Title: Prediction of return on equity using machine learning algorithms: evidence from India

Authors: Amit Hedau; S.V.S. Raja Prasad; Sasikanta Tripathy

Addresses: National Institute of Construction Management and Research, Hyderabad, India ' National Institute of Construction Management and Research, Hyderabad, India ' Department of Economics and Finance, College Business Administration, University of Bahrain, Kingdom of Bahrain

Abstract: The present study analysed and predicted the return on equity using machine learning algorithms from the historical financial data during April 2018-March 2022 for construction firms operating in India. The study considered sampling bias method to consider the listed 172 companies from construction sector, as this sector generates the second largest contribution to the GDP of India. The machine learning algorithms is used to model the regression equation. The results indicate that market capitalisation, sales, return on asset, current ratio, earning per share, promoter holdings and profit after tax significantly influence the return on equity for construction firm in India during the study period. We conclude that out of six classifiers, XGBoost is more accurate (86%) to predict the return on equity of the construction firms in India. Finally, a financial performance prediction tool is developed to predict the results.

Keywords: return on equity; ROE; construction; India; machine learning; XGBoost.

DOI: 10.1504/IJIDS.2025.150099

International Journal of Information and Decision Sciences, 2025 Vol.17 No.4, pp.410 - 429

Received: 02 Feb 2023
Accepted: 13 Mar 2024
Published online: 01 Dec 2025 *

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