Applying machine learning technique to study the influence of financial decisions on firm value - Van, Trang Do Thi; Viet, Thang Doan; Canh, Duong Nguyen; Ngoc, Huyen Pham; Xuan, Mai Nguyen Thi (Edelweiss Applied Science and Technology, 2025)
This study investigates the impact of financial decisions on the value of listed firms in Vietnam by employing both traditional statistical methods and advanced machine learning models. Using data from 646 firms listed on the Vietnamese Stock Exchange from 2012 to 2022, the research applies various predictive models, including traditional regression methods and machine learning approaches such as Linear Regression (LR), LASSO, Generalized Additive Model (GAM), Random Forests (RF), Gradient Boosting Regression Trees (GBRT), and Deep Neural Networks (DNN). The empirical results suggest a positive correlation between investment and financing decisions and firm value during the research period. Furthermore, the impact of dividend payment decisions on company value was statistically insignificant. Notably, machine learning models, particularly GBRT, outperform traditional statistical models, demonstrating superior predictive accuracy and robustness in capturing complex financial patterns. The study underscores the growing relevance of machine learning in financial analysis, highlighting its ability to enhance forecasting and strategic decision-making. These findings provide valuable insights for corporate managers, investors, and policymakers in optimizing financial strategies to maximize firm value. Moreover, the integration of machine learning into financial modeling can mitigate limitations associated with conventional statistical methods, particularly in handling non-linear relationships and large-scale financial datasets.