Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis
Abstract
:1. Introduction
2. Literature
3. Materials and Methods
3.1. Data Set
3.2. Artificial Neural Networks
3.3. Multiple Linear Regression
3.4. Decision Trees Regression
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author/Year | Article Title | Variables | Machine Learning Technique |
---|---|---|---|
Pham Vu Hong Son, Le Tung Duong, 2024 [6] | Research on applying machine learning models to predict and assess return on assets (ROA) | Days sales outstanding, Days inventory outstanding, Days payable outstanding, Cash conversion cycle, Networking capital, Financial leverage, Current ratio, Growth rate, Return on equity, Earnings before interest taxes, Gross margin | Lasso, Ridge, K Neighbor Regressor, SVR, Random Forest, GBR, XGBoost, MLP Regression |
Caterina De Lucia, Pasquale Pazienza, and Mark Bartlett, 2020 [7] | Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe | Emission score, CO2 equivalent emissions, Resource use score, ESG score, Environmental innovation score, Salary gap, Number of employees in the CSR reporting, Number of women employees, Workforce score, Management score, Operating income, Net income, Change in total equity | Random forest, Support Vector regression, K-Nearest Neighbor Model, Artificial Neural Network Model, Ridge Regression |
Ting-Hsuan Chen, Rong-Cih Chang, 2021 [8] | Using machine learning to evaluate the influence of FinTech patents: The case of Taiwan’s financial industry | (Interest income − Interest expense)/Total assets, Net income after tax/Operating income, Log(Total assets), Qualified capital/Risk weighted assets, Patents | Logistic Regression, Multilayer Perceptron, Decision Tree, Random Forest, Naive Bayes Classifier, Bayesian Network, Support Vector Machine |
Pratibha Rai, Bibhuti Bhusan Mohapatra, Vanita Jain, 2023 [9] | Major Determinants of Bank Profitability in India: A Machine Learning Approach | Financial crisis, Unemployment, Inflation, GDP growth rate, Real effective exchange rate | The random forest (RF) classification algorithm is executed using the CARET package in R. The results obtained from feature selection are corroborated the RF classification findings. |
Potta Chakri, Saurabh Pratap, Lakshay, Sanjeeb Kumar Gouda 2023 [10] | An exploratory data analysis approach for analyzing financial accounting data using machine learning | Total Revenue | Linear Regression, KNN (K Nearest Neighbor), SVR (Support Vector Regression), and Decision Tree |
Stewart Jones, William J. Moser, Matthew M. Wieland 2023 [11] | Machine learning and the prediction of changes in profitability | On Average Rnoa, Net Operating Assets | TreeNet, OLS Regression, PZ Regression |
Nicholas D. Belesis, Georgios A. Papanastasopoulos and Antonios M. Vasilatos 2023 [12] | Predicting the Profitability of Directional Changes Using Machine Learning: Evidence from European Countries | ROA/ROE/RNOA/FCF/CFO | Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Trees (DT), Random Forest (RF) |
Xinqi Dong, Bo Dang, Hengyi Zang, Shaojie Li, Danqing M 2024 [13] | The Prediction Trend of Enterprise Financial Risk based on Machine Learning ARIMA Model | Profit, Revenue, Expenditure, Assets, Liabilities | Support Vector Machine, Logistic Regression, Neural Network, ARIMA regression model |
