A Study of Early Warning System in Volume Burst Risk Assessment of Stock with Big Data Platform

Abstract

The high remuneration of stock market has attracted most investors. However, there are many factors affecting the trading price fluctuations in the stock market. In order to reduce the investment risk investor may use many measuring tools. The prediction of stock trading price is one of the most important topics in financial market. Financial time series forecasting is a popular application of machine learning methods. Previous research reports that advanced forecasting methods can accurately predict the trading price changes in financial markets. This study intends to use the real-time stream data processing architecture in the big data Spark framework to analyze the real-time stock trading volume according to the bursting comprehensive index of trading volume, and send risk notifications according to different trading volume levels. Investors can choose the best timing to invest. The results of this study show that investors who want to operate in high-risk environment can use stock trading volume risk rating criteria to increase profit.