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.

IT is recognized as the engine of the digital world. The fact that this technology has multiple sub-sectors makes it the driving force of the economy. With these characteristics, the sector is becoming the center of attention of investors. Considering that investors prioritize profitability, it becomes a top priority for managers to make accurate and reliable profitability forecasts. The aim of this study is to estimate the profitability of IT sector firms traded in Borsa Istanbul using machine learning methods. In this study, the financial data of 13 technology firms listed in the Borsa Istanbul Technology index and operating between March 2000 and December 2023 were used. Return on assets (ROA) and return on equity (ROE) were estimated using machine learning methods such as neural networks, multiple linear regression and decision tree regression. The results obtained reveal that the performance of artificial neural networks (ANN) and multiple linear regression (MLR) are particularly effective.

In the last decade, companies producing electricity from renewable energy sources in Turkey have gained momentum both quantitatively and qualitatively with the incentives carried out within the framework of the government’s carbon emission reduction programs. In this study, the financial performances of companies in terms of financial ratios of SWARA and WASPAS, which are multi-criteria decision-making techniques used in the field of numerical methods, were tried to be measured. In terms of scope, the financial performances of four renewable energy companies listed on Borsa Istanbul Stock Market were analysed for three years between the years 2018 and 2020. In the findings obtained by applying the SWARA technique of the study, it was concluded that the most important factor determining the financial performance of companies that participated in the research is the financial leverage ratio. As a result of the data obtained from the WASPAS technique, it has been determined that the companies that produce electricity through a single renewable energy source are financially more successful than the companies that produce electrical energy through more than one renewable energy source.

This study explores the Environmental, Social, and Governance (ESG) disclosure practices of 31 information technology firms listed on Borsa Istanbul (BIST), with a particular emphasis on transparency and accountability. Building on legitimacy, stakeholder, and signalling theories, the study develops a composite ESG disclosure index based on 18 binary indicators covering strategy, environmental performance, social impact, stakeholder engagement, and governance structures. Each indicator is equally weighted and combined into environmental, social, and governance sub-indices, which are then aggregated into a firm-level ESG disclosure score using a single min–max normalisation scheme. K-means clustering, validated through the elbow method and silhouette coefficient, is applied to identify groups of firms with similar ESG disclosure profiles. The empirical results show substantial heterogeneity in disclosure intensity across Turkish IT firms. A small group of companies exhibits proactive and comprehensive ESG communication, whereas many firms disclose only limited and fragmented information. Governance- and reporting-related indicators (C6 and C7) are particularly influential, underscoring the importance of standardised ESG reporting and board-level oversight in strengthening transparency. The study contributes to the emerging ESG disclosure literature by providing a methodologically consistent framework for assessing ESG transparency in the IT sector and offering practical insights for regulators, investors, and corporate decision-makers aiming to improve the reliability of ESG reporting in Turkey.

Methane gas emission into the atmosphere is rising due to the use of fossil-based resources in post-industrial energy use, as well as the increase in food demand and organic wastes that comes with an increasing human population. For this reason, methane gas, which is among the greenhouse gases, is seen as an important cause of climate change along with carbon dioxide. The aim of this study was to predict, using machine learning, the emission of methane gas, which has a greater effect on the warming of the atmosphere than other greenhouse gases. Methane gas estimation in Turkey was carried out using machine learning methods. The R² metric was calculated as logistic regression (LR) 94.9%, artificial neural networks (ANNs) 93.6%, and support vector regression (SVR) 92.3%. All three machine learning methods used in the study were close to ideal statistical criteria. LR had the least error and highest prediction success, followed by ANNs and then SVR. The models provided successful results, which will be useful in the formulation of policies in terms of animal production (especially cattle production) and the disposal of organic human wastes, which are thought to be the main causes of methane gas emission.