A dynamic financial distress forecast model with multiple forecast results under unbalanced data environment

Abstract

Corporate financial distress forecasts are important for companies, investors and regulatory authorities. However, as most financial distress forecast (FDF) models in previous studies were based on a single time dimension, they have tended to ignore the two key financial distress data characteristics, imbalanced data sets and concept drift of data stream. To overcome these problems, this study proposes a new dynamic financial distress forecasting (DFDF) approach, the Adaptive Neighbor SMOTE-Recursive Ensemble Approach (ANS-REA), that allows for multiple forecast results from unbalanced data streams. An empirical experiment was conducted on 373 financially distressed samples and 1119 matching normal Chinese listed companies from 2007 to 2017. With an overall average AUC, it was found that the Random Forest (RF) classifier outperformed other commonly used classifiers such as Support Vector Machine (SVM), Decision Tree (DT), baggingDT, oblique random forests (obRF), Kernel ridge regression (KRR) and Bayes in the classification of DFDF data. In addition, the proposed ANS-REA algorithm had better performance than SMOTE, ANS, Random Walk Over-Sampling Approach (RWO), Rapidly Converging Gibbs sampling Technique (racog), SMOTEboost, RUSboost, SMOTEbagging, wRACOG and Majority Weighted Minority Oversampling Technique (MWMOTE) methods in dealing with imbalanced data sets classification. Further, we found that the proposed model that combined the multiple forecast results is the effective way to solve the financial distress forecast problem.