ROA and ROE Forecasting in Iron and Steel Industry Using Machine Learning Techniques for Sustainable Profitability
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Set
2.2. Artificial Neural Networks
2.3. Support Vector Regression
2.4. Multiple Linear Regression
3. Result and Discussion
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Author/Year | Article Title | Variables | Machine Learning Technique |
---|---|---|---|
(Mousa et al., 2022) [14] | Using Machine Learning Methods to Predict Financial Performance: Does Disclosure Tone Matter? | Output: Financial Performance (Earnings per Share) Input: Financial Leverage, Bank Size, Market to Book Ratio, Beta of The Company, Bank Age | Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Random Forest |
(Zhang et al., 2019) [15] | A Contrastive Study of Machine Learning on Energy Firm Value Prediction | Output: Deal value Input: EBIT, ROE, ROA, CAPEX, M&A Type, Asset Turnover, Cash Debit Ratio, Total Debt to Assets, Firm Type, Nationality, Acquisition Year, Share | Decision Tree Regression, Supported Vector Regression, Artificial Neural Network |
(Erdal & Karahanoğlu, 2016) [16] | Bagging ensemble models For Bank Profitability: Empirical Research on Turkish Development and Investment Banks | Output: ROE Input: Non-Interest Income/Total Income, Other Revenues/Total Assets, Equity/Total Assets, Loans/Total Assets, Liquid Assets/Total Assets, Non-Performing Loan/Total Loans, Personnel Expenditure/Other Expenditure, Foreign Currency Assets/Total Assets, Total Credits/Total Assets, Net Currency Position/Total Equity | Decision Stump, Reduced Error Pruning Tree, Random Tree |
(JC et al., 2022) [17] | AI-Based Prediction of Capital Structure: Performance Comparison of ANN SVM and LR Models | Output: Total Debt/Equity Input: Total Liabilities/Equity, Revenues/Cash and Equivalents, Revenues/Current Assets, Revenues/Equity, Net Income/Equity, Gross Margin, EBITDA Margin, Net Income Margin, Current Assets/Current Liabilities, Current Liabilities/Equity, Total Liabilities/Total Assets, EBIT/Interest Expenses | Artificial Neural Network, Support Vector Regression, and Linear Regression |
(Saberi et al., 2016) [18] | Forecasting the Profitability in the Firms Listed in Tehran Stock Exchange Using Data Envelopment Analysis and Artificial Neural Network | Output: ROA Input: Return on Investment, Capitalization of Exploration Cost, Dupont Ratios | Artificial Neural Network |
(Skobic et al., 2020) [19] | Machine learning algorithms in the profitability analysis of Casco insurance | Output: Client Profitability Input: Client Age, Client Gender, Discount, Casco claims through history, Car insurance claims through history, Number of Casco policies through history, Casco profit through history, Casco profit for client | Logistic regression, Artificial Neural Network, Decision tree |
(de Andrés et al., 2004) [20] | The Use of Machine Learning Algorithms for the Study of Business Profitability: A New Approach Based on Preferences | Output: Business Profitability Input: Use of Fixed Capital, Debt Quality, Indebtedness, Short-Term Liquidity, Debt Cost, Share of Labor Costs, Average Sales per Employee, Net Sales | Learning to Assess from Comparison Examples and Recursive Feature Elimination Algorithms |
(Kuzey et al., 2014) [21] | The Impact of Multinationalism on Firm Value: A Comparative Analysis Of Machine Learning Techniques | Input: Firm Value Output: Asset Structure and Growth Rate, Size, Leverage, Asset Structure and Growth Rate, Sales Growth, Capital Expenditure, Profitability, Liquidity | Artificial Neural Networks and Decision Trees |
(Zahariev et al., 2022) [22] | Estimation of Bank Profitability Using Vector Error Correction Model and Support Vector Regression | Input: ROE and ROA Output: Inflation and other macroeconomic determinants | Support Vector Regression and Vector Error Correction Model |
Company Code | Company Name | Operating Country |
---|---|---|
