLE 2
Descriptive Statistics and Correlation Matrix for the Variables in the Modified Cross-Section model by Stice (1991)" Panel A Descriptive Statistics"
Variable" " n" " Mean" " Std.Dev" Min" " Max" " 25th" " Median"" 75th"
A/R! ! ! 252! ! 0.154! ! 0.126! ! 0.003! ! 0.552! ! 0.062! ! 0.127! ! 0.212 INV! ! ! 252! ! 0.136! ! 0.136! ! 0.001! ! 0.618! ! 0.031! ! 0.094! ! 0.191 GROWTH! ! 252! ! 0.206! ! 0.758! ! -0.812! ! 5.811! -0.058! ! 0.054! ! 0.227 FC! ! ! 252! ! 1.023! ! 11.753! ! -69.570!! 29.189! !0.871`! ! 2.412! ! 4.383 BIG4! ! ! 252! ! 0.722! ! 0.449! ! 0! ! 1 TENURE! ! 252! ! 0.849! ! 0.359! ! 0! ! 1 MV! ! ! 252! ! 5.188! ! 2.615! ! -1.339! ! 11.356! ! 4.383! ! 2.412! ! 4.383 INDLIT! ! ! 252! ! 0.420! ! 0.494! ! 0! ! 1 AUDSPECIAL! ! 252! ! 0.246! ! 0.432! ! 0! ! 1 BIG4SPECIAL! ! 252! ! 0.246! ! 0.432! ! 0! ! 1!
Panel B: Correlation Matrix"
! ! SUIT! A/R" INV GROWTH FC" BIG4 TENURE MV INDLIT AUDSPECIAL BIG4SPECIAL"!
SUIT! ! ! 1.000! A/R! ! ! 0.065! 1.000! ! ! INV! ! ! 0.017 ! 0.251**** 1.000 ! GROWTH! ! 0.005! 0.058! 0.019 1.000 FC! ! ! 0.057 ! 0.015! -0.136 -0.049 1.000 BIG4! ! -0.056 -0.199**** -0.252**** -0.101**** 0.318**** 1.000 TENURE! -0.001 -0.091**** 0.022 -0.083* 0.032 0.011 1.000 MV! ! -0.085* -!0.241**** -0.297**** -0.009 0.361**** 0.544**** 0.544**** 1.000 INDLIT! ! ! 0.040! 0.169**** 0.117*** 0.057 -0.082* -0.098 -0.099* -0.125** 1.000 AUDSPECIAL! -0.085* -0.180**** -0.069 -0.068 0.117*** 0.354**** 0.354**** 0.284**** -0.188**** 1.000 BIG4SPECIAL! -0.085* -0.180**** -0.069 -0.068 0.117*** 0.353**** 0.354**** 0.284**** -0.188**** 1.000 1.000 !
4. Empirical results!
4.1 Probit regression results!
Table 3 includes the results from the probit regression including the coefficients and p-values. The first thing to notice is the fact that BIG4SPECIAL variable was omitted. Because all industry specialists in my research sample, were big four auditors, the outcome of the dummy-variable was exactly the same as that of AUDSPECIAL. Due to collinearity of BIG4SPECIAL, I was forced to drop this variable and therefore was unable to test my third hypothesis. The only significant relationship in my results is the AUDSPECIAL variable, which is negative (-0.860) with a p-value of 0.070. These results suggest that there is enough evidence in support of my first hypothesis. Again, the BIG4SPECIAL variable would have gotten the exact same results, but has therefore been dropped. The results on the BIG4 variable do not find support my second hypothesis, as the relationship is positive instead of the hypothesized negative. Although the p-value (0.449) is not low enough to state that this finding is of any significance. The same holds for most other control variables within the model. Although GROWTH (p-value: 0.984) and FC (p-value: 0.602) seem to have no relationship with the possibility of lawsuits for auditors. The fact that these variables are so far off, could help explain the rather low pseudo R-squared of 0.0982. The F-statistic is 9.47 with a p-value of 0.395.!
