Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset

Abstract

Abstract:In recent years, weakened by the fall of economic growth, many enterprises fell into the crisis caused by financial difficulties. Bankruptcy prediction, a machine learning model, is a great utility for financial institutions, fund managers, lenders, governments, and economic stakeholders. Due to the number of bankrupt companies compared to that of non-bankrupt companies, bankruptcy prediction faces the problem of imbalanced data. This study first presents the bankruptcy prediction framework. Then, five oversampling techniques are used to deal with imbalance problems on the experimental dataset which were collected from Korean companies in two years from 2016 to 2017. Experimental results show that using oversampling techniques to balance the dataset in the training stage can enhance the performance of the bankruptcy prediction. The best overall Area Under the Curve (AUC) of this framework can reach 84.2%. Next, the study extracts more features by combining the financial dataset with transaction dataset to increase the performance for bankruptcy prediction and achieves 84.4% AUC.

A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset

“…Other methods combined undersampling and oversampling for obtaining better accuracy. For the experimental dataset in this study, i.e., KBD, Le et al [28] conducted an experiment to compare several oversampling techniques to predict bankruptcy. Moreover, the authors analyze the relationship between bankruptcy and the income and outcome transactions of one company.…”

“…Then, the resampled set, the results of the first module, will be used to train the CBoost classifier, which is then used to predict bankruptcy for the testing set. The proposed framework will be verified by the KBD dataset introduced in [28], which has a high balancing ratio. The experimental results of this study show that the proposed framework outperforms the GMBoost algorithm [24], the oversampling-based framework [28], and the clustering-based undersampling framework [20] for KBD.…”

“…The proposed framework will be verified by the KBD dataset introduced in [28], which has a high balancing ratio. The experimental results of this study show that the proposed framework outperforms the GMBoost algorithm [24], the oversampling-based framework [28], and the clustering-based undersampling framework [20] for KBD. The main contributions of this study are highlighted as follows: (1) We propose the CBoost algorithm, which is a boosting algorithm with initial weight based on the clustering; (2) a robust framework using the CBoost algorithm and IHT (RFCI) is then proposed for effective bankruptcy prediction; (3) several experiments were conducted to find the optimal number of clusters using the Elbow method for KBD.…”

“…The results on this study found that bagging, boosting, and random forest classifiers are better than other models. Recently, Le et al [28] presented a Korean bankruptcy dataset (KBD) with a highly imbalanced ratio, i.e., the number of bankrupt companies is much smaller than the number of normal companies. Moreover, the authors presented a framework for bankruptcy prediction using effective oversampling techniques.…”

“…Next, the novel features extracted from the transaction dataset were added to the original features of KBD to enhance the performance. Although the proposed framework in [28] yields 84.4% in terms of AUC on KBD, it is still necessary to develop another method to improve the performance for this dataset, which is very important for the government and investors.…”