DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
This work addresses short-term stock prediction for investors by combining news sentiment with differential privacy, but it appears incremental as it builds on existing LSTM and ARMA models.
The paper tackled stock price prediction by integrating financial news articles into a deep neural network, achieving a 0.32% improvement in mean MPA and up to 65.79% improvement in MSE for S&P 500 index prediction.
Stock price prediction is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent years. In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism. First, based on the autoregressive moving average model (ARMA), a sentiment-ARMA is formulated by taking into consideration the information of financial news articles in the model. Then, an LSTM-based deep neural network is designed, which consists of three components: LSTM, VADER model and differential privacy (DP) mechanism. The proposed DP-LSTM scheme can reduce prediction errors and increase the robustness. Extensive experiments on S&P 500 stocks show that (i) the proposed DP-LSTM achieves 0.32% improvement in mean MPA of prediction result, and (ii) for the prediction of the market index S&P 500, we achieve up to 65.79% improvement in MSE.