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International Conference on AI and Mobile Services

AIMS 2022: Artificial Intelligence and Mobile Services – AIMS 2022 pp 76–90Cite as

Indicator-Specific Recurrent Neural Networks with Co-teaching for Stock Trend Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13729))

Abstract

Stock trend prediction is a challenging problem due to the complexity of stock data. Recently, many works applied deep learning methods for stock trend prediction and achieve impressive results. However, these methods still suffer from two limitations: 1) Various types of technical indicators are input into a single model, making it difficult for the model to learn differentiated features. 2) Noisy data in the stocks is not handled effectively. Therefore, in this paper, we propose a stock trend prediction framework using indicator-specific recurrent neural networks with co-teaching. Specifically, we first collect data from Chinese stock market and divide them into fourteen categories. Then we apply multiple RNNs to extract features separately from different technical indicator categories which can learn comprehensive features. In addition, we leverage multi-head attention for effective feature interaction and fusion. At last, we utilize co-teaching method during the training process to reduce the impact of noisy data. Experimental results show both the effectiveness and superiority of our method.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (62006062, 62176076), Shenzhen Foundational Research Funding JCYJ20200109113441941, Shenzhen Key Technology Project JSGG20210802154400001 and Joint Lab of HITSZ and China Merchants Securities.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Hongling Xu, Jingqian Zhao, Yang Sun, Jianzhu Bao & Ruifeng Xu

  2. China Merchants Securities Co., Ltd., Shenzhen, China

    Xiaoqi Yu & Yixue Dang

  3. Joint Lab of HITSZ-CMS, Shenzhen, China

    Hongling Xu, Jingqian Zhao, Xiaoqi Yu, Yixue Dang, Yang Sun, Jianzhu Bao & Ruifeng Xu

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  1. Hongling Xu
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  2. Jingqian Zhao
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  3. Xiaoqi Yu
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  4. Yixue Dang
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  5. Yang Sun
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  6. Jianzhu Bao
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  7. Ruifeng Xu
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Corresponding author

Correspondence to Ruifeng Xu .

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Editors and Affiliations

  1. Minzu University of China, Beijing, China

    Xiuqin Pan

  2. Hainan University, Haikou, China

    Ting Jin

  3. Kingdee International Software Group Co., Ltd., Shenzhen, China

    Liang-Jie Zhang

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Xu, H. et al. (2022). Indicator-Specific Recurrent Neural Networks with Co-teaching for Stock Trend Prediction. In: Pan, X., Jin, T., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2022. AIMS 2022. Lecture Notes in Computer Science, vol 13729. Springer, Cham. https://doi.org/10.1007/978-3-031-23504-7_6

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  • DOI: https://doi.org/10.1007/978-3-031-23504-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23503-0

  • Online ISBN: 978-3-031-23504-7

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