Chuqing Zhang, Han Zhang, AND Dunnan Liu 2019 [14] | A Contrastive Study of Machine Learning on Energy Firm Value Prediction | Deal value Input: EBIT, ROE, ROA, CAPEX, M&A Type, Asset Turnover, Cash Debt Ratio, Total Debt to Assets, Firm Type, Nationality, Acquisition Year, Share | Decision Tree Regression, Supported Vector Regression, Artificial Neural Network |
Andrey Zahariev, Petko Angelov, Silvia Zarkova (2022) [15] | Estimation of Bank Profitability Using Vector Error Correction Model and Support Vector Regression | ROE and ROA | Support Vector Regression and Vector Error Correction Model |
Kristof Tamas and Miklos Virag (2022) [16] | What drives financial competitiveness of industrial sectors in Visegrad Four countries? Evidence by use of machine learning techniques. | ROA and ROE | The K-Nearest Neighbor (KNN) and Random Forest (RF) Methods |
Vic Anand, Robert Brunner, Kelechi Ikegwu, Theodore Sougiannis (2019) [17] | Predicting Profitability Using Machine Learning | ROA (return on assets), ROE (return of equity), CFO (cash flow from operations), RNOA (return on net operating assets) and FCF (free cash flow) | Random Forests |
Md. Nur-Al-Ahad, Syeda Nusrat, Vagavi Prakash (2019) [18] | Nexus Between Corporate Governance and Firm Performance in Malaysia: Supervised Machine Learning Approach | ROA and ROE | Supervised Machine Learning Approach |
Companies | Codes |
---|---|
Alcatel Lucent Teletas Telecommunications Co. (Boulogne-Billancourt, France) | ALCTL |
Arena Computer Industry and Trade Co. (İstanbul, Turkey) | ARENA |
Despec Computer Marketing and Trading Co. (Dubai, United Arab Emirates) | DESPC |
Datagate Computer Materials Trading Co. (Nicosia, Cyprus) | DGATE |
Fonet Information Technologies Co. (Ankara, Turkey) | FONET |
Indeks Computer Systems Engineering Industry and Trade Co. (İstanbul, Turkey) | INDES |
Ingram Micro Computer Systems Co. (Irvine, CA, USA) | INGRM |
Karel Electronics Industry and Trade Co. (Istanbul, Turkey) | KAREL |
Kron Technology Co. (Jersey City, NJ, USA) | KRONT |
Link Computer Systems Software and Hardware Industry and Trade Co. (Istanbul, Turkey) | LINK |
Logo Software Industry and Trade Co. (Gebze, Turkey) | LOGO |
Netas Telecommunications Co. (Istanbul, Turkey) | NETAS |
Plastikkart Smart Card Communication Systems Industry and Trade Co. (Istanbul, Turkey) | PKART |
Dependent Variables | |
---|---|
Return of Assets (ROA) | |
Return of Equities (ROE) | |
Independent Variables | |
Receivables Turnover | Operational Ratios |
Inventory Turnover | |
Fixed Asset Turnover | |
Fixed Assets/Total Assets (%) | |
Current Assets/Total Assets (%) | |
Tangible Fixed Assets Turnover | |
Price Earnings Ratio | Stock Exchange Ratios |
Earnings per Share | |
Cost of Sales/Net Sales (%) | Profitability Ratios |
EBITDA Margin (%) | |
Net Profit Growth (%) | |
Net Profit Margin (%) | |
Leverage Ratio (%) | Financial Structure Ratio |
Current Ratio | Liquidity Ratio |
ANN | MLR | DTR | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | MAE | MSE | RMSE | R2 | MAE | MSE | RMSE | R2 | MAE | MSE | RMSE | |
ALCTL | 0.836 | 0.124 | 0.025 | 0.159 | 0.778 | 0.114 | 0.018 | 0.136 | 0.683 | 0.253 | 0.097 | 0.312 |
ARENA | 0.707 | 0.095 | 0.014 | 0.119 | 0.88 | 0.056 | 0.007 | 0.082 | 0.958 | 0.258 | 0.119 | 0.345 |
