Comparative study on classifying overinvestment by machine learning

What the paper says

This research delves into the prevalent issue of overinvestment in Vietnamese firms, employing a range of machine learning algorithms to classify such behaviour. The study utilises data from Vietnam's stock exchanges, applying a wide array of machine learning algorithms - ranging from Logistic Regression to complex deep learning architectures - to identify the most effective tool in classifying overinvestment. The findings from this study reveal that certain machine learning algorithms, particularly ensemble methods like AdaBoost, XGBoost, LightGBM, and CatBoost, demonstrate exceptional proficiency in classifying overinvestment scenarios among Vietnamese firms. These models outperform traditional statistical approaches by effectively capturing the complex, non-linear dynamics that characterise overinvestment behaviour. Notably, the research uncovers that AdaBoost and XGBoost are particularly adept in this context, offering high accuracy and robustness in detecting overinvestment patterns. The research concludes that these machine-learning tools are crucial for understanding and mitigating overinvestment risks, providing a significant analytical resource for Vietnam's financial sector. The study recommends further adoption of machine learning in corporate financial analysis to enhance decision-making and market stability.