Research on Quantitative Investment Strategy of Stock Index Futures Based on XGBoost Model

Hongxin Zhu, Anmin Zhu

2022

Abstract

In the past two decades, China’s economy has been developing continuously. The emergence of various emerging industries has continuously introduced more value choices to the financial market, which has also made the stock market more volatile. People have been studying and predicting the stock market for a long time, hoping to find the rule of stock price fluctuations. It is believed that similar stock price fluctuations will occur in the previous performance of stock price fluctuations at a certain time in the future, therefore improving the accuracy of stock forecasts. In this paper, the prediction of the CSI 300 Index Futures is studied based on the XGBoost model. It designs a quantitative investment strategy to trade the CSI 300 Index Futures based on the prediction, to study the accuracy of the XGBoost model applied to financial market forecasts.

Paper Citation

in Harvard Style

Zhu H. and Zhu A. (2022). Research on Quantitative Investment Strategy of Stock Index Futures Based on XGBoost Model. In Proceedings of the 3rd International Symposium on Automation, Information and Computing - Volume 1: ISAIC; ISBN 978-989-758-622-4, SciTePress, pages 31-36. DOI: 10.5220/0011825700003612

in Bibtex Style

@conference{isaic22,
author={Hongxin Zhu and Anmin Zhu},
title={Research on Quantitative Investment Strategy of Stock Index Futures Based on XGBoost Model},
booktitle={Proceedings of the 3rd International Symposium on Automation, Information and Computing - Volume 1: ISAIC},
year={2022},
pages={31-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011825700003612},
isbn={978-989-758-622-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Symposium on Automation, Information and Computing - Volume 1: ISAIC
TI - Research on Quantitative Investment Strategy of Stock Index Futures Based on XGBoost Model
SN - 978-989-758-622-4
AU - Zhu H.
AU - Zhu A.
PY - 2022
SP - 31
EP - 36
DO - 10.5220/0011825700003612
PB - SciTePress