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The Frontiers of Society, Science and Technology, 2022, 4(6); doi: 10.25236/FSST.2022.040605.

An Empirical Study on Stock Price Forecasting Based on ARIMA Model


Han Peng, Zhou Yang

Corresponding Author:
Han Peng

School of Management, University of Shanghai for Science and Technology, Shanghai, 200082, China


The time series data in the financial market contains historical information, which can reveal the operation law of the system.  This paper analyzes and predicts the stock price of China Merchants Bank based on the ARIMA model, selects 243 sample data of the daily stock closing price of China Merchants Bank from July 1, 2021 to July 1, 2022 as the research object, establishes the ARIMA model with Eviews, and uses Python for drawing.  Finally, the closing price of the next 3 working days is predicted based on the model.  The empirical results show that the ARIMA model has a good effect on the short-term prediction of stock prices, but in the long-term prediction, it can be considered to combine with other forecasting methods, and pay more attention to the stock market information and national macro policies, so as to improve the accuracy of the model prediction.


time series; ARIMA model; Stock price forecasting

Cite This Paper

Han Peng, Zhou Yang. An Empirical Study on Stock Price Forecasting Based on ARIMA Model. The Frontiers of Society, Science and Technology (2022) Vol. 4, Issue 6: 30-37. https://doi.org/10.25236/FSST.2022.040605.


[1] G.E.P Box, G.M Jenkins. Time Series Analysis Forecasting and Control [M]. San Francisco: San Francisco, 1978.

[2] DIMITRIOS D. THOMAKOS, PRASAD S. BHATTACHARYA. Forecasting Inflation, Industrial Output and Exchange Rates: A Template Study for India[J]. Indian Economic Review, 2005,40(2).

[3] Jeffrey E Jarrett Ph.D., Eric Kyper Ph.D. ARIMA Modeling with Intervention to Forecast and Analyze Chinese Stock Prices [J]. International Journal of Engineering Business Management, 2011,3(3).

[4] Zhu Libin. Application of ARIMA model in stock market prediction [J]. Jiangsu Statistics, 1999(01):27-28.

[5] Ai Xiaowei, Wang Youyuan. Analysis of Shenzhen Component Index return rate based on ARIMA model [J]. Statistics and Decision, 2008(19):138-140.  

[6] Wu Yuxia, Wen Xin. Based on ARIMA model to predict the short-term stock price [J]. Journal of statistics and decision, 2016(23):83-86.DOI:10.13546/j.cnki.tjyjc. 2016.23.051.

[7] Liu Song, Zhang Shuai. An empirical study on stock price prediction using ARIMA model [J]. Economic Research Guide, 2021(25):76-78.