Research on Prediction of Decision Tree Algorithm on Different Types of Stocks

Shipei Du, Xiao Li, Dongjie Yang

2023

Abstract

The stock market is an important part of the financial market and is closely connected to a country’s market growth and economic patterns. Because of how much the stock market changes and the non-linear nature of it, accurately guessing what will happen in the stock market is really hard. In this article, we suggest a model for predicting the stock market using a decision tree algorithm. We will use historical trading data from multiple A-shares for our research. We use artificial intelligence and the decision tree algorithm to study the financial industry and make predictions about stock prices. Our research found that using the decision tree algorithm to predict stocks gave us good results. This is helpful information for guiding both big institutions and individuals in making stock investments.

Paper Citation

in Harvard Style

Du S., Li X. and Yang D. (2023). Research on Prediction of Decision Tree Algorithm on Different Types of Stocks. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 178-181. DOI: 10.5220/0012277000003807

in Bibtex Style

@conference{anit23,
author={Shipei Du and Xiao Li and Dongjie Yang},
title={Research on Prediction of Decision Tree Algorithm on Different Types of Stocks},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={178-181},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012277000003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - Research on Prediction of Decision Tree Algorithm on Different Types of Stocks
SN - 978-989-758-677-4
AU - Du S.
AU - Li X.
AU - Yang D.
PY - 2023
SP - 178
EP - 181
DO - 10.5220/0012277000003807
PB - SciTePress