Stock Price Prediction Based on ARIMA-GARCH and LSTM

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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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  -