Measuring audit quality

Rajgopal, Shivaram; Srinivasan, Suraj; Zheng, Xin

doi:10.1007/s11142-020-09570-9

Cited by 87 publications

(56 citation statements)

References 47 publications

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“…Second, the audit cost also showed the same change as the ratio of the audit fees to the total fees, from the pre-to the post-regulation period. Third, empirical evidence documented in Rajgopal et al (2021) indicates that AFRatio is a robust audit proxy in the setting of testing the auditor independence and internal control weakness.…”

Section: Discussionmentioning

confidence: 99%

“…Adapting empirical models from previous studies (e.g., Carcello and Li 2013;Cunningham et al 2019;Rajgopal et al 2021), we used the following baseline model Equation (1) to test our H1 and H2. We suppressed the subscripts for firm (j) and fiscal year (t) in the Equation (1).…”

Section: Empirical Modelsmentioning

confidence: 99%

“…Second, Rajgopal et al (2021) suggest that the probability of restating financial statements and the ratio of audit fees to total fees offer good predictive power for the degree of auditor independence and the strength of the internal control, the two unobserved audit input qualities. Third, in the PCAOB comments and letters, the supporters of PCAOB Rule 3211 argued that disclosing the EP's name could motivate the EP to devote more effort to the audit engagement, increase independence (e.g., proxied by the ratio of audit fees to total fees), and reduce the likelihood of being associated with adverse audit outcomes, such as restatements or material internal control weakness.…”

Section: Empirical Modelsmentioning

confidence: 99%

“…Controlk represents firm j's value of the kth control variable in the fiscal year t. As into previous research (Carcello and Li 2013;Rajgopal et al 2021), in Equation (1), we controlled for factors that may have influenced the outcome variables (audit quality and costs): size, lLeverage, the occurrence of loss in operation (Loss), market-to-book ratio (MB), sale growth rate (Growth), return on assets (ROA), the occurrence of material internal control weakness issued by the auditor (AUD_MW), foreign operations (Foreign), firm's busyness (Busy), Big4 audit firms (Big4), and industry dummy variables. In the first differencing regressions, the dependent and independent variables are the changes in the values between current year and one-year prior.…”

Section: Empirical Modelsmentioning

confidence: 99%

“…We hypothesized that, in the disclosure regime, (1) audit quality would improve because, given the reputation costs for EPs, they would devote more effort to audit engagements to prevent audit failure; (2) audit costs would increase due to the performance of more audit procedures. To test our hypotheses, we used discretionary accruals (denoted by ABAQ), the occurrence of restating financial statements (denoted by Restatement), and the ratio of audit fees to total fees (denoted by AFRatio) to measure audit quality (e.g., Rajgopal et al 2021). We used audit fees (LNAF) to measure the audit costs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Does the Disclosure of an Audit Engagement Partner’s Name Improve the Audit Quality? A Difference-in-Difference Analysis

John

Liu

2021

JRFM

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On 15 December 2015, the Public Company Accounting Oversight Board (PCAOB) passed Rule 3211, requiring audit firms registered with PCAOB in the U.S. to disclose the audit engagement partner’s name in the Form AP, effective 31 January 2017. The regulation aims to improve the transparency and quality of audits, thereby increasing investors’ confidence in financial statements. Using the audit firms registered with the PCAOB and their clients as the treated sample, we employed a difference-in-difference analysis to investigate whether and the extent to which implementing Rule 3211 impacts audit quality and audit costs. We compared the audit quality (proxied by the abnormal discretionary accruals quality, the probability of restating the financial statements, and the ratio of the audit fees to the total fees) and audit costs (proxied by the total audit fees) from one year (up to three years) pre- to one year (up to three years) post-Rule 3211, to a control sample (comprised of U.K. audit firms, which were not subject to such regulation during the sample period). The empirical results generally indicate that there was an increase in the audit quality and in the audit costs from the pre- to the post-Rule 3211 period and also suggest that auditor independence increased in the post-regulation period compared to the pre-regulation period. Our empirical results are new and contribute to the research on the PCAOB and audits.

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Section: Discussionmentioning

confidence: 99%

Section: Empirical Modelsmentioning

confidence: 99%

Section: Empirical Modelsmentioning

confidence: 99%

Section: Empirical Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Does the Disclosure of an Audit Engagement Partner’s Name Improve the Audit Quality? A Difference-in-Difference Analysis

John

Liu

2021

JRFM

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Factors Impact the Audit Quality: Evidence from Kingdom of Bahrain

Hussain,

Al-Absy

2024

The AI Revolution: Driving Business Innovation and Research

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Is artificial intelligence improving the audit process?

Fedyk

Hodson²,

Khimich

et al. 2022

Rev Account Stud

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How does artificial intelligence (AI) impact audit quality and efficiency? We explore this question by leveraging a unique dataset of more than 310,000 detailed individual resumes for the 36 largest audit firms to identify audit firms’ employment of AI workers. We provide a first look into the AI workforce within the auditing sector. AI workers tend to be male and relatively young and hold mostly but not exclusively technical degrees. Importantly, AI is a centralized function within the firm, with workers concentrating in a handful of teams and geographic locations. Our results show that investing in AI helps improve audit quality, reduces fees, and ultimately displaces human auditors, although the effect on labor takes several years to materialize. Specifically, a one-standard-deviation change in recent AI investments is associated with a 5.0% reduction in the likelihood of an audit restatement, a 0.9% drop in audit fees, and a reduction in the number of accounting employees that reaches 3.6% after three years and 7.1% after four years. Our empirical analyses are supported by in-depth interviews with 17 audit partners representing the eight largest U.S. public accounting firms, which show that (1) AI is developed centrally; (2) AI is widely used in audit; and (3) the primary goal for using AI in audit is improved quality, followed by efficiency.

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Measuring audit quality

Cited by 87 publications

References 47 publications

Does the Disclosure of an Audit Engagement Partner’s Name Improve the Audit Quality? A Difference-in-Difference Analysis

Does the Disclosure of an Audit Engagement Partner’s Name Improve the Audit Quality? A Difference-in-Difference Analysis

Factors Impact the Audit Quality: Evidence from Kingdom of Bahrain

Is artificial intelligence improving the audit process?

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