Big Data Business Actual Analysis: Stock Price Prediction Based on Time Series Model

Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v2i2.327

Aiwen Rui

Tianjin University of Finance and Economics


This paper selects the daily closing price data of the Shanghai Composite Index from January 1, 2016 to December 31, 2017, excluding holidays, and preprocesses the data. After taking the logarithm and converting it into the rate of return data, the first-order difference is performed to make it into a stable time series, and then the ARMA(p,q) model is constructed. Through parameter significance test, residual test and characteristic root test, according to the minimum principle of AIC, the optimal model is finally determined to be ARMA(2,5) of sparse coefficient, and the expression of the model is obtained. The GARCH(1,1) model is established for the residual of ARMA(2,5), and the model expression is obtained. In order to directly predict the return rate of the Shanghai Composite Index, the ARIMA(2,1,5) model of the sparse coefficient is constructed for the return rate of the Shanghai Composite Index, and the model expression is obtained. By predicting the Shanghai Composite Index return data on January 2, 2018, it is found that the prediction error of the model is small, and it can be used for subsequent predictions.


ARMA(2,5) of sparse coefficient, ARIMA(2,1,5), GARCH(1,1)


[1] Xu Jun. Empirical analysis of gold futures prices based on the ARMA model-523 sets of data from the New York Stock Exchange from 2006 to 2016. Industrial Economic Forum. 2017; 04(04): 16-22.
[2] Guo Xue, Wang Yanbo. Forecast of Shanghai stock index based on ARMA model. Times Economics and Trade. 2006; (S3): 58-59.
[3] Deng Jun, Yang Xuan, Wang Wei, Jiang Zhehui. Empirical research on stock price prediction using ARMA model. Enterprise Herald. 2010; (06): 266-267.
[4] Huang Lixia. Analysis and forecast of stock price based on ARIMA model — Taking Ping An of China as an example. Science and Technology Economic Market. 2020; (10): 62-63.

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