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Robust technical trading with fuzzy knowledge-based systems (Forthcoming in "Frontiers in Artificial Intelligence and Applications".)

Author

Listed:
  • Masafumi Nakano

    (Graduate School of Economics, University of Tokyo)

  • Akihiko Takahashi

    (Graduate School of Economics, University of Tokyo)

  • Soichiro Takahashi

    (Graduate School of Economics, University of Tokyo)

Abstract

This paper proposes a framework of robust technical trading with fuzzy knowledge-based systems (KBSs). Particularly, our framework consists of two modules, i.e., (i) a module for preparing candidate investment proposals and (ii) a module for their evaluation to construct a well-performed portfolio. Moreover, our framework effectively utilizes fuzzy KBSs for representation of human expert knowledge: Precisely, in the 1st module, three sets of fuzzy IF-THEN rules implement linguistic technical trading rules, which are designed specifically for getting well performance in different market phases. On the other hand, the 2nd module exploits fuzzy logic to evaluate the prepared investment candidates in terms of multilateral performance measures frequently used in practice. In an out-of-sample numerical experiment, our framework successfully generates a series of portfolios, which show long-term satisfactory records in the prolonged slumping Japanese stock market.

Suggested Citation

  • Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Robust technical trading with fuzzy knowledge-based systems (Forthcoming in "Frontiers in Artificial Intelligence and Applications".)," CARF F-Series CARF-F-413, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    • Handle: RePEc:cfi:fseres:cf413
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    File URL: https://www.carf.e.u-tokyo.ac.jp/old/pdf/workingpaper/fseries/F413.pdf
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    References listed on IDEAS

    as
    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Srichander Ramaswamy, 1998. "Portfolio selection using fuzzy decision theory," BIS Working Papers 59, Bank for International Settlements.
    3. Fang, Yong & Lai, K.K. & Wang, Shou-Yang, 2006. "Portfolio rebalancing model with transaction costs based on fuzzy decision theory," European Journal of Operational Research, Elsevier, vol. 175(2), pages 879-893, December.
    4. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    5. Li, Jun & Xu, Jiuping, 2009. "A novel portfolio selection model in a hybrid uncertain environment," Omega, Elsevier, vol. 37(2), pages 439-449, April.
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    Citations

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    Cited by:

    1. Masaaki Fujii & Akihiko Takahashi & Masayuki Takahashi, 2019. "Asymptotic Expansion as Prior Knowledge in Deep Learning Method for high dimensional BSDEs (Forthcoming in Asia-Pacific Financial Markets)," CARF F-Series CARF-F-456, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. Masaaki Fujii & Akihiko Takahashi & Masayuki Takahashi, 2017. "Asymptotic Expansion as Prior Knowledge in Deep Learning Method for high dimensional BSDEs," CIRJE F-Series CIRJE-F-1069, CIRJE, Faculty of Economics, University of Tokyo.
    3. Masaaki Fujii & Akihiko Takahashi & Masayuki Takahashi, 2017. "Asymptotic Expansion as Prior Knowledge in Deep Learning Method for high dimensional BSDEs," CARF F-Series CARF-F-423, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Masaaki Fujii & Akihiko Takahashi & Masayuki Takahashi, 2017. "Asymptotic Expansion as Prior Knowledge in Deep Learning Method for high dimensional BSDEs," Papers 1710.07030, arXiv.org, revised Mar 2019.

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