This dissertation investigates how economically-comparable peer firms can be used to obtain inferences about a company’s accounting quality in two different research settings. The first Chapter examines whether auditor industry specialization, measured using auditor market share by industry, improves audit quality. After matching clients of specialist and non-specialist auditors according to industry, size and performance, there are no significant differences in audit quality between these two groups of auditors. In addition, this Chapter uses two analyses that do not rely primarily on matched samples. First, examining a sample of Arthur Andersen clients that switched auditors in 2002, there is no evidence of industry-specialization effects following the auditor change. Second, using a simulation approach, this study shows that client characteristics, and particularly client size, influence the observed association between auditor industry specialization and audit quality. Overall, these findings do not imply that industry knowledge is not important for auditors, but that the methodology used in extant studies examining this issue may not fully parse out the effects of auditor industry expertise from client characteristics. The second Chapter examines whether account-level expectation models for analytical audit procedures can be enhanced by using information from economically-comparable peer firms. This Chapter assesses the effectiveness of three main types of expectation models, with and without including information from peer firms: heuristic, time-series, and industry cross-sectional models. Information from peer firms improves the accuracy of all models and improves the detection power of time-series and industry cross-sectional models. Comparing between models, one-period heuristic models are generally unreliable, and industry cross-sectional models can be more effective than time-series models. These findings may help auditors of public companies and financial analysts in selecting expectation models and finding peer firms to assess the reasonability of a company’s financial information at the account-level.

This dissertation investigates how economically-comparable peer firms can be used to obtain inferences about a company’s accounting quality in two different research settings. The first Chapter examines whether auditor industry specialization, measured using auditor market share by industry, improves audit quality. After matching clients of specialist and non-specialist auditors according to industry, size and performance, there are no significant differences in audit quality between these two groups of auditors. In addition, this Chapter uses two analyses that do not rely primarily on matched samples. First, examining a sample of Arthur Andersen clients that switched auditors in 2002, there is no evidence of industry-specialization effects following the auditor change. Second, using a simulation approach, this study shows that client characteristics, and particularly client size, influence the observed association between auditor industry specialization and audit quality. Overall, these findings do not imply that industry knowledge is not important for auditors, but that the methodology used in extant studies examining this issue may not fully parse out the effects of auditor industry expertise from client characteristics. The second Chapter examines whether account-level expectation models for analytical audit procedures can be enhanced by using information from economically-comparable peer firms. This Chapter assesses the effectiveness of three main types of expectation models, with and without including information from peer firms: heuristic, time-series, and industry cross-sectional models. Information from peer firms improves the accuracy of all models and improves the detection power of time-series and industry cross-sectional models. Comparing between models, one-period heuristic models are generally unreliable, and industry cross-sectional models can be more effective than time-series models. These findings may help auditors of public companies and financial analysts in selecting expectation models and finding peer firms to assess the reasonability of a company’s financial information at the account-level.