Developing Financial Distress Prediction Models Based on Imbalanced Dataset: Random Undersampling and Clustering Based Undersampling Approaches

الموضوعات :

Seyed behrooz Razavi ghomi ¹ ( Department of Accounting, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran )
Alireza Mehrazin ² ( Department of Accounting, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran )
Mohammad reza shoorvarzi ³ ( Department of Accounting, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran )
Abolghasem Masih Abadi ⁴ ( Department of Accounting, Sabzevar Branch, Islamic Azad University, Sabzevar, Iran )

تاريخ الإرسال : 26 الأربعاء , رمضان, 1443 تاريخ التأكيد : 15 الخميس , ذو الحجة, 1443 تاريخ الإصدار : 18 السبت , شوال, 1445

الکلمات المفتاحية: Imbalanced datasets, Undersampling , Machine Learning, Financial distress prediction models, Financial ratios,

ملخص المقالة :

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

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