Dinamik Sermaye Yapısı Belirleyicileri ve Optimal Kaldıraç Oranı Uyarlama Hızı: Türkiye’de Finans Şirketleri Üzerine Bir Çalışma
Hülya YılmazBu çalışma Türkiye’de faaliyet gösteren finansal şirketlerin sermaye yapılarını oluştururken bir hedef doğrultusunda hareket edip etmedikleri ve uyarlama maliyetlerinin olduğu bir ortamda uyarlama hızlarının ne olduğunun tespit edilmesi amacıyla yapılmıştır. Finansal şirketlerin borç uyarlama hızlarını analiz etmek üzere gerekli olan veri Amerikan “Standard and Poor’s” firmasının küresel şirket verilerini tuttuğu S&P Compustat Fundementals veri tabanından derlenmiştir. Veri setinde 114 finansal şirkete (mevduat bankaları, katılım bankaları, menkul ve gayrimenkul yatırım ortaklıkları ve sigorta şirketleri) ait 1996-2018 yıllarını kapsayan toplam 1570 firma-yılı gözlem bulunmaktadır. Dinamik panel veri analiz yöntemlerinden GMM-SYS kullanılarak yapılan analizler sonucu, finansal şirketlerin cari borçlarını optimal borç oranına uyarlama hızı 26% olarak tespit edilmiştir. Ancak bu hızın finansal şirketlerin toplam borçlanma oranlarının art arda düştüğü 2008 yılını da içine alan küresel finans krizi sürecinde (2007-2010) 19%’a gerilediği görülmüştür. Modellerden elde edilen bulgular yorumlandığında, finansal şirketlerin sermayelerini yapılandırırken alternatif finansman tercihleri arasında hiyerarşik bir sıralama yapmak yerine, borç ve özsermaye oranlarını belli bir seviyede tutmak amacıyla hedef bir borç oranı etrafında dengeleme davranışı sergilediklerini söylemek yanlış olmayacaktır. Araştırma keşfedici nitelikte bir çalışma olduğundan, uyarlama hızını etkileyen faktörler ve bu faktörlerin farklı dönemlerde uyarlama hızını ne ölçüde arttırdığı veya azalttığı çalışmanın kapsamı dışındadır. Firma yönetimlerine optimal sermaye yapısı kararlarında yardımcı olunabilmesi için gelecekteki çalışmaların uyarlama hızına etki eden kurumsal ve makroekonomik bazı değişkenler üzerine yoğunlaşması uygun olacaktır.
Determinants of Dynamic Capital Structure and the Speed of Adjustment to Optimal Leverage: A Study on Financial Institutions in Turkey
Hülya YılmazThis study examines whether financial institutions in Turkey revise target debt ratios when determining capital structures, in addition to investigating the speed of adjustment to optimal capital structure in the presence of adjustment costs. The data were obtained from the COMPUSTAT Fundamentals database, which is maintained by Standard & Poor’s. The dataset covers 1,570 firm-year observations from 114 financial institutions (deposit banks, Islamic banks, securities, real estate investment trusts, and insurance companies) between 1996 and 2018. Using the dynamic panel data analysis method GMM-SYS, the speed of adjustment to optimal capital structure is determined to be 26%; however, during the global financial crisis (2007–2010), when financial institutions’ total debt ratios declined, this speed plummeted to 19%. Based on the results of the analysis, it is reasonable to conclude that financial firms conduct balancing behavior around a target debt ratio to maintain optimal debt ratios when structuring capital, rather than making a hierarchical selection among alternative financing options. As the study is an exploratory endeavor to determine the speed of adjustment, the factors affecting the adjustment speed and the extent to which these factors increase or decrease adjustment speeds in different periods are beyond the scope of the study. Future research should consider institutional and macroeconomic determinants of adjustment speed to help advise firm executives about the optimal capital structures of their respective firms.
This study examines whether financial institutions in Turkey revise target debt ratios when determining capital structures, in addition to investigating the speed of adjustment to optimal capital structure in the presence of adjustment costs. The trade-off theory of capital structure proposes that firms consider the costs and advantages of debt and equity prior to making a final decision on an optimal (target) debt-to-equity ratio. According to this theory, firms may deviate from the target debt level and then gradually return to this level due macroeconomic changes and financial and administrative decisions. When firms are over- or under-leveraged in terms of optimal leverage, they tend to adjust capital structures to the targets in the next period. Trade-off theory has been tested using both static and dynamic methods. The static approach assumes that current debt ratios are already optimal, whereas the dynamic approach asserts that optimal debt ratios can only be estimated, as they cannot be directly observed. The underlying assumption of dynamic trade-off theory is that firms cannot maintain optimal debt because of adjustment costs, as it takes some time for firms to adjust current ratios to optimal ratios. Some studies demonstrate that firms in Turkey follow an optimal debt ratio; however, such studies are mostly conducted on non-financial firms. This study attempts to fill the current gap by analyzing financial data from 114 financial institutions between 1996 and 2018. The dependent variable measures total debt ratios, whereas independent variables include lagged-values of total debt, profitability, the tangibility of assets, growth opportunities, and firm size. Blundell–Bond’s (1991) GMM-SYS approach was adopted as the estimation method. The findings indicate that financial firms in Turkey revise target debt ratios when determining capital structures and the speed of adjustment is 26%, with a half-life of 1.93 years. It takes almost two years for financial firms to adjust current debt to half of the target debt ratios. In contrast, the speed of adjustment was around 19% between 2007 and 2010, when the debt ratios plummeted due to the global financial crisis. The speed of adjustment was around 37% between 1996 and 2007, and 43% between 2011 and 2018. The findings also indicate that profitability, the tangibility of assets, growth opportunities, and firm size have no effect on when the lagged version of total debt is endogenized. In contrast, the estimated coefficients for profitability and tangibility seem to follow a pattern across different time periods. While profitability had a positive effect on total debt in all models, the effect was negative during the crisis. Conversely, the effect of tangible assets on total debt almost doubled during the crisis. Based on the results of analysis, it is reasonable to conclude that financial firms conduct balancing behavior around a target debt ratio to maintain optimal debt ratios when structuring capital, rather than making a hierarchical selection among alternative financing options. Due to missing observations in the data set, it was not possible to establish a model that would allow measuring the speed of adjustment during the 2001 economic crisis. The autocorrelation (AR(1) and AR(2)) and Hansen (1982) test results indicate that the models fit the data well. As the study is an exploratory endeavor to determine the speed of adjustment, the factors affecting the adjustment speed and the extent to which these factors increase or decrease adjustment speeds in different periods are beyond the scope of the study. Future research should consider institutional and macroeconomic determinants of adjustment speed to help advise firm executives about the optimal capital structures of their respective firms.
