Hybrid Model for Financial Distress Prediction: Evidence from Vietnamese Listed Firms

This work develops a financial distress prediction model by combining a multi-layer perceptron artificial neural network (MLP-ANN) with the conventional Altman Z-Score model. Financial data comprising 7,126 observations from 509 listed firms in Vietnam, collected between 2006 and 2019, were used to build the hybrid model. Our predictive model accurately classifies the financial status of listed firms into three categories: low bankruptcy probability, high bankruptcy probability, and a gray region of bankruptcy, with an accuracy rate of 98.4%. Additionally, the results segmented by industry show that the trading company model achieves the highest accuracy (99.2%) in identifying the firm's financial status, followed by the models for the service and manufacturing industries (98.6% and 97.4%, respectively). In Vietnam, the Z-score and ANN hybrid model proves to be both appropriate and useful. Banks, securities firms, and investors can utilize this application to forecast the financial status of businesses in advance, aiding in making well-informed decisions.