A deep learning-based model for stock price prediction under consideration of financial news publishers

Abstract

Stock price prediction with financial news is beneficial for making correct investment decisions. Recent researches mainly focus on extracting sentiments from news. The publishers as a crucial part of news, which can reflect the authority and reliability, but are usually neglected. In this paper, we propose a novel hybrid deep learning model for stock price prediction that considers the financial news publishers. Specifically, a novel feature about financial news publishers is constructed by classifying financial news publishers. Furthermore, a novel hybrid model named 2d-CNN-AM-LSTM is proposed by combining Convolutional Neural Network (CNN), Attention Mechanism (AM) and Long Short-term Memory, so that it can fuse textual and numerical data in-depth. To verify the superiority of the proposed model, numerous baselines are employed to conduct experiments with representative stock datasets. The experimental analysis demonstrates that the proposed model considering financial news publishers surpasses other benchmarks and achieves outstanding prediction performance.