IDEAS home Printed from https://ideas.repec.org/p/tky/fseres/2017cf1053.html
   My bibliography  Save this paper

Robust Technical Trading with Fuzzy Knowledge-based Systems

Author

Listed:
  • Masafumi Nakano

    (Graduate School of Economics, The University of Tokyo)

  • Akihiko Takahashi

    (Faculty of Economics, The University of Tokyo)

  • Soichiro Takahashi

    (Graduate School of Economics, The 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 speci cally 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," CIRJE F-Series CIRJE-F-1053, CIRJE, Faculty of Economics, University of Tokyo.
  • Handle: RePEc:tky:fseres:2017cf1053
    as

    Download full text from publisher

    File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2017/2017cf1053.pdf
    Download Restriction: no
    ---><---

    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. 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.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Li, Jun & Xu, Jiuping, 2009. "A novel portfolio selection model in a hybrid uncertain environment," Omega, Elsevier, vol. 37(2), pages 439-449, April.
    5. Srichander Ramaswamy, 1998. "Portfolio selection using fuzzy decision theory," BIS Working Papers 59, Bank for International Settlements.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. C. Veeramani & R. Venugopal & S. Muruganandan, 2023. "An Exploration of the Fuzzy Inference System for the Daily Trading Decision and Its Performance Analysis Based on Fuzzy MCDM Methods," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1313-1340, October.
    3. Akihiko Takahashi & Soichiro Takahashi, 2022. "A state space modeling for proactive management in equity investment "Forthcoming in International Journal of Financial Engineering"," CARF F-Series CARF-F-543, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. 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.
    5. 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.
    6. 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.
    7. Akihiko Takahashi & Soichiro Takahashi, 2022. "A State Space Modeling for Proactive Management in Equity Investment," CIRJE F-Series CIRJE-F-1197, CIRJE, Faculty of Economics, University of Tokyo.
    8. Masaaki Fujii & Akihiko Takahashi & Masayuki Takahashi, 2019. "Asymptotic Expansion as Prior Knowledge in Deep Learning Method for High dimensional BSDEs," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(3), pages 391-408, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Fuzzy Logic-based Portfolio Selection with Particle Filtering and Anomaly Detection," CIRJE F-Series CIRJE-F-1037, CIRJE, Faculty of Economics, University of Tokyo.
    2. 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.
    3. Liu, Yong-Jun & Zhang, Wei-Guo, 2015. "A multi-period fuzzy portfolio optimization model with minimum transaction lots," European Journal of Operational Research, Elsevier, vol. 242(3), pages 933-941.
    4. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Fuzzy Logic-based Portfolio Selection with Particle Filtering and Anomaly Detection (Subsequently published in "Knowledge-Based Systems")," CARF F-Series CARF-F-405, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    5. Liu, Yong-Jun & Zhang, Wei-Guo & Zhang, Pu, 2013. "A multi-period portfolio selection optimization model by using interval analysis," Economic Modelling, Elsevier, vol. 33(C), pages 113-119.
    6. Madlener, Reinhard & Glensk, Barbara & Weber, Veronika, 2011. "Fuzzy Portfolio Optimization of Onshore Wind Power Plants," FCN Working Papers 10/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Jul 2014.
    7. Tsaur, Ruey-Chyn, 2013. "Fuzzy portfolio model with different investor risk attitudes," European Journal of Operational Research, Elsevier, vol. 227(2), pages 385-390.
    8. 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.
    9. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Fuzzy Logic-based Portfolio Selection with Particle Filtering and Anomaly Detection," CIRJE F-Series CIRJE-F-1037, CIRJE, Faculty of Economics, University of Tokyo.
    10. Gupta, Pankaj & Mittal, Garima & Mehlawat, Mukesh Kumar, 2013. "Expected value multiobjective portfolio rebalancing model with fuzzy parameters," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 190-203.
    11. Yong-Jun Liu & Wei-Guo Zhang, 2018. "Multiperiod Fuzzy Portfolio Selection Optimization Model Based on Possibility Theory," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 941-968, May.
    12. Smimou, K. & Bector, C.R. & Jacoby, G., 2008. "Portfolio selection subject to experts' judgments," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 1036-1054, December.
    13. Zhang, Wei-Guo & Liu, Yong-Jun & Xu, Wei-Jun, 2012. "A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs," European Journal of Operational Research, Elsevier, vol. 222(2), pages 341-349.
    14. Sehgal, Ruchika & Sharma, Amita & Mansini, Renata, 2023. "Worst-case analysis of Omega-VaR ratio optimization model," Omega, Elsevier, vol. 114(C).
    15. Hasuike, Takashi & Ishii, Hiroaki, 2009. "On flexible product-mix decision problems under randomness and fuzziness," Omega, Elsevier, vol. 37(4), pages 770-787, August.
    16. Zhao, Daping & Bai, Lin & Fang, Yong & Wang, Shouyang, 2022. "Multi‐period portfolio selection with investor views based on scenario tree," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    17. P. Kumar & Jyotirmayee Behera & A. K. Bhurjee, 2022. "Solving mean-VaR portfolio selection model with interval-typed random parameter using interval analysis," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 41-77, March.
    18. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Creating Investment Scheme with State Space Modeling ," CIRJE F-Series CIRJE-F-1038, CIRJE, Faculty of Economics, University of Tokyo.
    19. Bilbao-Terol, Amelia & Arenas-Parra, Mar & Cañal-Fernández, Verónica, 2016. "A model based on Copula Theory for sustainable and social responsible investments," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 19(1), pages 55-76.
    20. Woodside-Oriakhi, M. & Lucas, C. & Beasley, J.E., 2013. "Portfolio rebalancing with an investment horizon and transaction costs," Omega, Elsevier, vol. 41(2), pages 406-420.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tky:fseres:2017cf1053. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CIRJE administrative office (email available below). General contact details of provider: https://edirc.repec.org/data/ritokjp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.