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ROA and ROE Forecasting in Iron and Steel Industry Using Machine Learning Techniques for Sustainable Profitability

Author

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  • Mehmet Kayakus

    (Department of Management Information Systems, Faculty of Social Sciences and Humanities, Akdeniz University, Antalya 07058, Turkey)

  • Burçin Tutcu

    (Department of Accounting and Taxation, Korkuteli Vocational School, Akdeniz University, Antalya 07058, Turkey)

  • Mustafa Terzioglu

    (Department of Accounting and Taxation, Korkuteli Vocational School, Akdeniz University, Antalya 07058, Turkey)

  • Hasan Talaş

    (Department of Accounting and Taxation, Korkuteli Vocational School, Akdeniz University, Antalya 07058, Turkey)

  • Güler Ferhan Ünal Uyar

    (Department of Business Administration, Faculty of Economics and Administrative Sciences, Akdeniz University, Antalya 07058, Turkey)

Abstract

Return on equity (ROE) and return on assets (ROA) are important indicators that reveal the sustainability of a company’s profitability performance for both managers and investors. The correct prediction of these indicators will provide a basis for the strategic decisions made by the company managers. The estimation of these signs is a significant factor in supporting the decisions and up-to-date knowledge of potential investors. In this study, return on equity and return on assets were estimated using artificial neural networks (ANNs), multiple linear regression (MLR), and support vector regression (SVR) on the financial data of thirteen companies operating in the iron and steel sector. The success of predicting ROA in the designed model was 86.4% for ANN, 79.9% for SVR, and 74% for MLR. The success of estimating the ROE of the same model was 85.8% for ANN, 80.9% for SVR, and 63.8% for MLR. It is concluded that ANN and SVR can produce successful prediction results for ROA and ROE both accurately and reasonably.

Suggested Citation

  • Mehmet Kayakus & Burçin Tutcu & Mustafa Terzioglu & Hasan Talaş & Güler Ferhan Ünal Uyar, 2023. "ROA and ROE Forecasting in Iron and Steel Industry Using Machine Learning Techniques for Sustainable Profitability," Sustainability, MDPI, vol. 15(9), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7389-:d:1136146
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    2. Emilia Herman & Kinga-Emese Zsido, 2023. "The Financial Sustainability of Retail Food SMEs Based on Financial Equilibrium and Financial Performance," Mathematics, MDPI, vol. 11(15), pages 1-26, August.

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