This study identifies key factors significantly impacting financial risk in listed manufacturing enterprises on the Vietnamese stock exchange, using the Least Absolute Shrinkage and Selection Operator (LASSO) variable selection method and constructing a machine learning model to predict financial risk. The research results show that the LASSO model identified five financial indicators influencing the financial risk faced by enterprises over five years (2020-2024), including: Short-term solvency ratio (Current Assets/Short-term Liabilities); Total asset turnover ratio (Revenue/Total Assets); ROA (Net Profit/Total Assets); Long-term Debt-to-Total Assets ratio; and Short-term Debt-to-Total Liabilities ratio. The Artificial Neural Network (ANN) model, when combined with the LASSO method for selecting appropriate predictive variables, enhances the performance of forecasting models compared to not performing variable selection for financial risk assessment. Based on these findings, the research team proposed several solutions and policies for businesses.
Hung et al. (Wed,) studied this question.
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