A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Research on applying machine learning models to predict and assess return on assets (ROA)
Return on Assets (ROA), a profitability measure, is crucial in corporate finance for assessing how efficiently a company uses assets to generate profit. 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 financial 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 findings indicate potential real-world uses in predicting and boosting business performance. In conclusion, the integration of machine learning in financial analysis and prediction represents substantial progress. It enhances both accuracy and efficiency and holds promise for future advancements in financial management practices. This study aims to encourage more research and development in this area, leading to more advanced and efficient financial management tools.
Research on applying machine learning models to predict and assess return on assets (ROA)
Return on Assets (ROA), a profitability measure, is crucial in corporate finance for assessing how efficiently a company uses assets to generate profit. 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 financial 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 findings indicate potential real-world uses in predicting and boosting business performance. In conclusion, the integration of machine learning in financial analysis and prediction represents substantial progress. It enhances both accuracy and efficiency and holds promise for future advancements in financial management practices. This study aims to encourage more research and development in this area, leading to more advanced and efficient financial management tools.
Research on applying machine learning models to predict and assess return on assets (ROA)
Asian J Civ Eng
Son, Pham Vu Hong (author) / Duong, Le Tung (author)
Asian Journal of Civil Engineering ; 25 ; 4269-4279
2024-07-01
11 pages
Article (Journal)
Electronic Resource
English
Research on applying machine learning models to predict and assess return on assets (ROA)
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