Stock Price Prediction Method Based on XGboost Algorithm

DOI

10.2991/978-94-6463-030-5_60How to use a DOI?

Keywords

XGboost; Stock Price

Abstract

In this paper, I use the XGboost algorithm model with parameters controlled by the Grid SearchCV search algorithm to forecast stock prices based on the daily time series characteristics of stocks. The results of the model demonstrate that by using a computer algorithm to analyze stock price information with a large amount of data, the model is able to capture the high-frequency time series fluctuation trend of the stock more accurately under control of overfitting and underfitting, and thus can obtain more accurate prediction results about the stock price.

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.

Download article (PDF)

TY  - CONF
AU  - Yifan Zhang
PY  - 2022
DA  - 2022/12/20
TI  - Stock Price Prediction Method Based on XGboost Algorithm
BT  - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
PB  - Atlantis Press
SP  - 595
EP  - 603
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-030-5_60
DO  - 10.2991/978-94-6463-030-5_60
ID  - Zhang2022
ER  -