E3S Web Conf.
Volume 275, 20212021 International Conference on Economic Innovation and Low-carbon Development (EILCD 2021)
|Number of page(s)||4|
|Section||Energy Application and Ecological Resource Sustainability|
|Published online||21 June 2021|
Stock Price Prediction Based on XGBoost and LightGBM
Southwest Minzu University, Chengdu, China
2 JinagXi University Of Fianance and Economics Jiangxi, China
3 South China University of Technology GuangZhou, China
4 Shanghai University of Finance and Economics Yue Yang and Peikun Wang are co-first author. Shanghai, China
Stock trading, as a kind of high frequency trading, generally seeks profits in extremely short market changes. And effective stock price forecasting can help investors obtain higher returns. Based on the data set provided by Jane Street, this paper makes use of XGBoost model and LightGBM model to realize the prediction of stock price. Since the given training set has a large amount of data and includes abnormal data such as missing value, we first carry out feature engineering processing on the original data and take the mean value of the missing value, so as to obtain the preprocessed data that can be used in modeling.
The experimental results show that the combined model of XGBoost and LightGBM has better prediction performance than the single model and neural network.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.