Research on financial fraud identification of listed companies based on text data mining

Hualing Liu; Long Tao; Mengyao Liu

doi:10.1117/12.2581398

10 November 2020 Research on financial fraud identification of listed companies based on text data mining

Hualing Liu, Long Tao, Mengyao Liu

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115841X (2020) https://doi.org/10.1117/12.2581398
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Identifying the financial fraud behavior of listed companies in a timely manner and helping investors avoid risks is a key measure to promote the healthy development of the capital market. Aiming at the lag of traditional fraud identification method, a text classification model suitable for Chinese capital market is constructed based on user generated content (UGC) on social media platform. To detect fraudulent companies,TF-IDF features, topic features and explosive news quantity features are extracted guided by systemic functional linguistics (SFL) theory. Empirical analysis is conducted by crawling the news and comments of 124 companies from Eastmoney and JRJ.com.The empirical analysis results show that the model constructed by the article can effectively extract the implicit information in unstructured data and improve the timeliness of screening fraudulent behaviors of listed companies. The topic and keyword features in text comments play an important role in distinguishing the fraud of listed companies. On the one hand, it helps individual and institutional investors avoid investment traps, on the other hand, it helps regulators detect companies with fraudulent potential in time to prevent market risks.

Citation Download Citation

Hualing Liu, Long Tao, and Mengyao Liu "Research on financial fraud identification of listed companies based on text data mining", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115841X (10 November 2020); https://doi.org/10.1117/12.2581398

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KEYWORDS

Data modeling

Data mining

Explosives

Machine learning

Feature extraction

Databases

Neural networks

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