A multi-channel cross-residual deep learning framework for news-oriented stock movement prediction

Stock market movement prediction remains challenging due to random walk characteristics. Yet through a potent blend of input parameters, a prediction model can learn sequential features more intelligently. In this paper, a multi-channel news-oriented prediction system is developed to capture intricate moving patterns of the stock market index. Specifically, the system adopts the temporal causal convolution to process historical index values due to its capability in learning long-term dependencies. Concurrently, it employs the Transformer Encoder for qualitative information extraction from financial news headlines and corresponding preview texts. A notable configuration to our multi-channel system is an integration of cross-residual learning between different channels, thereby allowing an earlier and closer information fusion. The proposed architecture is validated to be more efficient in trend forecasting compared to independent learning, by which channels are trained separately. Furthermore, we also demonstrate the effectiveness of involving news content previews, improving the prediction accuracy by as much as 3.39%.