DESPC | 0.878 | 0.077 | 0.01 | 0.101 | 0.774 | 0.169 | 0.038 | 0.196 | 0.626 | 0.252 | 0.126 | 0.355 |
DGATE | 0.79 | 0.103 | 0.022 | 0.149 | 0.753 | 0.07 | 0.009 | 0.094 | 0.917 | 0.265 | 0.094 | 0.306 |
FONET | 0.969 | 0.063 | 0.007 | 0.082 | 0.919 | 0.078 | 0.013 | 0.112 | 0.816 | 0.196 | 0.085 | 0.292 |
INDES | 0.654 | 0.072 | 0.008 | 0.088 | 0.84 | 0.059 | 0.005 | 0.073 | 0.189 | 0.023 | 0.078 | 0.28 |
INGRM | 0.745 | 0.115 | 0.022 | 0.147 | 0.662 | 0.117 | 0.035 | 0.186 | 0.54 | 0.316 | 0.118 | 0.343 |
KAREL | 0.869 | 0.11 | 0.015 | 0.123 | 0.924 | 0.091 | 0.013 | 0.116 | 0.814 | 0.303 | 0.157 | 0.397 |
KRONT | 0.723 | 0.103 | 0.015 | 0.124 | 0.857 | 0.093 | 0.019 | 0.136 | 0.487 | 0.218 | 0.077 | 0.278 |
LINK | 0.973 | 0.061 | 0.007 | 0.086 | 0.937 | 0.063 | 0.007 | 0.085 | 0.983 | 0.336 | 0.24 | 0.49 |
LOGO | 0.961 | 0.081 | 0.009 | 0.095 | 0.886 | 0.094 | 0.014 | 0.118 | 0.657 | 0.365 | 0.165 | 0.407 |
NETAS | 0.913 | 0.019 | 0.001 | 0.03 | 0.773 | 0.044 | 0.003 | 0.056 | 0.484 | 0.2 | 0.096 | 0.311 |
PKART | 0.806 | 0.082 | 0.019 | 0.137 | 0.947 | 0.09 | 0.012 | 0.111 | 0.725 | 0.241 | 0.13 | 0.361 |
ANN | MLR | DTR | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | MAE | MSE | RMSE | R2 | MAE | MSE | RMSE | R2 | MAE | MSE | RMSE | |
ALCTL | 0.847 | 0.292 | 0.143 | 0.179 | 0.503 | 0.178 | 0.045 | 0.212 | 0.913 | 0.17 | 0.05 | 0.224 |
ARENA | 0.945 | 0.086 | 0.015 | 0.123 | 0.89 | 0.084 | 0.01 | 0.099 | 0.654 | 0.298 | 0.152 | 0.39 |
DESPC | 0.95 | 0.094 | 0.014 | 0.118 | 0.908 | 0.108 | 0.018 | 0.133 | 0.998 | 0.269 | 0.132 | 0.363 |
DGATE | 0.714 | 0.155 | 0.037 | 0.193 | 0.967 | 0.045 | 0.003 | 0.255 | 0.906 | 0.317 | 0.163 | 0.404 |
FONET | 0.875 | 0.097 | 0.012 | 0.108 | 0.879 | 0.096 | 0.012 | 0.107 | 0.716 | 0.28 | 0.145 | 0.381 |
INDES | 0.674 | 0.151 | 0.038 | 0.196 | 0.561 | 0.154 | 0.049 | 0.22 | 0.753 | 0.286 | 0.149 | 0.386 |
INGRM | 0.437 | 0.177 | 0.043 | 0.207 | 0.837 | 0.273 | 0.113 | 0.336 | 0.916 | 0.291 | 0.118 | 0.343 |
KAREL | 0.938 | 0.058 | 0.005 | 0.071 | 0.376 | 0.122 | 0.021 | 0.144 | 0.537 | 0.267 | 0.09 | 0.301 |
KRONT | 0.925 | 0.059 | 0.005 | 0.07 | 0.651 | 0.112 | 0.027 | 0.164 | 0.991 | 0.25 | 0.126 | 0.356 |
LINK | 0.912 | 0.096 | 0.022 | 0.127 | 0.947 | 0.112 | 0.017 | 0.132 | 0.897 | 0.218 | 0.104 | 0.323 |
LOGO | 0.995 | 0.076 | 0.008 | 0.09 | 0.993 | 0.084 | 0.008 | 0.091 | 0.797 | 0.206 | 0.058 | 0.241 |
NETAS | 0.411 | 0.109 | 0.045 | 0.153 | 0.252 | 0.131 | 0.038 | 0.196 | 0.451 | 0.111 | 0.047 | 0.216 |
PKART | 0.984 | 0.051 | 0.004 | 0.065 | 0.977 | 0.049 | 0.005 | 0.067 | 0.613 | 0.156 | 0.056 | 0.236 |
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Tutcu, B.; Kayakuş, M.; Terzioğlu, M.; Ünal Uyar, G.F.; Talaş, H.; Yetiz, F. Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis. Appl. Sci. 2024, 14, 7459. https://doi.org/10.3390/app14177459
Tutcu B, Kayakuş M, Terzioğlu M, Ünal Uyar GF, Talaş H, Yetiz F. Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis. Applied Sciences. 2024; 14(17):7459. https://doi.org/10.3390/app14177459
Chicago/Turabian StyleTutcu, Burçin, Mehmet Kayakuş, Mustafa Terzioğlu, Güler Ferhan Ünal Uyar, Hasan Talaş, and Filiz Yetiz. 2024. "Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis" Applied Sciences 14, no. 17: 7459. https://doi.org/10.3390/app14177459