Stock Price Prediction Based on Machine Learning

DOI

10.2991/978-94-6463-036-7_189How to use a DOI?

Keywords

Stock Market; Prediction; Machine Learning

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.

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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TY  - CONF
AU  - Lixing Liu
AU  - Bingxi Peng
AU  - Jieming Yu
PY  - 2022
DA  - 2022/12/31
TI  - Stock Price Prediction Based on Machine Learning
BT  - Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)
PB  - Atlantis Press
SP  - 1277
EP  - 1282
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-036-7_189
DO  - 10.2991/978-94-6463-036-7_189
ID  - Liu2022
ER  -