Article
citation information:
Kiracı, K., Yaşar, M., Asker, V. Determinants
of capital structure in aircraft leasing firms: Theoretical and empirical
perspectives. Scientific Journal of
Silesian University of Technology. Series Transport. 2025, 129, 115-130. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2025.129.7
Kasım KİRACI[1],
Mehmet YAŞAR[2],
Veysi ASKER[3]
DETERMINANTS OF
CAPITAL STRUCTURE IN AIRCRAFT LEASING FIRMS: THEORETICAL AND EMPIRICAL
PERSPECTIVES
Summary. The aviation industry
encompasses a variety of stakeholders. In recent years, the growing reliance of
airlines on leased aircraft has elevated leasing companies to a critical
position within the sector. Despite their importance, the capital structure of leasing
companies remains an underexplored area in the aviation literature. This study
is a pioneering effort to investigate both the theoretical and empirical
aspects of leasing companies’ capital structure. Using panel data analysis, the
research examines the capital structure behavior of
these companies over the period from 2013 to 2023. Six different models are
developed to provide a more in-depth analysis of the effects of short-term and
long-term financing decisions on the capital structure. The findings generally
indicate that the financing behavior of leasing
companies is in line with the pecking order theory, which suggests seeking
internal financing before seeking external debt or equity.
Keywords: leasing, capital structure, panel data, pecking order theory,
trade-off theory
1.
INTRODUCTION
The air transportation industry is widely
recognized as one of the most capital-intensive sectors. To manage and reduce
capital expenditures, airlines employ various financial strategies, the most
prominent of which is leasing aircraft instead of purchasing them outright.
This approach not only supports a more flexible and resilient capital structure
but also contributes to mitigating financial risk. Empirical evidence suggests
that aircraft leasing plays a significant role in enhancing the financial stability
of airlines (Oum, Zhang, & Zhang, 2000). Over the past few decades, the air
transport sector has shifted significantly from a model in which airlines owned
the entirety of their fleets to one in which a substantial proportion of
aircraft are leased. Since the establishment of the first aircraft leasing
company in the 1970s, the industry has undergone considerable structural
transformation. While leased aircraft accounted for less than 5% of the global
fleet in the 1980s, this figure surpassed 50% by 2020 Wandelt
et al. (2023) The primary motivation behind aircraft leasing is to lower
borrowing costs and to enhance organizational flexibility. European-based
airlines engage in leasing activities not only to optimize their workforce
allocation but also to reduce operational costs through the outsourcing of
specific services. For instance, carriers such as Air Baltic, Finnair, and
Lufthansa maintain extensive contractual arrangements with leasing firms. Given
that aircraft leasing represents a strategic effort to balance high capital
expenditures and limited financial resources with the need for both operational
and financial flexibility, leasing decisions are often embedded in complex
strategic planning processes (Gavazza, 2011).
Therefore, aircraft leasing is one of the important long-term strategic
investment decisions for the airline industry.
Although the demand for aircraft leasing by
airlines has generally followed an upward trajectory over the past three
decades, fluctuations have been observed periodically. In particular, during
times of crisis, leasing activities tend to decline, and airlines may opt to
return aircraft acquired through optional lease agreements. Such developments
impose additional costs and elevate financial risks for leasing companies.
Considering the severe impact of the COVID-19 pandemic on the air transport
sector Shortall et al. (2021) a decline in aircraft leasing activity was
observed during the 2020–2021 period compared to 2019, as expected. However,
leasing activity rebounded rapidly in 2022 and beyond, even surpassing
pre-pandemic levels by 2024. There are three potential factors that may explain
this upward trend. First, aircraft leasing fees have become more favorable compared to the pre-pandemic period. Second, as
the air transport industry began to recover in 2022 and thereafter, airlines
demonstrated a renewed willingness to expand capacity (Wandelt
et al. 2023) Third, acquiring new aircraft directly from manufacturers entails
a higher long-term borrowing cost, while leasing offers a lower-cost
alternative to meet growing capacity needs (Bourjade,
Huc, & Muller-Vibes, 2017).
According to the CAPA database, at least 50% of
the global commercial aircraft fleet is leased (CAPA, 2024). This highlights
the critical role that leasing companies play in the air transport sector. The
presence of numerous leasing firms worldwide, combined with the fact that 53%
of leased aircraft are owned by the 15 largest lessors, has made the leasing
market increasingly competitive. This heightened competition enables airlines
to secure lease agreements at more favorable rates (Marintseva & Athousaki,
2024). Consequently, leasing companies are engaged in intense rivalry, which
makes analyzing their capital structures particularly
crucial.
