Bankruptcy prediction using hybrid data mining models based on misclassification penalty
Bankruptcy prediction using hybrid data mining models based on misclassification penalty
Subject Areas : Financial engineering
Atiye Torkaman 1 , AmirAbbas Najafi 2
1 - Department of Financial Engineering, Faculty of Industrial Engineering, Khajeh Nasiruddin Toosi University of Technology, Tehran, Iran
2 - Department of Financial Engineering, Faculty of Industrial Engineering, Khajeh Nasiruddin Toosi University of Technology, Tehran, Iran
Keywords: Support vector machine, Data mining, Bankruptcy Prediction, K-Nearest Neighbor, Penalty Coefficients,
Abstract :
In recent years, data mining, particularly the support vector machine, has gained considerable interest among investors, managers, and researchers as an effective means of bankruptcy prediction. However, studies indicate that it is highly sensitive to the selection of parameters and input variables. Hence, the aim of this research is to improve bankruptcy prediction accuracy by combining an advanced support vector machine model with the k-nearest neighbor approach to eliminate erroneous entries. To achieve this, first, by using five financial ratios: current ratio, net profit margin, debt ratio, return on assets, and return of investment from 150 companies listed on the Tehran Stock Exchange during the 10-year period (2010-2019), and k-nearest neighbor algorithm, the training data will be refined. Then, relying on a support vector machine based on classification penalty, a prediction model will be constructed. The parameters will be estimated, and its validity will be assessed using test data. Finally, a comparison will be made between the outcomes of the proposed model and traditional models.The findings demonstrate that the combination of the k-nearest neighbor models and support vector machine reduces the overall prediction error, and the penalty coefficients of the support vector machine exhibit a high level of statistical significance.
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