Stock Market Prediction via Deep Learning Techniques: A Survey

Jinan Zou, Qingying Zhao, Yang Jiao, Haiyao Cao, Yanxi Liu, Qingsen Yan, Ehsan Abbasnejad, Lingqiao Liu and Javen Qinfeng Shi

Papers from arXiv.org

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.

Date: 2022-12, Revised 2023-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-fmk
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Citations: View citations in EconPapers (2)

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