Numerous studies in the literature have
investigated the determinants of corporate capital structure. For instance, Kiracı & Aydın (2018) examined the factors
influencing the capital structure of traditional airlines, drawing on prominent
capital structure theories. Ramli et al., (2019) analyzed
the impact of capital structure determinants on the financial performance of
firms in Indonesia and Malaysia. Kiracı &
Asker (2020) explored the capital structure determinants of airlines that are
members of strategic alliances, employing panel data analysis. Sikveland et al.
(2022) studied how geographical concentration and seasonality affect capital
structure decisions in the accommodation industry using fixed-effects panel
regression. Rehan et al. (2023) investigated the capital structure determinants
of firms operating across various sectors in Malaysia. Zhao & Zhang (2024) analyzed the relationship between ESG performance and
capital structure in Chinese firms, with a particular focus on equity and debt
financing. Lastly, Ashraf et al. (2025) assessed the impact of the COVID-19
pandemic on the capital structure decisions of tourism and accommodation
enterprises.
Numerous studies in the literature have explored
various aspects of aircraft leasing companies. Some have focused on the legal
challenges associated with leasing agreements, emphasizing the complexity of
drafting such contracts (Kuhle et al. 2021; Jackson et al., 2023). Others have
examined the leading companies and countries with the highest volumes of leased
aircraft (Karunakaran et al., 2021; Lin et al. 2022). Research has also
highlighted the significance of leasing for specific markets or airlines (Bowyer
& Davis, 2012; Richardson et al. 2014), as well as its influence on fleet
planning strategies (Chen et al. 2018; Şafak et al. 2022). More recently,
studies have addressed the impacts of the COVID-19 pandemic on the aircraft
leasing sector, including the risks that have emerged (Kiracı
& Asker, 2020; Güngör, 2022; Deveci et al. 2022). However, the determinants
of the capital structure of aircraft leasing companies have received limited
attention. This study addresses this gap by analyzing
the financial factors that influence capital structure decisions in the sector.
The remainder of the article is organized as
follows. Section 2 reviews the existing literature, while Section 3 outlines
the theoretical background. Section 4 describes the methods and data employed
in the study, followed by Section 5, which presents the research model. Section
6 discusses the findings, and the final section provides the discussion and
conclusion.
2. THEORETICAL
FRAMEWORK
There are several theories that attempt to explain capital structure,
among which the Modigliani & Miller (1958) theory is considered a
pioneering framework. This theory posits that a firm’s value depends on the
ability of its assets to generate value, regardless of whether the capital is
sourced internally or externally. Modigliani and Miller argued that firms
should maximize their use of debt to benefit from the tax shield on interest
payments, thereby reducing their overall tax burden. In other words, the
greater the proportion of debt in a firm’s capital structure, the higher its
value, due to the tax deductibility of interest expenses. This theory has been
fundamental in shaping our understanding of the relationship between capital
structure and firm value. Subsequently, the theory was revised to incorporate
corporate taxes (Modigliani & Miller, 1963) emphasizing that the tax
advantage of debt arises from the deductibility of interest expenses.
Another important theory that explains capital structure is the trade-off
theory, which gained prominence following debates around the Modigliani-Miller
theorem (Javed & Jahanzeb, 2012). This theory originated as an extension of
the Modigliani-Miller framework when corporate taxes were introduced,
highlighting the benefit of debt as it provides a tax shield on earnings.
According to the trade-off theory, firm managers evaluate and weigh the various
costs and benefits associated with different leverage options. It is generally
assumed that firms seek an optimal leverage level where the marginal benefits
of debt, such as tax savings, balance the marginal
costs, such as financial distress (Ahmadimousaabad et al., 2013). The trade-off
theory posits that a firm faces bankruptcy and agency costs in exchange for the
tax benefits derived from debt usage. Bankruptcy costs arise when the perceived
probability of default is greater than zero. These costs include liquidation
costs, which represent the loss in value resulting from the sale of the firm’s
net assets, and distress costs, which are incurred when stakeholders believe
the firm may cease operations. According to the trade-off theory, firms aim to
maintain an optimal or target debt ratio that balances these costs and benefits
(Jalilvand & Harris, 1984).
