Meta-heuristic Algorithm

The existence of bubbles in the market, especially the capital market, can be a factor in preventing the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. On the other hand, due to the goal of investors in achieving a portfolio of high returns with the least amount of risk, the need to pay attention to these markets increases. In this research, with the aim of maximizing return and minimizing investment risk, an attempt has been made to form an optimal portfolio in conditions where the capital market has a price bubble. According to the purpose, the research is of applied type, and in terms of data, quantitative and post-event, and in terms of type of analysis, it is of descriptive-correlation type. In order to identify the months with bubbles in the period from 2015 to 2021 in the Tehran Stock Exchange market, sequence tests and skewness and kurtosis tests were used. After identifying periods with bubbles, the meta-heuristic algorithms were used to optimize the portfolio. The results indicate the identification of 14 periods with price bubbles in the period under study. Also, in portfolio optimization, selected stock portfolios with maximum returns and minimum risk are formed. This research will be a guide for investors in identifying bubble courses and how to form an optimal portfolio in these conditions.

The present study is aimed to Rating the loans granted to the real customers of Bank Melli Iran in accordance with the credit factors of the customers using the multi-objective meta-heuristic algorithm of genetics-adaptive neuro-fuzzy network system (GENETIC-ANFIS). This research is a qualitative-quantitative design and exploratory based on purpose in terms of purpose and descriptive in terms in terms of data collection and analysis method and survey. Qualitative data was collected via the research of Rezaei et al. (2022) and the decision making team of the banking field, and quantitative data was collected through 1178 real customers of Bank Melli of Mazandaran province during the years 2012 to 2021 based on 14 types of loans. According to the rating of granted loans, the risk of each loan was measured separately for 4 personal, environmental, economic and credit factors. In Mudharabah loans, Musyarakah, debt purchase, Istisna and salaf, the economic factor showed the highest sensitivity. Also, the behavior of the research meta-heuristic model has indicated 78% reliability in the accuracy and interpretability of the model compared to genetic algorithm, neural network, fuzzy logic and neural-fuzzy network models..