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4.2 Propensity score matching!
The probit regression above, is run with a random sample. As additional test I have also conducted a propensity score matching analysis. In order to see what the effect of auditor specialization (H1) and big four classification (H2) is on litigation risk, I have run a propensity score matching on all control variables. This technique allows us to see what the real effect of these two variables is on litigation, as all other variables have to same propensity to be either specialized (AUDSPECIAL) or be classified as big four auditor (BIG4). Using STATA12 statistics utility, I have matched samples based on all control variables. The propensity score matching was done using the nearest-neighbor technique. The samples were therefore matched with the propensity scores that were closest to each other. Unfortunately I was unable to test my third hypothesis for the same reason as with the probit regression. The results would have been exactly the same as with AUDSPECIAL because my sample only included specialists that were also classified as a big four auditor. The results of the propensity score matching are shown in table 4, where panel A represents the results using AUDSPECIAL as treatment indicator and panel B represent the results using BIG4 as treatment indicator. If we look at panel A, we can see that the BIG4 variable was dropped. The reason for this lies in the fact that BIG4 predicts failure (AUDSPECIAL=0) perfectly, and was therefore ignored by the propensity score matching utility within STATA12. This has resulted in a
sample where 62 specialists have been matched with 120 non-specialists, for the treatment indicator AUDSPECIAL. Ideally, the differences between the control variables should be non-significant to proxy a good match. From panel A we can conclude that the matching was rather good, as non of the differences were significant, and were all smaller then 0.5. The variable of interest is the SUIT variable which shows a slight negative difference (-0.032). Due to the propensity score matching, we can attribute these findings to auditor specialization. While the difference is negative, there is no direct support for my first hypothesis as the results only show significance at the 30 per cent level. From the probit regression idn table 4 panel A, we can also conclude that AR and INDLIT are negatively and significantly related with AUDSPECIAL at the 10 per cent and 5 per cent level respectively. Next to this the, the probit regression shows that MV and INV are positively and significantly related to AUDSPECIAL at the 10 per cent level. The other variables do not seem to determine AUDSPECIAL significantly.
! To test my second hypothesis, I also matched propensity scores with BIG4 as treatment indicator to see whether big four auditors face less litigation risk. Panel B in table 4 show that the AUDSPECIAL variable was omitted, this because AUDSPECIAL predicts success (BIG4=1) perfectly, as was with the BIG4 variable in my previous matched sample (although that predicted failure perfectly). This has led to a matched sample of 120 big four auditors against 70 non big four auditors. Again, ideally the differences between control variables should not be significant, to proxy a good match. Unlike with AUDSPECIAL as treatment indicator, the match was not very good. If we therefore look at the difference between the treatment (BIG4=1)and non treatment (BIG4=0) for the SUIT (outcome) variable, we should be careful interpreting the results. Panel B shows that there is a positive difference (0.058) with a significance level of 5 per cent (p=0.031). This would suggest that other then my first probit regression, there is evidence to conclude that big four auditors are faced with higher rather then lower litigation risk which I hypothesized. However I will not conclude that these results will act as solid evidence, this because of the rather weak propensity score matching The determents (control variables) of BIG4, are all non significant except for MV, which is significant at the 1 per cent level. These results could be explained due to the fact that big four auditors accept many different clients in size and industries, and therefore the propensity score match was not very strong due to larger variances in the variables.
