Title: Stock prediction based on LightGBM with feature selection and improved grid search

Authors: Qihang Zhou; Changjun Zhou; Zhiqiang Liu

Addresses: College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, 321000, China ' College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, 321000, China ' College of Engineering, Zhejiang Normal University, Jinhua, 321000, China

Abstract: In order to improve the accuracy of stock forecasting, a stock forecasting model based on LightGBM is proposed. Firstly, based on the grid search, an improved grid search is proposed, and the improved grid search is used to search out the best super parameters for LightGBM. At the same time, the normalised function and loss function suitable for the model are also searched out, and the IGS-LightGBM model is constructed. Then, the feature selection in LightGBM is used to further optimise the model and obtained FS-IGS-LightGBM. The model is then applied to the actual stock data. In this paper, the closing prices of Shanghai Composite Index, Shenzhen Composite Index, Shanghai and CSI 300, Growth Enterprise Index, Chuanneng Power and Baiyun Airport are taken as experimental data. The experimental results show that FS-IGS-LightGBM is superior to XGBoost and LightGBM in the evaluation index of MAPE.

Keywords: LightGBM; grid search; stock forecast; optimisation; feature selection.

DOI: 10.1504/IJAIS.2022.10049031

International Journal of Adaptive and Innovative Systems, 2022 Vol.3 No.2, pp.100 - 118

Received: 11 Feb 2021
Accepted: 26 Apr 2021

Published online: 25 Jul 2022 *

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