Quantitative Forecasting Method of Stock Price Based on Time Series Model

DOI

10.2991/978-94-6463-270-5_6How to use a DOI?

Keywords

stock price; forecast; fitting; Shanghai and Shenzhen 300 index

Abstract

Nowadays, our economy is developing rapidly, and the financial market has gradually become a very important part of our economic development, in which the stock market is an important part of the financial market and closely related to our economy. For investors, a key problem in making decisions is how to accurately analyze the stock market by knowing the price fluctuation in time; For the managers of the stock market, it is also a very arduous task to create a relatively stable trading environment by mastering the real-time dynamics of the stock market. Because of this, it is of great significance for us to better understand the characteristics of stock market fluctuations and find out the laws. Taking the Shanghai-Shenzhen 300 Index, which reflects the overall trend of A-share market from 2009 to 2018, as an example, this paper uses stationarity test, pure randomness test and other test methods to fit ARIMA model, ARCH model and AR-GARCH model, compare their advantages and disadvantages in the stock price trend, and then make a short-term forecast of the stock price with the fitting model that has passed the test. Finally, it is found that the AR-GARCH model has a good fitting effect on the original sequence, and a more accurate prediction result is obtained.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lu Xu
PY  - 2023
DA  - 2023/10/29
TI  - Quantitative Forecasting Method of Stock Price Based on Time Series Model
BT  - Proceedings of the 3rd International Conference on Internet Finance and Digital Economy (ICIFDE 2023)
PB  - Atlantis Press
SP  - 44
EP  - 52
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-270-5_6
DO  - 10.2991/978-94-6463-270-5_6
ID  - Xu2023
ER  -