Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam

Abstract

The past decade has witnessed the rapid development of machine learning applied in economics and finance. Recent evidence suggests that machine learning models have produced superior results to traditional statistical models and have become the driving force for dramatic improvement in the financial industry. However, a much-debated question is whether the prediction results from black box machine learning models can be interpreted. In this study, we compared the predictive power of machine learning algorithms and applied SHAP values to interpret the prediction results on the dataset of listed companies in Vietnam from 2010 to 2021. The results showed that the extreme gradient boosting and random forest models outperformed other models. In addition, based on Shapley values, we also found that long-term debts to equity, enterprise value to revenues, account payable to equity, and diluted EPS had greatly influenced the outputs. In terms of practical contributions, the study helps credit rating companies have a new method for predicting the possibility of default of bond issuers in the market. The study also provides an early warning tool for policymakers about the risks of public companies in order to develop measures to protect retail investors against the risk of bond default.

RETRACTED ARTICLE: Explainable AI Model for Recognizing Financial Crisis Roots Based on Pigeon Optimization and Gradient Boosting Model

“…Table 9 summarizes the comparison results between the state-of-art models and the proposed XAI model. The classification performances metrics ( e.g., accuracy, precision, recall, and F1-score) clarify that the proposed XAI model has the best performance in recognizing and interpreting financial crisis classification results compared with the state-of-art models, NN [ 46 ], LASSO-CART [ 47 ], and Decision Trees [ 48 ], Extreme Gradient Boosting [ 49 ] models.…”

Exploring Trends and Advancements in Financial Distress Prediction Research: A Bibliometric Study

“…There have been many research studies in the context of financial distress prediction but still there is need to explore more in this area. Previous studies have explored this area with using AI and ML techniques used for predict financial distress (Zhang et al, 2022), (Tran et al, 2022). Some advanced AI model (weighted boosted tree-based tree) used for distress prediction which was given by (Liu et al, 2022), for Deep learning (Li and Wang, 2023).…”

Application of machine learning in classification of overinvestment: Evidence from listed firms in Vietnam stock exchange market

“…In Vietnam, there are studies on using machine learning models to support and predict financial-related problems. In another study, Tran et al utilized empirical evidence from listed companies in Vietnam between 2010 to 2021 to predict financial hardship using machine learning algorithms 12 . The research evaluated the predictive capability of different machine learning models and utilized SHAP values to interpret the obtained results.…”

“…In addition, in this study, we also build a completely new model for measuring overinvestment based on the existing foundational theories. This research focuses on the main objective to compare the performance in classify overinvestment companies of six classification algorithms: logistic regression 11 , support vector machine (SVM), decision tree 12 , random forest 13 , Naive Bayes (NB) and Knearest neighbor (KNN). With the aforementioned comparison, we indicate which is the most suitable algorithm for classifying overinvestment companies.…”