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- Title
Detecting Financial Statement Fraud through Multidimensional Analysis of Text Readability.
- Authors
Yang, Fang; David, Jeanne M.; Chang, Chun-Chia
- Abstract
This study uses Coh-Metrix to analyze multiple dimensions of readability of the MD&A section of the SEC Form 10-K. We incorporate the five main Coh-Metrix components of text easability (word concreteness, syntactic simplicity, referential cohesion, deep cohesion, and narrativity) into a logistic model to test their predictive power for financial misreporting. We find that compared to the MD&As of nonfraud firms, the MD&As of fraud firms connect clauses and sentences less coherently, use more story-like language, and show a higher number of vague and abstract words. Thus, referential cohesion, narrativity, and word concreteness significantly enhance predictive ability in fraud detection. The Coh-Metrix readability measures enhance the linguistic complexity assessment beyond traditional readability measures, such as the Fog Index and the Flesch Indexes. Financial analysts and investors can utilize the Coh-Metrix readability measures to supplement traditional readability measures and common financial statement variables in predicting financial misreporting. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G32; K42; M41; M48.
- Subjects
UNITED States. Securities &; Exchange Commission; FINANCIAL statements; LINGUISTIC complexity; FRAUD investigation; FINANCIAL analysts; FRAUD
- Publication
Journal of Forensic Accounting Research, 2023, Vol 8, Issue 1, p74
- ISSN
2380-2138
- Publication type
Article
- DOI
10.2308/JFAR-2021-019