Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Value Creation and Market Perception in the Construction Sector: Adjusted EVA and MVA Analysis
Kalkan, Yusuf
Purpose – This study aims to assess the financial performance of firms listed in Borsa Istanbul’s Construction and Public Works sector through the calculation of adjusted Economic Value Added (EVA) and Market Value Added (MVA). In this context, the association between the value created by these companies and their market values is examined. Design/methodology/approach – In the study, adjusted EVA and MVA values for the 2022–2024 period were calculated using the financial data of the relevant firms, and the relationships between these indicators and Market Value (MV) were tested through statistical analyses. Results – The results indicate that negative EVA values prevail across the sector, suggesting that most firms have been unable to cover their cost of capital. While a strong relationship was observed between MVA and Market Value (MV) in the first year, a negative and statistically significant relationship was found between EVA and MV for all years. However, no significant relationship was identified between EVA and MVA. Discussion – The findings reveal that investor behavior does not always align with companies’ internal performance indicators. This indicates that relying solely on a single metric in the evaluation of financial performance may be insufficient. On the other hand, the findings of the study show both similarities and differences compared to previous research. These differences may stem from sectoral and regional dynamics as well as variability in investor perceptions. Thus, this study reveals that financial performance indicators can carry different meanings depending on the sectoral context, and that contextual factors should be taken into account when interpreting these indicators.
- MakaleAçık Erişim2025Efficiency analysis using the machine learning algorithms: model development and verification
Soydaş, Şafak Sönmez; Kalkan, Yusuf; Çam, Alper Veli; Barut, Abdulkadir
Recently, machine learning (ML) algorithms have been employed intensively in the field of finance as in all sectors. The issues such as financial distress prediction, bank credit risk calculation, etc., have been analyzed using ML algorithms. This study aimed to determine firm performance with the data envelopment analysis (DEA) method, sensitivity analysis, and ML algorithms and analyze the efficiency of companies via artificial neural networks (ANNs), support vector machines (SVMs), and logistic regression (LR) classification algorithms. In the study, first, 10 financial ratios were categorized into two parts, such as output and input, and efficiency scores were determined in MS Excel software. The obtained scores were included in the ML algorithm as a categorical dependent variable. Secondly, the data were extracted and included in the analysis software as 80% training and 20% test data, and the accuracy of ML algorithms was tested. Lastly, a comparative analysis of the estimation and classification algorithms of active and inactive companies was conducted. As a result of the analysis, the best classification prediction was seen as the ANN algorithm. SVM and LR algorithms also made an acceptable level of classification prediction. It was expected that the study would have contributed to the literature in terms of testing the companies whose efficiency scores were determined by the DEA method with ML techniques and determining which technique was more successful.
- MakaleAçık Erişim2024Spatial Analysis of Economic Risk, Economic Freedom Index, Corruption Perceptions Index and Human Development Index; [Ekonomik Risk, Ekonomik Özgürlük İndeksi, Yolsuzluk Algısı İndeksi ve İnsani Gelişim İndeksi’nin Mekânsal Analizi]
Kalkan, Yusuf
This study aims to examine the spatial (neighbourhood) relations of 163 countries in terms of economic risk (ER), economic freedom index (EFI), corruption perception index (CPI) and human development index (HDI). Moran I, Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) methods were used for spatial analysis. The MGWR models demonstrated that countries globally have strong spatial relationships with their neighbouring countries regarding ER, EFI, and CPI but are not significant regarding HDI. This result can be explained by the fact that ER, EFI and CPI indicators are mostly related to the country's economy and that countries have become economically interdependent today. However, HDI may not have resulted in significant spatial relationships due to its focus on human quality of life and social-cultural differences among countries.
- MakaleAçık Erişim2023Spatial analysis of high-tech exports with geographically weighted regression
Kalkan, Yusuf
The development of High-Technology Exports (HTE) contributes to the increase of competitiveness and growth in national production, the development of commercial and economic cooperation with foreign countries, and the diversification of the economy. In addition, high technology exports reduce the current account deficit of the country's economy. Therefore, high-tech industries have become indispensable for economic development in a country. The aim of this study is to examine the existence of spatial (neighbourhood) relations of countries in high-technology exports. In this study, Exploratory Regression analysis was applied to determine the model that best explains HTE among the variables. Moran Index was used for spatial autocorrelation, Geographical Weighted Regression (GWR) method for spatial regression analysis, and Ordinary Least Squares (OLS) method, to compare with the GWR method. The GWR model established in this study showed that countries have a weak or strong spatial relationship with their neighboring countries in terms of HTE. It has been observed that this spatial relationship is strong in the countries neighboring China, which ranks first in HTE, while the spatial relationship is weak in countries where HTE is low.