IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v80y2009i3p506-521.html
   My bibliography  Save this article

Synthesis of fuzzy logic and Dempster–Shafer Theory for the simulation of the decision-making process in stock trading systems

Author

Listed:
  • Sevastianov, P.
  • Dymova, L.

Abstract

Modern computerized stock trading systems (mechanical trading systems) are based on the simulation of the decision-making process and generate advice for traders to buy or sell stocks or other financial tools by taking into account the price history, technical analysis indicators, accepted rules of trading and so on. Two stock trading simulating systems based on trading rules defined using fuzzy logic are developed and compared. The first is based on the so-called “Logic-Motivated Fuzzy Logic Operators” (LMFL) approach and aims to avoid certain disadvantages of the classical Mamdani’s method, which has been developed for use in fuzzy logic controllers and not for solving the decision-making problems of stock trading. The LMFL approach is based on the modified mathematical representation of t-norm and Yager’s implication rule. The second trading system combines the tools of fuzzy logic and Dempster–Shafer Theory (DST) to represent the features of the decision-making process more transparently. The fuzzy representation of trading rules based on the theory of technical analysis is used in these expert systems. Since the theory of technical analysis is based on the indicators used by experts to predict stock price movements, the method maps these indicators into new inputs that can be used in a fuzzy logic system. The only required inputs to calculate these indicators are past sequences (history) of stock prices. The method relies on fuzzy logic to choose an appropriate decision when certain price movements or certain price formations occur. The optimization procedure based on historical (teaching) data is used as it significantly improves the performance of such expert systems. The efficiency of the developed expert systems is measured by comparing their outputs versus stock price movements. The results obtained using real NYSE data allow us to say that the developed expert system based on the synthesis of fuzzy logic and DST provides better results and is more reliable. Moreover, such a conjunction of fuzzy logic, DST and technical analysis, makes it possible to make a profit even when trading against a dominating trend.

Suggested Citation

  • Sevastianov, P. & Dymova, L., 2009. "Synthesis of fuzzy logic and Dempster–Shafer Theory for the simulation of the decision-making process in stock trading systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(3), pages 506-521.
  • Handle: RePEc:eee:matcom:v:80:y:2009:i:3:p:506-521
    DOI: 10.1016/j.matcom.2009.06.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475409001980
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2009.06.027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sevastjanov, P.V. & Róg, P., 2003. "Fuzzy modeling of manufacturing and logistic systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 63(6), pages 569-585.
    2. Christian Haefke & Christian Helmenstein, "undated". "Forecasting Stock Market Averages to Enhance Profitable Trading Strategies," Computing in Economics and Finance 1996 _023, Society for Computational Economics.
    3. Beynon, Malcolm, 2002. "DS/AHP method: A mathematical analysis, including an understanding of uncertainty," European Journal of Operational Research, Elsevier, vol. 140(1), pages 148-164, July.
    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. Guodong Yu & Li Zhang & Huiping Sun, 2018. "A Method for Partner Selection of Supply Chain Using Interval-Valued Fuzzy Sets — Fuzzy Choquet Integral and Improved Dempster–Shafer Theory," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1777-1804, November.
    2. Ledermann, Daniel & Alexander, Carol, 2012. "Further properties of random orthogonal matrix simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 83(C), pages 56-79.
    3. Hong, Bingyuan & Shao, Bowen & Guo, Jian & Fu, Jianzhong & Li, Cuicui & Zhu, Baikang, 2023. "Dynamic Bayesian network risk probability evolution for third-party damage of natural gas pipelines," Applied Energy, Elsevier, vol. 333(C).

