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Financial News Quantization and Stock Market Forecast Research Based on CNN and LSTM

  • Shubin Cai
  • Xiaogang Feng
  • Ziwei Deng
  • Zhong Ming
  • Zhiguang Shan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)

Abstract

The changes of stock market and the predictions of the price have become hot topics. When machine learning emerged, it has been used in the stock market forecast research. In recent years, the vertical development of machine learning has led to the emergence of deep learning. Therefore, this paper proposes and realizes the CNN and LSTM forecasting model with financial news and historical data of stock market, which uses deep learning methods to quantify text and mine the laws of stock market changes and analyze whether they can predict changes. According to the results from this paper, this method has certain accuracy in predicting the future changes of the stock market, which provides help to study the inherent laws of stock market changes.

Keywords

Deep learning Text quantification CNN LSTM Stock market forecasting 

Notes

Acknowledgements

The research in this paper was supported by the National Natural Science Foundation of China (Nos. 61672358 and Nos. 61836005).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shubin Cai
    • 1
  • Xiaogang Feng
    • 1
  • Ziwei Deng
    • 1
  • Zhong Ming
    • 1
  • Zhiguang Shan
    • 2
  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  2. 2.State Information Center of ChinaBeijingChina

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