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Evaluation of the Degree of Uncertainty in the Type-2 Fuzzy Logic System for Forecasting Stock Index

Author

Listed:
  • Zuzana JANKOVÁ

    (Institute of Informatics, Faculty of Business and Management, Brno University of Technology, Brno, Czech Republic)

  • Petr DOSTÁL

    (Institute of Informatics, Faculty of Business and Management, Brno University of Technology, Brno, Czech Republic)

Abstract

The paper deals with investment analysis based on a new fuzzy methodology. Specifically, the interval type-2 fuzzy logic model is created to support decision-making for investors, financial analysts and brokers. The model is demonstrated on the time series of the leading stock index S&P 500 of the US market. Type-2 fuzzy logic membership features are able to include additional uncertainty resulting from unclear, uncertain or inaccurate financial data that are selected as inputs to the model.The paper deals mainly with the evaluation and comparison of different degrees of uncertainty of the functions of the membership of input variables. Several model situations with different levels of inaccuracy are created. Based on the results of the comparison, it can be said that the type-2 fuzzy logic with dual membership functions is able to better describe data from financial time series.

Suggested Citation

  • Zuzana JANKOVÁ & Petr DOSTÁL, 2022. "Evaluation of the Degree of Uncertainty in the Type-2 Fuzzy Logic System for Forecasting Stock Index," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 41-57, December.
    • Handle: RePEc:rjr:romjef:v::y:2022:i:4:p:41-57
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    References listed on IDEAS

    as
    1. Konstandinos Chourmouziadis & Dimitra K. Chourmouziadou & Prodromos D. Chatzoglou, 2021. "Embedding Four Medium-Term Technical Indicators to an Intelligent Stock Trading Fuzzy System for Predicting: A Portfolio Management Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1183-1216, April.
    2. George S. Atsalakis & Eftychios E. Protopapadakis & Kimon P. Valavanis, 2016. "Stock trend forecasting in turbulent market periods using neuro-fuzzy systems," Operational Research, Springer, vol. 16(2), pages 245-269, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    computational finance; fuzzy logic; type-1 fuzzy logic; T1FLS; type-2 fuzzy logic; T2FLS.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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