Design a Model to Predict the Financial Crisis of the Iranian Capital market Using Smart Web Models

As the managers due to decision-making and stakeholders, namely investors, tend to predict the occurrence or non-occurrence of financial crisis in the organization under their management, so the present study is aimed to provide a model for predicting this crisis. To achieve the research purpose, smart web models including grey wolf, ant colony optimization, particle swarm optimization and genetics algorithms were used. For this purpose, the data obtained from the questionnaire completed by 20 experts in the quality section and the data obtained from 173 companies from 2009 to 2019 listed in the Tehran Stock Exchange (TSE) were used. 38 indices from the categories of macroeconomic indicators, industry factors, corporate characteristics, political, cultural and behavioral events were identified using the review of the theoretical basics. Then, 25 indicators with high impact on the financial crisis were selected using expert opinion and MICMAC analysis. Then, by reviewing the financial statements of 173 companies listed on the Tehran Stock Exchange (TSE) and using Rahavard Novin software, the data were collected from 25 selected indicators and their impact on the financial crisis was examined using gray wolf, ant colony, particle swarm and genetics algorithm to determine the final model of the research. It was found that in terms of efficiency, the ant colony optimization method is the most efficient and the gray wolf method is the least efficient in predicting the financial crisis.