AUDITOR-CLIENT COMPATIBILITY AND AUDIT FIRM SELECTION. Stephen V. Brown * Arizona State University. W. Robert Knechel University of Florida

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1 AUDITOR-CLIENT COMPATIBILITY AND AUDIT FIRM SELECTION Stephen V. Brown * Arizona State University W. Robert Knechel University of Florida March 12, 2013 * Corresponding Author Acknowledgements We are grateful for feedback from Stephen Asare, Ryan Cerf, Praveen Pathak, and Jennifer Wu Tucker. We would also like to thank workshop participants at Arizona State University, Florida International University, Southern Methodist University, the University of Florida, the University of Illinois at Urbana Champaign, the University of Kentucky, the University of South Carolina, and the University of Virginia for their feedback on portions of this paper.

2 AUDITOR-CLIENT COMPATIBILITY AND AUDIT FIRM SELECTION Abstract We examine the degree of compatibility between clients and their auditors to test whether companies systematically prefer specific auditors based on this criterion. Using both financial statements and narrative disclosures, we introduce two new measures of compatibility based on how similar a client is to other clients of the same auditor. Our results strongly support the idea that auditor-client compatibility can predict auditor-client alignment. When compatibility is lower, clients are more likely to change auditors and are more likely to pick a replacement auditor with relatively high auditor-client fit. Interquartile changes in compatibility increase the probability of switching auditors by as much as 19 percent. Further, audit quality, as captured by discretionary accruals, increases as compatibility increases. Finally, we simulate a mandatory audit rotation regime in which clients base their choice of auditor on compatibility, finding changes in market share of as much as 9.7% from a first-best baseline. Key Words: Audit markets, Auditor switching

3 AUDITOR-CLIENT COMPATIBILITY AND AUDIT FIRM SELECTION 1. Introduction The process by which a client selects an auditor can be complex and may be influenced by a number of factors. Some of the factors that might affect the degree of compatibility between an auditor and a client includes pricing, expertise, location, interpersonal associations and the extent of agency problems in the client (Chaney et al. [1997], Johnson and Lys [1990], Knechel et al. [2008]). Some of these attributes are obviously more relevant than others for determining the overall quality of the resulting audit. A limited amount of research has examined alignment between clients and types of auditors, based on factors such as the size of the audit firm (Shu [2000], Landsman et al. [2009]) or degree of specialization. There is even less research on the compatibility of specific auditors and a specific client. What literature exists tends to be narrowly focused, such as research on the effects of a client hiring a former audit partner (e.g., Lennox and Park [2007]). In general, clients may have preferences about the audit process, its outcomes, and the nature of the relationship with their auditor. In this paper, we define auditor-client compatibility as the ability of the auditor to satisfy these preferences, given its own preferences and constraints. 1 If client preferences vary from company to company, and the ability of an auditor to meet a client s needs varies from firm to firm, the degree of fit between any two entities will also vary. In this paper, we develop a unique measure of auditorclient compatibility for Big 4 firms and use it to evaluate the nature of long term client relationships and changes in auditors. The degree to which a client and a specific auditor are compatible is not observable. However, to the extent a company has similar audit preferences as other companies, they would 1 In this paper, we use the term fit interchangeably with compatibility. 1

4 presumably choose a similar auditor, subject to various choice constraints (e.g., location). In this study, we compare the similarity of a company to the current clients of a specific auditor to generate a proxy for how well that company fits into the auditor s client base. When a company appears similar to other clients of the same auditor, ceteris paribus, we presume that the auditor is likely to have developed expertise and cost advantages related to that type of client. Therefore, we consider clients and auditors compatible when similarity to other clients of the audit firm is high; and we consider low compatibility to occur when there is very low similarity between the company and the audit firm s existing clients. We introduce two measures of inter-company similarity within an industry, one based on a client s financial statements and one based on their narrative disclosures. Each source of information reveals variation in what managers are disclosing and how they choose to disclose it. The financial statement similarity proxy relies on the Mahalanobis distance which is used in cluster analysis to divide objects into groups based on a vector of numeric measures associated with each object. The vector used in this paper incorporates financial statement components known to be important in an audit context, i.e., financial proxies for audit complexity, audit risk and auditor effort. The narrative disclosure similarity measure extends the similarity score introduced in Brown and Tucker [2011] which was based on year-on-year changes in Management s Discussion and Analysis (MD&A). In this paper, we consider narrative disclosures contained in the annual report including the company s business description, footnotes, and MD&A. Using each measure, we calculate the similarity of each client to other clients of an audit firm in the same industry and year (the reference group ). In general, we find that clients tend to hire auditors where compatibility is better. Depending on the proxy used, up to 59 percent of clients employ one of the two best-fitting 2

