علی نمکی

Holding companies collect funds from subsidiaries and allocate them to important areas (Internal Capital Market). Managers of holding companies have the ability to transfer funds between subsidiaries. Some specific orientations cause the non-optimal allocation of financial resources. One of the concerns of investors is investing in companies where transparency is fully implemented. Companies are trying to achieve this by implementing corporate governance mechanisms. In this research, using a systematic review method, the dimensions of corporate governance were extracted with the stakeholder theory approach. Finally, in order to examine the question of whether the managers of holding companies consider the interests of all stakeholders when using the internal capital market or not, according to the assumptions of the research, the dimensions of corporate governance on stakeholders have been investigated. To investigate this relationship, a set of questions based on Likert scale about the measured variables of the target society was designed. After data collection, finally, data analysis was done by statistical method using SPSS software and structural equations using SPLS software, and the results of path analysis and causal relationships between the research variables were interpreted in the conceptual model. The data analysis also showed that the value of the path coefficient, the effects of dimensions and components of corporate governance and stakeholders, is a positive value. The null hypothesis of the research is rejected and the opposite hypothesis is confirmed. This shows that there is a relationship between the effects of corporate governance dimensions and stakeholders. The direct effect value indicates a strong and high effect size. As a result, the interests of all stakeholders should be considered.

One of the essential factors that lead to severe disruptions in financial markets is price bubbles and subsequent crashes. Numerous models for detecting bubbles have been developed, one of which (LPPLS) has lately attracted considerable interest. This study aims to utilize this model to detect price bubbles in Tehran Stock Exchange's index (TEDPIX). Confidence multi-scale indicators for this model are presented by fitting the LPPLS model to the data of the TSE index from 2009 through 2020. The bubble is detected when the number of fits that are in our filter conditions increases which means the growth of the indicator's value. By applying this method on TSE data two significant crashes in 2013 and 2020 are detected. The proposed technique can be useful for market participants to detect financial crashes and bubbles.

Since the 1970’s developing and developed countries have experienced unprecedented public debt levels. This surge in public debt has emphasized the importance of public debt management. Since risks such as reducing economic growth, increasing inflation, and depreciation of the national currency accompany unplanned public debt accumulation, governments should be alert not to endanger economic growth with ill-considered borrowing. In this paper, we aim to analyze Iran’s external debt variations concerning major macroeconomic variables such as GDP growth as a proxy of economic growth, inflation, and sovereign oil generated incomes. The method that is applied in this research is cross wavelet transform which is a powerful mathematical approach for analyzing the financial data.      Our results show there are different patterns in small and large scales between variables and external debt as a dependent variable has different relations with endogenous and exogenous factors. In the short run, low oil prices as an exogenous variable, during the 1980s have shaped governments’ debt accumulation behavior but on larger scales, indigenous variables such as governments’ budget deficits have been much more dominant in shaping governments borrowing patterns. In a chronological view, US cruel sanctions and the Iran-Iraq war were major events affecting sovereign borrowing behavior.

Banked based financial sector of Iran leads us to focus on the banking industry and its components. One of the important aspects of this industry is its coupling structure. In this paper, we have analyzed the collective behavior of Iran banking sector by Random Matrix Approach (RMT). This technique is useful for splitting the information part of the correlation matrix from the random region. This research confirms good compliance with random matrix predictions. By removing the market mode of the system the average of the banking cross-correlation matrix changes. Then, by calculation of the participation ratio, node participation ratio and relative participation ratios of these banks, it is shown that the collective behavior of the system is so fragile. Also, by applying local and global perturbations on the banking sector, it is shown that this system is very sensitive to the global perturbation and the mean value of cross-correlations decreases rapidly that means some banks have crucial effects in the market. 

Networks are useful tools for presenting the relationships between financial institutions. During the previous years, many scholars have found that using single-layer networks cannot properly characterize and explain complex systems. The purpose of this research is to introduce a multiplex network in order to analyze, as accurately as possible, all aspects of communication between banks in capital market of Iran. In this article, each bank represents a node and three layers of return, trading volume and market Cap have been presented for analyzing the idea of multiplex networks. We have used the Granger causality method to determine the direction between nodes. For understanding the topology structure of these layers, different concepts have been used. The research findings show that the value layer topology has a significant similarity with the trading volume layer. Also according to the measure of centrality it can be seen that the centrality varies in different layers.