Linear Discriminate Analysis (LDA) is a valuable tool for two or multigroup classification. Assuming in LDA classes have multivariate normal distribution with common covariance matrix. The case of normal classes rarely holds. Therefore, for the purpose of effective classification in case of heterogeneity of classes it is natural to assume that the classes consist of subclasses with normal but unobserved distribution, classification in this case is called Mixture Discriminate Analysis (MDA), and estimation of the parameters is possible only through EM algorithm. Linear decision boundaries do not differentiate classes completely. In MDA, decision boundaries are nonlinear even with the assumption of equal covariance matrix among classes. This study with financial ratios  of Altman model tries to predict financial failure in Tehran stock market, for this purpose sample consist 100 survivor firms and 44 bankrupt firms from 1378 to 1384. Using this sample estimated parameters for LDA and compared MDA. It has been demonstrated MDA is more effective for predicting financial failure of various firms and MDA has been 92.34 percent correct classification for all firms.

Current collapses of big companies and the worse fluctuations of the financial markets have evoked the awareness of the stakeholders and managers to utilize suitable tools to predict the financial distress of companies. One of such tools is the application of financial ratios as independent variables and developing models to predict bankruptcy issue. The objective of this study is first to test the prediction power of original Altman (1983) and Ohlson (1980) models on the dataset of Iranian listed companies and secondly by applying Multiple Discriminant Analysis (i.e. MDA) and Logit Analysis statistical techniques on the same dataset, develop a suitable prediction model for bankruptcy of listed companies in the economic environment of Iran. It was finally concluded that both original Ohlson bankruptcy prediction model in 1980 without any modification of multipliers and coefficients and Logistic regression technique showed better prediction results than original Atman model in 1983 or Discriminant analysis technique.

One tools which is used in decision making for investing in a firm models of predicting bankruptcy. The aim of this research is to offer the best model of bankruptcy of firms in Iran. For this research, the logit model is used and a model is offered to predict bankruptcy in accepted firms in stock exchange of Tehran. For designing a model, the information of two groups of accepted firms are used in stock exchange of Tehran. The first group of evaluated firms was consisted of 20 bankrupt firms and the second group was also similar to first group and was consisted of 20 non-bankrupt firms. To design these model nine financial ratios were used. According to this research among these, the logit model with explanatory variables of working capital to total assets, current liabilities to current assets and gross interest to sales are liquidity ratios, liquidity and profit ability ratios respectively, which has the most powerful predictability about bankruptcy of firms in Iran. The accuracy and precision of predictability model for the year of bankruptcy is %87.5, for one year before bankruptcy is about %72.5 and for two years before that is % 52.5.Therefore the recent research has shown that the bankruptcy procedure of firms in Iran, not only is not a long term and gradual procedure but also these firms under the condition of economical fluctuations and political variables in short term were encountered with bankruptcy.

We developed three Logit models for estimating the rating of prediction of bankruptcy for listed firms in Tehran Stock Exchange (TSE).The first phase of study examines the existence of any significance. Statistical differences between the financial statement data of successful and unsuccessful firms, if there will be any significant association between financial ratios and probability of bankruptcy, we want to see which ratios have a greater association with probability of bankruptcy. There are some significant differences between the two groups, from the financial activity indexes, liabilities position (financial leverage) points of view. However, the differences for liquidity not passed the test of significance. In this study we developed the Logit model with a Wald methodology to predict the rating of the firms with a reasonable accuracy.

The use of traditional forecasting tools and methods has a high error and has a poorer performance compared to newer methods and nonlinear models. One of the most widely used methods and algorithms in predicting the use of machine learning. The main purpose of this study is to investigate the application of machine learning in providing a model for predicting the bankruptcy of 308 companies listed on the Tehran Stock Exchange in the period 1389 to 1398 (3080 years - company) to test the hypotheses of multiple regression of composite data. In order to implement the Medians-K clustering algorithm and related calculations, R statistical calculation software was used. The results show that among the financial ratios identified in the first model, only the ratio of net income to total assets and the ratio of market value of equity to total market value can improve the ability of the Altman bankruptcy prediction model. Also, in the second model, the specified financial ratios have the ability to improve the bankruptcy forecast model, and by adding the Devscore variable for groups based on industry and size, the modified model improves the bankruptcy forecast, The results shows that a company is more likely to go bankrupt if it has bankruptcy-related financial ratios that are lower than the average of its cluster peers..