Abstract:

[Objective] Identifying violations of listed companies and effectively preventing the occurrence of the violations have always been a topic of concern. [Application background] From new perspectives, study on association rules analysis of violations using the big data of the listed companies and association rule mining algorithms is different from traditional researches. It can provide clues for identifying and predicting violations of listed companies, which helps regulatory authorities to carry out case investigation according to the association rules of violations [Methods] Taking Chinese listed companies punished for violation of rules and regulations from 2000 to 2020 as samples, this paper uses Apriori algorithm and Sequence algorithm to mine the simple association rules and sequential association rules among the violations by Chinese listed companies from static and dynamic perspectives. [Results] The results show that violations by listed companies are not isolated, that is, one kind of violation is often associated with another or more violations. As the former item, operational violations are more likely to lead to wrongful information disclosure, omission or delay, and other violations, while the relationship between the former and the latter items conforms to the basic logic. [Conclusions] More diversified investigation clues and methods for regulatory authorities are provided by the study, which has important practical significance for promoting the new law enforcement in the big data environment.

Key words: violations, association rule mining, apriori algorithm, sequence algorithm, feature recognition