The trade-off theory asserts that firms are incentivized to use debt to
benefit from debt tax shields. Thus, it can be argued that firms have an
incentive to incur debt because generating annual profits enables them to take
advantage of these tax shields (Serrasqueiro & Caetano, 2015). According to
several studies (DeAngelo & Masulis, 1980; Fama & French, 2002;
López-Gracia & Sogorb-Mira, 2008) a positive relationship is expected
between the effective tax rate and the level of debt.
According to DeAngelo & Masulis (1980) non-debt tax shields – such as depreciation deductions and investment tax credits – can serve as substitutes for the tax benefits provided by debt.
Consequently, firms with higher levels of non-debt tax shields are expected to
carry lower levels of debt compared to those with lower levels of such shields.
The trade-off theory thus predicts a negative relationship between non-debt tax
shields and debt. The pecking order theory of capital structure is one of the
most influential approaches to explaining corporate leverage (Frank &
Goyal, 2003). According to Myers (1984) firms prefer to finance their
operations primarily through internal resources due to the adverse selection
problem. When external financing is necessary, debt instruments – which typically involve lower costs stemming from information asymmetry – are preferred over equity issuance. As a result, equity issuance is used
infrequently. The strength of the pecking order theory largely lies in its
ability to explain firms’ external financing behaviors (Myers, 2001) further
notes that the proportion of external financing in total capital formation
remains limited, with equity issuances constituting only a small fraction, and
most external financing being raised through debt.
The pecking order theory also has implications for the maturity and
priority of debt. According to the theory, firms should prefer securities with
lower information costs before resorting to those with higher information
costs. In this context, short-term debt should be used before long-term debt,
and capital leases and secured debt should be preferred over unsecured debt
(Frank & Goyal, 2003). Moreover, since the pecking order theory does not
explain broad patterns in corporate finance, it is more appropriate to examine
narrower subsets of firms. According to the theory, financing behavior is
driven by adverse selection costs, and it should perform best among firms that
face particularly severe adverse selection problems. Small firms with
high-growth rates are generally considered to experience significant
information asymmetries (Frank & Goyal, 2003)
Pecking order theory suggests that firms follow a specific hierarchy of
capital preferences when financing their business activities (Myers &
Majluf, 1984). Due to information asymmetries between firms and potential
investors, companies prefer to use internal funds first, then debt, and lastly
equity (Myers & Majluf, 1984). This behavior implies that firms can
mitigate information asymmetry by relying on retained earnings rather than
issuing new securities to finance investment opportunities. As the information
gap between insiders and external investors widens, equity financing becomes
increasingly costly. Therefore, firms facing high levels of information
asymmetry are more likely to choose debt to avoid selling equity at undervalued
prices. Developments that dilute the capital structure, such as the issuance of
new equity, may lead to a decline in the firm’s stock price (Chen & Chen,
2011).
3. LITERATURE
REVIEW
The
determinants of capital structure decisions have long been debated in the
literature and are known to vary based on contextual factors such as industry,
country, and period. While the Trade-Off Theory and the Pecking Order Theory
provide the main theoretical frameworks for understanding these decisions,
empirical evidence suggests that the relevance of each theory may vary
depending on specific contextual conditions.
While
studies such as Güner (2016) and Matias & Serrasqueiro
(2017) suggest that the Pecking Order Theory is more explanatory, the study by Kiracı & Aydın (2018) indicates that both the
Trade-Off Theory and the Pecking Order Theory may be valid for different types
of debt within the same industry. Conversely, studies such as Yildirim et al.
(2018), compare the capital structure determinants of Shari'ah-compliant
(SC) and non-compliant (SNC) firms using data from seven countries and
industries. Results show that key determinants affect SC and SNC firms
differently, and the influence varies by the leverage measure used.
Profitability, firm size, growth, and tangibility show mixed effects. The
findings suggest that Pecking Order Theory explains book leverage better, while
Trade-Off Theory fits market leverage.
Some
studies move beyond general theoretical frameworks and instead emphasize
sector-specific dynamics. For example, Capobianco & Fernandes (2004) and
Fernandes & Capobianco (2001) highlight that airlines can maintain stable
performance despite high levels of debt, and that management quality plays a
critical role in this resilience. In contrast, Guzhva
& Pagiavlas (2003) argue that poor debt
management strategies can significantly increase financial risk. These findings
suggest that even within the aviation industry, capital structure decisions
cannot be explained by a single model alone.
