چکیده:
In utilizing the system dynamics approach to model social and economic phenomena, due to the profound impact of the human factor and their decision-making in such phenomena, the certainty present in classical and modern views has diminished, making the need for a more flexible perspective on these phenomena essential. The aim of the present research is to simulate the interactions of linguistic variables affecting capital market value using a system dynamics approach. In this regard, to reflect the mental thinking patterns of investors, a fuzzy inference system has been used within the system dynamics approach. The proposed model of the research was simulated with Vensim DSS software, and its validity was assessed using statistical and instrumental tests. The results of scenario simulations show that simultaneous changes in endogenous capital market variables have a much greater effect and with less time delay on the capital market value variable compared to the individual changes of each variable. The impact of desirable changes in capital market efficiency variables, investment knowledge and culture, and capital market interventions leads to a 77 percent increase in capital market value in the 1404 horizon compared to the baseline simulation. Additionally, sensitivity analysis simulation results show that capital market value exhibits less sensitivity to individual changes in linguistic variables compared to their simultaneous changes.
خلاصه ماشینی:
Simulating the interactions of linguistic variables in the process of capital market development by utilizing 1 a fuzzy inference system and a system dynamics approach Ali Mohammadi 2, Alinaghi Mosleh Shirazi 3, Abbas Abbasi 4, Saeed Akhlaghpour 5 Abstract In utilizing the system dynamics approach for modeling social and economic phenomena, due to the profound impact of the human factor and its decision-making in such phenomena, the certainty present in classical and modern views has faded, making the need for a more flexible perspective toward these phenomena essential.
In this method, the non-fuzzy value related to the fuzzy variable is equal to the weighted average of the Maximum2, calculated in the following form: (Refer to the page image) Research Background In the field of modeling the internal dynamics of the capital market, most studies conducted have investigated the dynamics of the internal variables of the capital market separately and without using system dynamics methodologies.
Determining fuzzy modules and designing the fuzzy inference process Variables such as confidence in the capital market, efficiency, transparency, investment knowledge and culture, and the psychological tendency of investors must be entered into the system dynamics model through fuzzy inference, based on their linguistic nature.
Accordingly, in the present research, in order to model the effects of linguistic variables on the capital market development process, the fuzzy inference process was integrated into the system dynamics approach, and scenario simulation was used for this purpose.
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