Business Failure Prediction Based on a Cost-Sensitive Extreme Gradient Boosting Machine

Abstract

Business failure prediction is very important for the sustainable development of enterprises. Machine learning algorithms, especially ensemble algorithms, have shown great economic benefits in enterprise financial early warning. However, the highly imbalanced class distribution of financial risk data and the inexplainable of most machine learning-based early distress warning models limit their commercial application. To address the above limitations, we enhance the business failure prediction performance by tree-ensemble in a boosting manner. Moreover, to solve the class imbalanced issue in business failure datasets, a weighted objective function, weighted cross-entropy, is embedded into the boosted tree framework, making the weighted XGBoost a cost-sensitive business failure prediction model. Besides, to tackle the second issue, we explore the intrinsic interpretability of the proposed method by visualizing the feature importance and incorporating a partial dependence plot technique to locally interpret the individual business failure event. Experimental results on business failure datasets with different predictive horizons collected from China Security Market Accounting Research Database (CSMARD) show the proposed weighted XGBoost is a good solution to reduce the error on recognizing firms in business failure. Furthermore, the visualized feature importance score and partial dependence plot result both demonstrate that the cost-sensitive tree-based ensemble can be a good tool to guide the investors in making rational as well as provide interpretable business prediction results as a reference for the policymaking of the regulators.

Machine Learning for Credit Risk Prediction: A Systematic Literature Review

“…Consequently, in scenarios of increased demand, credit risks also escalate considerably, in a non-linear manner, considering the level of risk, rate, and terms of credit [19]. In the same manner, there is an expectation of an increase in fraud in the following years [20]. Another problem to consider is the consistency of the information recorded at the different stages of the process, such as sales data [21], cultural variables, environmental data [22], macroeconomics [23], innovation capacity management and development, exchange rate evolution, Gross Domestic Product (GDP) growth trends [23,24], economic activity, and experience [25].…”

“…These models have presented problems, especially in difficult times, such as "the financial crisis of 2008" [1,31], since financial institutions focus on loans that generate the most income, being, therefore, of higher risk due to payment defaults [19,22]. Automatic evaluation models, based on credit data, could confuse good paying customers for bad ones [20], and apply penalties on possible benefits [32]. The low explainability of advanced non-linear ML models is slowing down their implementation [33].…”

“…Here, the challenge of machine learning is to consider the existing multicollinearity in the input data [43,44] where there is a high correlation of variables and some that is not useful for classification [2]. This imbalance in the data actually used, with probable overfit [20], could generate biases in machine learning [45,46], causing chaotic reputation management, and malicious or criminal deception [47]. In other words, the challenge is to determine the effective, relevant features [48], for example, for the training of neural networks (NNs) (which is performed end-to-end by interactions).…”

Uncovering the Impact of Local and Global Interests in Artists on Stock Prices of K-Pop Entertainment Companies: A SHAP-XGBoost Analysis

“…They evaluated the effects of these indicators using the Shapley additive explanation value. Zou et al [50] created a weighted XGBoost cost-sensitive model for predicting business failure and analyzed its interpretability, depicting the feature importance with a partial dependence plot (PDP).…”

Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction

“…In another study, the authors used decision trees as a boosting method to improve business failure prediction performance. A weighted objective function, weighted cross-entropy, was incorporated into the boosted tree architecture to overcome the class imbalance issue in the business failure datasets, making the weighted XGBoost a cost-sensitive business failure prediction model [22].…”