Stock Price Prediction Based on Machine Learning and Deep Learning Methods

DOI

10.2991/978-94-6463-198-2_112How to use a DOI?

Keywords

Stock market; artificial intelligence; machine learning; deep learning

Abstract

Stock price prediction is one of the most challenging tasks in time series forecasting. Many methods are put forward to explore the nature of the stock market. However, most of them just focus on one kind of model. This paper mainly contributes in two experts: The first is that the author innovatively found that predicting stock price of different companies need to be applied by different methods after data analyses. The second is that the author applies many popular artificial intelligence methods to predict the stock price and makes a summary of their performances. In this paper, the author firstly attempts to apply plenty of methods like linear regression, SVR, Random Forest, KNN, Decision tree, Bagging, AdaBoost, XgBoost, MLP, RNN, LSTM, GRU to predict the stock price of Intel company, Coca-Cola company and Exxon Mobil Corporation. And the results would be evaluated by the metrics of R² and accuracy. After conducting out the experiment, it is found that Bagging method is the best model for the Intel company and Exxon Mobil Company and RNN is considered as the best method to predict the stock price of Coca-Cola Company. Due to the fact that these three companies are good representatives for technology, food and drink and energy fields, the approaches corresponding to these three companies can also be transferred to the same kind of other company. And the results prove that the selected methods are effective.

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  - Hanlin Wang
PY  - 2023
DA  - 2023/08/10
TI  - Stock Price Prediction Based on Machine Learning and Deep Learning Methods
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 1087
EP  - 1098
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_112
DO  - 10.2991/978-94-6463-198-2_112
ID  - Wang2023
ER  -