Inter-sectoral
differences also represent an important area of discussion. Tang & Jang
(2007), for instance, compared the software and accommodation sectors and
demonstrated that the effects of fixed assets and growth opportunities on debt
decisions vary by industry. Similarly, Sikveland et al. (2022) found that
seasonality and geographic concentration influence debt structures within the
accommodation sector. Pacheco & Tavares (2015) argue that the Pecking Order
Theory provides a better explanation for capital structure decisions in the
shoe industry. In the context of the Norwegian salmon farming sector, Sikveland
& Zhang (2020) show that profitability is negatively associated with both
short-term and total debt, while liquidity has a positive effect on
profitability.
Orlova
et al. (2020) argue that capital structure decisions involve not only
preferences between debt and equity, but also the diversity of debt sources and
access to financial markets. Barrachina-Fernández
& Sogorb-Mira (2024) further confirm
inter-sectoral heterogeneity by showing that corporate hedging strategies and
commodity prices influence capital structure decisions in the oil and gas
sector.
Regional
and national differences constitute another frequently emphasized area of
divergence in the literature. Studies such as Jõeveer
(2013), Mateev et al. (2013), and Proença et al. (2014) reveal that the capital
structures of firms in regions such as Eastern Europe and Portugal are
influenced by country-specific economic and institutional factors. Kayo &
Kimura (2011) and Vo (2017) have also shown that country-level determinants
exert indirect but significant effects on corporate financial decisions. Conversely,
studies such as Kahya et al. (2020) highlight that firm-level factors play a
more dominant role in companies operating under Islamic finance principles.
Zhang & Liu (2017) examined the relationship between Total Factor
Productivity (TFP) and leverage in unlisted Chinese firms, finding that TFP is
positively associated with leverage in private and foreign-owned firms.
Moreover, the impact of TFP on both formal and informal leverage becomes
stronger under conditions of financial constraints and within challenging
institutional environments.
Periods
of financial crisis and economic shocks also challenge the validity of
traditional assumptions regarding capital structure. Studies such as Moradi
& Paulet (2019) and Proença et al. (2014) demonstrate that debt ratios tend
to decline, and firms adopt more financially prudent behaviors
during times of crisis. Similarly, Ashraf et al. (2025) report that the
COVID-19 pandemic led to increased leverage in capital restructuring,
particularly among small and publicly listed firms. In contrast, Touil & Mamoghli (2020) highlight the importance of political
stability in mitigating bankruptcy costs and reducing information asymmetries.
Recently,
new variables such as ESG factors, governance structure, and individual
characteristics of directors have been increasingly incorporated into the
capital structure literature. Zhao & Zhang (2024) demonstrate that ESG
performance significantly influences capital structure decisions. Halford et
al. (2024) find that the bargaining power of labor
unions affects firms’ leverage, while Tripathi et al. (2024) reveal that the
relationship between board size and leverage has an impact on firm value. Le et
al. (2025) show that the CEO’s age, education, and tenure influence the
alignment with optimal capital structure. Additionally, Krystyniak & Staneva (2024) report that firms with female CFOs exhibit
similar risk preferences to those with male CFOs, suggesting that traditional
gender-based assumptions should be reconsidered.
Differences
in empirical methodologies also contribute to the diversity of findings in the
literature. For instance, Chang et al. (2009) employed the MIMIC model, Ramli
et al. (2019) utilized PLS-SEM, Bilgin & Dinc
(2019) applied the fractional regression model, Handoo & Sharma (2014) used
traditional regression analysis, Duguleană et al.
(2024) adopted the GMM approach, while Rehan et al. (2023) combined MRA, ARDL,
panel data techniques, and GMM in their analysis. The use of different models
can lead to varying effects of the same variables. Furthermore, several studies
specifically focus on the aviation sector, emphasizing the influence of
sectoral dynamics on capital structure decisions. For example, Ramírez-Orellana
et al. (2025) contend that governance practices and leasing standards help
reduce the cost of capital, whereas Li & Islam (2019) highlight the role of
sector-specific factors in shaping corporate capital structure.
