Abstract: Stock trend prediction,as a classic and challenging task,can help traders make trading decisions for greater returns.Recently,deep learning related models have achieved obvious performance improvement on this task.However,most of the current deep learning related works only leverage the historical data on stock price to complete the trend prediction,which cannot capture the market dynamics other than price indicators,thus having an accuracy limitation to a certain extent.To this end,this paper combines social media texts with stock historical price information,and proposes a novel deep cross-modal information fusion network(DCIFNet) for stock trend prediction.DCIFNet first utilizes temporal convolution operations to encode stock prices and twitter texts,so that each element can have sufficient knowledge of its neighborhood elements.Then,the results are fed into a transformer-based cross-modal fusion structure to fuse stock prices and important information in Twitter texts more effectively.Finally,a multi-graph attention convolutional network is introduced to describe the interrelationships among different stocks,which well captures the industry,wiki and correlation relationship among related stocks,leading to the accuracy improvement of stock prediction.We have performed trend prediction and simulated trading experiments on high-frequency trading datasets in nine different industries,and ablation studies as well as compared experiments with multipronged attention network for stock forecasting(MAN-SF) demonstrate the effectiveness of the proposed DCIFNet method.In addition,with the optimal accuracy of 0.6309,it obviously outperforms representative methods on the stock prediction application.

Key words: Stock trend prediction, Social media text, Cross-modal information fusion, Graph convolutional network, Temporal convolution