Research on Stock Price Prediction Based on Machine Learning

DOI

10.2991/978-94-6463-512-6_72How to use a DOI?

Keywords

Stock Prediction; Support Vector Machine; Deep Learning; Random Forest

Abstract

Stock forecasting has always been a popular research topic in the economic market. In current years, machine learning methods have been generally used in stock forecasting, which has unique advantages over traditional stock forecasting methods. This paper collects the relevant literature in recent years, first introduces the traditional methods of stock prediction, quantitative analysis, fundamental analysis and technical analysis, and then introduces modern stock analysis methods: deep learning, support vector machines and random forests, introduces various improved prediction models, as well as the practical application and research status of different models, and summarizes the application characteristics of these models. The article summarized the advantages and disadvantages of machine learning algorithms at the end, and provided a outlook on the development of machine learning algorithms based on practical situations.

Copyright

© 2024 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  - Shijin Yang
PY  - 2024
DA  - 2024/09/23
TI  - Research on Stock Price Prediction Based on Machine Learning
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 693
EP  - 698
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_72
DO  - 10.2991/978-94-6463-512-6_72
ID  - Yang2024
ER  -