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

Author

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  • Xinyi Li
  • Yinchuan Li
  • Hongyang Yang
  • Liuqing Yang
  • Xiao-Yang Liu

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.

Suggested Citation

  • Xinyi Li & Yinchuan Li & Hongyang Yang & Liuqing Yang & Xiao-Yang Liu, 2019. "DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News," Papers 1912.10806, arXiv.org.
    • Handle: RePEc:arx:papers:1912.10806
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    References listed on IDEAS

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    1. Xinyi Li & Yinchuan Li & Yuancheng Zhan & Xiao-Yang Liu, 2019. "Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation," Papers 1907.01503, arXiv.org.
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    Cited by:

    1. Jingyi Gu & Fadi P. Deek & Guiling Wang, 2023. "Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network," Papers 2302.14164, arXiv.org.
    2. Pratyush Muthukumar & Jie Zhong, 2021. "A Stochastic Time Series Model for Predicting Financial Trends using NLP," Papers 2102.01290, arXiv.org.
    3. Dhruhi Sheth & Manan Shah, 2023. "Predicting stock market using machine learning: best and accurate way to know future stock prices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 1-18, February.
    4. Yang Li & Yi Pan, 2020. "A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News," Papers 2007.12620, arXiv.org.

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