5 auditors among the Big 4. We also observe that the poorer the fit with an existing auditor, the greater the probability the client will choose to switch to a new auditor. Further, when a client switches auditors, the successor auditor is generally the one of the three remaining firms that has the best fit. Extending the analysis of compatibility, we also examine the association between fit and audit quality. We find that discretionary accruals are lower when the auditor-client compatibility is better. The primary contribution of this study is the introduction of two measures of how similar a company is to a reference group of other companies clustered by audit firm and industry. The financial statement measure (Mahalanobis distance) has been used in other contexts before, but not in the accounting literature. While the financial statement similarity score is used in Brown and Tucker [2011], we extend their pairwise, year-on-year, measure to allow for comparison of entities within a specified group. Within the audit context, we provide insight into the manner in which auditors and clients gravitate to each other. We add to the limited literature on auditorclient fit by considering the suitability of a specific auditor for a specific client. Such insight can be potentially useful in considering what might happen if mandatory audit firm rotation is adopted by the PCAOB or European Union since forcing an auditor change could have negative implications for the engagement if the client is currently served by a highly compatible auditor. The rest of the paper proceeds as follows. The next section develops the hypotheses and discusses prior literature. Following that section is the rationale and foundation for the similarity measures, a demonstration of how they are calculated, and a discussion of observed trends. A description of the design and results of the empirical tests follows, while the next section examines these results for their sensitivity to changes in the similarity measures. The final section contains the conclusion. 3

6 2. Hypotheses and Prior Literature 2.1. Auditor-Client Compatibility and Selection of Auditor To make predictions about the choice of an auditor based on auditor-client compatibility requires two conditions: (1) variation in client preferences regarding the audit and auditor and (2) variation across auditors in their ability to satisfy those preferences. If auditors are all essentially equivalent (no auditor variation), clients could randomly choose between them. On the other hand, if auditors vary but clients all have the same preferences, then all the clients would prefer the same auditor subject to capacity constraints. The audit literature has documented substantial evidence of variation in client preferences and auditor capability. For example, a large, multinational client is more likely to choose a Big N auditor (Chaney et al. [2004]), at least in part because a smaller auditor does not have the resources and capability of auditing such a company. Therefore, the US audit market has enough variation to suggest that auditor-client compatibility is an issue worth examining (Numan and Willekens [2012]). In a related vein, a large number of studies have also focused on industry specialization as a differentiator of both demand and supply. Industry specialists are those auditors that have invested significantly in expertise in auditing a particular industry, typically reflected by larger market shares in an industry. Specialists are considered to deliver higher quality, e.g., they are better at detecting errors (Owhoso et al. [2002]), and are associated with clients having higher earnings response coefficients (Balsam et al. [2003], Gul et al. [2009]) and lower discretionary accruals (Krishnan [2003]). They are also better at improving audit quality through knowledge spillovers from non-audit services (Lim and Tan [2008]). Beyond variation in quality, there are also cost structure differences among specialists. While most studies have found specialists charge higher audit fees (Gramling and Stone [2001]), there is also the possibility of cost savings 4

7 through the leveraging of expertise (Cahan et al. [2008], Craswell et al. [1995], Willenborg [2002]). Further, audits by industry specialists tend to be more efficient (Cairney and Young [2006]). As a result, client preferences for certain levels of cost and quality will lead them to choose an auditor with structural characteristics that best meet their needs. 2 While most prior literature has focused on broad categories of auditors (e.g., Big 4 or non-big 4, specialist or non-specialist), a handful of studies have examined client selection at a more micro level. For instance, Lennox and Park [2007] find that a company is more likely to engage a particular auditor when a former employee of that auditor is on the management team of the client. Research on client-auditor disagreements finds that a client is more likely to switch auditors in the presence of more conservative accounting treatments, presumably in an effort to find an auditor who is more amenable to the company s preferences (DeFond and Subramanyam [1998]; Krishnan [1994]). Bamber and Iyer [2007] find that auditors who more strongly identify with the client will be more likely to allow the desired accounting treatment. While this opinion shopping may sound disreputable, Dye [1991] shows that the firm may simply be trying to better communicate its internal information to the market. The broad conclusion is that interpersonal relationships and opinion shopping are just two ways a client can find one auditor to be a more compatible than others. In sum, given sufficient variation among clients and auditors, each is likely to choose a counterparty that best matches its preferences and needs. While auditors can appear very similar on the surface, there are likely to be subtle differences that make one auditor a better fit than 2 Investor preference can also play a role in the decision. Switches to larger auditors and specialist auditors are associated with positive market reactions (Fried and Schiff [1981], Knechel et al. [2007], Nichols and Smith [1983]). 5

