Machine learning and statistical modeling in firm value prediction - Van, Trang, Do Thi; Thu, Huyen, Giang Thi (Edelweiss Applied Science and Technology, 2025)
Determining the best approach model—machine learning or statistical model—to solve the analysis challenges of firm value prediction has garnered attention from many scholars. This article used both traditional statistical models and machine learning to predict the firm value of 435 non-financial companies that are listed on the Vietnam Security Exchange during 2014-2021. Based on the empirical results, the machine learning models provided evidence that they forecast firm value better than traditional models and identify the number of firm value determinants. The paper applied six machine learning models to find the best-performing one, including the multiple regression model (LM), Lasso, generalized additive model (GAM), random forests (RF), gradient boosting regression trees (GBM), and neural networks (NNET). The findings indicated that the RF is the best-performing model, selecting firm size, ROA, tangibility, GDP, quality, financial leverage, and inflation as reliable predictors of market firm value. The results suggest several recommendations for internal managers, investors, and creditors when choosing the appropriate model to forecast firm value for making financial decisions.