A novel interval dual convolutional neural network method for interval-valued stock price prediction

• Published in: Pattern recognition 2024-01, Vol.145, p.109920, Article 109920
• Main Authors: Jiang, Manrui, Chen, Wei, Xu, Huilin, Liu, Yanxin
• Format: Article
• Language: English
• Subjects: Convolutional neural network; Interval-valued stock price; Interval-valued time series; Relevant stock information; Stock price prediction
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2023.109920

Accurate interval-valued stock price prediction is challenging and of great interest to investors and for-profit organizations. In this study, by considering individual stock information and relevant stock information simultaneously, we propose a novel interval dual convolutional neural network (Dual-CNNI) model based method to predict interval-valued stock prices. First, the individual and relevant stock information are collected and transformed into images. Then, the Dual-CNNI model is proposed to predict interval-valued stock prices. Specifically, two convolutional neural network (CNN) models with different structures are constructed to respectively extract individual stock features and relevant stock features, and then an interval multilayer perceptron (MLPI) model is used for final interval-valued stock price prediction. Finally, extensive experiments are conducted based on six randomly selected stocks, with comparison to several popular machine learning model based methods and interval-valued time series (ITS) prediction methods. The experimental results indicate that the proposed Dual-CNNI based method has superior predictive ability. •A method is proposed for interval-valued stock price prediction.•Both the individual and relevant stock information are considered.•An interval dual convolutional neural network (Dual-CNNI) is designed.•The experimental results show that Dual-CNNI method has better predictive ability.•The importance of the individual and relevant stock information are discussed.