8 another. Considering all a client s needs and preferences, one specific auditor will likely provide a higher net benefit to the client, leading to our first hypothesis: 3 H1: The auditor used by a client will tend to be more compatible with the client than other auditors in the market Auditor-Client Compatibility and Auditor Switching Under H1, clients will prefer higher compatibility with their auditors. If a client has low compatibility with their current auditor, it is likely they will eventually consider a change in auditor to increase the level of fit in the audit. Johnson and Lys [1990] demonstrate companies are more likely to switch auditors as the client s operating, investing, and financing activities change over time. They interpret the increased likelihood of switching as a rational, efficient response to temporal changes in the company s audit preferences. In effect, the auditor-client compatibility that was utility-maximizing in the past has shifted such that another auditor may now be a better fit. Shu [2000] examines auditor-client fit based on whether the client is with a Big N auditor when an empirical model would predict a non-big N auditor, or vice versa, finding clients are more likely to change auditors when there is a mismatch between the two. We extend the concept of auditor-client compatibility to examine whether mismatches with a specific auditor are more likely to lead to auditor switches. Based on the degree of fit between the auditor and client, we expect that poor compatibility is more likely to lead to an auditor switch, leading to our second hypothesis: H2: Clients having relatively poorer compatibility with their current auditor are more likely to change auditors than those with high compatibility. 3 The decision is also subject to various constraints. For example, Coca-Cola is unlikely to choose the same auditor as PepsiCo due to competitive concerns. We do not specifically address this issue in this paper, but the effect will be to shift a client away from its apparent best fit, thus working against our findings. Further, we are unable to incorporate the effect of specific partners who might service a client due to lack of partner data in the U.S. 6

9 Once a client has made the decision to change auditors, 4 it will need to choose from the other available auditors. If the company limits itself to Big 4 auditors, a maximum of three auditors remain, subject to constraints arising from competitor auditor choices and current nonaudit service providers. If compatibility is low with the present auditor who is to be replaced (H1), it then follows that a company will generally prefer a new auditor that has a better compatibility from among the remaining options. 5 This perspective leads to our third hypothesis: H3: A client switching auditors will tend to choose a new auditor that has a relatively higher degree of compatibility among the non-incumbent auditors Effect of Changes in Auditor-Client Compatibility While it is relatively intuitive to grasp how auditor-client compatibility can influence the decision to change or retain an auditor, it is less clear how increases in compatibility would influence audit quality. In experimental studies, Hammersley [2006] shows matched specialists defined as those operating within their industry of expertise are more likely to process experimental cues regarding misstatements than mismatched specialists, while Low [2004] shows that an industry-based mismatch affects audit planning and risk assessments. These studies imply that audit quality will be higher when there is a better fit between the auditor and client. Johnstone, Li, and Luo [2011] find that clients within the same supply chain a measure of relatedness, if not similarity tend to have higher audit quality. On the other hand, a sizable literature finds decreased audit quality when the auditor closely identifies with the client. In essence, the possibility exists that auditor-client compatibility could become so good that an auditor s independence is compromised. Lennox [2005] shows that companies are more likely to 4 The decision to actually change auditors is complex and would take into considerations beyond that of fit. For example, the transaction costs of an auditor change can be quite high so the degree of incompatibility would probably have to be relatively large to justify an auditor change on a cost/benefit basis. 5 We do not predict that the new auditor will necessarily have a better fit than the previous auditor, primarily because of the endogenous nature of compatibility a client may appear to become a better fit for an auditor over time as the auditor s preferences modify the client s observable financial statements and related disclosures. 7

10 receive a clean audit opinion if the auditor becomes affiliated with the client by hiring its former auditors. Menon and Williams [2004] find clients employing former audit partners in executive positions tend to report higher levels of discretionary accruals. Taken together, these potentially offsetting effects lead to our fourth (nondirectional) hypothesis: H4: Audit quality is associated with auditor-client compatibility. 3. Measurement of Auditor-Client Compatibility When clients are similar to each other in terms of industry, operations, risks or transactions, auditors have the opportunity to specialize in those companies for both reputational and audit production reasons. 6 While prior literature typically uses an all-or-nothing industry membership test to organize clients into similar groups, we develop two continuous proxies for the degree of compatibility between a company and an auditor s existing client base. The first proxy is based on cluster analysis and is derived by grouping similar entities together based on a small set of numeric data (e.g., financial statement accounts). The second proxy is based on information (text) retrieval which uses narrative-based documents as the underlying data. The two proxies facilitate the development of measures of client-auditor similarity for both financial statement and narrative textual disclosures. While the accounting systems and underlying economics of multiple organizations are not separately observable, their joint effect is presumed to be reflected in the financial statements and related narrative disclosures. The narrative disclosures are especially flexible, giving management the opportunity to communicate firm-specific information or to influence the market s view of the company. In general, the numeric proxy and the text-based proxy serve the same purpose, namely, they provide an indication of how similar a specific client is to all of the other clients audited by a specific 6 Gramling and Stone [2001] summarize the industry specialization literature, which generally finds differences in both quality and audit fees for industry specialists versus non-specialists. 8