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TABLE 3Probit Regression Results"
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" " " " " " " " " Coefficients ! ! ! ! ! ! Predicted! ! (p-values in ! ! ! ! ! ! Sign!! ! parentheses)*" Constant (_cons)! ! ! ! ! ! ! ! -2.475 ! ! ! ! ! ! ! ! ! ! (0.000)Accounts receivable/Total Assets (AR)! ! ! ! ! 1.22
! ! ! ! ! ! ! ! ! ! (0.301)
Inventory/Total Assets (INV)! ! ! ! ! ! ! 1.085
! ! ! ! ! ! ! ! ! ! (0.403) Growth (GROWTH)! ! ! ! ! ! ! ! -0.004 ! ! ! ! ! ! ! ! ! ! (0.984) Financial Condition (FC)! ! ! ! ! ! ! 0.011 ! ! ! ! ! ! ! ! ! ! (0.602) Name/brand (BIG4)! ! ! ! -" " " " 0.322 ! ! ! ! ! ! ! ! ! ! (0.449)
Auditor tenure (TENURE)! ! ! ! ! ! ! -0.308
! ! ! ! ! ! ! ! ! ! (0.449)
Natural Log of firm Market Value (MV)! ! ! ! ! 0.116
! ! ! ! ! ! ! ! ! ! (0.124)
Industry Litigation Risk (INDLIT)! ! ! ! ! ! -0.230
! ! ! ! ! ! ! ! ! ! (0.443)
Auditor Specialization (AUDSPECIAL)! -! ! ! ! -0.860
! ! ! ! ! ! ! ! ! ! (0.070)
Specialist and Big-Four (BIG4SPECIAL)! -! ! ! ! 0 (omitted)
F-Statistic! ! ! ! ! ! ! ! ! 9.47**
! ! ! ! ! ! ! ! ! ! (0.395)
Pseudo R-Squared! ! ! ! ! ! ! ! 0.0982!
*All p-values are two-tailed with a 95% confidence interval ** Chi-square statistic on log likelihood ratio
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TABLE 4Propensity Score Matching Results" Panel A : Treatment Indicator AUDSPECIAL"
" " AUDSPECIAL = 1 (n=62)" AUDSPECIAL=0 (n=120)
Variable" " Mean" " " " Mean" " " Difference S.E." t-statistic" p-value*
SUIT! ! ! 0.016! ! ! ! 0.048! ! ! -0.032 0.047 -1.01! 0.313 AR! ! ! 0.115! ! ! ! 0.100! ! ! 0.015 0.020! 0.96! 0.337 INV! ! ! 0.120! ! ! ! 0.109! ! ! 0.011 0.025! 0.57! 0.572 GRWOTH! ! 0.116! ! ! ! 0.127! ! ! -0.011 0.066! -0.21! 0.873 FC! ! ! 3.425! ! ! ! 3.867! ! ! -0.442 1.141! -0.57! 0.573 BIG4! ! ! 1! ! ! ! 1! ! ! .! ! . . . TENURE! ! 0.806! ! ! ! 0.839! ! ! -0.033! 0.088! -0.47! 0.642 MV! ! ! 6.486! ! ! ! 6.580! ! ! -0.094 0.463! -0.25! 0.804 INDLIT!! ! 0.258! ! ! ! 0.274! ! ! -0.016 0.109! -0.20! 0.841!
Probit regression (Dependent variable: AUDSPECIAL)"
Variable" " Coefficient " " Standard Error" z " p-value* CONSTANT! ! -0.363! ! 0.411! -0.88! 0.376 AR! ! ! -2.160! ! 1.126! -1.92! 0.055 INV! ! ! 1.704! ! 0.954 1.79! 0.077 GROWTH! ! -0.174! ! 0.206! -0.84 0.399 FC! ! ! 0.010! ! 0.020! 0.50! 0.614 BIG4 0 (omitted) TENURE ! -0.403! ! 0.280! -1.44! 0.149 MV ! ! ! 0.088! ! 0.048! 1.84! 0.065! INDLIT!! ! -0.459! ! 0.221! -0.88! 0.038"
Panel B : Treatment Indicator BIG4"
" " BIG4 = 1 (n=120)" " BIG4 =0 (n=70)
Variable" " Mean" " " " Mean" " " Difference" S.E." t-statistic" p-value*
SUIT! ! ! 0.075! ! ! ! 0.017! ! ! 0.058! 0.076 2.17 0.031 AR! ! ! 0.151! ! ! ! 0.178! ! ! -0.027! 0.050 -1.59! 0.112 INV! ! ! 0.113! ! ! ! 0.113! ! ! 0.000 0.043 -0.00! 0.999 GROWTH! ! 0.181! ! ! ! 0.135! ! ! 0.046! 0.169! 0.69! 0.491 FC! ! ! 3.290! ! ! ! 4.852! ! ! -1.562! 4.401! -1.62! 0.107 TENURE ! ! 0.875! ! ! ! 0.758! ! ! 0.117! 0.103 2.35 0.019 MV! ! ! 5.853! ! ! ! 5.636! ! 0.217! 0.592 0.79! 0.428 INDLIT!! ! 0.458! ! ! ! 0.542! ! ! -0.084 0.157 -1.29! 0.198 AUDSPECIAL!! 0 ! ! ! ! 0! ! ! - ! -! ! - -! Probit regression (Dependent variable: BIG4)"
Variable" " Coefficient " " Standard Error" z " p-value* CONSTANT! ! -0.882! ! 0.438! -2.01! 0.044 AR! ! ! -0.291! ! 0.808! -0.36! 0.719 INV! ! ! -0.996! ! 0.833 -1.20! 0.232 GROWTH! ! -0.151! ! 0.129! -1.17 0.242 FC! ! ! 0.013! ! 0.011! 1.22! 0.222 TENURE! ! -0.175 0.294! -0.60 0.550 MV ! 0.345! ! 0.060! 5.74! 0.000 INDLIT ! ! 0.147! ! 0.222! 0.66! 0.509! AUDSPECIAL!! 0 (omitted)" * One-tailed test
5. Discussion!
