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On the Study of Trading Strategies Within Limited Arbitrage Based on SVM

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Genetic and Evolutionary Computing (ICGEC 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 536))

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Abstract

Limited arbitrage will impede the operation of market, then confuses the well-known “Efficient Market Hypothesis” in theory and investment decision of market participants in practice. We develop a contrarian trading strategy, tailored to this kind of situation, with the trading signals derived the technical indicator: BIAS, to indirectly verify the existence of limited arbitrage by testing all listed stocks in Taiwan. Further, we use the well-known machine learning, SVM, to confirm the classification method in this study being free of subjective discretion. Thus we have a robust evidence to support the usefulness of this trading strategy.

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References

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Author information

Authors and Affiliations

  1. Department of Finance, National United University, Miaoli, Taiwan

    Hui-Huang Tsai

  2. Department of Mathematics, Soochow University, Taipei, Taiwan

    Mu-En Wu

  3. Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan

    Wei-Ho Chung

  4. PIXNET Media, Taipei, Taiwan

    Cheng-Yu Lu

Authors
  1. Hui-Huang Tsai
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  2. Mu-En Wu
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  3. Wei-Ho Chung
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  4. Cheng-Yu Lu
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Corresponding author

Correspondence to Mu-En Wu .

Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

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

    Jerry Chun-Wei Lin

  3. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Chia-Hung Wang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Xin Hua Jiang

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Tsai, HH., Wu, ME., Chung, WH., Lu, CY. (2017). On the Study of Trading Strategies Within Limited Arbitrage Based on SVM. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_15

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  • DOI: https://doi.org/10.1007/978-3-319-48490-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48489-1

  • Online ISBN: 978-3-319-48490-7

  • eBook Packages: EngineeringEngineering (R0)

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