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Stock price prediction is important for value investments in the stockmarket. In particular, short-term prediction that exploits financial newsarticles is promising in recent years. In this paper, we propose a novel deepneural network DP-LSTM for stock price prediction, which incorporates the newsarticles as hidden information and integrates difference news sources throughthe differential privacy mechanism. First, based on the autoregressive movingaverage model (ARMA), a sentiment-ARMA is formulated by taking intoconsideration the information of financial news articles in the model. Then, anLSTM-based deep neural network is designed, which consists of three components:LSTM, VADER model and differential privacy (DP) mechanism. The proposed DP-LSTMscheme can reduce prediction errors and increase the robustness. Extensiveexperiments 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 ofthe market index S&P 500, we achieve up to 65.79% improvement in MSE.

DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News