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Deep Stock Ranker: A LSTM Neural Network Model for Stock Selection

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Data Mining and Big Data (DMBD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10943))

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Abstract

Stock prediction is a great challenge for the past decades because of the fact that it is a non-stationary, noisy, chaotic environment. Traditional stock prediction models including statistical and machine learning based methods almost use handcrafted features as input. With the development of deep learning, end-to-end models achieve state-of-the-art in many other tasks. However financial time series data is too noise to apply end-to-end models straightly, instead of predicting stocks’ absolute future return, we propose a novel stock selection model DeepStockRanker to predict stocks’ future return ranking. Experimental results show that our method is able to extract information from raw data to predict stocks’ future return ranking and achieves much better performance compared with several advanced models.

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Acknowledgment

This work was supported by the Natural Science Foundation of China (NSFC) under grant no. 61673025 and 61375119 and Supported by Beijing Natural Science Foundation (4162029), and partially supported by National Key Basic Research Development Plan (973 Plan) Project of China under grant no. 2015CB352302.

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Authors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Xiaolin Zhang & Ying Tan

Authors
  1. Xiaolin Zhang
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  2. Ying Tan
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Corresponding author

Correspondence to Ying Tan .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Zhang, X., Tan, Y. (2018). Deep Stock Ranker: A LSTM Neural Network Model for Stock Selection. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_58

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  • DOI: https://doi.org/10.1007/978-3-319-93803-5_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93802-8

  • Online ISBN: 978-3-319-93803-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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