Stock Index Prediction Based on ARIMA Model

Abstract

Stock index price forecasting is a very important thing for financial markets. Stock indices select the most representative stocks in the stock market, which are the most favorable representatives of the industry, sector or market. Successful prediction of them can guide investors to a good payoff and allow researchers to understand the workings of the market economy. Time series models are widely used in stock price forecasting precisely because they have the advantage of being able to predict in complex environments such as large shocks. Therefore, this paper introduces the process of using time series model - ARIMA model to establish the prediction of index price fluctuation. And the five-year-long stock index data from May 2017 to April 2022 of the CSI 300 index is used as the research sample to successfully predict the volatility trend of the CSI 300 index in May 2022. The outcomes demonstrate that the ARIMA model has excellent capability for short-term forecasting. and is capable of forecasting complex situations that cannot be accomplished by linear models.