Does explanatory language convey the auditor’s perceived audit risk? A study using a novel big data analysis metric

审计 会计 联合审计 质量审核 解释力 审计报告 审计证据 业务 代理(统计) 解释模型 精算学 走查试验 心理学 内部审计 计算机科学 统计 哲学 数学 认识论 机器学习
作者
Seung Uk Choi,Hyung Jong Na,Kun Chang Lee
出处
期刊:Managerial Auditing Journal [Emerald Publishing Limited]
卷期号:38 (6): 783-812
标识
DOI:10.1108/maj-10-2021-3342
摘要

Purpose The purpose of this study is to examine the relationship between explanatory language, audit fees and audit hours to demonstrate that auditors use explanatory language in audit reports to explain perceived audit risk. Design/methodology/approach The authors construct the sentiment value, a novel audit risk proxy derived from audit reports, using big data analysis. The relationship between sentiment value and explanatory language is then investigated. The authors present the validity of their new metric by examining the relationship between sentiment value and accounting quality, taking audit fees and hours into account. Findings The authors first find that reporting explanatory language is positively related to audit fees. More importantly, the authors provide an evidence that explanatory language in audit reports is indicative of increased audit risk as it is negatively correlated with sentiment value. As a positive (negative) sentimental value means that the audit risk is low (high), the results indicate that auditors describe explanatory language in a negative manner to convey the inherent audit risk and receive higher audit fees from the risky clients. Furthermore, the relationship is strengthened when the explanatory language is more severe, such as reporting the multiple numbers of explanatory language or going-concern opinion. Finally, the sentiment value is correlated with accounting quality, as measured by the absolute value of discretionary accruals. Originality/value Contrary to previous research, the authors’ findings suggest that auditors disclose audit risks of client firms by including explanatory language in audit reports. In addition, the authors demonstrate that their new metric effectively identifies the audit risk outlined qualitatively in audit report. To the best of the authors’ knowledge, this is the first study that establishes a connection between sentiment analysis and audit-related textual data.

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