Financial Distress Prediction: A Hybrid Tracking Model Approach

Abstract

The purpose of this study was to build a highly accurate corporate financial distress tracking and prediction model based on hybrid machine learning technology. The research data were from Taiwan Economic Journal, and the research subjects were enterprises with financial distress risk announced in September 2022. In consideration of enterprise features, this study excluded the finance and insurance industries. The research period was three years (2019, 2020, and 2021) before the distress announcement. This study matched enterprises with financial distress and enterprises without financial distress (normal enterprises) at a ratio of 1:1 for each year. The sample size for each year included 374 enterprises with financial distress and 374 enterprises without financial distress. This study applied several machine learning technologies. At first, important variables were screened by applying artificial neural networks (ANNs). Next, prediction models were built based on decision tree C5.0 and random forest (RF) and were compared. According to the empirical result, the ANN-RF model provided a higher accuracy.

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

“…In recent years, machine learning approaches have gained traction in financial distress prediction. The application of machine learning has further promoted studies on financial distress prediction and improved financial distress prediction accuracy [20]. Machine learning algorithms have become extensively utilized in predicting company financial difficulties in recent years [21].…”

“…Several studies have highlighted the superiority of machine learningbased models over traditional methods in predicting corporate financial distress [11]. These machine learning techniques include support vector machines, deep learning models, hybrid machine learning technologies, genetic algorithms, and neural network models [21], [22], [20], [27], [28]. [29] compared traditional methods such as logistic regression with machine learning models like random forest and neural networks to identify the model with the highest predictive accuracy of financial distress.…”