Now that we have seen the results of both the probit regression and comparison of means using propensity scores to match samples, it is time to put these results into context of the academic literature. In section 2.2 I provide an adjusted research framework from Balsam et al. (2003, p.72), in which I place this thesis into recent research in the area of auditor industry specialization and litigation risk. Past research has mainly focused on two areas to proxy audit quality, namely brand name (big four versus non big four) and auditor industry specialization. These studies conclude that industry specialization by auditors significantly reduces the risk of SEC enforcements (Carcello & Nagy, 2004), but also enhances earnings quality (Balsam et al., 2003). Next to this, Dunn & Mayhew (2004) argue that disclosure quality from specialist auditors, is believed to be of higher by financial analysts. The proxy to measure financial reporting quality which is most important for this thesis, is litigation risk. Cenker & Nagy (2009) argue that specialist auditors suffer less litigation risk, but only conclude this by looking at auditor resignations. Lys & Watts (1994) however fail to find a negative relation between auditor specialization and litigation risk in their empirical research. My results in section 4 however, do show a significant negative relationship (p=.069) between auditor industry specialization and auditor litigation risk. The fact that my results differ, could be due to the difference in time of which the sample was drawn. While Lys & Watss (1994) use a sample that consist of litigation between 1966 and 1991, my sample includes cases from 1999 until 2012. The fact that the samples differ, does not immediately suggest that results should always be different, though it is important to keep in mind that time can have effect on the relationship. This thesis therefore provides the first empirical evidence that auditor industry specialization lowers litigation risk for auditors. To gain more insight I also conducted a propensity score matching analysis to see whether the findings of the probit regression hold. While the results show that auditor specialization can lower litigation risk for auditors, these results lack significance. Unfortunately I was unable to conclude that brand name (hypothesis 2) has a significant negative influence on litigation risk. Results show that (while insignificant) brand name actually increases litigation risk for the probit regression. The same holds for my additional propensity score matching analysis on hypothesis 2. While the results suggest that big four auditors are faced with higher litigation risk, I do not conclude that this is always the case because of the weak propensity score matching. Higher litigation risk would however be in line with the deep pocket hypothesis which was discussed in section 2.1.2.1. Another reason for these results could be the rather small sample size, which is due to the low number of litigation cases I was able to find. Regarding my third hypothesis, I already explained that the results are exactly the same as for industry specialization. In my sample, only big four auditors were industry specialist, and I was therefore unable to test my third hypothesis. These results could however mean that only big four auditors are able specialize in certain industries. As is with a lot of other studies in the auditing area, size
seems to matter. Smaller companies are perhaps unable to specialize due to a lack of resources and number of clients within industries. On a final note I would like to mention the industries in which auditors operate, do not significantly alter litigation risk. While in some industries, auditors are more prone to litigation then other industries, this cannot be concluded from the results of this thesis. Again the rather small sample size could be the reason for the failure to find a significant relationship.!
