Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty
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Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However,type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited de-scriptiveness. ,is paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets mayinclude additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs arecalculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzysets and compared with the type-1 fuzzy model. ,e case study aims to create a model to support investment decisions inExchange-Traded Funds (ETFs) listed on international equity markets. ,e created models of type-2 fuzzy logic are comparedwith the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dualfuzzy sets is able to better describe data from financial time series and provides more accurate outputs. ,e results reflect thecapability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic modelsdecreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine thedifferent levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.
Document typePeer reviewed
Document versionFinal PDF
SourceMathematical Problems in Engineering. 2021, vol. 2021, issue 1, p. 1-16.
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