4. DATA AND
METHODOLOGY
Panel data is a type of data commonly used in estimating economic
variables. Since panel data analysis provides more observations compared to
cross-sectional or time series data, it enhances the efficiency of econometric
estimation (Hsiao, 2014). Panel data consist of N cross-sectional units
observed over T time periods. The indices i and t denote
the individual units and time periods, respectively (Tatoğlu, 2013). In
linear panel data models, where the dependent variable is represented by Y
and the independent variables by X, i refers to the
cross-sectional unit (i = 1, ..., N), and t indicates the time
period (t = 1, ..., T). The model can be expressed as: 
In this study, the capital structure data of 12 aircraft leasing
companies over the period 2013-2023 were analyzed using panel data methods. The
data were obtained from Thomson Reuters Refinitiv. Detailed information on the
variables used in the analysis is presented in Table 1.
Tab. 1
Dependent and independent variables
|
Dependent |
TDR |
Total debt/
total assets |
|
LDR |
Long term debt/total assets |
|
|
TDC |
Total debt/total capital |
|
|
SDC |
Short-term debt & current port/total
capital |
|
|
LDC |
Long-term debt/total capital |
|
|
Independent |
ETA |
Ebit/total
assets |
|
FTA |
Property, plant & equip net/total assets |
|
|
TS |
Depreciation and depletion/total assets |
|
|
FS |
Log (total assets) |
|
|
CTA |
Cash/total capital |
|
|
ITA |
Operating income/total assets |
In
this study, five models were developed to assess the impact of debt structure
on firm financial indicators within the framework of capital structure
theories. Six models were constructed using the following dependent variables:
total debt/total assets, long-term debt/total assets, total debt/total capital,
short-term debt & current portion/total capital, and long-term debt/total
capital. The independent variables included EBIT/total assets, property, plant,
and equipment net/total assets, depreciation and depletion/total assets, log
(total assets), cash/total capital, and operating income/total assets. Panel
regression analysis was employed to test the hypotheses. However, to obtain
more precise and robust results, previous studies have emphasized the importance
of using robust estimation techniques rather than solely relying on fixed or
random effects models. Robust models also effectively address issues of
heterogeneity and correlation. The following panel regression models were
tested:
In
the models, 
represents the intercept term, which is the
constant of the model. The subscript i denotes
the individual aircraft leasing companies, while t corresponds to the
annual time periods. The hypotheses of the study were tested using panel
regression analysis. The Hausman test results strongly support the
appropriateness of the random effects model. Additionally, heteroscedasticity
and autocorrelation test results indicated the need to address these issues
using robust estimation techniques. Consequently, the study reports results
from both random effects and fixed effects models to demonstrate the
sensitivity and stability of the estimated coefficients. Furthermore, robust
regression analysis results are also presented to ensure the reliability of the
findings.
5. RESULTS
This section presents the descriptive statistics, correlation matrix, and
regression analysis results of the models. Table 2 provides the descriptive
statistics for the dependent and independent variables used in the study. The
ratio of total debt to total assets ranges from a minimum of 0 to a maximum of
0.827. The ratio of total debt to total capital reaches a maximum value of
3.315, which is the highest among the debt-related variables. The ratio of
long-term debt to total assets varies between 0 and 0.652, while the ratio of
long-term debt to total capital ranges from 0 to 0.781. Earnings before
interest and taxes (EBIT) is the only variable with negative values due to some
leasing companies reporting negative earnings. The highest standard deviation is
observed in firm size, measured as the natural logarithm of total assets, with
a value of 16.669.
Tab. 2
Descriptive statistics
|
Variable |
Obs. |
Mean |
Std. Dev. |
Min |
Max |
|
TDR |
132 |
0.4448 |
0.2708 |
0.0009 |
0.8279 |
|
LDR |
132 |
0.2862 |
0.2279 |
0.0000 |
0.6525 |
|
TDC |
132 |
0.9600 |
0.6822 |
0.0013 |
3.3152 |
|
SDC |
132 |
0.1586 |
0.1800 |
0.0000 |
0.6819 |
|
LDC |
132 |
0.4870 |
0.2565 |
0.0000 |
0.7811 |
|
ETA |
132 |
0.0423 |
0.0388 |
-0.0900 |
0.2172 |
|
FTA |
132 |
0.3549 |
0.3898 |
0.0019 |
0.9845 |
|
TS |
130 |
0.0224 |
0.0315 |
0.0004 |
0.2032 |
|
FS |
132 |
16.669 |
2.3260 |
11.502 |
20.562 |
|
CTA |
132 |
0.1259 |
0.1335 |
0.0135 |
0.6004 |
|
ITA |
132 |
0.0429 |
0.0358 |
-0.0277 |
0.2425 |
This
section of the study includes the correlation matrix of the variables and the
analysis results. Table 1 shows the correlation between the variables. The
presence of high correlation in panel data analysis may cause multicollinearity
problems. The correlation coefficient between the variables used in the study
is low, so there is no multicollinearity problem.
