logistic regression

It is very important to choose an efficient monitoring system to assess the financial distress of banks, therefore, one of the most important monitoring systems used to assess the financial distress of banks is the CAMELS monitoring system. Which includes six indicators; Capital adequacy, asset quality, management quality, revenue quality, liquidity, market risk sensitivity. Therefore, in this study, the criterion of financial helplessness of banks is CAMELS indicators. Initially, 17 banks listed on the Tehran Stock Exchange in the fiscal year 1399 were ranked and divided into healthy and helpless financial groups by CAMELS indicators. Then, models, Data Envelopment Analysis and logistic Regression were used to predict the financial distress of banks. Then, with the pairwise comparison test (T), the prediction accuracy of both models was investigated. In logistic regression method, binary model with ForwardlR method was used. And in data envelopment analysis method, SBM model with different application was used. The results showed that the overall accuracy of the logistic regression model is higher than the data envelopment analysis model in assessing financial distress and also the CAMELS monitoring system can be a good assessor for banks' financial distress.

This study with aim to designing a model for fiscal discipline of companies listed in the Tehran stock exchange using logistic regression was conducted. In this regard, in this study, 15 independent variables were considered as dependent variables to explain fiscal discipline. For this purpose we examined of data related on 129 firms that were accepted in Tehran stock exchange during 21/3/2015 to 20/03/2019 and were analyzed using Eviews9 software. The result showed that accordingly with significance level, there is a positive and significant relationship between the previous year's fiscal discipline and audit firm size separately with dependent variable. Also, there is an inverse and significant relationship between exchange rate fluctuations, earnings management, audit firm rotation and credit risk with fiscal discipline. However, for other explanatory variables, no significant relationship was observed with the dependent variable.

The present research with emphasis on the use of the combined model of logistic regression, artificial neural networks, and symbolic regression, the current research aims to identify the factors that affect the credit risk of real customers in selected branches of Sepah Shiraz, with the aim of effective monitoring of micro-loan. By examining a sample of 351 real customers, 17 variables included; loan amount, repayment term, interest rate, income, age, number of bounced checks, debt history, account lifetime, type of collateral, education, gender, spouse's employment, marital status, property status, job, type of loan, obligatory or non-obligatory status for the classification of customers into good and bad accounts were categorized and extracted. Using forward selection technique of parent, 5 variables affecting credit risk were selected and used to train a neural network with three neurons in the hidden layer. The optimal cutting point was selected based on the characteristic curve of the system performance. The results of the output of the artificial neural network on the test data showed that the accuracy of the combined model of logistic regression-artificial neural networks in the classification of good customers is 0.89 and in the classification of bad customers is 0.83. The results of logistic regression, and the combined logistic regression-symbolic regression model is better.

Market timing Investment decisions are made with a mechanical trading strategy based on certain macroeconomic criteria. In this study, investor sentiment index and macroeconomic indicators such as inflation, exchange rate, employment growth and real GDP of representative variables were used to market timing to predict the direction and return of the total index of Tehran Stock Exchange. In this regard, four models of logistic regression, Lasso, Ridge and Elastic Net were used using monthly data in the period 1395 to 1399. In order to develop the sentiment index, using the exploratory factor analysis model, six different emotional variables were used, and finally, three variables of stock ratio in the portfolio of investment funds, Tehran price index and top 50 index were selected. The output of the logistic regression model for forecasting based on a single index was compared with the value of forecasting based on other indicators, which showed that logistics forecasting based on all variables was superior to logistic forecasting based on a single index. Comparison of Lasso, Ridge and Elasticnet models for prediction showed that the strength and accuracy of Ridge regression model was more than the other two models, in addition, Lasso and Elasticnet models were almost equally accurate. The results of this research can be useful for investment companies and portfolio managers, analysts and investors.

The purpose of this study is to validate customers and the impact of its components on non-current receivables of Bank Mellat. For this purpose, in the first stage, first, by using the documentary and library method, 15 indicators affecting the validation of customers in three sections: individual, financial and social identified, and then the final data related to the indicators, including the files of 7330 real customers of Bank Mellat, were collected during 2013-2019. To study the credit rating indicators of customers and the impact of its components on non-current receivables of Bank Mellat, multiple logistic regression was used in four categories of timely receipt, past due, delinquent and doubtful receivables. The results showed that in general, individual, social, and economic conditions affecting the credit of real customers have a significant effect on reducing the non-current receivables of Bank Mellat. Using the results of research model estimation can help bank managers and monetary and banking policymakers to make the right decision regarding customers' credit ratings and reduce banks' non-current receivables.

