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Stock Market Prediction via Deep Learning Techniques: A Survey

Author

Listed:
  • Jinan Zou
  • Qingying Zhao
  • Yang Jiao
  • Haiyao Cao
  • Yanxi Liu
  • Qingsen Yan
  • Ehsan Abbasnejad
  • Lingqiao Liu
  • Javen Qinfeng Shi

Abstract

Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we point out several future directions by sharing some new perspectives on stock market prediction.

Suggested Citation

  • Jinan Zou & Qingying Zhao & Yang Jiao & Haiyao Cao & Yanxi Liu & Qingsen Yan & Ehsan Abbasnejad & Lingqiao Liu & Javen Qinfeng Shi, 2022. "Stock Market Prediction via Deep Learning Techniques: A Survey," Papers 2212.12717, arXiv.org, revised Feb 2023.
    • Handle: RePEc:arx:papers:2212.12717
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    References listed on IDEAS

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    2. Nurhuda Nizar & Ahmad Danial Zainudin & Ali Albada & Chua Mei Shan, 2024. "Forecasting Short-Term FTSE Bursa Malaysia Using WEKA," Information Management and Business Review, AMH International, vol. 16(2), pages 104-114.

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