R-Net: robustness enhanced financial time-series prediction with differential privacy

Artificial intelligence has been investigated to conduct automatic predictions on financial time series such as stock. However, they faced two challenges. Firstly, the stock movement is affected by both technical fundamentals and external textual information. Secondly, resource data are often highly-noisy and heterogeneous, and prediction based on noisy data usually leads to significant errors. We propose a robust and accurate model (R-Net) that incorporates both technical information and qualitative sentiment derived from news reports for daily stock movement prediction in response to these challenges. A variety of enhancement strategies are adopted to improve the prediction model's robustness and accuracy. Specifically, a multimodal CNN and LSTM neural networks are applied to extract semantics from text and model complex temporal characteristics for stock market prediction. Further, based on the connection between the robustness of deep neural networks and differential privacy, we utilize provable noise injection and heterogeneous Gaussian mechanisms to enhance model robustness and accuracy. Experimental results on S&P 500 stocks demonstrate that our proposed R-Net, which integrates four enhancements, achieves 12.7% and 0.67% improvement in prediction accuracy for trends and price value prediction, respectively.