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

Zuzana Janková () and Petr Dostál ()
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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

Journal for Economic Forecasting, 2022, issue 4, 41-57

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.

Keywords: computational finance; fuzzy logic; type-1 fuzzy logic; T1FLS; type-2 fuzzy logic; T2FLS. (search for similar items in EconPapers)
JEL-codes: C63 G11 G15 G17 (search for similar items in EconPapers)
Date: 2022
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