The Prediction of Stock Prices

DOI

10.2991/978-94-6463-304-7_77How to use a DOI?

Keywords

Stock Prices; Deep Learning; Modelling

Abstract

The prediction of stock prices has consistently captured the attention of numerous analysts and researchers. Due to the influence of various variables such as economics, politics, investment psychology, and trading techniques on the price trends of stocks, forecasting stock prices inherently presents a challenging problem. In order to accurately predict the changing trends of stock prices, this study proposes a hybrid forecasting model known as ARIMA-SVM. This model is capable of simultaneously accommodating both the linear and nonlinear features of stock price data. Empirical research is conducted using stock price data from four sectors, and a comparison is made between the predictive accuracy of the ARIMA-SVM model, ARIMA model, and SVM model. The results indicate that the predictive accuracy of the ARIMA-SVM model, which integrates two individual models is enhanced.

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  - Dingwei Bai
PY  - 2023
DA  - 2023/12/04
TI  - The Prediction of Stock Prices
BT  - Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023)
PB  - Atlantis Press
SP  - 735
EP  - 745
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-304-7_77
DO  - 10.2991/978-94-6463-304-7_77
ID  - Bai2023
ER  -