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Real-time Impact of Securities Price Volatility Based on Big Data Mining

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DOI: 10.38007/Proceedings.0001661


Fanjin Feng

Corresponding Author

Fanjin Feng


With the development of big data, a large number of fast information on the Internet provides rich data support for predicting the changes of the securities market, so how to use the big data analysis technology to mine the real-time changes of the securities market is a hot issue of current research. This paper aims to study the real-time impact of securities price volatility based on big data mining. This paper uses AISI model to study the index change value of market influence. If it is negative, it means that it has a negative impact on the market; if it is positive, it means that it has a positive impact on the market. In this paper, according to the recent time of Internet information on the stock market change prediction experiment, data collection, analysis and prediction are carried out, and the actual verification is carried out according to the results and the subsequent situation. The experimental data show that the extraction method, change relationship and influence model of information content and each characteristic factor in the Internet are constructed, and the Internet information indicators for the large market, industry and individual stocks are discussed to reflect the support degree of the data. The experimental results show that the AISI prediction indexes of 8 trading days in 10 days are: - 2, 1, - 5, - 1, 3, - 22, 1 and - 3, which can realize the method of forecasting the stock market based on the comprehensive characteristic factor measurement. Therefore, big data mining method has good feasibility and will bring obvious academic and commercial value.


Big Data Mining, Stock Price Fluctuation, Internet Information, Stock Research