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Pattern Recognition Based on the Nonparametric Kernel Regression Method in A-share Market

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Proceedings of International Conference on Soft Computing Techniques and Engineering Application

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 250))

Abstract

Fourteen kinds of technical pattern in A-share market have been automatically identified in this paper, using nonparametric kernel regression method and quantitative recognition rules that are constrained and improved to make the model more feasible and effective for investment practice. K–S test shows that the distribution of the stock yield following the recognized patterns statistically differs from that of random sampling. Clustering analysis also suggests that most of the identified technical patterns have distinguishable effects on the subsequent stock price movement. These findings provide useful guidance for the development of innovative investment models in high-frequency automatic stock trading.

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Author information

Authors and Affiliations

  1. Economics and Management School, Wuhan University, Wuhan, China

    Huaiyu Sun

  2. Research Department, China Investment Securities Co., Ltd, Shenzhen, China

    Huaiyu Sun & Feng He

  3. Product Design Department, UBS SDIC Fund Management Co., Ltd, Shenzhen, China

    Mi Zhu

Authors
  1. Huaiyu Sun
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  2. Mi Zhu
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  3. Feng He
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Corresponding author

Correspondence to Huaiyu Sun .

Editor information

Editors and Affiliations

  1. Dept of Computer Science and Engineering, SOA University, Bhubaneswar, Orissa, India

    Srikanta Patnaik

  2. Electronics and Computer Engg Tech., Indiana State University, Indiana, Indiana, USA

    Xiaolong Li

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Sun, H., Zhu, M., He, F. (2014). Pattern Recognition Based on the Nonparametric Kernel Regression Method in A-share Market. In: Patnaik, S., Li, X. (eds) Proceedings of International Conference on Soft Computing Techniques and Engineering Application. Advances in Intelligent Systems and Computing, vol 250. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1695-7_35

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  • DOI: https://doi.org/10.1007/978-81-322-1695-7_35

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1694-0

  • Online ISBN: 978-81-322-1695-7

  • eBook Packages: EngineeringEngineering (R0)

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