Türk bankacılık sektörü verimliliğinin stokastik sınır analizi ile değerlendirilmesi
Efficiency of the turkish banking sector: A stochastic frontier approach
Yazar: SONER KIZILKAYA
Danışman: PROF. DR. UFUK CEBECİ
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Endüstri Mühendisliği Ana Bilim Dalı / Mühendislik Yönetimi Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Bankacılık = Banking
Dizin: Türk bankacılık sektörü = Turkish banking sector

Özet:
Türkiye gibi gelişmekte olan ekonomilerde, reel sektörün temel finansman sağlayıcısı olan bankalar, finansal aracılık sürecinde özellikle kredi ve likidite riskleri gibi birçok riskle karşılaşmaktadır. Yönetimi verimli bir şekilde yapılamayan bu riskler bankacılık sisteminin finansal performansına olumsuz etki etmektedir. İstikrarlı bir ekonomik büyüme ve refah düzeyi için finansal sistemin performansı önemli bir gösterge haline gelmiştir. 
Finansal sistemin temel dinamiğini oluşturan bankaların verimliliğini ölçmek için kullanılan finansal oran analizlerinin birden fazla girdi ve çıktıyı kullanma konusunda yetersiz olmasından dolayı verimlilik incelemesi parametrik veya parametrik olmayan yöntemlerle gerçekleştirilmektedir. Bu çalışmada girdiler, çıktılar ve çevresel faktörler arasındaki maliyet, kar veya üretim ilişkisi için işlevsel bir form belirleyen ve rastgele hataya izin veren Stokastik Sınır Analizi kullanılmıştır. Türkiye'de faaliyet gösteren bankaların 2017 - 2022 dönemi için teknik verimliliği analiz edilmiştir. 
Bu çalışmada bankacılık davranışının modellenmesinde, bankaların mevduat sahipleri ile borçlular arasında aracılık yaptığını ve bankaların girdileri çıktılara dönüştürdüğünü varsayan finansal aracılık yaklaşımı uygulanmıştır. Ek olarak bu çalışmada stokastik girdi mesafe fonksiyonu kullanılmıştır. Çok girdi, çok çıktı teknolojisini modelleme avantajına sahip olan girdi mesafesi fonksiyonunda bankalar, üç girdiden üç çıktı üreten çok ürünlü firmalar olarak modellenmiştir. Banka girdileri emek, sermaye ve ödünç verilebilir fonlardan oluşurken, banka çıktıları toplam krediler, bilanço dışı kalemler ve diğer kazanç sağlayan varlıklardan oluşmaktadır. 
Çalışmanın amacı sadece bankacılık sektörünün durumunu yansıtmak değil, aynı zamanda Türk bankacılık sektörünü banka sahipliği (yabancı, özel, devlet), banka türü (geleneksel, katılım), borsa kotasyon durumu (kote, kote değil) ve banka büyüklüğü gibi çeşitli bakış açılarından değerlendirmektir. Ek olarak, bu çalışma aynı zamanda olağanüstü koşulların (Koronavirüs salgınından etkilenen makroekonomik ortam) banka verimliliği üzerindeki etkisini de değerlendirmektedir.
Sonuçlar, bankacılık hizmetlerinin aracılığı sırasında belirli miktarda banka kaynağının israf edildiğini, yönetimin girdi ve çıktıları daha iyi kontrol ederek verimliliği artırabileceğini göstermektedir. Bir başka ilginç sonuç ise katılım bankalarının daha az verimli olduğu, yerli özel bankaların ve devlet bankalarının ise yabancı bankalara göre daha verimli olduğudur. Ayrıca sonuçlar banka büyüklüğünün, borsa kotasyon durumunun ve pandeminin banka verimliliği üzerinde etkili olduğunu da göstermektedir.

Abstract:
Banks, the main source of financing for real sector, in developing economies such as Turkey, are exposed to many risks, especially credit and liquidity risks, during the financial intermediation process. Unless they are managed effectively, these risks may have adverse impact on the performance of the banking sector. The performance of the financial system has become an important indicator for a stable economic growth and welfare.
Since financial ratio analyzes used to determine bank efficiency, which constitute the basic dynamics of the financial system, are insufficient to use multiple input and output, efficiency analysis is carried out by parametric or non-parametric methods. The Stochastic Frontier Analysis is the most used parametric method to measure bank efficiency. Alongside other parametric methods, there are also non-parametric empirical estimation techniques. Parametric methods involve defining the frontier through a functional form such as trans-logarithmic specification, while non-parametric methods do not assume any explicit functional form for the frontier and derive it from observed input-output ratios using mathematical programming techniques. As non-parametric methods do not allow statistical noise and do not incorporate a functional form, they regard any deviation from the frontier as inefficiency, leading to a restrictive assumption. The Stochastic Frontier Analysis differentiate inefficiency from other stochastic errors. Therefore, it is considered superior to non-stochastic models in estimating bank efficiency. 
