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A firm bankruptcy prediction is one of the important challenges of investors and other stakeholders in the capital market, because timely detection of firms exposed to bankruptcy can largely prevent potential losses. Nevertheless, despite numerous efforts in this field (generally considering quantitative criteria) , a comprehensive method for predicting bankruptcy has not been developed yet. Therefore, considering qualitative criteria along with quantitative criteria in order to predict bankruptcy may be beneficial. This study attempts to investigate the effect of tone of Board activity reports as one of the qualitative criteria, on predicting a firm bankruptcy. The present study uses data of 476 year-firm listed in Tehran Stock Exchange in the period from 2011 to 1396. In order to test the hypotheses, logistic regression method is used.Findings indicate that the qualitative criteria (negative and judicial tone) of financial reports may not predict a firm bankruptcy. In other words, tone of financial reports does not provide investors and stakeholders with a suitable tool for predicting bankruptcy. ‌

The main purpose of this study is to investigate the relation between auditor characteristics and probability of bankruptcy. This study uses variables such as auditor fees, audit-firm size, audit-firm tenure, and auditor industry specialization as auditor characteristics. Also the study uses Altman (2000) and Kordestani at el (2014) as bankruptcy Indices. Data collected from 98 firms over the period 2005-2015 are analyzed using logistic regression. The results indicate that there is no significant relation between the auditor's characteristics and the probability of bankruptcy obtained through Altman's model. On the other hand, findings show that firms which have higher audit fees and are audited by large audit firms are more likely to default (calculated by the Kordestani model). In addition, firms audited by long-tenured auditor firms and industry-expert auditors are less likely to go bankrupt. ‌

Objective: In the present era, businesses have developed to a large extent which has, in turn, forced them to manage their resources and expenditures wisely for the sake of competition. This is mainly because the competitive market has severely reduced the flexibility of companies, which means that their ability respond to different economic situations has reduced and this puts most firms at the constant risk of bankruptcy and contraction. Therefore, in this study, we have tried to predict the bankruptcy of manufacturing companies through preventing the occurrence of such risks. Methods: In this study, the "Kernel Extreme Learning Machine" has been used as one of the artificial intelligence models for predicting bankruptcy. Given that machine learning methods require an optimization algorithm we have used one of the most up-to-date, "Gray Wolf Algorithm" which has been introduced in 2014. Results: The above model has been implemented on the 136 samples that were collected from the Tehran Stock Exchange between 2015 and 2018. All of the performance evaluation criteria including the classification, accuracy, type error, second-order error and area under the ROC curve showed better performance than the genetic algorithm which was presented and its significance was confirmed by t-test. Conclusion: Considering the gray wolf algorithm’s high accuracy and its performance compared to the genetic algorithm, it is necessary to use the gray wolf algorithm to predict the bankruptcy of Iranian manufacturing companies either for investment purposes and for validation purposes, or for using internal management of the company

This study has been carried out with purpose to present the method of meta analysis as a tool to aggregate and reprocessing findings and investigate its ability to assess field studies of accounting and financial. In this respect, studies on the topic of bankruptcy predictions were evaluated, which indicate that the lack of integrity and agreement on bankruptcy predictor variables as the fundamental basis of these studies. Accordingly, regardless of the techniques used and the purpose of them, extracting a set of predictor variables and integration them in this area is necessary Therefore, three studies were assessed by using Meta analysis. The main reason for the use of these three studies was lack of sufficient data for Meta analysis of studies that provide evidence of weakness in standard presentation and reporting of studies by researchers. Among of techniques of quantitative Meta analysis for aggregating the results, is used combining result approach and effect size r. After reviewing studies and extraction 42 unique variables, finally the seven variables were selected based on the four classification of r.

The usefulness of accounting information has always been source of concern for standard setters that view the main goal of financial reporting as the issue required for generating useful information to facilitate decision making process according to conceptual framework. Predicting bankruptcy is one of the main areas in which researchers have studied the usefulness of accounting information. However, to assess the usefulness of accounting information in current condition, it is necessary to compare it’s predicting ability in relation to other information resources such as market information. To this end, using genetic algorithm, models with different mixture of accounting and market variables are designed and tested. To improve the generalization of the results, cross-validation is used. The samples used in this study include the companies listed in Tehran Stock Exchange between 2001 to 2009. The fiundings show that accounting information is more useful in predicting bankruptcy than market information, and that market variables are not a good complement to accounting information in bankruptcy prediction.

