Stock Price Prediction Based on Machine Learning

Lixing Liu, Bingxi Peng and Jieming Yu ()
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Lixing Liu: Macau University of Science and Technology, Faculty of Information Technology
Bingxi Peng: Macau University of Science and Technology, Faculty of Information Technology
Jieming Yu: Beijing University of Technology, Faculty of Information Technology

A chapter in Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), 2022, pp 1277-1282 from Springer

Abstract: Abstract The stock market is riddled with uncertainty and risks, taking one fallacious decision could lead to huge loss. Therefore, stock market prediction is of great interest to many stock investors. The paper adopts four machine learning models including Decision Tree Regression, Linear Regression, Random Forest Regression Support Vector Regression, respectively, to make prediction on the price of Apple Inc. During the experiment, data in the recent three years were used to train the models in order to make prediction. Moreover, by calculating the mean squared error, the comparison between different models were made. The obtained results showed that the Support Vector Linear Regression model shows a better performance than other models, which is instrumental to the related stock investors in financial markets.

Keywords: Stock Market; Prediction; Machine Learning (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-036-7_189

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DOI: 10.2991/978-94-6463-036-7_189

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