A Stock Price Analysis and Prediction Method Based on Machine Learning

DOI

10.2991/978-94-6463-198-2_152How to use a DOI?

Keywords

machine learning; the stock price forecast; stock prices; support vector machine (SVM)

Abstract

Changes in the stock market have a significant impact on individual investment management and are closely related to economic research and market development trends in the country as a whole. If the trend of stock prices can be predicted more correctly, investors will make more scientific investment decisions based on it, which is of great significance to promote the effective allocation of resources and improve market efficiency from a macro perspective. In recent years, big data and artificial intelligence technology have begun to rise, machine learning, as an important technology in the field of artificial intelligence, has excellent performance in simulating the specific characteristics of objects and processing complex and large amounts of data. Therefore, this paper takes the daily closing price of stocks S1 and S2 as the essential data, and uses the support vector machine (a machine learning model) of python to analyze, prove and predict the stock quotations in China. The analysis results show that the accuracy of using the support vector classifier to predict stocks is as high as 90%, We performed parameter optimization for the second time on the basis of this model, and the accuracy of the support vector machine for stock prediction is as high as 90%. This conclusion indicates that the stock prediction method based on machine learning model has high accuracy, and has certain reference value for practical application.

Copyright

© 2023 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  - Runqi Wang
PY  - 2023
DA  - 2023/08/10
TI  - A Stock Price Analysis and Prediction Method Based on Machine Learning
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 1471
EP  - 1477
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_152
DO  - 10.2991/978-94-6463-198-2_152
ID  - Wang2023
ER  -