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Open Access Article
1 - Experimental Comparison of Financial Distress Prediction Models Using Imbalanced data sets
Seyed Behrooz Razavi Ghomi Alireza Mehrazin Mohammad Reza Shoorvarzi Abolghasem Masih AbadiFrom machine learning perspective, the problem of predicting financial distress is challenging because the distribution of the classes is extremely imbalanced. The goal of this study was comparing the performance of financial distress prediction models for the imbalance MoreFrom machine learning perspective, the problem of predicting financial distress is challenging because the distribution of the classes is extremely imbalanced. The goal of this study was comparing the performance of financial distress prediction models for the imbalanced data sets with different proportions. In this study, the data of the previous year before financial distress was used for 760 company year for the time period of 2007-2017. Besides using traditional classifications such as logistic regression, linear discriminant analysis, artificial neural network, and the classification models of least square support vector machine with four kernel functions, random forest and the Knn algorithm, the measures of the area under the curve and Friedman and Nemenyi tests were also utilized to determine the average rank and the difference significance of the Auc of the models. For selecting the models´ optimal parameters, the combined method of grid search optimization and cross validation was used. The results of this experimental study showed that for the balanced and imbalanced datasets with lower proportions, the best performance was for the random forest. For more imbalanced datasets, the best performance belonged to the least square support vector machine with sigmoid, radial, and linear kernel functions; performance of Knn algorithm had no significant difference from the other models and the performance of the artificial neural network was average or appropriate. Also, the performances of the linear logistic regression and linear discriminant analysis were weaker than other nonlinear models. Manuscript profile -
Open Access Article
2 - 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 AbadiSo 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 MoreSo 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
List of Articles Mohammad Reza Shourvarzi
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