Author(s)

Chao Liu ¹, Kaijun Li ¹, Shutong Liang ¹, Shixing Han ¹

Affiliation(s)

¹ College of Engineering, Tibet University, Lhasa, 850000, China

Corresponding Author

Shixing Han

ABSTRACT

Stock trading volume is a performance of supply and demand, which refers to the number of transactions in a time unit. The trading volume is an important basis for judging the stock trend, and provides an important basis for analyzing the main behaviors. Therefore, this paper will extract indicators based on the data information of the "digital economy" sector index every five minutes from July 14, 2021 to December 31, 2021, so as to combine ARIMA and multiple linear regression model to obtain the "digital economy" sector index trading volume analysis and prediction model based on ARIM (p, q) model, and then through the comparative analysis of the two models, obtain the best. The example shows that the model has strong applicability and practicability for the prediction of closing price.

KEYWORDS

Stock, Digital economy, Volume forecast, Arim (p, q) model

CITE THIS PAPER

Chao Liu, Kaijun Li, Shutong Liang, Shixing Han, Research on the Analysis and Forecast of "Digital Economy" Sector Index Trading Volume Based on Arim (p, q) Model . Financial Engineering and Risk Management (2023) Vol. 6: 85-93. DOI: http://dx.doi.org/10.23977/ferm.2023.060113.

REFERENCES

[1] Liu Min, Hou Junhua. Research on the forecast of crude oil stock turnover based on GS-ARIMA model [J]. Foreign Trade and Economic Cooperation, 2021 (12): 38-40.
[2] Wang Jingle. Markov Chain Analysis and Prediction of Stock Trading Volume [J]. Career Circle, 2007 (13): 13-14.
[3] Sun Yanlin, Chen Shoudong, Liu Yang. Prediction of Stock Price Fluctuation Based on Volume Information of Stock Market and Foreign Exchange Market [J]. System Engineering Theory and Practice, 2019, 39 (04): 935-945.
[4] Lin Jie, Zhu Jiaming, Chen Fuyuan. Analysis and prediction of trading volume of stock “Qingdao Haier” based on ARMA model [J]. Journal of Natural Science of Harbin Normal University, 2016, 32 (06): 6-10.
[5] Guo Qinghuang Predictive ability of dynamic changes in trading volume to future stock returns [D]. Nanjing University of Technology, 2014.

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