Abstract
The decision-making process on investing in financial markets is a very complex and difficult task, mainly due to the chaotic behavior and high uncertainty in the development of the prices of investment instruments. For this reason, financial markets are increasingly using means of artificial intelligence, namely fuzzy logic, which is able to capture the nonlinear behavior.Fuzzy logic provides a way to draw definitive conclusions from vague, ambiguous, or inaccurate information.However, there are some drawbacks associated with type-1 fuzzy logic, so the type-2 fuzzy logic comes forward, which can work with greater uncertainty. Type-2 fuzzy logic works with a new third dimension fuzzy set that provides additional degrees of freedom and allows to model and process numerical and linguistic uncertainties directly. The paper applies type-2 fuzzy logic to the stock market with the aim to create a simple and understandable model for deciding on investing in investment instruments, which is important for investors in this area. The proposed type-2 fuzzy model uses return, risk, dividend and total expense ratio of ETF as input variables. The created system is able to generate aggregated models from a certain number of language rules, which allows the investor to understand the created financial model. Using type-2 fuzzy logic can lead to more realistic and accurate results than type-1 fuzzy logic.
Highlights
Linear models are widely used for forecasting, but these models are greatly limited, especially when applied to seasonal and nonlinear uncertainty issues
Artificial intelligence models used in many fields can be used for this purpose
This paper introduces the implementation of type2 fuzzy logic to the investment decision problem
Summary
Linear models are widely used for forecasting, but these models are greatly limited, especially when applied to seasonal and nonlinear uncertainty issues. Nonlinear methods such as neural networks, fuzzy logic, and genetic algorithms attract more and more attention. Fuzzy logic is able to work with inaccurate data and information in a relatively simple way as well as to understand the meaning of words in natural language. The potential of fuzzy logic to improve forecasting models can be found in various applications (such as Jana & Ghosh, 2018) due to its known ability to bridge the gap between numerical data (quantitative information) and language expression (qualitative information), In particular, financial markets are influenced by deterministic and random factors. Predicting and analyzing financial data is a non-linear and time-dependent problem. Chang et al (2011) conclude and state that stock market forecasts can only be successful with the use of tools and techniques that can overcome the problem of price uncertainty and nonlinearity. Wang &Wang (2015) report that fuzzy logic and neural networks are increasingly being used in financial
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