Research on Decision Tree in Price Prediction of Low Priced Stocks

Shipei Du, Jin Qiu, Wenhui Ding

2023

Abstract

The decision tree is a commonly used machine learning algorithm that can be used for problems such as predicting stock prices. In the study of predicting low stock prices, decision trees can be used to analyze factors such as fundamentals and technical factors of stocks to predict the price trend of stocks. This research selects the historical transaction data of 4 China’s Shanghai Stock Exchange A shares as the research object. The common feature of these 4 stocks is that the price is relatively low, no more than 10 yuan. This research proposes a prediction of low-price stocks based on the decision tree algorithm Model, our research results show that decision tree model can help predict the price trend of low-priced stocks and help investors make investment decisions.

Paper Citation

in Harvard Style

Du S., Qiu J. and Ding W. (2023). Research on Decision Tree in Price Prediction of Low Priced 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 386-390. DOI: 10.5220/0012284500003807

in Bibtex Style

@conference{anit23,
author={Shipei Du and Jin Qiu and Wenhui Ding},
title={Research on Decision Tree in Price Prediction of Low Priced Stocks},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={386-390},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012284500003807},
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 Decision Tree in Price Prediction of Low Priced Stocks
SN - 978-989-758-677-4
AU - Du S.
AU - Qiu J.
AU - Ding W.
PY - 2023
SP - 386
EP - 390
DO - 10.5220/0012284500003807
PB - SciTePress