جستجوی مقالات مرتبط با کلیدواژه « ریسک نکول » در نشریات گروه « مالی »

This study seeks to investigate the effect of variables and data related to people's social network on their credit score. In this study, two main goals are pursued: reducing information asymmetry and increasing financial inclusion. Achieving the above goals is done by finding meaningful information about people's social data to measure how such data affects their credit score. The basic hypothesis of this study is that people with a high credit score have social relationships with people who are similar and of the same age. A data set of more than 300,000 loans that have been paid by an Iranian bank to real people has been used to confirm and explain the effect of social network variables on credit score. In order to determine the variables of the study, an in-depth interview was conducted with a number of banking experts and people actively involved in the field of credit scoring, and at the end, the variables were determined and classified into three categories: financial, behavioral, and social. The study continued with the logistic regression method and finally with various regression models based on deep machine learning, including gradient. The results of the analyses conducted using the logistic regression method show that, statistically, people's social variables can predict the probability of their loan default. The results of machine learning algorithms also indicate that social network information can significantly improve the performance of loan default prediction.

Currently, one of the most important issues that banks and financial institutions always face is the issue of credit risk or the possibility of non-fulfillment of obligations by applicants receiving credit facilities. Therefore, the main purpose of this study is to explain and review the credit rating system of banks in facility files of companies receiving facilities based on the accepted regressions in credit risk and review of financial ratios of these companies. The statistical population of this research consists of 97 listed companies in the period from 2016 to 2021. The results show that the coefficients of independent variables in data mining techniques are higher and the logit model has a higher fit and is more accurate than the good probit model. Current ratios (CUR), turnover (STA) and return on sales (NTS) also play a greater role in assessing default risk.

Competition is one of the issues that economists always refer to as a solution for economic growth and optimal use of economic resources. Increasing competition and efficiency in the banking market can increase the quality and variety of banking services as well as reduce transaction costs. This is an important reason for the need to measure banking risks by considering the competition between them. In this study, for banks listed on the stock exchange, the relationship between banking competition and various types of important banking risks has been investigated. The risks in question are default risk, assets, market, capital and liquidity, which are measured as a dependent variable of their relationship with the competition index (Herfindal). The results show that banking competition has a negative and significant relationship with asset risk and a positive and significant

Measuring credit risk and estimating the likelihood of companies failing is one of the most important challenges in the credit sector. Structural and non-structural models are the two main frameworks for estimating default risk and credit risk. But each of these models has its strengths and weaknesses, and it seems that combining these two frameworks and providing a hybrid model can provide a more accurate prediction of companies' default risk. In the present study, a hybrid model has been used to measure the default risk of listed and over-the-counter companies in the period between 2007-2017, which has been transferred to the basic market according to the laws of the Iranian capital market. First, the Merton model (structural models) was used to measure the risk of default of these companies, and then the results of this model were compared with the Z-Altman model (from non-structural models). Then, by regression analysis of different financial ratios, significant variables were identified and Morton's and Z-Altman models were statistically and comparatively compared. The findings show that the hybrid model offers a more accurate prediction of the risk of default than structural and non-structural models. By entering the results of each of these two models into the hybrid model, the statistical power of the hybrid model increases. Therefore, using a combined model will help banks and credit institutions to provide resources to healthier companies with less risk.

Study of spillover effects of default risk plays a very significant role in financial institutions, their relation & interaction with each other and in portfolio selection, risk management and the credit rating of such institutions as well.This research aims to study the spillover effects of default risk between holding Companies and their subsidiaries active in Iran’s capital market. For such a purpose, in the first phase, default probability was calculated for Iran Khodro Investment Development Co. and its three subsidiaries by Black-Scholes-Merton (BSM) option pricing model, then default probability spillover for the period of 2011-2018 was measured on daily basis by multivariate GARCH model (BEKK model).As concluded, there is a spillover effect of default risk from the holding companies to their subsidiaries and vice versa.

Systemic risk has been considered as one of the new concepts in the field of finance since 2008 and this risk is more considered in the banking industry due to the close relationship between banks in their daily operations. Therefore, identifying the factors affecting this risk in the banking industry is the main purpose of this study. In this study, the relationship between macroeconomic indicators (interest rate, economic growth rate and inflation), risk (liquidity and default risk) and competitiveness (Herfindahl-Hirschman index and asset size) using banks' data from 2009 to date and with The GMM data panel method was tested. The results of modeling show that there is a significant and direct relationship between default risk index (credit) and systemic risk of the banking industry. Also, in all competition indicators, including Herfindahl-Hirschman index and the size of banks, there is a direct relationship, and in macroeconomic indicators, the relationship between interest rates and inflation with the systemic risk of banks is direct and significant.

This study is examines the effect of default risk on explanatory power of Fama-French five factor model from 2009 to 2017 in Tehran Stock Exchange. In this regard, time series regression and GRS tests were used. For reaching the research purpose, Fama-French five factor model results compared with six factor model results, which default risk regarded as sixth variable using GRS test. In addition, for investigating the effect of default risk on return, time series regression was used. The research results showed that default risk variable has not a significant effect on the explanatory power of the Fama-French five factor model. In other words, default risk factor is not significant in explaining return change. Also, the results showed that among 36 investigated portfolios in this research, in most portfolios there is a positive relationship between default risk and portfolio’s return but only in 8 portfolios this relationship was significant.