! Now that I have put the results into past and recent academic literature, we should also look at the practical implications of these results. As mentioned in previous sections, past research suggest that industry specialization can benefit auditors in several ways. These for instance include benefits of economies of scale and spreading industry specific trainings (Mayhew & Wilkins (2003), section 2.1.2.2.). The results of this thesis show that as auditors become industry specialists, litigation will lower. As litigation can be quite costly for auditors, specialization can have a positive effect. As auditors engage in risk management, specialization in industries can help auditors to make an acceptation decision with clients from certain industries. From a client perspective the results can also have implications. Past research uses auditor litigation as a proxy for the quality of financial reporting. A decrease in litigation risk, can be seen as increase in financial reporting quality. Clients can therefore look at auditor industry specialization, to perhaps increase their financial reporting quality. Next to this, as is the case with the auditors, litigation can be quite costly. Therefore it might be beneficial for future clients to look for an industry specialist auditor .!
! After discussing the results, I would like to suggest some ideas for further research. In the first place I would like to suggest that future research tries to include/find more litigation cases against auditors. Due to a lack of resources available, I was only able to find 146 litigation cases over the last 12 years. However I am quite sure that more cases could have been found when looking at other resources such as the litigation database from AuditAnalytics (which I was unable to access). Increasing sample size in future research will hopefully lead to more solid results. My thesis also used the number of clients audited within an industry as proxy for industry specialization. Balsam et al. (2003) argue that until this moment it is unclear whether this proxy for market share should be in the number of clients or market share as proxied by the amount of audited sales by clients per industry. Future research can add to this thesis by using a different proxy for industry specialization. Finally I would like to suggest that future research could look more into the mediating effect of big four auditors on the relationship between industry specialization and litigation risk. Due to my small sample size I was unable to find non big four specialists. Though we should keep in my mind that it might be impossible for smaller audit firms to specialize.!
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6. Conclusion!
This thesis started with the claim of Hogan & Jeter (1999) that there are possible returns/benefits for auditors to invest in industry specialization. Looking at possible returns, this thesis focused on litigation risk for auditors and its relationship with industry specialization, where lower litigation risk stands for one of the these possible returns of investing in specialization. The main research objective of this thesis was to investigate to what extent auditor industry specialization will alter litigation risk for auditors. Not only focusing on the direct relationship but also looking at the mediating of big four auditors on this relationship. I therefore constructed three hypothesis to look for an answer on the main objective of this paper. The first hypothesis stated that auditor industry specialization will lower litigation risk for auditors while the second hypothesis was concerned with the auditor brand/name. This stated that big four auditors will face less litigation due to their higher perceived ability to deliver high quality audits. The third and final hypothesis combined the first and second hypothesis, to state that the negative relationship between auditor industry specialization and litigation risk will stronger for big four audit firms. Running a probit regression and providing additional analysis using propensity score matching, with an overall sample 252 observation, I find significant support for my first hypothesis and therefore conclude that auditor industry specialization does indeed lower litigation risk for auditors. Unfortunately I was unable to draw a conclusion regarding my second and third hypothesis due to insignificant results. My research only included big four auditors to be identified as industry specialist, therefore I was unable to test my third hypothesis. I argue that the difficulty of finding evidence for my second and third hypothesis, could be due to the rather small sample size. However, the results for the third hypothesis could suggest that only large auditors are able to specialize. The results can have implication for the auditors as well as for the client, with regard to risk management and financial reporting quality. Further research is needed to confirm/reject my second and third hypothesis, but also the use of different proxies will be interesting, this to see whether the findings of this thesis will hold. I look forward to reading more about industry specialization and litigation in the future.!
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References !
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