11 accounting firm on a year and industry basis (i.e., each metric is specific to a firm-year-industry nexus) Financial Statement Similarity Algorithms used in cluster analysis of numeric data include the Euclidean distance, cityblock distance, Chebychev distance, and Mahalanobis distance (Hair et al. [2006]). Of these, the Mahalanobis distance-squared (D 2 ) is particularly sophisticated in its ability to weight each variable according to its individual scale, as well as account for covariances among the various components. Further, the D 2 statistic imposes few restrictions on the underlying variables (Mahalanobis [1936]). After scaling and accounting for covariance of the variables, an observation s distance from the group is larger when the variables for the observation are jointly more unusual. The D 2 measure is the generally preferred algorithm in cluster analysis (Hair et al. [2006]) 7 but there has been limited use of the D 2 measure in the accounting literature. Rege [1984] uses it to test the effectiveness of a discriminant function in classifying data into likely and unlikely takeover targets. If the distance between the two groups is significant then the discriminant function is considered effective. Iyer [1998] employs the statistic in the context of distinguishing audit firm alumni that can help the firm market its services. Guilding and McManus [2002] use the measure to test for potentially influential outlying observations. However, all these studies use the measure in a statistical context and do not examine the 7 Outside the accounting literature, D 2 has been used in management to compare the distance between countries along multiple dimensions, including economic, financial, political, cultural, demographic, and geographic location (Berry et al. [2010]). Climatologists have used the measure to look for boundaries between different regional climates (Mimmack et al. [2001]). And chemists have used it for multivariate calibration, pattern recognition and process control (De Maesschalck et al. [2000]). 9

12 properties of the distance itself. 8 We use the D 2 measure in this paper to capture financial statement similarity. We use five financial variables that are generally believed to be important in an audit context (Hay et al. [2006]) to construct our measure of financial similarity: (1) company size measured as the log of total assets (SIZE) to capture the scope and potential importance of the client, (2) the combination of inventory and receivables as a proxy for inherent risk (IRISK), (3) total accruals as an indicator of audit quality (TACC, as calculated by DeFond and Subramanyam [1998]), (4) cash and equivalents (CASH) as a proxy for liquidity, and (5) return on assets (ROA) as a measure of profitability. All measures except SIZE are scaled by total assets. 9 To calculate the D 2 distance for the sample, these five variables are gathered from Compustat for companies having assets greater than $1 million and a consistent fiscal year end throughout the sample period. We exclude utilities or financial services industries due to inherent differences in the structure of their financial statements. The sample begins in 1997, when EDGAR data is first widely available and ends in Former Arthur Andersen clients are included but only after they have not engaged Andersen for at least one year to limit the potential confounding effect of changes due to the collapse of Andersen. A similarity metric can be calculated for a single company by comparing it to some welldefined group of other companies called the reference group. In this paper, we define the reference group to be all other clients of an accounting firm in the same industry and same year. 10 We use industries to define our reference groups because of potential systematic 8 The abnormal accrual model could be conceptualized as a distance measure. It empirically models the relationship between total accruals and various explanatory variables to attempt detection of accrual levels that are unusuallooking relative to peer companies. 9 Other potential inputs, various permutations of those inputs, and alternative measurement approaches are described in the robustness section. 10 Hogan and Jeter [1999] document the increasing importance of auditor restructuring along industry lines at the national level to take better advantage of internal teams of experts. While client compatibility may vary at the office 10

13 differences and a lack of comparability in the structure of financial statements and narrative disclosures across different industries. We exclude any reference groups that do not have at least five observations because the similarity score is unlikely to be reliable in small groups. Further, possible reference groups are rarely large enough for non-big 4 auditors so we explicitly limit the sample to Big 4 clients. Finally, a company is omitted from the reference group in the year that they switch auditors. Each observation in the sample consists of a vector of five financial statement variables (n=5), x T = (x 1, x 2,, x n ) = (SIZE, IRISK, TACC, CASH, ROA). The mean of these same financial statements variables for the entire auditor-industry-year is contained in the vector, μ T = (μ 1, μ 2,, μ n ). Finally, the group has a covariance matrix, Σ, for the five inputs. The Mahalanobis distance-squared is then calculated as: 11 ( ) ( ) The (x μ) portion of the measure is calculated by subtracting the mean values of the five financial variables of the reference group (i.e., auditor-industry-year combination) from the values for the company-year being analyzed. The Σ covariance matrix is calculated at the industry-year level to account for different scales and covariances across industries and over time that are unlikely to vary much across auditors. 12 Because D 2 is a measure of dissimilarity, it is converted to a similarity measure by calculating its inverse. Finally, the natural log level, there are few offices with enough clients to calculate similarity measures within an industry. Calculating the scores at the office-industry level would lead to a 63% reduction in sample size (and a 30% reduction if calculated at the office-sector level). 11 The Euclidian distance between observations is a special case of the Mahalanobis distance. If the covariance matrix (Σ) is the identity matrix, the square root of D 2 simplifies to the familiar [(x-μ) T (x-μ)] ½, which is the Pythagorean theorem if the vector has length two. 12 Multicollinearity is not problematic as it would be in a regression. However, as the variables approach nearly perfect multicollinearity, the covariance matrix will not be invertible, which can be a concern when using a small vector of variables in a small industry. While unusual in the sample, we exclude industry-years that do not have at least ten company-year observations (twice the number of variables). This restriction is almost always met given the earlier restriction of at least five observations with an auditor-industry-year. 11

