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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/2008
Title: Comprehensive Machine Learning Applications in Corporate Bankruptcy Risk Prediction: Evidence from Vietnam
Authors: Nguyen Dinh Anh Khoa 
Keywords: bankruptcy risks, machine learning, Elastic Net, LASSO, neural network
Issue Date: Dec-2024
Source: IEEE
Abstract: 
This study aims to comprehensively examine bankruptcy risk prediction in Vietnam by applying
and evaluating the performance of several machine learning models, such as neural networks and
tree-based algorithms, alongside advanced feature selection techniques. This is the first study to
apply Elastic Net for feature selection in this context, alongside LASSO regression. Utilizing
financial data from over 1500 publicly traded companies on Vietnam stock exchanges, the largest
dataset in the field to date, the study explores the applicability of predictive modeling in predicting
corporate bankruptcy risks and identifying key financial indicators of financial distress. The
findings reveal that most models performed very well despite the class imbalance and missing
values within the dataset. The study also demonstrates that Elastic Net outperform LASSO in
feature selection by retaining more relevant features and maintaining models’ stability, particularly
with kNN, SVM, and LSTM. Key financial metrics, including debt ratios, profitability measures,
liquidity indicators, and market confidence variables, emerge as significant predictors. Moreover,
findings also suggest missing values may carry valuable insights in predicting financial distress.
These obtained results have practical implications for credit risk assessment, early warning systems
for regulators, and corporate financial planning.
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/2008
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Finance & Accounting (FA)

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