Subject. The article addresses data mining methods, their applicability in the securities market. Objectives. The aim is to identify the specifics of using modern data mining methods in the stock market, outline basic tenets for building a combined data mining model. Methods. The study rests on logical and systems approaches, general scientific methods of analysis and synthesis, and comparative analysis. Results. Based on the analysis of existing works and models, the paper unveils the specifics of using modern methods of data mining in the stock market, defines the main postulates for building a data mining model. The findings can be used by financial market participants, State authorities, and research and educational organizations. Conclusions. Today, data mining methods are an alternative to the traditional portfolio analysis and management methods, being a logical continuation of them due to the ability to work with a large amount of diverse information and use approaches that overcome the shortcomings and limitations of other methods. To build a model of data mining to evaluate assets and portfolio management in the securities market, it is necessary to combine several methods of different models, combine the fundamental and technical analysis, and create systems for identifying asset volatility.
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