Tab. 3
Correlation matrix
|
ETA |
FTA |
TS |
FS |
CTA |
ITA |
|
|
ETA |
1 |
|||||
|
FTA |
0.174 |
1 |
||||
|
TS |
0.105 |
0.634 |
1 |
|||
|
FS |
-0.216 |
-0.245 |
-0.380 |
1 |
||
|
CTA |
-0.113 |
-0.534 |
-0.335 |
-0.061 |
1 |
|
|
ITA |
0.748 |
0.419 |
0.555 |
-0.482 |
-0.246 |
1 |
Table
4 presents the regression results of the random effects models. The findings
indicate that higher firm profitability generally has a positive impact on
leverage, suggesting that airlines predominantly rely on internal resources
when borrowing. Tangible fixed assets positively affect long-term debt levels;
specifically, the ability to use aircraft as collateral facilitates borrowing
at lower costs. The results for the tax shield (TS) proxy variable are mixed.
While TS has a negative relationship with the ratio of long-term debt to total
assets, it exhibits a positive effect on the ratios of total debt to capital
and short-term debt to capital. These findings suggest that higher depreciation
levels may substitute for long-term debt, likely due to the tax shield benefits
they provide. Firm size is positively associated with debt levels, which may
reflect larger firms’ ability to borrow at more favorable
terms. Additionally, higher cash flow appears to increase borrowing capacity by
reducing firm risk, as evidenced by the positive effects observed across the
models. Operating profits, however, show a significant negative effect on
leverage in all models, indicating that firms with higher operating profits
tend to rely less on external financing, preferring instead to fund investments
through retained earnings.
Tab. 4
Random effects model regression results
|
Variable/Model |
TDR |
LDR |
TDC |
SDC |
LDC |
|
ETA |
1.924** |
1.649* |
-0.182 |
0.275 |
1.356*** |
|
FTA |
0.578* |
0.583* |
0.146 |
-0.005 |
0.492* |
|
TS |
0.394 |
-1.567** |
4.138*** |
1.961* |
-0.416 |
|
FS |
0.026* |
0.005 |
0.147* |
0.020* |
0.041* |
|
CTA |
0.858* |
0.079 |
2.221* |
0.779* |
0.270*** |
|
ITA |
-4.685* |
-2.922* |
-5.797** |
-1.763** |
-3.975* |
|
cons |
-0.185 |
0.073 |
-1.658* |
-0.258** |
-0.277*** |
***p < 0.1, **p < 0.05, *p <
0.01
Table
5 presents the fixed effects model regression results to evaluate the validity
of the model across different specifications. The fixed effects model controls
for time-invariant heterogeneity among the observations in the panel data. The
results are consistent with those of the random effects model. Additionally,
the Random-Effects GLS regression, which accounts for heteroskedasticity and
autocorrelation inherent in the panel structure, yields results that are also
consistent with the previous models. Therefore, the findings demonstrate that
the model is robust across different specifications and that the observations
in the sample exhibit similar relationship patterns under various panel data
assumptions. Furthermore, the overall consistency reinforces the robustness of
the relationships between the explanatory variables and the dependent variable.
