The Comparison of Stock Price Prediction Based on Linear Regression Model and Machine Learning Scenarios

DOI

10.2991/978-94-6463-030-5_82How to use a DOI?

Keywords

Stock Prediction; Machine Learning; OLS; Lightgbm; XGBoost; Random Forest; LSTM; GRU

Abstract

Financial price prediction always plays a vital role for investment decision. This paper investigates the prediction of the close price of LONGi based on linear models and machine learning approaches, including ordinary least square (OLS), Lightgbm, XGBoost, random forest, LSTM and GRU models. Specifically, according to our result, the LSTM and the GRU perform relatively better results and the random forest is the worst. Based on the analysis, all the models can predict the trend of the close price. These results offer a guideline for investors that desires to forecast the price trend of a specific underlying assets. These results shed light on comprehending the characteristics of different regression models.

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.

Download article (PDF)

TY  - CONF
AU  - Xiwen Jin
AU  - Chaoran Yi
PY  - 2022
DA  - 2022/12/20
TI  - The Comparison of Stock Price Prediction Based on Linear Regression Model and Machine Learning Scenarios
BT  - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
PB  - Atlantis Press
SP  - 837
EP  - 842
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-030-5_82
DO  - 10.2991/978-94-6463-030-5_82
ID  - Jin2022
ER  -