Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)

Stock Price Prediction Based on ARIMA-GARCH and LSTM

Authors
Xingdan Huang1, Panlu You2, Xiaolian Gao2, Dapeng Cheng2, *
1School of Statistics, Shandong Technology and Business University, Yantai, China
2School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
*Corresponding author. Email: 1259601112@qq.com
Corresponding Author
Dapeng Cheng
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_45How to use a DOI?
Keywords
static forecasting; dynamic forecasting; ARIMA; LSTM
Abstract

Stock price prediction is a hot topic in the financial industry, and accurate stock price prediction is an important method to prevent risk and protect market stability. To this end, this paper constructs time series models and deep learning model, respectively, and compares the prediction results of the two types of models from the perspective of dynamic and static forecasting based on SSE index data. The results show that the forecasting methods of the models affect their forecasting effects, and the ARIMA-GARCH model has the highest average forecasting accuracy in static forecasting, while the LSTM model has the most accurate forecasting effect in dynamic forecasting, with an RMSE value of only 6.32%.

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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Volume Title
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-198-2_45
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_45How to use a DOI?
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.

Cite this article

TY  - CONF
AU  - Xingdan Huang
AU  - Panlu You
AU  - Xiaolian Gao
AU  - Dapeng Cheng
PY  - 2023
DA  - 2023/08/10
TI  - Stock Price Prediction Based on ARIMA-GARCH and LSTM
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 438
EP  - 448
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_45
DO  - 10.2991/978-94-6463-198-2_45
ID  - Huang2023
ER  -