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 this study, decision tree regression, artificial neural networks (ANN) and support vector machines (SVM) methods are applied by using monthly data for the period 2013-2020 in the estimation of housing sales in Turkey. In the analysis, the volume of individual mortgage loans offered by banks, the average annual interest rate of mortgage loans from macroeconomic and market variables, the consumer price index (CPI), the BIST 100 index, the benchmark bond interest rate, gold prices and the values of the US dollar and Euro Turkish lira and the housing sales price per square meter in Turkey are used. As a result of the analysis carried out on the model created house sales prices in the Turkish housing market have been successfully estimated and in the light of these estimates, it is determined that banks can guide banks in the creation of various credit packages and appropriate loan targets to support the housing sector.

Amaç: Bu araştırmanın amacı, Türkiye’de faaliyet gösteren özel ve kamu sermayeli mevduat bankalarının finansal performanslarının çok kriterli karar verme yöntemleri ile değerlendirilmesidir. Tasarım/Yöntem: Çalışmanın veri setini Türkiye’de faaliyet gösteren 15 mevduat bankasının 2015-2019 yıllarına ait yıllık finansal rasyoları oluşturmaktadır. Bankaların finansal performansı çok kriterli karar verme yöntemlerinden VIKOR analizi tekniğiyle değerlendirilmiştir. Bulgular: Belirlenen finansal oranlar çerçevesinde yıllar itibariyle finansal performans açısından en başarılı bankaların ilk üç yıl için T.C. Ziraat Bankası A.Ş., sonraki iki yıl için ING Bank A.Ş. olmuştur. Başarısı en düşük bankalar ise yıllar itibariyle ilk iki yıl HSBC Bank A.Ş., sonraki iki yıl için Alternatif Bank A.Ş. ve son yıl için Türkiye Halk Bankası A.Ş. olmuştur. Sınırlılıklar: Çalışmanın örneklemini 15 banka oluşturduğu için performans değerlendirmesi sadece bu bankalar arasında geçerlidir. Özgünlük/Değer: Bankaların performans değerlendirmesi genel olarak TOPSIS yöntemi kullanılarak yapılmaktadır. Bu çalışmanın gerek VIKOR yönteminin kullanılması açısından gerekse de daha güncel veri seti kullanılması açısından literatüre katkı sağlayacağı düşünülmektedir.

COVID-19 has caused a major crisis all over the world. To manage this crisis, a fixed shift system was applied to nursing home staff in Turkey to protect nursing home residents from the COVID-19 pandemic. Staff were not allowed to leave the institution during fixed shifts. It is thought that this practice for the COVID-19 outbreak, while protecting nursing home residents on the one hand, increased the workload and related stress of nursing home staff on the other hand. To the best of our knowledge, there is no study examining the workload and stress caused by the COVID-19 pandemic for nursing home staff in Turkey. The aim of this study was to examine the level of workload and work stress experienced by staff working in nursing homes during the COVID-19 pandemic in Turkey. Methods: A cross-sectional research design was used in the study. The sample of the study consisted of nursing home personnel working in nursing homes in the provinces of Istanbul, Ankara, Izmir and Antalya between October 2021 and January 2022. A personal information form and a workload and stress scale were used for collecting the data. Cluster analysis was performed with SPSS software. Results: In total, 154 nursing home personnel participated in the study. A statistically significant difference was found between the mean age of the two clusters. The first cluster was described as “old” and the second as “young”. Statistically significant and high values were found in the quantitative workload, qualitative workload, job organization, social work area and fatigue factors in the nursing home staff in the older participant cluster. Conclusion: The results of this study provide new information about the concepts of workload and work stress experienced during the COVID-19 pandemic in Turkey, which will serve as a guide for the management of future pandemics. Therefore, this study will contribute to the strategies to be followed in future pandemics in Turkey.

Banks have expanded their marketing activities to uphold their existing customers and gain new customers to accentuate their market share in the sector. This study aims to emphasize the importance of customer forecasting and investigate the factors that are effective in determining the number of customer forecasting. Machine learning, artificial neural networks, and support vector machines methods are used. The variables commonly used in the literature, such as number of branches, number of employees, total deposits, total loans, were selected to determine customer estimates by considering the monthly data of deposit banks in the Turkish banking sector for the period 2011-2020. As a result of the analysis, the number of customers estimates of deposit banks in Turkey was successfully obtained. In light of these estimates, it is thought that banks can facilitate the creation of targets for identifying target customers they aim to provide services to in the future.

This study examines the relationship between banks’ ESG performance and core risk and balance sheet indicators in the Turkish banking sector. Using an unbalanced panel of eight banks listed on Borsa Istanbul over the period 2008–2023, we estimate bank fixed-effects models with one-year-lagged ESG measures and controls and report Driscoll–Kraay standard errors. Two complementary specifications are employed: one based on the composite ESG score and another based on its environmental (E), social (S), and governance (G) pillars. The findings suggest that the composite ESG score is positively associated with non-performing loans and capital adequacy, while its relationship with liquidity risk and net balance sheet position/equity is less stable across specifications. When the ESG pillars are examined separately, substantial heterogeneity emerges across the E, S, and G dimensions. In particular, the environmental score is negatively associated with capital adequacy, whereas the social score is negatively associated with net balance sheet position/equity. Governance-related results appear weaker and more sensitive to specification choice. Overall, the findings indicate that ESG does not operate through a uniform risk channel in banking and should be interpreted as associational rather than causal. The study contributes evidence from an emerging-market banking system and highlights the importance of disaggregated ESG analysis.