Bankruptcy predication is one of the main matters in classifying the bankrupt corporations. Investors, Owners, Managers, Creditors, Governmental Agencies are interested in evaluating the financial conditions of the companies. Since in the case of bankruptcy, it will cost them a lot. This research intended to study the bankruptcy prediction in Tehran Stock Exchange (TSE) by using the Altman’s Z-model, Genetic Algorithms and finally intends to determine efficiency of the best model. The samples under the investigation were 36 bankrupt companies and 36 non-bankrupt companies during fiscal years of 1384 – 1386. The variables used in Genetic Algorithms Model and Altman’s Z- Model were 5 variables. The Genetic Algorithms Model had respectively an equivalent accuracy between 90, 91.5 percent in average during one year and two years before the indicator year (base year).The Altman‘s Z-Model had equivalent accuracy of 83.32 and 83.32 percent. According to the finding, Genetic Algorithms Model (GAM) was more accurate in predicting bankruptcy as a result; it is an appropriate means for predicting bankruptcy.

The main purpose of this paper is prediction of TSE corporate financial bankruptcy using Artificial Neural Networks. The mean values of key ratios reported in past bankruptcy studies were selected for neural network inputs (Working capital to total assets, Net income to total assets, Total debt to total assets, Current assets to current liabilities, Quick assets to current liabilities). The neural network used in this paper is Multilayer Perceptron (MLP) that trained with back propagation algorithm, and contained three-layer feed forward neural network with 5,4,1 number of neurons in input, hidden and output layer respectively. The samples of this paper consist of bankrupt and non-bankrupt groups. Bankruptcy group was Manufacturing Corporations that were included Article 141 of Mercantile law within 1378-1385 and non-bankruptcy group selected by random sampling. The same set of data is analyzed using more traditional method of bankruptcy prediction, multivariate discriminate analysis. A comparison of the predictive abilities of both the neural network and the discriminate analysis method was presented. Also, prediction accuracy of neural network is presented by ROC curve. The results showed that there is significant difference between MDA and ANN, Also, according to the results, lower error type 1 has priority to error type 2.

Bankruptcy prediction is one of the major business classification topics. Owners, managers, investors, creditors and business partners, as well as governmental institutions are interested in assessing the financial position of firms. Nowadays several models have been used to predict bankruptcy.‌The goal of this study is the presentation of theoretical basis of bankruptcy and comparing the results of prediction of Altman and Fulmer models. So the data collected were examined during the years 2000-2004. To analyze the data the nonparametric statistical models were used. The results show that in the prediction of bankruptcy, there is a significant difference between two models. Altman model has done more conservatively than Fulmer model as well.

Financial distress and bankruptcy of companies may cause the resources to be wasted and the investment opportunities to be faded. Bankruptcy prediction by providing warnings can make the companies aware of the occurrence of bankruptcy and financial distress so that they could take appropriate decisions. The aim of this study is to model financial distress prediction of listed companies in Tehran stocks exchange (TSE) using Bayesian Networks (BNs). In order to accomplish this aim, two naïve bayes models and a logistic regression model using information of listed companies in Tehran Stock Exchange are developed. The accuracy of the first naïve bayes model's performance that is based upon conditional correlation is 90% and the accuracy of the second naïve bayes model that is based upon conditional likelihood is 93% and eventually the accuracy of the logistic regression model that is a linier model is 90%.

One of the main issues in financial management is choosing the best way of utilizing investment. Investors would like to invest their capitals in a way to minimize their risks. Bankruptcy is one of the risk factors which affect the decision of investors. Prediction of bankruptcy can help investors to reduce the risks in the capital markets and recognize the best opportunities for alternative investment. This study aims to predict the bankruptcy of companies by using the technique of Artificial Neural Network (ANN). Moreover, discriminant Analysis and logestic regression techniques are employed to compare the results. The data used in this study covers the firms in the Kerman Province of Iran over the period 1975- 2007. The results show that ANN model perfom much better than the discriminant analysis and logestic regression techniques. Moreover, the results confirm that the accuracy of ANN model is higher than the discriminant analysis and logestic regression techniques for predicting of bankruptcy. The analysis also shows that none of the firms will bankrupt in the year after the period covered in this study.