Using machine learning to predict auditor switches: How the likelihood of switching affects audit quality among non-switching clients |
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| Institution: | 1. Mississippi State University, United States;2. University of Texas at Arlington, United States;3. University of Arkansas, United States;1. Oregon State University, United States;2. Purdue University, United States;1. Kindai University, Faculty of Business Administration, 3-4-1 Kowakae, Higashiosaka City, Osaka 577-8502 Japan;2. Kobe University, Graduate School of Business Administration, 2-1 Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan;1. University of Richmond, Richmond, VA 23173, United States;2. Bentley University, Waltham, MA 02452, United States;1. Ted Rogers School of Management, Ryerson University, Toronto, ON, Canada;2. Lazaridis School of Business and Economics, Wilfrid Laurier University, Waterloo, ON, Canada;1. Weatherhead School of Management, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, United States;2. Department of Economics and Accounting, Hunter College, City University of New York, New York, NY 10065, United States;3. Georgia Institute of Technology, United States |
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| Abstract: | In this paper, we utilize machine learning techniques to identify the likelihood that a company switches auditors and examine whether increased likelihood of switching is associated with audit quality. Building on research that finds a deterioration in audit quality associated with clients that engage in audit opinion shopping, we predict and find lower audit quality among companies that are more likely to switch auditors but remain with their incumbent auditor. Specifically, we find that companies more likely to switch auditors have a higher likelihood of misstatement and larger abnormal accruals. These results are consistent with auditors sacrificing audit quality to retain clients that might otherwise switch. Our findings are especially concerning because there is no public signal of this behavior, such as an auditor switch. Our methodology is designed such that it could be implemented by investors, audit firms and regulators to identify companies with a higher probability of switching auditors and preemptively address the deterioration in audit quality. |
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| Keywords: | Auditor change Audit quality Auditor independence Machine learning Data analytics |
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