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

Mingtao Jia (), Haichen Xu () and Sicong Zhang ()
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Mingtao Jia: University of Sydney, Business School
Haichen Xu: Boston University, College of Art and Science
Sicong Zhang: Warren College, University of California (Sandiego)

A chapter in Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022), 2022, pp 1607-1614 from Springer

Abstract: 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.

Keywords: component; Stock Price Trend Prediction; Arima; Machine Learning; Linear Regression; Random Forest; Decision Tree; Gradient Boosting (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-052-7_178

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DOI: 10.2991/978-94-6463-052-7_178

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