Research on applying machine learning models to predict and assess return on assets (ROA)

Abstract

Return on Assets (ROA), a pro tability measure, is crucial in corporate nance for assessing how e ciently a company uses assets to generate pro t. Currently, the prediction of the ROA index at present is a tedious, manual process. It usually involves making educated guesses or waiting for the accurate data, which becomes available only after nancial reports have been compiled. This paper introduces a machine learning model for predicting the ROA index. The model draws data from 78 companies listed on the Vietnam Stock Exchanges (HOSE and HNX) over the span of 2012 to 2022.The Random Forest (RF) model was put to the test using datasets from selected Vietnamese businesses in 2023. The results demonstrated a high level of precision, with an error rate of less than 1%, an R2 value of 0.9762, and a Root Mean Square Error (RMSE) of 0.5826. These ndings indicate potential real-world uses in predicting and boosting business performance. In conclusion, the integration of machine learning in nancial analysis and prediction represents substantial progress. It enhances both accuracy and e ciency and holds promise for future advancements in nancial management practices. This study aims to encourage more research and development in this area, leading to more advanced and e cient nancial management tools.