The main purpose of this study is to investigate the effects of investor sentiment on bubble formation in the stock market. This study aims to find a significant relationship between investors' sentiments and the creation and explosion of the bubble in the Tehran stock exchange. For this purpose, daily index data is used as well as investor sentiment index for the 2009 - 2019 period. By using Dicky – Fuller test, we identify bubbles in the Tehran stock market and calculate the indicator of investor sentiment. Afterwards, we study the relationship between investor sentiment and bubble formation by using Logistic regression. According to the research findings, the hypothesis of the influence of investor's feelings in the capital market on the formation of the financial bubble is rejected.Also according to the results of logistic regression and probit regression, The probability value of the variable EMSI (Equity Market Sentiment Index) at conventional statistical levels is meaningless. Further, the author observes that, Investor's sentiment have no effect on the formation of a bubble in the Stock market.

One of the most important risks of the banking system is the credit risk. Considering the portfolios of institutes and the mutual relationship of their items, any tension in refunding the overdone facilities can lead to essential problems such as liquidity risk, interest rate, and even bankruptcy. In this way, finance and credit institutes look for models, achieving experience, and improving credit evaluation models they use to validate the credit of their credit customers. However, the list of the important variables for credit validation and selection of the more appropriate and effective model has been a crucial question for many of these institutes. The present study investigates the LOGIT, PROBIT, and Z Altman models, using a set of qualitative and quantitative variables of the legal customers of depository institutions. The findings prove that qualitative and financial variables of the legal customers for this institute proved to be explanatory for the credit risk probability at a high degree of confidence. These models successfully predicted credit risk for 80% of the facilities and LOGIT was more successful than other models.

In current competitive environment, the risk of financial distress of companies has increased, in this situation, individuals and corporations and financial institutions make a lot of effort to learn about the status of Investee companies to protect their capital. The assessment and recognition of the firm's financial condition requires utilizing efficient measures with less error probability, Therefore, the purpose of this study is to compare the different criteria for identifying financially distressed companies. For this purpose, after reviewing and identifying the most important criteria and models for identifying distressed companies from non-distressed corporations, the distressed companies of Tehran Stock Exchange during the years 2006 to 2018 were separated from healthy firms and compared with the results of logistic regression and artificial intelligence methods, Article 141 of the trade law, Altman (1968), Altman (1995) and Asquith et al (1994). The results of this study showed that during the period under review and in the conditions of the Iranian companies based on Tehran Stock Exchange that Asquith et al (1994) criteria is the best way to identify distressed companies and to predict the financial position of companies and the Altman criteria (1995), Article 141 of the trade law and Altman (1968) are among the top priorities in identifying distressed enterprises.

This research predicts financial distress of companies listed on Tehran Stock Exchange using Factor Analysis, Decision Tree, and Logistic Regression models. For this purpose, 33 financial ratios have been investigated in the 5-year time horizon. In order to reduce the dimensions of the data and to find the internal relationship the variables, factor analysis model has been used. Then, the variables according to their relationships with financial distress are classified in eight factors. In the following, the results of the decision tree and the logistic regression models are compared with each other. The results show that both models have the ability to predict financial distress, but the decision tress model has a higher predictive power than the logistic regression model.

Stock market activists are the acquiring and using methods to predict future stock prices, increasing their capital gains. Therefore, it seems necessary that appropriate, correct, and scientific principles are used to determine the future price of the stock of investor stock options.stock price prediction is an important part of investment, and in most cases it is the field of research for researchers, because it ultimately leads to the choice of appropriate investment. Different methods have now been developed to achieve this goal. Have been introduced that are often statistical methods and artificial intelligence. In this research, using a randomized approach approach that is among artificial intelligence classification methods, along with technical indicators that include: power index Relative Price, Stochastic, Equilibrium Balance, Williams R%, Daily Returns, and Mac.d Series Markets, are looking for stock price trends. This model is compared with logistic regression method and completely randomized method (dice throw). The results of the research on daily data of Tehran Stock Exchange Index from 1393 to 1395 indicate that the accuracy of the proposed method in estimating market trend is 64%, which is more than two methods of logistic regressionand completely randomized method of accuracy Has a higher rate.

