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

Understand

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.