# Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis
## 1 Introduction  
In the new world order, where digitalization is at the center, it is known that the only way to ensure sustainable growth is through technology breakthroughs. Some of the world's leading economists describe the current period in the economy as the \"IT (Information Communication Technologies) Techno-Economic Paradigm\". In this period, which is still in its early stages, it is expected that the activities of the IT sector, such as hardware, software, communication and telecommunication services, and content production of information technology services, are both the driving force behind the economy and can radically change activities in other sectors of the economy. The global IT market size will grow by 13% to USD 4.3 trillion in 2023, while the information technology market size will increase by 25.7% and the communication technology market size will increase by 1.4%, as reported by TUBISAD. In 2026, this increase is projected to reach USD 5.6 trillion.  
The IT sector has consistently been an attractive sector for investors since the mid-1990s. However, after the bursting of the dot-com bubble in 2000, it is seen that the profitability of these companies is prioritized in the investment feasibility of these companies, not the idea or product that the company comes up with. In the same way, the managers of these companies prioritize the realized profitability and aim to increase the profitability rates of the companies in order to support investors' decisions.  
In this process, ROA (return on assets) and ROE (return on equity) are the ratios that managers especially work on. ROA shows how much profit a company makes for the assets it owns, and it shows how efficiently it uses its assets. ROE, on the other hand, expresses how effectively the investments made by shareholders in the company are used to generate profit. In this respect, it is known to be one of the most important profitability indicators for investors. The predictability of ROA and ROE plays an important role in the growth and sustainability of companies. Accurate estimation of these indicators, on the other hand, will form the basis for strategic decisions to be taken by managers.  
In profitability forecasting, machine learning methods are known to provide more successful results in different sectors than traditional methods such as regression. The reason for this is that these methods generally produce effective solutions in cases of nonlinearity and multicollinearity in financial data sets.  
The fact that the IT sector is considered as the locomotive of the sustainable world makes the decisions to be taken by managers much more important. The fact that managers use past period data in their decisions makes future forecasts much more important. The motivation of this study is to increase the number of investors by predicting profitability in the IT sector, which is the key to the digital world, and to determine the sustainability of businesses. For this purpose, profitability studies conducted with machine learning methods in different sectors will first be examined in the literature section. Then, the model designed in the methodology section will be tested using machine learning methods, and the results will be discussed.  
This study differs from other studies in that it examines the IT sector, which has not been addressed as a sector in the literature before. The fact that companies in the IT sector need to follow the pace of technology and make extra efforts in innovation compared to companies in other sectors requires these companies to have a strong financial structure and the potential to generate profit. Therefore, the study will contribute to the literature in terms of creating the ideal model with ideal machine learning methods.