The paper employs a stochastic input distance function, which is flexible and advantageous for modeling multi-output multi-input technology. Unlike specific behavioral objectives such as cost minimization or profit maximization, the input distance function does not require any particular behavioral objective. Therefore, it is useful in banking analysis in which it is inappropriate to assume an optimizing behaviour due to short or long run constraints. Moreover, given that the banking sector typically has more control over inputs than outputs, this supports the adoption of an input-oriented approach in banking studies. 
The choice of input and output variables for estimating bank efficiency depends on the approach being pursued. In this study, the intermediation approach is utilized to model banking behavior. This approach assumes that banks function as intermediaries between depositors and debtors, converting inputs into outputs. The intermediation approach incorporates interest expenses, which frequently constitute more than half of total costs, making it deemed superior for evaluating entire financial institutions. In the input distance function, the banks in the sample are depicted as multi-product firms that generate three outputs from three inputs. Bank inputs consist of labor, physical capital, and loanable funds, while bank outputs include total loans, off-balance sheet items, and other earning assets. In this study, the analysis also incorporates exogenous variables (macroeconomic variables such as the GDP growth rate and real interest rate, and cross bank observations to account for the types of bank ownership, orientation, and listing status. Inclusion of these factors allows for bank efficiency levels to vary systematically across banks as these are directly associated with technical inefficiency. 
This study, aimed at exploring the efficiency of the Turkish banking sector, seeks answers to numerous questions addressed in the efficiency literature through a comprehensive approach. The efficiency of the banking sector has been scrutinized across various dimensions, including bank size, ownership structure, type (such as deposits or participation banks), and listing status on the stock exchange. Additionally, the impact of the pandemic period on bank efficiency has also been investigated.
The sample consists of annual balanced panel data for 28 commercial banks operating in Turkey. Out of the 28 banks, 12 are categorized as foreign banks (with more than 50% of their shares owned by non-residents), eight are domestically owned commercial banks (with more than 50% of their shares owned by Turkish residents), three are designated as state-owned commercial banks (predominantly owned by the Turkish government), and five are participation banks (commonly known as Islamic banks, engaging in interest-free financial activities based on the profit-loss sharing principle). Among them, 11 banks have publicly quoted shares. The total asset size of the banks in the study sample represents 97% of the total asset size of the Turkish banking sector. Hence, it is accurate to assert that the study reflects the Turkish banking sector. The final dataset comprises 168 bank observations spanning the period 2017–2022, rendering this investigation one of the most recent studies on bank efficiency concerning Turkish banks.
During the years under investigation, the average efficiency of the banking sector stood at 73%, indicating that approximately 27% of bank resources were inefficiently utilized during the intermediation of banking services. Management could enhance efficiency through better control of inputs and outputs. Although the efficiency of the banking sector was 67% in 2017, it rose to 81% by 2019, only to decline to 66% in both 2020 and 2021, marking a 15-point decrease. This decrease in efficiency during 2020 and 2021 can be attributed to the global impact of the pandemic on economic activities. However, in 2022, efficiency recovered and reached 79%.
When banks are categorized based on their ownership structures and types, it becomes evident that participation banks differ significantly from deposit banks, exhibiting notably lower bank efficiency. The diminished efficiency of participation banks could be attributed to limited opportunities for product diversification and a less standardized banking system. 
Empirical evidence suggests that foreign banks generally underperform compared to domestic banks, supporting the home field advantage hypothesis. Domestic banks' inherent managerial skills are better suited to the local market, while foreign banks often encounter challenges such as establishing an effective branch network and attracting new retail clients.
When banks are compared based on asset size, it is evident that larger banks exhibit higher efficiency, underscoring the significance of economies of scale in Turkey's emerging financial system. The widespread branch networks of large banks, coupled with their ease of market access, enable them to reduce costs and enhance output efficiency through economies of scale.
Finally, when comparing the listing status of banks (those traded on the stock exchange) with their efficiency, it is found that listed banks tend to be more efficient. Listed banks are subject to closer scrutiny by market participants for their transparency through a focus on corporate governance rules and the publication of bank data. This contributes to these banks maintaining higher quality portfolios and therefore reducing input costs.