Effective Bankruptcy Prediction Models for North American Companies

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Author(s): Rachel Cardarelli (Bryant University, USA), Son Nguyen (Bryant University, USA), Rick Gorvett (Bryant University, USA)and John Quinn (Bryant University, USA)
Copyright: 2023
Pages: 15
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch108

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Bankruptcy prediction is a widely researched topic. However, few studies focus on dealing with the imbalance problem. This article proposes a new technique that applies a bagging undersampling procedure to balance the data and compares it to random undersampling and five oversampling techniques. The performance of the algorithm is evaluated by a random forest's balanced accuracy, sensitivity, and specificity. The results show that models trained after applying the oversampling techniques are prone to overfitting, and the model trained after applying the proposed method had the highest balanced accuracy without overfitting.

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Effective Bankruptcy Prediction Models for North American Companies

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