List of Articles Financial Distress Prediction

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  1 - The Role of Earnings Management in Theoretical Development and Improving the Efficiency of Accounting-Based Financial Distress Prediction Models
  Abbas Ramezanzadeh Zeidi Khosro Faghani Makarani Ali Jafari
  10.22034/amfa.2019.1879394.1300
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  2 - Experimental Comparison of Financial Distress Prediction Models Using Imbalanced data sets
  Seyed Behrooz Razavi Ghomi Alireza Mehrazin Mohammad Reza Shoorvarzi Abolghasem Masih Abadi
  10.22034/amfa.2021.1905055.1461
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  3 - Developing Financial Distress Prediction Models Based on Imbalanced Dataset: Random Undersampling and Clustering Based Undersampling Approaches
  Seyed behrooz Razavi ghomi Alireza Mehrazin Mohammad reza shoorvarzi Abolghasem Masih Abadi
  10.22034/amfa.2022.1956898.1743
  So far, distress prediction models have been based on balanced, such sampling is not consistent with the reality of the statistical community of companies. If the data are balanced, the bias in sample selection may lead to an underestimation of typeI error and an overes More

  So far, distress prediction models have been based on balanced, such sampling is not consistent with the reality of the statistical community of companies. If the data are balanced, the bias in sample selection may lead to an underestimation of typeI error and an overestimation of the typeII error of models. Although imbalanced data-based models are compatible with reality, they have a higher typeI error compared to balanced data-based models. The cost of typeI error is more important to Beneficiaries than the cost of typeII error. In this study, for reducing typeI error of imbalanced data-based models, random and clustering-based undersampling were used. Tested data included 760 companies since 2007-2007 with 4 different degrees and the results of the H1 to H3 test represented them. In all cases of the typeI error, typeII error of balanced data-based models were lower and more, respectively, compared to imbalanced data-based models; also, in most cases, the geometric mean of balanced data-based models was higher compared to imbalanced data-based models, respectively. The results of testing H4 to H6 show that in most cases, typeI error, typeII error and the geometric mean criterion of models based on modified imbalanced data were less, more, and more, respectiively compared to the models based on imbalanced data, in other words, applying Undersampling methods on imbalanced training data led to a decrease in typeI error and an increase in typeII error and geometric mean criteria. As a result using models based on modified imbalanced data is suggested to Beneficiaries Manuscript profile
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  4 - Predicting Banks' Financial Distress by Data Envelopment Analysis Model and CAMELS Indicators
  Abass Paidar Morteza Shafiee Fariborz Avazzadeh Hashem Valipour
  10.30495/jsm.2021.1935059.1499
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  5 - Expansion of Financial Distress Modeling Using Corporate Earnings Management in the Iran's Economic Environment
  Abbas Ramezanzadeh Zeidi Khosro Faghani Makrani Ali jafari
  The purpose of this study is to provide a model for financial distress predicting with real earnings management.So the redesignthe financial distress prediction model of Altman (1983) with the real earnings management variable as a predictor variable, the performance of More

  The purpose of this study is to provide a model for financial distress predicting with real earnings management.So the redesignthe financial distress prediction model of Altman (1983) with the real earnings management variable as a predictor variable, the performance of the unadjusted model and the adjusted model in predicting of financial distress among companies accepted in the Tehran Stock Exchange was compared.The statistical sample consists of 179 Companies during the years 2008- 2017.Data analysis and hypothesis testing were performed using multiple logistic regression.The results show that the overall accuracy of the adjusted model is higher than the unadjusted model. Manuscript profile
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  6 - Comparing Different Feature Selection Methods in Financial Distress Prediction of the Firms Listed in Tehran Stock Exchange
  Mohammad Namazi Mostafa Kazemnezhad M. Mahdi Nematollahi
  Research in financial distress and bankruptcy emphasize the design of more sophisticated classifiers, and less feature (variables) selection and their appropriate methods. In this regard, the purpose of this study is to compare performance of different feature selection More

  Research in financial distress and bankruptcy emphasize the design of more sophisticated classifiers, and less feature (variables) selection and their appropriate methods. In this regard, the purpose of this study is to compare performance of different feature selection methods in financial distress prediction of the companies listed on Tehran Stock Exchange (TSE). In this regard, we investigated and compared five feature selection methods, including t-test, stepwise regression, factor analysis, relief, wrapper subset selection and RFE-SVM feature selection. To obtain comparable experimental results (reliable comparison), three different classifiers (including neural networks, support vector machine and AdaBoost) were used in this study. In overall, the experimental results confirmed the usefulness of variable selection methods and significant difference among level (amount) of different methods performance. In other words, the application of the feature selection methods increases the mean of accuracy, and reduces the occurrence of type I and type II errors. Furthermore, the results indicated that wrapper subset selection method outperforms the other feature selection methods. Manuscript profile