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

TL;DR

This paper introduces DP-LSTM, a novel deep neural network that integrates financial news with differential privacy to improve short-term stock prediction accuracy and robustness.

Contribution

The paper presents a new DP-LSTM model combining sentiment analysis, ARMA-based modeling, and differential privacy for enhanced stock prediction.

Findings

0.32% improvement in mean MPA for stock prediction

Up to 65.79% reduction in MSE for S&P 500 index

Demonstrates robustness and accuracy improvements

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.