İlgi alanları

Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2026/r/gumushane/19403
  Value 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.

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• MakaleAçık Erişim2025/r/gumushane/12519
  Efficiency 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.

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• MakaleAçık Erişim2024/r/gumushane/12547
  Spatial 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.

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• MakaleAçık Erişim2023/r/gumushane/20288
  Spatial 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.

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• Doktora TeziAçık Erişim2021/r/gumushane/21029
  Coğrafi bilgi sistemi tabanlı ekonomik ve finansal risk analizi: Avrupa ve Orta Asya örneği

  Yusuf Kalkan

  Çalışmanın amacı; ICRG rating kuruluşunun belirlediği ekonomik ve finansal riski oluşturan her bir faktörü farklı bir sınıflandırma tekniği kullanarak risk sınıflandırması yapmak ve bu sınıflandırmaya göre ülkerin tematik haritalarını oluşturarak karşılaştırmaktır. Diğer amaç ise ICRG rating kuruluşunun belirlediği ekonomik ve finansal risk faktörlerine göre ülkelerin, Coğrafi Bilgi Sistemi (CBS) analiz tekniklerinden biri olan coğrafi ağırlıklı regresyon yöntemini kullanarak dinamik ve görsel sonuçlar ortaya koyacak şekilde ekonomik ve finansal risk haritalarını oluşturmaktır. Bu amaç doğrultusunda International Country Risk Guide (ICRG) derecelendirme kuruluşunun ülke riski modeline göre ülke riski kategorilerinden ekonomik ve finansal riskin içinde yer alan 10 faktör kullanılmıştır. Ekonomik riski belirleyen faktörler; kişi başına düşen GSYİH, reel GSYİH artışı, enflasyon oranı, GSYİH'nın % olarak bütçe dengesi, GSYİH'nın % olarak cari hesap değişkenleri kullanılmıştır. Finansal riski belirleyen faktörlerde ise GSYİH'nın % olarak dış borç, mal ve hizmet ihracatının % olarak dış borç, mal ve hizmet ihracatının % olarak cari hesap, ithalat yapıldığı aylarda net uluslararası likidite, döviz kuru istikrarı değişkenleri kullanılmıştır. Çalışmada 2018 ve 2019 yıllarını kapsayacak şekilde Avrupa ve Orta Asya'daki 48 ülke belirlenmiştir. Çalışmada bu parametreler kullanılarak Avrupa ve Orta Asya'daki 48 ülkenin 2018 ve 2019 yılları için ekonomik ve finansal risk haritaları, coğrafi ağırlıklı regresyon (CAR) analizi ile oluşturulmuştur. CAR analizi için ArcGIS 10.8 programından yararlanılmıştır. Araştırma kapsamında yer alan değişkenler CAR analizi yapılmadan önce mekansal otokorelasyonun temel ölçümü olarak Moran's Indeks analizi yapılmış ve ekonomik ve finansal risk bağımlı değişkenlerinin p-değerleri 0,05 anlamlılık düzeyinden küçük olması nedeniyle istatiksel açıdan anlamlı ve pozitif otokorelasyona yani mekansal bağımlılığa sahip olduğu görülmüştür. Ayrıca kurulan modellerin performansını gösteren düzeltilmiş R2 değerlerinin hem 2018 hem de 2019 yıllarında 0,999 olduğu ve bağımsız değişkenlerin bağımlı değişkenleri %99,9 gibi yüksek bir oranda açıkladığı tespit edilmiştir. CAR analizi sonuçlarına göre incelenen ülkelerin risk düzeyi beş sınıfa (çok yüksek risk, yüksek risk, orta düzey risk, düşük risk ve çok düşük risk) ayrılmış ve CAR analizi sonucunda elde edilen ekonomik ve finansal risk puanalarının tahmini (predicted) değerlerine göre risk haritaları oluşturularak daha dinamik, anlamlı ve görsel sonuçlar ortaya konmuştur. Çalışmada elde edilen CAR analizi sonuçlarının ekonomi ve finans alanında CBS tabanlı uygulamaların kullanılabileceği görülmüş ve bu doğrultuda ülkelerin ekonomik ve finansal riskini belirlemede literatüre disiplinler arası yeni bir bakış açısı kazandırılmıştır.

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