Using machine learning to predict auditor switches: How the likelihood of switching affects audit quality among non-switching clients

2020 ◽  
pp. 106785
Author(s):  
Joshua O.S. Hunt ◽  
David M. Rosser ◽  
Stephen P. Rowe
Keyword(s):  
Machine Learning ◽  
Audit Quality ◽  
Auditor Switches

Timing of auditor terminations and client firm risk

2019 ◽  
Vol 34 (6) ◽  
pp. 650-672
Author(s):  
Young-Won Her ◽  
Jennifer Howard ◽  
Myungsoo Son
Keyword(s):  
Financial Reporting ◽  
Internal Control ◽  
Audit Quality ◽  
Operating Performance ◽  
Firm Risk ◽  
Content Type ◽  
Auditor Resignation ◽  
Client Firm ◽  
Auditor Switches ◽  
Available Information

Purpose The purpose of this study is to examine whether the timing of auditor terminations signals the riskiness of client firms. Design/methodology/approach This empirical study uses a sample of auditor switches during 2003-2014 to conduct univariate tests and multivariate regression analyses. Auditor switches occurring after the audit report date but before the shareholders’ meeting are classified as “planned” terminations and auditor switches that occur outside of this window are classified as “abrupt” terminations. Findings First, abrupt terminations are more strongly related to client risk factors than planned terminations. Second, relative to planned terminations, abrupt terminations are more likely to result from an auditor resignation rather than a client dismissal. Third, abrupt termination firms are more likely to have internal control weaknesses and experience delistings in the following year. Future operating performance is also worse after an abrupt termination. Finally, auditors and investors view abrupt terminations as riskier than planned terminations. Practical implications As the timing of the auditor termination is publicly available information, it can provide an important signal of deteriorating financial performance to shareholders and potential investors. Abrupt terminations could be costly to shareholders because those firms likely have lower quality financial reporting (due to internal control weakness) and deterioration of future operating performance. Originality/value While concurrent studies investigate the relation between the timing of new auditor appointment and audit quality, this is the first study to document the relation between the timing of auditor termination and the riskiness of client firms.

Double machine learning with gradient boosting and its application to the Big N audit quality effect

2020 ◽  
Vol 216 (1) ◽  
pp. 268-283
Author(s):  
Jui-Chung Yang ◽  
Hui-Ching Chuang ◽  
Chung-Ming Kuan
Keyword(s):  
Machine Learning ◽  
Audit Quality ◽  
Gradient Boosting

Classifying Restatements: An Application of Machine Learning and Textual Analytics

2021 ◽  
Author(s):  
Louise Hayes ◽  
J. Efrim Boritz
Keyword(s):  
Machine Learning ◽  
Audit Quality ◽  
Learning Algorithm ◽  
Reporting Quality ◽  
Financial Statements ◽  
Net Income ◽  
Machine Learning Classification ◽  
Large Samples ◽  
Automated Methods ◽  
Manual Classification

Restatements of audited financial statements are used for evaluating reporting quality and audit quality, and for other evaluative purposes. We constructed a machine learning algorithm to classify restatements by management intent based on the language in restatement announcements. Our machine learning classification is as reliable as other commonly used automated methods such as those based on market reaction, restatement direction, and magnitude. Our method does not require a dictionary of words and is applicable when other automated methods are not, for example, when restatements are announced contemporaneously with financial results and when net income is not restated. For large samples, the use of such a classification algorithm is less tedious and less time-consuming, and more consistent, replicable and scalable than manual classification.

Does Auditor Industry Specialization Matter? Evidence from Market Reaction to Auditor Switches

2007 ◽  
Vol 26 (1) ◽  
pp. 19-45 ◽  
Author(s):  
W. Robert Knechel ◽  
Vic Naiker ◽  
Gail Pacheco
Keyword(s):  
Audit Quality ◽  
Market Reaction ◽  
Abnormal Returns ◽  
Brand Name ◽  
Market Reactions ◽  
Industry Specialization ◽  
Big 4 ◽  
Audit Firms ◽  
Auditor Switches ◽  
Differential Costs

