Corporate Bankruptcy Prediction: An Approach Towards Better Corporate World. (17th June 2020)

• Record Type: Journal Article
• Authors: Alam, Talha Mahboob; Shaukat, Kamran; Mushtaq, Mubbashar; Ali, Yasir; Khushi, Matloob; Luo, Suhuai; Wahab, Abdul
• Is Part Of: Computer journal. Volume 64:Number 11(2021)
• Page Start: 1731
• Page End: 1746
• Publication Date: 2020-06-17
• Subjects: corporate bankruptcy prediction -- imbalanced dataset -- random undersampling -- SMOTE -- data mining -- machine learning -- deployment; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxaa056
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: The area of corporate bankruptcy prediction attains high economic importance, as it affects many stakeholders. The prediction of corporate bankruptcy has been extensively studied in economics, accounting and decision sciences over the past two decades. The corporate bankruptcy prediction has been a matter of talk among academic literature and professional researchers throughout the world. Different traditional approaches were suggested based on hypothesis testing and statistical modeling. Therefore, the primary purpose of the research is to come up with a model that can estimate the probability of corporate bankruptcy by evaluating its occurrence of failure using different machine learning models. As the dataset was not well prepared and contains missing values, various data mining and data pre-processing techniques were utilized for data preparation. Within this research, the task of resolving the issues induced by the imbalance between the two classes is approached by applying different data balancing techniques. We address the problem of imbalanced data with the random undersampling and Synthetic Minority Over Sampling Technique (SMOTE). We used five machine learning models (support vector machine, J48 decision tree, Logistic model tree, random forest and decision forest) to predict corporate bankruptcy earlier to the occurrence. We use data from 2009 to 2013 on Poland manufacturing corporates and selected the 64 financial indicators to be broken down. The main finding of the study is a significant improvement in predictive accuracy using machine learning techniques. We also include other economic indicators ratios, along with Altman's Z -score variables related to profitability, liquidity, leverage and solvency (short/long term) to propose an efficient model. Machine learning models give better results while balancing the data through SMOTE as compared to random undersampling. The machine learning technique related to decision forest led to 99% accuracy, whereas support vector machine (SVM), J48 decision tree, Logistic Model Tree (LMT) and Random Forest (RF) led to 92%, 92.3%, 93.8% and 98.7% accuracy, respectively, with all predictive financial indicators. We find that the decision forest outperforms the other techniques and previous techniques discussed in the literature. The proposed method is also deployed on the web to assist regulators, investors, creditors and scholars to predict corporate bankruptcy.