Financial risk

The main objective of this article is to investigate the impact of various financial, economic, and political risks and economic complexity on the development of the Sukuk market in the Iranian economy. The data required to conduct this research based on the variables of the proposed model were used from the Capital Market Central Asset Management Company, the International Country Risk Guide (ICRG) database, and the MIT University website. The data relating to 2010-2022 is seasonal, and REVIEWS 13 software was used. The model estimation results using the Nonlinear Autoregressive Distributed Lag Model Approach (NARDL) show that the negative shock of political risk reduces the development of the Sukuk market in the short and long term. The negative shock of financial risk in the long term has a negative impact on the development of the Sukuk market. The negative shock of economic complexity reduces the development of the Sukuk market in the short term. The positive shocks of political risk, financial risk, economic risk, and economic complexity in the short and long term led to the development of the Sukuk market. Among the three types of risk, political risk and financial risk have the most impact on sukuk market development. The error correction coefficient in this estimate is negative and statistically significant, which shows that 0.42% of the short-term imbalance is adjusted to reach the long-term balance every year.

This research uses a fuzzy Delphi approach to identify the dimensions and components of investor risk and financing for IT-based startup companies. The statistical population for this study consisted of 30 experts and university professors familiar with the research concepts surveyed to select the dimensions and components identified from the literature and prior research. The results from the fuzzy Delphi method showed that the financing and investor risk dimensions were selected in 9 dimensions and 29 components. The weighting results for the research dimensions and components using the Best-Worst Method (BWM) prioritized each. The weighting results indicate that the industry status ranked first, followed by scientific factors and other components, ranked third to ninth in the LINGO software. Additionally, the intensity of relationships among the research dimensions was assessed using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. The analysis of the intensity of relationships among the dimensions shows that the government factors dimension had the highest numerical value based on the row sum, making it the most influential dimension among those examined in financing startup companies using the DEMATEL technique. Conversely, based on the D-R analysis, the geographic factors dimension received the lowest value and was recognized as the most affected dimension. Using the fuzzy Delphi method, this research has identified specific and novel dimensions and components, such as governmental, geographical, and scientific factors, which have been less addressed in the existing literature.