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

Abstract

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.

arXiv commentarXiv admin note: text overlap with arXiv:1908.01112

References in corpus (5)

• Adam: A Method for Stochastic Optimization
• Deep Learning with Differential Privacy
• A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
• Differential Privacy and Machine Learning: a Survey and Review
• Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction

Cited by in corpus (4)

• Applications of deep learning in stock market prediction: recent progress
• A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News
• ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
• A Stochastic Time Series Model for Predicting Financial Trends using NLP