According to what is stated in behavioral finance issues is one of the most important factors affecting the demand for formal financial sector, behavioral characteristics of individuals. The present study aimed to explore the relationships between some individual and behavioral characteristics and demand for financial services, is trying to score as behavioral characteristics, including financial literacy, per capita expenditure, age, risk aversion, interest in financial matters, fatalism , education and the savings on the demand for financial services. The statistical population of the present study is Householders metropolis of Mashhad and surrounding rural areas in the period of 2015-2016. The sample size was 290 people who were selected by using available sampling method .Binary logistic regression was used to analyze the research hypotheses. The results showed that the behavioral characteristics of individuals only expenditure per capita, age, interested in financial issues and the savings impact on the demand for financial services. Furthermore, as the interaction effect between age and the rural and urban on willingness to invest in life insurance and investment.

In this research, five major bankruptcy predictions model to study and among the components of the five models, redesigned bankruptcy prediction is provided that consists of eight variables. The main issue in this research is that by examining the financial statements of listed companies in Tehran Stock Exchange we can offer a model to predict corporate bankruptcy. In order to design data from two groups of companies accepted in the Tehran Stock Exchange use the first group consists companies surveyed non-bankrupt company and second group included bankrupt company. The study period financial statements of exchange data during the years have been 2005 to 2014. The study results in relation to the ability to predict model reflects the fact that the model could be two years before the bankruptcy of companies provide accurate predictions about the crisis and bankruptcy. The results show that the predictive power of the model for one year before bankruptcy 91% and two years before the bankruptcy 83%.

One of the threats facing the companies is the financial distress and the bankruptcy accordingly. The purpose of this study is to investigate the effect of earning quality as a moderator in companies’ financial distress prediction. The population of study included 41 distressed and 41 non distressed companies listed in Tehran Stock Exchange during 2003 to 2013. The selection criterion to choose distressed companies was the use of five specific criteria for financial distress. Firstly, to verify the separation accuracy of two samples, distress and Non-distress, using the Wilcoxon test, the average of two independent variables were analyzed. In order to investigate the financial distress prediction, three models included Altman, Ohlson and Bharath and Shumway in a binary logistic regression were used. The findings of the study show that the earning quality significantly increases the accuracy of predicting financial distress. Also Altman model, in comparison to other models, is more accurate in predicting financial distress, so it is considered as a more suitable tool in prediction.

In this study, the relationship between tax evasion and future stock price crash risk of Companies listed in Tehran stock exchange (TSE), based on data from 70 companies during the years 2005-2012 is examined. To do this, two hypothesis were specified. The statistical methods used in testing hypotheses are logistic and panel data regression. Findings show that tax evasion is positively associated with future stock price crash risk. This finding is consistent with the following view: Tax avoidance facilitates managerial rent extraction and bad news hoarding activities. The hoarding and accumulation of bad news for extended periods lead to stock price crashes when the accumulated hidden bad news crosses a tipping point, and thus comes out all at once. Moreover, the second hypothesis, the positive relation between tax evasion and crash risk is attenuated when firms have strong external monitoring mechanisms such as institutional investors and non executive directors, was not approved.

The purpose of this study is to investigate the big shareholders’ perspective on block divestiture of shares in business financing through propensity score matching method in Tehran stock exchange. The statistical population of this study includes all of listed companies in Tehran stock exchange. 102 companies were examined from 2003 to 2012 and their data was collected through website of Tehran stock exchange and software of Novin Rahavard. In order to test the hypotheses، Logistic regression and matching based on the attitude score methods were used. The results of this study revealed that attitudes of big shareholders toward divestiture influence business policies negatively. As a result، shareholders seek to eliminate obstacles of appropriate divestiture opportunities.

This research has been done with the aim of identification of effective factors which influence credit risk and designing model for estimating credit Rating of the companies which have borrowed from a commercial Bank in the one-year period by using Data Envelopment Analysis and neural network model and comparison of these two models . For this purpose the necessary sample data on financial and non-financial information of 146 companies (as random simple) was selected. In this research, 27 explanatory variables (include financial and non-financial variables) were obtained, by application of factor analysis and Delphi method for examination. Finally 8 variables which had significant effect on credit risk were selected and entered to DEA model. Efficiency of companies was calculated with these variables. Also variables as well as the input vector three-layer perceptron neural network models were added to the model .finally data was processes with logistic regression. Results from data envelopment analysis model and Neural network and Logistic regression in comparisons to the actual results obtained from neural network models to predict credit risk legal customers and credit rating suggest that neural network is more efficient than data envelopment analysis and logistic regression.