14 transformation reduces skewness and outliers, yielding our measure of financial statement similarity: ( ) 3.2. Narrative Disclosure Similarity The information retrieval literature has developed numerous methods for measuring the similarity of two documents, often in the context of matching a user s Internet search query to the closest applicable web pages (Singhal [2001]). 13 A common method for analyzing text documents is the Vector Space Model (VSM) from the document retrieval literature. The VSM maps a document into a numeric vector (Salton et al. [1975]). There are numerous ways to calculate the similarity of document vectors. The most common approach is to calculate the cosine of the angle between any two vectors (Singhal [2001]), an approach used in the accounting literature by Brown and Tucker [2011]. They use the VSM cosine statistic to measure year-on-year dissimilarities in MD&A as a proxy for changes in narrative disclosure. Because Brown and Tucker [2011] were interested in the differences between just two documents at a time, they only calculate pairwise similarity scores. In contrast, for this paper, we aggregate these pairwise scores to get a measure of the similarity between one company s narrative disclosure and the disclosures issued by a reference group of clients within the same auditor-industry-year. 13 In theory, the D 2 measure can be used in a document context, however, practical considerations limit its usefulness in such a setting. When mapping a list of words from narrative text into variable vectors, the high dimensionality makes calculation of the D 2 measure impractical. The problem is that the size of the covariance matrix depends on the unique words used in the documents being analyzed rather than the number of documents. For example, a covariance matrix using the 98,519 unique words for the MD&A in the sample would contain 9.7 billion elements. This matrix would need to be inverted in the computational process, making the approach obviously impractical. 12

15 The VSM-based similarity score for narrative disclosure similarity is calculated based on three different types of text disclosure in a company s annual 10-K. 14 We select annual report disclosures that are common to virtually all 10-K s, reviewed by the auditor, not voluntary, and considered important by the capital markets and regulators. It is preferable to use more than one disclosure item because there is considerable variation in the characteristics of different disclosures in terms of subject matter, time-horizon, and audit requirements. As a result, we examine the company s business description, 15 management discussion and analysis (MD&A), 16 and financial footnotes 17 because they are usually the longest disclosure items in the 10-K. Excluding exhibits, there are an average of 6,338 words in the business description, 7,054 in the MD&A, and 8,623 in the footnotes (see Table B-1), comprising 17, 18, and 21 percent, respectively, of the length of the typical 10-K. 18 The data for the narrative disclosure score is taken from 10-K s filed electronically via the SEC s EDGAR system for fiscal years 1997 through 2009 for the clients of Big 4 audit firms 14 While it is not feasible to calculate the D 2 statistic for documents, it is feasible to calculate the VSM similarity for financial statements. Using such an approach, Jaffe [1986] uses vectors of different categories of patent applications to examine the overlap of R&D spending within industries. However, the D 2 statistic is superior for numeric data because VSM does not account for variances and covariances of the variable components, which reduces its statistical power. 15 Item 101 of Regulation S-K requires the 10-K item 1 contain a detailed narrative description of the business, including industrial and geographic segments, principal products and services, R&D spending, and competitive conditions. This is the disclosure that is likely to have the least formal structure but may not change much over time. 16 Item 303(a) requires that the MD&A contain a discussion of liquidity, capital resources, results of operations, offbalance sheet arrangements, and contractual obligations. The MD&A is intended to be an interpretation of past and future operations through the eyes of management (SEC [2003]). Given certain conditions, any forward-looking statements receive Safe Harbor protection (Item 303(c) of Regulation S-K). This is the only disclosure that is specifically forward-looking to some extent. 17 Although there is some topical overlap with the other two disclosures, footnote content is the only disclosure specifically determined by GAAP. This is the only disclosure that is formally audited while the other disclosures are reviewed by the auditor (AU Sections 550; 551) 18 The footnotes and MD&A are particularly important to stakeholders, given the large number of accounting standards requiring or encouraging specific footnote disclosures and the relatively frequent guidance by the SEC on MD&A (e.g., SEC [1987], [1989], [2003]). Prior studies have demonstrated the usefulness of footnotes (e.g., Shevlin [1991], Amir [1993], Wahlen [1994], Riedl and Srinivasan [2010]). Other research has shown some of the potential information contained in MD&A (e.g., Feldman et al. [2010], Feng Li [2010], Sun [2010]). Of the three narrative disclosures, the business description is relatively unexplored except in studies of the full 10-K as a single document (e.g., Li [2008]). 13

