Prediction of European Stock Indexes Using Neuro-fuzzy Technique


  • Zuzana Janková Brno University of Technology Faculty of Business and Management Institute of Informatics
  • Petr Dostál Brno University of Technology Faculty of Business and Management Institute of Informatics



ANFIS, financial market, fuzzy logic, neural networks, soft computing


Purpose of the article: The paper is focused on the forecast of stock markets of the Central European countries, known as V4, by means of soft computing. The tested model is constructed by a combination of fuzzy logic and artificial neural networks. A total of four SAX, PX, BUX, WIG stock indices differing in their liquidity and efficiency are selected for the forecast. Methodology/methods: The methods of analysis, synthesis and techniques of mathematical neuro-fuzzy modelling were used to achieve this goal. The proposed neuro-fuzzy decisionmaking model consists of 3 input variables, one block of rules (with 21 fuzzy rules) and one output variable predicting the normalized price of stock indexes of the selected countries. The input variables have three attributes (L – large, M – medium, and S – small). Scientific aim: The aim of the paper is to create a suitable model that will be used to forecast stock indices of the Central European countries with a relatively low error. Findings: The developed ANFIS model is a suitable tool for predicting stock indexes. The importance of the neuro-fuzzy model can be seen especially in the fact that it shows a strong predictive capacity of both efficient and less efficient stock markets. Conclusions: The paper discussed the design of the neuro-fuzzy model as a supporting tool for predicting the selected stock indexes listed on the European stock markets. For further research, it would be appropriate to extend the proposed model with other significant fundamental indicators, or to incorporate technical and psychological indicators and to monitor the strength of the revised model also in several stock markets, for example according to the geographical distribution.