Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022)

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Comparison Analysis of ARIMA and Machine Learning Methods for Predicting Trend of US Semiconductor Stocks

Authors
Mingtao Jia1, *, Haichen Xu2, *, Sicong Zhang3, *
1Business School, University of Sydney, Sydney, 00026A, Australia
2College of Art and Science, Boston University, Boston, 02215, USA
3Warren College, University of California (Sandiego), Sandiego, 92092, USA
*Corresponding author. Email: mjia6176@uni.sydeny.edu.au
*Corresponding author. Email: xhcbu@bu.edu
*Corresponding author. Email: s3zhang@ucsd.edu
Corresponding Authors
Mingtao Jia, Haichen Xu, Sicong Zhang
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-052-7_178How to use a DOI?
Keywords
component; Stock Price Trend Prediction; Arima; Machine Learning; Linear Regression; Random Forest; Decision Tree; Gradient Boosting
Abstract

The stock price trend prediction has some challenges for the investors because there are many unknown risks and great variation in the stock market. Some researchers have studied how to give the prediction of the stock price trend with high accuracy. However, the systematic analysis of the comparisons for this field is still insufficient. In this paper, the Arima and machine learning methods are applied to predict the trend of US semi-conductor stocks. The comparison analysis of the Arima-based method and machine learning-based methods are given to evaluate their performances. The comparison results indicate that the Arima-based method has a better performance than that of machine learning methods in the application of fitting the variation of the stock prices. Our research has great significance in the application of stock price trend prediction.

Copyright
© 2022 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 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022)
Series
Advances in Economics, Business and Management Research
Publication Date
27 December 2022
ISBN
978-94-6463-052-7
ISSN
2352-5428
DOI
10.2991/978-94-6463-052-7_178How to use a DOI?
Copyright
© 2022 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

risenwbib
TY  - CONF
AU  - Mingtao Jia
AU  - Haichen Xu
AU  - Sicong Zhang
PY  - 2022
DA  - 2022/12/27
TI  - Comparison Analysis of ARIMA and Machine Learning Methods for Predicting Trend of US Semiconductor Stocks
BT  - Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022)
PB  - Atlantis Press
SP  - 1607
EP  - 1614
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-052-7_178
DO  - 10.2991/978-94-6463-052-7_178
ID  - Jia2022
ER  -