Numerous capital market studies have investigated the stock market's reaction to firms switching to and from brand name auditors (Big 8/6/5/4 auditors). However, audit firm brand name is only one possible indication of the quality of an auditor. This study contributes to the existing literature on auditor switching, by examining how the market reacts to auditor switches to or from audit firms that are considered to be industry specialists. Consistent with our hypotheses, we find that firms switching between Big 4 auditors experience significant positive abnormal returns when the successor auditor is an industry specialist, and they experience significant negative abnormal returns when the successor auditor is not a specialist. We also find that these market reactions are more likely to be due to changes in perceived audit quality rather than differential costs of using specialist auditors. In supplemental analysis of switches involving non-Big 4 auditors, we find that firms that switch from a specialist Big 4 auditor to a non-Big 4 auditor suffer the largest negative market reaction. Surprisingly, we also observe that the market reacts most positively when a company switches from a non-Big 4 auditor to a Big 4 auditor who is not a specialist. These results suggest that the market does perceive audit quality differences based on industry specialization to be relevant to the valuation of a company's market value.

Auditor Response to Estimated Misstatement Risk: A Machine Learning Approach

2021 ◽  
Author(s):  
Emily Hunt ◽  
Joshua O.S. Hunt ◽  
Vernon J. Richardson ◽  
David Rosser
Keyword(s):  
Machine Learning ◽  
Risk Premium ◽  
Audit Quality ◽  
Audit Fees ◽  
Risk Assessments ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Auditor Switching ◽  
Learning Techniques ◽  
Machine Learning Approach

In this paper, we investigate whether misstatement risk estimated using advanced machine learning techniques, hereafter referred to as estimated misstatement risk (EMR), approximates auditors' risk assessments in practice. We find that auditors price EMR and that auditor turnover is more likely to occur when EMR increases, indicating that EMR is associated with auditors' risk assessment. We also find evidence that EMR is positively and significantly associated with audit fees and auditor switching for companies with Big N auditors but not for other companies, suggesting that Big N auditors are more responsive to risks captured by EMR. Additional analyses reveal that companies switching auditors when EMR increases are more likely to engage non-Big N auditors. Surprisingly, we find little evidence that the association between audit quality and EMR differs by auditor type. Our findings are consistent with the notion that the documented association between audit fees and EMR primarily reflects a risk premium in our setting.

Demand for Audit Quality: The Case of Laventhol and Horwath's Auditees

2000 ◽  
Vol 15 (2) ◽  
pp. 183-198 ◽  
Author(s):  
Brad J. Reed ◽  
Mark A. Trombley ◽  
Dan S. Dhaliwal
Keyword(s):  
Audit Quality ◽  
Management Ownership ◽  
Self Selection ◽  
Auditor Switches ◽  
Big Six

This study investigates the demand for audit quality for the firms being audited by Laventhol and Horwath (LH) at the time LH declared bankruptcy. The demand for audit quality by the former LH clients is inferred from their decisions to select Big Six or non–Big Six auditors. Because the change in auditors was involuntary, the sample avoids self-selection issues associated with voluntary auditor switches. LH clients that selected Big Six auditors tended to be more highly leveraged, have less management ownership, and issue more securities in the year after selecting the new auditor than LH clients that selected non–Big Six auditors.

scholarly journals Reputation of low-quality big 4 and non-big 4 auditors: evidence from auditor switches of former Chuoaoyama clients

2013 ◽  
Vol 2 (2) ◽  
pp. 7-23 ◽  
Author(s):  
Hikaru Murase ◽  
Shingo Numata ◽  
Fumiko Takeda
Keyword(s):  
Audit Quality ◽  
Capacity Constraints ◽  
Audit Firm ◽  
Big 4 ◽  
Auditor Switches ◽  
Selection Of

We examine how an auditor’s reputation for audit quality affects the selection of new auditors in a unique setting. Specifically, we investigate forced auditor switches after the collapse of ChuoAoyama and its successor, Misuzu, in a low litigation country, Japan, where the insurance value of auditing is minimal. We find that former ChuoAoyama clients with greater reputation concerns tended to switch away from Misuzu, a low-quality Big 4 audit firm. Our results also indicate that auditors’ sensitivity to reputation decreased after the collapse of Misuzu, perhaps because of intensified capacity constraints and decreased differences in perceived audit quality between Big 4 and Non-Big 4 auditors after the audit scandal and the introduction of the J-SOX.

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