Identification of Implicit Collusion of Investors in Financial Markets by Text Mining Tools

Background and Objective: The digitalization of the securities market provides participants of financial markets with new opportunities, which require specific methods to monitor market safety and identify violations. The purpose of this paper was to develop a methodology for identifying implicit collusion of financial market participants based on Text Mining tools. Materials and Methods: Concurrent with the traditional comparison of data on the trading volumes and price of individual securities, which let us visualize abnormal dynamics, the extracting sentiment method was utilized to accomplish this objective. The methodology was tested in the form of a case study on the shares of a Russian company currently in circulation. Results: The results demonstrated five main combinations of measures, which describe specific exchange situations, based on which it is likely to identify possible collusion of stock market participants. The classification of the information field is presented to determine the type of information that has the greatest impact on the dynamics of the course. Conclusion: The proposed methodology solves the problem of identifying investor collusion in financial markets. The scientific significance lies in the development of a new approach to identifying collusion between investors and other participants based on trading data and news flow, through their processing using Text Mining tools.