Why are expanded audit reports not informative to investors? Evidence from the United Kingdom

Lennox, Clive S.; Schmidt, Jaime J.; Thompson, A. H.

doi:10.1007/s11142-021-09650-4

Cited by 115 publications

(130 citation statements)

References 30 publications

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“…That is, (exp(0.248)À1)*100 = 28%.24 These results differ fromLennox et al (2021), who find that KAMs…”

mentioning

confidence: 85%

“…Several prior archival studies that examine investor reactions to KAMs use short‐window information content (e.g., Bédard et al, 2019; Gutierrez et al, 2018; Lennox et al, 2021; Liao et al, 2019). This approach measures whether KAM disclosures are incrementally relevant to investors.…”

Section: Hypotheses Developmentmentioning

confidence: 99%

“…P is the stock price seven trading days after the audited financial statements are released to the stock exchange, NI is net income per share, BVE is the book value of equity per share and KAMk is a set of k KAM characteristics (described in the next section). However, due to the multicollinearity in Equation (i.e., mean VIF is 36.1 and the minimum 32.7), we follow Lennox et al (2021) and estimate the value relevance of NI and BVE separately:

italicβ0 goodbreak+ italicβ1 ((), NI) goodbreak+ italicβ2 ((), KAMk) goodbreak+ italicβ3 ((), italicNI x italicKAMk),

italicβ0 goodbreak+ italicβ1 ((), BVE) goodbreak+ italicβ2 ((), KAMk) goodbreak+ italicβ3 ((), italicBVE x italicKAMk) .

…”

Section: Research Designsmentioning

confidence: 99%

“…Our second set of KAM classification is designed to capture the ‘news’ element in KAMs and is depicted in Figure 1 and Table 2 (c). We classify KAMs as account‐level and non‐account level (Doyle et al, 2007; Lennox et al, 2021). Account level KAMs relate to line items in the financial statements (e.g., revenue recognition and inventory).…”

Section: Sample and Descriptive Statisticsmentioning

confidence: 99%

“…2 With regard to key audit matter disclosures, Bédard et al (2019) examine Justification of Assessments in France. Gutierrez et al (2018), Reid et al (2019) and Lennox et al (2021) examine the UK's adoption of Risk of Material Misstatements. 3 The adoption of KAMs is examined in Australia (Wei et al, 2017), Hong Kong (Liao et al, 2019) and New Zealand (Li et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Auditor, client and investor consequences of the enhanced auditor's report

Al‐mulla¹,

Bradbury²

2021

Int J Auditing

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This study examines the impact of the enhanced auditor's report (ISA 701) in New Zealand on audit effort (audit fees and audit delay); audit quality (absolute abnormal accruals); investors (value relevance) and client disclosures (inventory). A notable feature of the enhanced report is a section termed Key Audit Matters (KAMs). The purpose of a KAM is to disclose financial reporting risks, thereby enhancing the communication value of the audit report. We find that by examining audit fees and value relevance, both auditors and investors price the information in KAMs (in both the first year of KAM reporting and in the prior year). We note that client disclosures related to inventory are greater for firms with inventory KAMs than firms not reporting inventory KAM, in both the first year of KAM reporting and in the prior year.

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“…That is, (exp(0.248)À1)*100 = 28%.24 These results differ fromLennox et al (2021), who find that KAMs…”

mentioning

confidence: 85%

Section: Hypotheses Developmentmentioning

confidence: 99%

italicβ0 goodbreak+ italicβ1 ((), NI) goodbreak+ italicβ2 ((), KAMk) goodbreak+ italicβ3 ((), italicNI x italicKAMk),

italicβ0 goodbreak+ italicβ1 ((), BVE) goodbreak+ italicβ2 ((), KAMk) goodbreak+ italicβ3 ((), italicBVE x italicKAMk) .

…”

Section: Research Designsmentioning

confidence: 99%

Section: Sample and Descriptive Statisticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Auditor, client and investor consequences of the enhanced auditor's report

Al‐mulla¹,

Bradbury²

2021

Int J Auditing

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Audit committee characteristics and Key Audit Matters (KAMs) disclosures

Bepari¹

2022

J Corp Accounting Finance

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In this study we have examined if audit committee characteristics in terms of audit committee members’ gender, audit committee members’ accounting and finance backgrounds, audit committee members’ industry expertise and audit committee members’ legal expertise affect the number and content characteristics of KAMs reported. We examine the issue based on a sample of 1112 firm‐year‐observations and 3013 KAMs spanning for the period 2017–2020. We use multivariate regression models to examine the research questions. We also use a variety of other specifications for robustness test. Our findings suggest that female audit committee members reduce the number and lengths of KAMs. Our results further suggest that auditors disclose more specific information, identify more firm specific KAMs and produce more readable KAMs in the presence of female members in the audit committee. We find similar results for audit committee members’ accounting and finance backgrounds and audit committee members’ industry expertise. However, audit committee members’ legal expertise reduces disclosure specificity, readability, and lengths of KAMs. Our results suggest that the audit committee strength/diversity affects the number and content characteristics of KAMs disclosure. This study makes a unique contribution by showing that audit committee characteristics affect the content specificity and idiosyncrasy of KAMs disclosed by external auditors. Moreover, this study supports the previous findings in the UK context that audit committee characteristics affect the number and readability of KAMs. Given the global adoption of ISA 701, evidence from current study will add to the global effort to understand the determinants of KAMs reporting practices. For regulators this study contributes by showing that audit committee members’ diversity affects the content characters of KAM.

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Machine learning in corporate credit rating assessment using the expanded audit report

et al. 2022

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We investigate whether key audit matter (KAM) paragraphs disclosed in extended audit reports-paragraphs in which the auditor highlights significant risks and critical judgments of the company-contribute to assess corporate credit ratings. This assessment is a complicated and expensive process to grade the reliability of a company, and it is relevant for many stakeholders, such as issuers, investors, and creditors. Although credit rating evaluations have attracted the interest of many researchers, previous studies have mainly focused only on financial ratios. We are the first to use KAMs for credit rating modelling purposes. Applying four machine learning techniques to answer this real-world problem-C4.5 decision tree, two different rule induction classifiers (PART algorithm and Rough Set) and the logistic regression methodology-, our evidence suggests that by simply identifying the KAM topics disclosed in the report, any decision-maker can assess credit scores with 74% accuracy using the rules provided by the PART algorithm. These rules specifically indicate that KAMs on both external (such as going concern) and internal (such as company debt) aspects may contribute to explaining a company's credit rating. The rule induction classifiers have similar predictive power. Interestingly, if we combine audit data with accounting ratios, the predictive power of our model increases to 84%, outperforming the accuracy in the existing literature. KeywordsCorporate credit rating • Machine learning techniques • Accounting ratios • Expanded audit report • Key audit matters

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Why are expanded audit reports not informative to investors? Evidence from the United Kingdom

Cited by 115 publications

References 30 publications

Auditor, client and investor consequences of the enhanced auditor's report

Auditor, client and investor consequences of the enhanced auditor's report

Audit committee characteristics and Key Audit Matters (KAMs) disclosures

Machine learning in corporate credit rating assessment using the expanded audit report

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