Detection of fraud statement based on word vector: Evidence from financial companies in China

Yi Zhang, Ailing Hu, Jiahua Wang and Yaojie Zhang

Finance Research Letters, 2022, vol. 46, issue PB

Abstract: This paper aims to detect plausible frauds of financial companies via text analysis of annual and quarterly reports of China's listed companies. The Management Discussion and Analysis (MD&A) is digitized as vectors. The empirical results indicate that compared with various vector indexes, the bag-of-words (BoW) model and machine learning algorithm have a prediction effect and the ability to recognize frauds where the BoW model can correctly recognize 77% of the fraud reports. This would be helpful for audit authorities to identify fraud reports.

Keywords: Public sector audit; Text analysis; Word vector; Bag-of-words; Machine learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612321004578
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:46:y:2022:i:pb:s1544612321004578

DOI: 10.1016/j.frl.2021.102477

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().