Decision-Making Process Using Neuro-Fuzzy Model for Capital Market
Keywords:
fuzzy logic, artificial neural networks, stock market, stock trading, soft computing, ETF, ANFISAbstract
Purpose of the article The paper discusses the proposal of a neuro-fuzzy model as support in decision-making on investments in exchange traded funds (ETF) focused on the real estate sector and listed on the American exchange. The created model is based on the neuro-fuzzy inference system (ANFIS).
Methodology/methods The methods of analysis, synthesis and mathematical neuro-fuzzy modelling were used to achieve the goal. Selected financial indicators represent the fuzzy system input. Based on the Gauss membership function, the neural network generates the fuzzy rules. The model output is a signal to buy or sell the ETF stock.
Scientific aim The paper aims to creat a suitable model for decision-making on investments in analyzed investment instruments based on selected financial indicators.
Findings The proposed neuro-fuzzy decision-making model consists of 4 input variables, one rule block (with 81 fuzzy rules) and one output variable (to invest or not to invest). The input variables and the output variable have three attributes (L – large, M – medium, S – small). The created ANFIS model is a suitable tool for investment decisions on buying or selling ETF stock. The model significance lies mostly in the fact it provides the expert analyst or potential investor with enough space to express and incorporate their subjective evaluation in the model.
Conclusions The paper discussed the proposal of a neuro-fuzzy model as support in decision-making on ETF investment opportunities listed on the American market. For further research, the proposed model should be extended by other significant fundamental indicators, possibly incorporate technical and psychological indicators and monitor the strength of the revised model in other capital markets as well.
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