    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. Arán Carrión, J. & Espín Estrella, A. & Aznar Dols, F. & Zamorano Toro, M. & Rodríguez, M. & Ramos Ridao, A., 2008. "Environmental decision-support systems for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2358-2380, December.
    2. A.H.T. Shyam Kularathna & Sayaka Suda & Ken Takagi & Shigeru Tabeta, 2019. "Evaluation of Co-Existence Options of Marine Renewable Energy Projects in Japan," Sustainability, MDPI, vol. 11(10), pages 1-26, May.
    3. Jorge L. García-Alcaraz & Aidé A. Maldonado-Macías & Juan L. Hernández-Arellano & Julio Blanco-Fernández & Emilio Jiménez-Macías & Juan C. Sáenz-Díez Muro, 2016. "Agricultural Tractor Selection: A Hybrid and Multi-Attribute Approach," Sustainability, MDPI, vol. 8(2), pages 1-16, February.
    4. Wang, Ying-Ming & Yang, Jian-Bo & Xu, Dong-Ling & Chin, Kwai-Sang, 2006. "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees," European Journal of Operational Research, Elsevier, vol. 175(1), pages 35-66, November.
    5. Liu, Qiang, 2021. "Reliability evaluation of two-stage evidence classification system considering preference and error," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Ozdemir, Mujgan S. & Saaty, Thomas L., 2006. "The unknown in decision making: What to do about it," European Journal of Operational Research, Elsevier, vol. 174(1), pages 349-359, October.
    7. Narimah Samat & Mohd Amirul Mahamud & Mou Leong Tan & Mohammad Javad Maghsoodi Tilaki & Yi Lin Tew, 2020. "Modelling Land Cover Changes in Peri-Urban Areas: A Case Study of George Town Conurbation, Malaysia," Land, MDPI, vol. 9(10), pages 1-16, October.
    8. Justin Moskolaï Ngossaha & Raymond Houé Ngouna & Bernard Archimède & Mihaela-Hermina Negulescu & Alexandru-Ionut Petrişor, 2024. "Toward Sustainable Urban Mobility: A Multidimensional Ontology-Based Framework for Assessment and Consensus Decision-Making Using DS-AHP," Sustainability, MDPI, vol. 16(11), pages 1-22, May.
    9. Beynon, Malcolm J., 2005. "Understanding local ignorance and non-specificity within the DS/AHP method of multi-criteria decision making," European Journal of Operational Research, Elsevier, vol. 163(2), pages 403-417, June.
    10. Amel Alnaqbi & Muataz Al Hazza, 2023. "Utilizing Industry 4.0 to Overcome the Main Challenges Facing UAE to Achieve the (SDG6.b) Goal of the United Nation Sustainable Development," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 98-107, September.
    11. Helena Gaspars-Wieloch, 2024. "AHP based on scenarios and the optimism coefficient for new and risky projects: case of independent criteria," Annals of Operations Research, Springer, vol. 341(2), pages 937-961, October.
    12. Ganji, Seyedreza Seyedalizadeh & Rassafi, Amir Abbas & Bandari, Samaneh Jamshidi, 2020. "Application of evidential reasoning approach and OWA operator weights in road safety evaluation considering the best and worst practice frontiers," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    13. Xu, Dong-Ling & Yang, Jian-Bo & Wang, Ying-Ming, 2006. "The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1914-1943, November.
    14. Frikha, Ahmed & Moalla, Hela, 2015. "Analytic hierarchy process for multi-sensor data fusion based on belief function theory," European Journal of Operational Research, Elsevier, vol. 241(1), pages 133-147.
    15. Jiménez Capilla, J.A. & Carrión, J. Arán & Alameda-Hernandez, E., 2016. "Optimal site selection for upper reservoirs in pump-back systems, using geographical information systems and multicriteria analysis," Renewable Energy, Elsevier, vol. 86(C), pages 429-440.
    16. Wang, Ying-Ming & Yang, Jian-Bo & Xu, Dong-Ling, 2006. "Environmental impact assessment using the evidential reasoning approach," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1885-1913, November.
    17. Amel Ennaceur & Zied Elouedi & Eric Lefevre, 2016. "Belief AHP Method — AHP Method with the Belief Function Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 553-573, May.
    18. Niloofar Vahabzadeh Najafi & Alireza Arshadi Khamseh & Abolfazl Mirzazadeh, 2020. "An Integrated Sustainable and Flexible Supplier Evaluation Model under Uncertainty by Game Theory and Subjective/Objective Data: Iranian Casting Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 21(4), pages 309-322, December.
    19. Beynon, Malcolm J., 2005. "A novel technique of object ranking and classification under ignorance: An application to the corporate failure risk problem," European Journal of Operational Research, Elsevier, vol. 167(2), pages 493-517, December.
    20. Sevastjanov, P. & Figat, P., 2007. "Aggregation of aggregating modes in MCDM: Synthesis of Type 2 and Level 2 fuzzy sets," Omega, Elsevier, vol. 35(5), pages 505-523, October.

    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:eee:matcom:v:80:y:2009:i:3:p:506-521. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.