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A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News

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  • Yang Li
  • Yi Pan

Abstract

In recent years, machine learning and deep learning have become popular methods for financial data analysis, including financial textual data, numerical data, and graphical data. This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep learning model to predict future stock movement. The blending ensemble model contains two levels. The first level contains two Recurrent Neural Networks (RNNs), one Long-Short Term Memory network (LSTM) and one Gated Recurrent Units network (GRU), followed by a fully connected neural network as the second level model. The RNNs, LSTM, and GRU models can effectively capture the time-series events in the input data, and the fully connected neural network is used to ensemble several individual prediction results to further improve the prediction accuracy. The purpose of this work is to explain our design philosophy and show that ensemble deep learning technologies can truly predict future stock price trends more effectively and can better assist investors in making the right investment decision than other traditional methods.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2007.12620
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    References listed on IDEAS

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    1. 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.
    2. Hyeong Kyu Choi, 2018. "Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model," Papers 1808.01560, arXiv.org, revised Oct 2018.
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