16 having at least five other observations available for comparison within the same auditor-industryyear reference group. Appendix A describes the selection and extraction process, which yields 33,355 business description, 31,280 MD&A, and 14,439 footnote observations. Treating the three narrative disclosure items of the annual report as separate data sets, the similarity score for each is calculated using an extension of the approach in Brown and Tucker [2011] that allows for a comparison of a company to its peers (reference group). The process, summarized in Appendix B, produces three variables SIM BUS, SIM MD&A, and SIM NOTES that proxy for the degree of similarity between one client and all other clients in the same auditor-industry-year. Higher similarity scores correspond to greater auditor-client compatibility Patterns in Client Similarity Panel A of Table 1 contains descriptive statistics for the financial statement and narrative disclosure similarity measures. The sample size is largest for SIM FS (57,035), smallest for SIM NOTES (14,439), and similar for SIM BUS (33,355) and SIM MDA (31,280). SIM FS is always negative because of the log transformation. Higher similarity scores indicate greater similarity in relation to the reference group. 19 The narrative disclosure similarities (SIM BUS, SIM MD&A, and SIM NOTES ) are approximately centered around zero. 20 Similarity scores are significantly higher for companies in the top quartile of size than for those in the bottom quartile (untabulated), indicating that bigger clients tend to be at the core of the auditor s portfolio in terms of their similarity. <<<<< Insert Table 1 about here >>>>> 19 The average auditor-industry-year reference group size is 38 clients for financial statements, 23 for the business description, 22 for MD&A, and 12 for the footnotes. 20 As noted in Appendix B, we maximize the sample size for making the length adjustment by using all available observations, including non-big 4 auditors. After restricting the study sample to Big 4 clients, the mean is slightly above zero. 14

17 The four similarity scores are not directly comparable to one another because of variations in how they are calculated (e.g., a score of 0.20 for MD&A is not necessarily larger than a score of 0.15 for footnotes). To compare across measures, the scores are standardized to have a mean of zero and standard deviation of one. Figure 1 plots the similarity scores against auditor tenure the number of years with the current auditor. A tenure of zero corresponds to the year prior to the current auditor obtaining the client, while a value of one indicates the first year of a successor auditor s tenure (i.e., the first year after an auditor switch). For visual comparability, all scores are adjusted to begin at zero when auditor tenure is zero. The graph only presents tenure up to year ten, after which the decreasing number of observations leads to heightened volatility in the scores. All similarity measures increase over the length of the auditor-client relationship, indicating that auditor-client compatibility improves over time. Auditors might find this trend beneficial to the extent that it improves the quality or reduces the effort involved in the audit engagement. Likewise, clients might benefit from adopting best practices arising from the auditor s expertise developed in similar client engagements. <<<<< Insert Figure 1 about here >>>>> The business description experiences a rapid increase in similarity during the first two years of the engagement, after which point the similarity becomes more stable. MD&A similarity also increases quickly in the first two years and then rises more slowly until approximately year seven. The trends in financial statement and footnote similarities are highly correlated, which is not surprising since those disclosures are intended to be closely aligned by regulation. Both of these measures increase gradually over time with less of a sudden jump in the early years. To test for the statistical significance of these trends, we compare the similarity scores for short tenure (less than four years) and long tenure (nine or more years) engagements. In all cases, the 15

18 similarity scores are significantly higher for longer-tenure clients than for shorter ones. We perform a related test using year-on-year changes in similarity and find that the annual changes for long-tenure clients are not as large as the changes seen in newer clients. 21 These patterns all demonstrate that auditor-client compatibility is not a static component of the relationship, but is partially a function of auditor tenure Validation of Similarity Measures Table 2 reports the Pearson pairwise correlations among the similarity measures. The reported correlations are limited to observations with scores available for all four disclosures (narrative and financial), although the unrestricted correlations are similar. The four similarity scores are all positively correlated with one another, indicating they measure related constructs. The correlations are higher among the three narrative disclosures (ranging from 0.61 to 0.72) than they are with the financial statements (from 0.05 to 0.11). <<<<< Insert Table 2 about here >>>>> As a means of validation, the table also contains the correlations between the similarity measures and various proxies for client differences. For each variable used to produce SIM FS, we calculate the client s absolute difference from the mean for the reference group (auditor-clientyear), i.e., SIZEDIFF, IRISKDIFF, TACCDIFF, CASHDIFF, and ROADIFF. 22 We expect the correlation with the similarity scores to be negative in all cases. The correlations with SIM FS, range from to and all are significant. More importantly, the significant correlations between these difference variables and the narrative disclosure scores are all negative, indicating the proper functioning of the narrative scores even though they did not explicitly include any 21 The t-statistics for the difference in means between long and short tenure clients of the financial statements, business description, MD&A, and footnotes are: 24.10, 4.61, 5.13, and 8.72, respectively. The corresponding t- statistics for the difference in annual changes are: 6.55, 2.94, 2.29, and These difference variables should be negatively correlated with SIM FS by design, but these correlations document the SIM FS measure is working as expected. Performing a series of univariate correlation tests is also substantially different from the joint difference measure produced using the D 2 technique. 16

