Analyzing Stock Market Fraud Cases Using a Linguistics-Based Text Mining Approach
نویسندگان
چکیده
The paper proposes a linguistics-based text mining approach to demonstrate the process of extracting financial concepts from the Security Exchange Commission (SEC) litigation releases (LR). The proposed approach presents the extracted information as a knowledge base to be used in market monitoring surveillance systems. Also, it facilitates users’ acquisition, maintenance and access to financial fraud knowledge and improves search results in the SEC enforcement portal. Answering questions such as: who are the agents involved in the manipulation? Which patterns are associated with this manipulation? When was this manipulative action performed? This paper used the financial ontology for fraud purposes introduced by [19] to provide underlying framework for the extraction process and capture financial fraud concepts from the SEC-LR. In particular, text mining analysers have been developed to extract metadata concepts (e.g. ‘LR No.’, ‘dates’) and stock market fraud concepts (e.g. agents and manipulation types) from the actual SEC fraud case.
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