BOW PL | Bowim sa | Poland |
CHMF RUM | Severstal | Russia |
COG PL | Cognor holding sa | Poland |
EREGL IS | Eregli demir celik | Turkey |
FER PL | Ferrum sa | Poland |
IZS PL | Izostal sa | Poland |
KRDMA IS | Kardemir | Turkey |
MAGN RUM | Mmk | Russia |
OZBAL IS | Ozbal celik boru | Turkey |
SZR PL | Stalprodukt sa | Poland |
TRMK RUM | Tmk | Russia |
URKZ RUM | Uralskaya kuznica | Russia |
ZRE PL | Zremb-chojnice sa | Poland |
Output Variables | Input Variables |
---|---|
ROA | Total Assets |
ROE | Current Ratio |
Leverage Ratio | |
Assets Turnover | |
Working Capital |
Company Code | ANN | SVR | MLR | ||||||
---|---|---|---|---|---|---|---|---|---|
MSE | RMSE | R2 | MSE | RMSE | R2 | MSE | RMSE | R2 | |
BOW PL | 0.004 | 0.064 | 0.921 | 0.001 | 0.030 | 0.887 | 0.001 | 0.009 | 0.991 |
CHMF RUM | 0.011 | 0.104 | 0.797 | 0.003 | 0.053 | 0.698 | 0.001 | 0.038 | 0.878 |
COG PL | 0.001 | 0.031 | 0.981 | 0.040 | 0.199 | 0.840 | 0.015 | 0.124 | 0.833 |
EREGL IS | 0.014 | 0.119 | 0.835 | 0.019 | 0.137 | 0.750 | 0.001 | 0.030 | 0.506 |
FER PL | 0.010 | 0.099 | 0.911 | 0.146 | 0.383 | 0.834 | 0.012 | 0.108 | 0.309 |
IZS PL | 0.014 | 0.118 | 0.811 | 0.052 | 0.228 | 0.776 | 0.015 | 0.121 | 0.694 |
KRDMA IS | 0.017 | 0.131 | 0.698 | 0.010 | 0.098 | 0.737 | 0.005 | 0.074 | 0.711 |
MAGN RUM | 0 | 0 | 0.996 | 0.046 | 0.214 | 0.729 | 0.012 | 0.110 | 0.887 |
OZBAL IS | 0.015 | 0.123 | 0.805 | 0.001 | 0.035 | 0.778 | 0.025 | 0.159 | 0.69 |
SZR PL | 0.004 | 0.065 | 0.943 | 0.074 | 0.272 | 0.809 | 0.038 | 0.195 | 0.716 |
TRMK RUM | 0.029 | 0.170 | 0.626 | 0.070 | 0.265 | 0.917 | 0.016 | 0.126 | 0.84 |
URKZ RUM | 0.003 | 0.051 | 0.966 | 0.001 | 0.019 | 0.722 | 0.016 | 0.126 | 0.781 |
ZRE PL | 0.005 | 0.067 | 0.941 | 0.005 | 0.069 | 0.910 | 0.013 | 0.114 | 0.789 |
Average | 0.010 | 0.089 | 0.864 | 0.036 | 0.154 | 0.799 | 0.013 | 0.103 | 0.740 |
Company Code | ANN | SVR | MLR | ||||||
---|---|---|---|---|---|---|---|---|---|
MSE | RMSE | R2 | MSE | RMSE | R2 | MSE | RMSE | R2 | |
BOW PL | 0.004 | 0.066 | 0.919 | 0.034 | 0.185 | 0.974 | 0.148 | 0.384 | 0.513 |
CHMF RUM | 0.004 | 0.062 | 0.916 | 0.527 | 0.726 | 0.681 | 0.065 | 0.255 | 0.699 |
COG PL | 0.003 | 0.053 | 0.947 | 0.212 | 0.461 | 0.738 | 0.110 | 0.331 | 0.458 |
EREGL IS | 0.015 | 0.121 | 0.833 | 0.252 | 0.502 | 0.716 | 0.079 | 0.28 | 0.880 |
FER PL | 0.005 | 0.069 | 0.883 | 1.040 | 1.020 | 0.865 | 0.179 | 0.423 | 0.963 |
IZS PL | 0.008 | 0.087 | 0.929 | 0.068 | 0.260 | 0.875 | 0.205 | 0.453 | 0.629 |
KRDMA IS | 0.017 | 0.132 | 0.628 | 1.822 | 1.350 | 0.992 | 0.045 | 0.212 | 0.575 |
MAGN RUM | 0.003 | 0.050 | 0.95 | 0.360 | 0.600 | 0.761 | 0.510 | 0.226 | 0.909 |
OZBAL IS | 0.008 | 0.090 | 0.816 | 1.671 | 1.293 | 0.900 | 0.138 | 0.371 | 0.804 |
SZR PL | 0.007 | 0.081 | 0.904 | 0.494 | 0.703 | 0.638 | 0.187 | 0.432 | 0.659 |
TRMK RUM | 0.010 | 0.099 | 0.865 | 0.203 | 0.450 | 0.802 | 0.057 | 0.238 | 0.305 |
URKZ RUM | 0.005 | 0.068 | 0.937 | 0.190 | 0.436 | 0.871 | 0.149 | 0.387 | 0.524 |
ZRE PL | 0.017 | 0.129 | 0.627 | 0.437 | 0.661 | 0.701 | 0.028 | 0.167 | 0.377 |
Average | 0.008 | 0.085 | 0.858 | 0.562 | 0.665 | 0.809 | 0.146 | 0.320 | 0.638 |
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Kayakus, M.; Tutcu, B.; Terzioglu, M.; Talaş, H.; Ünal Uyar, G.F. ROA and ROE Forecasting in Iron and Steel Industry Using Machine Learning Techniques for Sustainable Profitability. Sustainability 2023, 15, 7389. https://doi.org/10.3390/su15097389
Kayakus M, Tutcu B, Terzioglu M, Talaş H, Ünal Uyar GF. ROA and ROE Forecasting in Iron and Steel Industry Using Machine Learning Techniques for Sustainable Profitability. Sustainability. 2023; 15(9):7389. https://doi.org/10.3390/su15097389
Chicago/Turabian StyleKayakus, Mehmet, Burçin Tutcu, Mustafa Terzioglu, Hasan Talaş, and Güler Ferhan Ünal Uyar. 2023. "ROA and ROE Forecasting in Iron and Steel Industry Using Machine Learning Techniques for Sustainable Profitability" Sustainability 15, no. 9: 7389. https://doi.org/10.3390/su15097389