19 financial statement variables. Of the difference variables, ROADIFF and TACCDIFF have the highest negative correlation with SIM FS, so client profitability and accruals could be important determinants of financial statement similarity. On the other hand, IRISKDIFF has the most negative correlation with the three narrative scores. 23 We also consider the absolute value of unexpected discretionary accruals as a measure of client differences. Following DeFond and Jiambalvo [1994], accruals are estimated using a cross-sectional modified Jones model run within SIC 2-digit industries. The residual from this model is DACC, the unexpected discretionary accruals. As expected, the similarity scores are negatively correlated with the absolute value of DACC. Finally, we examine the stability of the similarity scores since it would be expected that auditor-client compatibility will not change dramatically over short time periods due to the relative stability in financial statements, related disclosures, and client portfolios from year to year. In untabulated analysis, we find that the autocorrelation coefficients for SIM FS (0.55), SIM BUS (0.93), SIM MD&A (0.92), and SIM NOTES (0.92) are high, demonstrating a high degree of time-series stability in all four measures. 4. Analysis of Auditor-Client Alignment 4.1. Sample For the hypotheses tests, data is collected from Compustat for each observation with at least one similarity score available. The variables used in our testing are summarized in Table 1 (Panel B). The earliest data come from 1997, corresponding with the earliest availability of the narrative disclosure data, and end in Auditor tenure is calculated based on the current auditor information in Compustat beginning in This is consistent with the importance of the numerous risk-related disclosures in annual report items (e.g., Kravet and Muslu [2010], Campbell et al. [2010]). 17

20 4.2. Auditor-Client Alignment If clients and auditors randomly choose to enter into an audit engagement without regard to auditor-client alignment, one would expect a relatively equal distribution of clients across firms when sorting on the compatibility score (i.e., approximately a 25% market share to each firm after omitting clients of non-big 4 firms). According to the first hypothesis, a client is more likely to use an auditor that also has other clients similar to itself, and are least likely to use an auditor having a less similar set of clients. Table 3, Panel A summarizes auditor-client alignment using each of the similarity scores. 24 In each case, the probability of using a specific auditor monotonically declines as auditor-client alignment decreases. For financial statements, slightly more clients use an auditor having the best fit (26%) than with the worst fit (24%). When examining the business description, 27% of clients use the auditor with which they are best aligned, while only 23% use the auditor with which they are least aligned. The MD&A pattern is even stronger, with 30% of clients using the most aligned auditor and just 21% using the least aligned auditor. The strongest pattern occurs based on similarity within the footnotes, where 32% of clients use the most similar auditor and only 20% use the least similar auditor. In combination, these results suggest that clients are indeed associated with highly compatible auditors. <<<<< Insert Table 3 about here >>>>> To evaluate the statistical significance of these patterns, we assign a rank between one and four, where one corresponds to the most compatible auditor and four to the least compatible. The average auditor-client alignment rank is then compared to the expected rank under the null 24 We do not expect to see extremely large differences from an equal distribution for existing client-auditor combinations because of the costs of auditor switching and the duration of auditor tenure in the sample. Many factors can influence auditor-client alignment when the relationship is stable, i.e., fit as measured by our compatibility score is just one dimension to alignment. As a result, we feel that the best test of auditor-client compatibility relates to auditor switches (to be discussed) since that is the point in time when one alignment would be most salient to a client and we would expect the best match between an auditor and a client. 18

21 of a random distribution (i.e., the average rank with four options is 2.5). Comparing the average rank of each score to the null provides a test of the tendency of clients to be associated with a more closely aligned auditor than with a less aligned one, without requiring that they be with the most similar auditor. Table 3, Panel B shows the average rank of the incumbent auditor based on auditor-client alignment. The average ranks are 2.47 (t = 5.67) when using the financial statements, 2.43 (t = 10.86) for the business description, 2.36 (t = 21.42) for MD&A, and 2.29 (t = 19.56) for the footnotes, all of which are significantly less than 2.5. Based on the content of their disclosures, the overall conclusion is that clients are significantly more likely to be associated with more compatible auditors Likelihood of Auditor Change In a stable client-auditor relationship, we see that clients are more likely to be aligned with compatible auditors (H1). However, such alignment at any point in time may reflect legacy costs and inertia as much as it does a desire for fit. Further, the simple descriptive analysis in the previous section does not address whether this pattern occurs because clients and auditors jointly choose an engagement that already has a higher alignment, or whether clients merely become more similar to their auditor over time as a side effect of the audit process. As previously noted, a client is likely to search out the most compatible auditor when going through a change in auditors (H2). Therefore, this section presents several analyses surrounding auditor switches. First, we develop a model of auditor change based on variables from the existing literature on auditor switching, augmented with the auditor-client similarity measures. We use the following logit model to analyze auditor switches based on a company s attributes in the year prior to the switch (firm and year subscripts are suppressed): 19

