An empirical study on big data stock volatility forecasting algorithm based on multivariate hybrid criterion fuzzy model

Abstract

The fluctuation of the stock market has always been a matter of great concern to investors. People always hope to judge the trend of the stock market through the trend of the K line, so as to obtain the price difference through trading, Therefore, it is a theoretical research concerned by the academic circles to carry out empirical research through big data stock volatility prediction algorithm, so as to establish a model to predict the trend of the stock market. After decades of development, China's stock market has gradually matured in continuous exploration. However, compared with the stock market in developed countries, there are still imperfections. For example, the market value of China's stock market does not improve well with economic growth. Year-on-year growth and the development of the real economy. By studying the historical data from 2002 to 2017, we use the Multivariate Mixed Criterion Fuzzy Model (MMCFM) to predict the price changes in the stock market, and obtain the market in China through error statistical analysis. (SSE) is more unstable than the US stock market. Therefore, Multivariate Mixing Criterion (MMC) can be used as a reference indicator to visually measure market maturity. In this paper, we establish a multivariate mixed criteria fuzzy model, and use big data to predict the stock volatility. The algorithm verifies the reliability and accuracy of the model, which has a good reference value for investors.