scholarly journals Data Mining for Municipal Financial Distress Prediction

2018 ◽  
pp. 296-308 ◽  
Author(s):  
David Alaminos ◽  
Sergio M. Fernández ◽  
Francisca García ◽  
Manuel A. Fernández
Keyword(s):  
Data Mining ◽  
Financial Distress ◽  
Financial Distress Prediction ◽  
Distress Prediction

scholarly journals Financial Distress Prediction of Iranian Companies Using Data Mining Techniques

Organizacija ◽  
2013 ◽  
Vol 46 (1) ◽  
Author(s):  
Mahdi Moradi ◽  
Mahdi Salehi ◽  
Mohammad Ebrahim Ghorgani ◽  
Hadi Sadoghi Yazdi
Keyword(s):  
Data Mining ◽  
Financial Distress ◽  
Data Mining Techniques ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Using Data

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

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