22 (1) The dependent variable, SWITCH, is an indicator set to one if the client will change auditors in the subsequent year; all other variables are measured in the current year. Because larger firms tend to change auditors less frequently, the natural log of total assets (SIZE) in the year before the switch is included in the model. Following Landsman et al. [2009], the model includes a variety of controls for audit and financial risk. For audit risk, the model includes growth, inherent risk, the nature of the audit opinion, and auditor tenure. GROWTH in assets is associated with greater litigation risk for the auditor. IRISK is defined as receivables plus inventories, scaled by total assets. MODOPIN is a dummy set to one for anything other than an unqualified, clean opinion. The audit risk variables are expected to be positively associated with the probability of an auditor switch. TENURE10 is the number of years the client has engaged its current auditor, with a maximum value of 10 years, and is expected to be negatively associated with auditor changes. Financial risk is proxied by return on assets (ROA) and a dummy variable when a company incurs a loss (LOSS). Both of these variables are expected to be positively associated with auditor switches. Higher cash and equivalents scaled by total assets (CASH) proxies for the financial risk for a client. 25 Because M&A activity can lead to an increased likelihood of changing auditors when the previously separate entities engaged different auditors, a dummy variable is included to indicate that a company has engaged in acquisition activity that exceeds ten percent of total assets (ACQUIS) All continuous control variables are winsorzed at the 1 st and 99 th percentiles and and log-transformed. 26 We omit the leverage variable in Landsman et al. [2009] since it is not significant in either their study or this one. Absolute discretionary accruals is omitted because it also proxies for the relative unusualness of the client relative to the industry, which is the construct of interest in this paper. Including it does not change the results of the analyses. 20

23 The correlations among these variables and the similarity scores are presented in Panel A of Table 4. The correlations with the similarity scores imply larger (SIZE), more profitable (ROA), and less risky companies (IRISK) are more likely to have a better fit with their auditor. This suggests that smaller, less profitable, and more risky companies may be constrained in their auditor selection (i.e., may not be able to engage their first-best choice) if that auditor declines the engagement due to concerns about audit or financial risk. <<<<< Insert Table 4 about here >>>>> Model (1) is augmented with each of the proxies for auditor-client fit introduced separately. The results are presented in Table 4, Panel B. The controls are generally consistent with prior literature. All similarity measures except for SIM BUS are negatively related to auditor switches. SIM FS (t = 4.72), SIM MD&A (t = 3.40) and SIM NOTES (t = 3.78) have negative coefficients, supporting H2 that clients having a poorer auditor fit are more likely to switch to a new auditor. 27 Holding all the other variables at their means, an interquartile decrease in financial statement similarity is associated with a 9.2 percent higher probability of switching. The increase for MD&A is 11.8 percent and for footnotes is 18.9 percent. This result explains one of the mechanisms through which the patterns in Table 3 may occur: clients tend to utilize a better-fitting auditor because they are more likely to change auditors when the fit is poor Auditor Choice Conditional on Decision to Switch Auditors Hypothesis H3 predicts that a company that hires a new auditor will select the firm that is most compatible from the set of firms available (i.e., excluding the incumbent that has been replaced). Since the company has already decided the net benefits of a change outweigh the 27 As an alternative composite test, all similarity scores are converted to quintile rankings and then summed across the four similarity scores (result ranges from 4 (maximum similarity) to 16 (minimum similarity). We then use this variable in the auditor switch model. The coefficient is negative as expected (p<.0001). 21

24 switching costs possibly due to fee or service considerations rather than compatibility it is expected that the new auditor, on average, will be the most compatible of the non-incumbent firms. Table 3, Panel C, summarizes the average compatibility rank of the new auditor (with 1 being most similar, 3 least similar). Consistent with the earlier results for incumbent auditors, we see that the likelihood that a firm is selected is a strictly decreasing function of the similarity scores in all cases. Further, these clients generally choose a new auditor with better auditorclient fit. To test the statistical significance of this pattern, the average rank of the new auditor is compared to what the average rank would be if a new auditor was randomly selected from the remaining 3 firms (i.e., 2.0). 28 In Table 3, Panel D, the average rank of the financial statements is 1.96 (t = 1.91; p-value = 0.028), the business description is 1.94 (t = 2.11; p-value = 0.018), the MD&A is 1.86 (t = 4.85; p-value < 0.001), and the footnotes is 1.89 (t = 2.19; p-value = 0.015). All four are significantly different from the rank expected for random selection. Thus, all similarity measures imply clients are significantly more likely to choose a better-fitting auditor when switching among the Big 4. Taken together, these tests provide insight into whether the commonly observed pattern is due to (1) a decision made by clients to choose an auditor with better fit or (2) clients gradually becoming more aligned with their auditors as the auditors influence the clients disclosures over time. The results in Table 3 support the argument that auditor-client compatibility is partially the result of the auditor selection process, i.e., when a company decides to change its auditor, it tends to gravitate to one that is relatively compatible. The pattern in Figure 1 then suggests that the compatibility continues to grow over the tenure of the auditor. Presumably this evolution 28 A random choice among auditors would imply a rank of ( )/3 = 2. 22