A textual analysis of the US Securities and Exchange Commission's accounting and auditing enforcement releases relating to the Sarbanes–Oxley Act

Abstract

Summary We focus on textual analysis of the US Securities and Exchange Commission's accounting and auditing enforcement releases (AAERs). Our research question is: Did the Sarbanes–Oxley Act (SOX) 2002 affect the qualitative linguistic content of the AAERs in the post‐SOX period? To answer this question, we test the null hypotheses that there will be no differences in the qualitative verbiage and sentiment of AAERs in the time periods that we study related to the enactment of SOX: pre‐SOX and post‐SOX. To resolve the research question, we applied several text mining methods and classification machine‐learning methods. We first used two basic text‐mining methods, generating a bag of words and topic modeling, for descriptive analysis of the AAER content before the enactment of SOX and after the enforcement of SOX. We then conducted sentiment analysis using four sentiment dictionaries on the content of the two time periods: before SOX and after SOX. Finally, we developed three different classification models based on well‐known supervised learning algorithms and determined that SOX‐related AAERs could be distinguished from non‐SOX‐related AAERs. Based on the results, we conclude that there were significant linguistic differences in the AAER content of the post‐SOX period compared with the pre‐SOX period. In other words, post‐SOX‐related AAERs are qualitatively different in terms of linguistic contents and sentiment values compared with the non‐SOX‐related AAERs.

Auditing and Assurance: Ensuring Financial Integrity and Transparency

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Audit Data and Management Platform Based on Intelligent Algorithm

“…Online analysis and processing is a complex analysis method based on large-scale data. It can analyze and process data in data warehouse in multi-dimensional and efficient ways from multiple perspectives [8][9].…”

Costs associated with exit or disposal activities: A topic modeling investigation of disclosure and market reaction

“…LDA employs a Bayesian model to analyze the complete corpus of 8‐K Item 2.05 filings and to generate probabilities that the various texts relate to the same underlying topic (Blei et al, 2003). LDA has been used by accounting researchers to examine narratives contained within 10‐Ks (e.g., Brown et al, 2020; Dyer et al, 2017), to analyze comment letters to the FASB (Khondkar et al, 2019), and to investigate the SEC's accounting and auditing enforcement releases relating to the Sarbanes–Oxley Act (Davalos & Feroz, 2022). Fisher et al (2016) provide a literature review of the applications of the natural language processing (NLP) methods, including LDA, to accounting, auditing, and finance.…”