A Fuzzy Based Recommendation System for Stock Trading

Pinto, Érico Augusto Nunes; Schnitman, Leizer; Reis, Ricardo

doi:10.1007/978-3-319-95312-0_28

Cited by 2 publications

(2 citation statements)

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“…Researches [17,18] apply triangular fuzzy functions to the moving average prediction and the obtained forecast is implemented on the share at Bombay Stock Exchange. Paper [19] develops a fuzzy trading system integrating technical indicators on the basis of which they predict the development of the stock index. [20] predict the stock price of the State Bank of India (SBI) and the Dow-Jones Industrial Average (DJIA) using a fuzzy model.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Type Problem [16] Gaussian T1FS stock index prediction [23] Triangular T2FS stock index prediction [17] Triangular T1FS stock index prediction [18] Triangular T1FS stock index prediction [19] Trapezoidal T2FS stock index prediction [20] Triangular T1FS stock index prediction [22] Bell ANFIS stock index prediction [21] Bell T1FS stock prediction [24] Triangular T1FS; T2FS stock index prediction [25] Triangular T2FS profit prediction [26] Triangular T2FS stock index prediction For this reason, the authors of this paper consider this area to be very important, especially a detailed examination of membership functions in terms of not only their appropriate shape (T1FL and T2FL) resulting from the nature of the selected dataset, but also the appropriate degree of uncertainty between dual membership functions (T2FLS) resulting from the nature of the stock market.…”

Section: Author Fuzzy Setsmentioning

confidence: 99%

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Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market

Janková

Rakovská

2022

Applied Sciences

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This article deals with the determination and comparison of different types of functions of the type-2 interval of fuzzy logic, using a case study on the international financial market. The model is demonstrated on the time series of the leading stock index DJIA 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. Data on the financial situation of companies are prone to inaccuracies or incomplete information, which is why the type-2 fuzzy logic application is most suitable for this type of financial analysis. This paper is primarily focused on comparing and evaluating the performance of different types of type-2 fuzzy membership functions with integrated additional uncertainty. For this purpose, several model situations differing in shape and level or degree of uncertainty of membership functions are constructed. The results of this research show that type-2 fuzzy sets with dual membership functions is a suitable expert system for highly chaotic and unstable international stock markets and achieves higher accuracy with the integration of a certain level of uncertainty compared to type-1 fuzzy logic.

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