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JSAI International Symposium on Artificial Intelligence

JSAI-isAI 2017: New Frontiers in Artificial Intelligence pp 140–156Cite as

Detecting Short-Term Mean Reverting Phenomenon in the Stock Market and OLMAR Method

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10838))

Abstract

In this study, we examined the “short-term Mean Reverting Phenomenon” from two aspects. First, we clarified that excess return can be obtained by using the short-term Mean Reverting Phenomenon for the On-Line Moving Average Reversion (OLMAR) method, which is a portfolio selection algorithm and reportedly exhibits high performance. Then, we examined why the method was able to maintain superiority over the long term. In addition, we proposed an evaluation index of the short-term Mean Reverting Phenomenon present in the stock price dataset and analyzed it. The OLMAR method proved that excessive return was obtained by using the characteristic property showing the mean reverting tendency, which can be selected by the moving average divergence rate. Then, we confirmed that the advantage of the OLMAR method disappears by invalidating the above property existing in the stock price dataset. In addition, we proposed an evaluation index of the Mean Reverting and analyzed the stock price dataset using it.

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Author information

Authors and Affiliations

  1. Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8502, Japan

    Kazunori Umino, Masaaki Kunigami & Takao Terano

  2. Keio University, 4-1-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8526, Japan

    Takamasa Kikuchi

  3. Yamaguchi University, 1677-1, Yoshida, Yamaguchi, 753-8541, Japan

    Takashi Yamada

Authors
  1. Kazunori Umino
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  2. Takamasa Kikuchi
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  3. Masaaki Kunigami
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  4. Takashi Yamada
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  5. Takao Terano
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Corresponding author

Correspondence to Kazunori Umino .

Editor information

Editors and Affiliations

  1. Chiba University, Chiba, Japan

    Sachiyo Arai

  2. National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan

    Kazuhiro Kojima

  3. Ochanomizu University, Tokyo, Japan

    Koji Mineshima

  4. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

  5. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  6. Fujitsu Laboratories Limited, Kanagawa, Japan

    Yuiko Ohta

Appendices

Appendix 1 Algorithm 1: MAD-MRC and Return Curve Algorithm

figure a

Appendix 2 Algorithm 2: OLMAR_Random Method

The OLMAR_random method, once simulated by the OLMAR method, extracts the composition weight of the portfolio and randomly replaces the selected stocks. In addition, a sequence of weights greater than 0 is recognized as one chunk and is copied to new randomly selected stock_id. The OLMAR_random method generates a portfolio based on the new weight data generated by Algorithm 2.

figure b

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Umino, K., Kikuchi, T., Kunigami, M., Yamada, T., Terano, T. (2018). Detecting Short-Term Mean Reverting Phenomenon in the Stock Market and OLMAR Method. In: Arai, S., Kojima, K., Mineshima, K., Bekki, D., Satoh, K., Ohta, Y. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2017. Lecture Notes in Computer Science(), vol 10838. Springer, Cham. https://doi.org/10.1007/978-3-319-93794-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-93794-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93793-9

  • Online ISBN: 978-3-319-93794-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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