Tab. 5
Fixed effects model regression results
|
Variable/Model |
TDR |
LDR |
TDC |
SDC |
LDC |
|
ETA |
2.039** |
1.808*** |
-0.450 |
0.230 |
1.454*** |
|
FTA |
0.576* |
0.583* |
0.130 |
-0.008 |
0.491* |
|
TS |
0.634 |
-1.581** |
5.522** |
2.215* |
-0.354 |
|
FS |
0.027* |
0.043 |
0.158* |
0.022* |
0.041* |
|
CTA |
0.879* |
0.694 |
2.376* |
0.809* |
0.271*** |
|
ITA |
-4.818* |
-3.090* |
-5.583*** |
-1.728** |
-4.099* |
|
cons |
-0.208 |
0.9034 |
-1.878* |
-0.298** |
-0.271 |
***p < 0.1, **p < 0.05, *p <
0.01
Tab. 6
Random-effects GLS regression results
|
Variable/Model |
TDR |
LDR |
TDC |
SDC |
LDC |
|
ETA |
1.9239* |
1.6489* |
-0.1816 |
0.2749 |
1.3560** |
|
FTA |
0.5784* |
0.5833* |
0.1464*** |
-0.0049 |
0.4917* |
|
TS |
0.394 |
-1.5666* |
4.1378** |
1.9606* |
-0.4157 |
|
FS |
0.0258* |
0.0053 |
0.1474* |
0.0205* |
0.0410* |
|
CTA |
0.8583* |
0.0788 |
2.2206* |
0.7795* |
0.2701** |
|
ITA |
-4.6852* |
-2.9221* |
-5.7968 |
-1.7631** |
-3.9754** |
|
cons |
-0.1851 |
0.0725 |
-1.6581* |
-0.2576* |
-0.2775 |
***p < 0.1, **p < 0.05, *p <
0.01
6. DISCUSSION
The findings of this study offer valuable insights into the determinants
of capital structure decisions in aircraft leasing companies, a topic that has
been relatively underexplored in the aviation finance literature. The results
derived from panel data regression models of 12 aircraft leasing firms over the
period 2013-2023 generally align with the Pecking Order Theory, while also
providing conditional support for the Trade-Off Theory.
The positive effect of EBIT on both total and long-term debt levels
suggests that firms primarily utilize internal resources and resort to
borrowing only when external financing is necessary. This finding aligns with
the Pecking Order Theory. However, it contrasts with the negative relationship between profitability and debt reported in some
empirical studies (Mateev et al., 2013; Serghiescu & Văidean, 2014). This
discrepancy may stem from the asset-intensive and capital-demanding nature of
the aircraft leasing industry. The positive association between fixed tangible
assets (FTA) and long-term debt supports both the Pecking Order and Trade-Off
theories. This result is consistent with previous studies such as Capobianco
& Fernandes (2004) and Kiracı & Aydın (2018), indicating that
high-value assets like aircraft facilitate borrowing by serving as collateral.
The effects of the depreciation variable (TS) vary across different debt
measures. It has a negative impact on the long-term debt ratio while exhibiting
a positive effect on total and short-term debt ratios. This finding aligns with
the Trade-Off Theory, which posits that the tax shield effect can serve as a
substitute for long-term debt. Similar observations were made by DeAngelo &
Masulis (1980).
The positive relationship between firm size (FS) and debt is consistent
with prior studies such as Moradi & Paulet (2019) and Vo (2017). Larger
firms typically face lower perceived risk, enabling them to borrow at lower
costs and thus increasing their reliance on debt. This finding aligns with both
theoretical frameworks. The positive association between cash assets (CTA) and
debt levels suggests that, contrary to traditional expectations, excess cash
does not reduce firms’ need for debt; rather, it enhances their borrowing
capacity. Similar results were found by Bilgin & Dinc (2019) in their study
on factoring. Moreover, the negative relationship between operating profit
(ITA) and debt levels indicates that firms with higher operational income
prefer to finance investments internally, resorting less to external financing.
This finding fully supports the Pecking Order Theory as articulated by Frank
& Goyal (2003) and Myers & Majluf (1984). When compared to other
sectors such as accommodation (Sikveland et al., 2022), footwear (Pacheco &
Tavares, 2015), and e-commerce (Duguleană et al., 2024), aircraft leasing
companies share some common determinants of capital structure – such as
profitability and firm size – but operate under different dynamics due to their
capital-intensive nature.
7. CONCLUSION
Capital structure decisions directly influence financing
and investment choices in the aircraft leasing sector, as in many other
industries. Aircraft leasing companies typically possess substantial fixed
assets and require significant capital investment. Additionally, these
companies must secure sufficient funds to stay abreast of technological
advancements, promptly respond to customer needs and expectations, and remain
competitive in an increasingly dynamic market environment. Therefore,
understanding how aircraft leasing companies shape their capital structures,
the factors influencing these structures, and how they achieve an optimal
debt-equity balance is crucial. Within this framework, this study examines the
capital structure data of 12 aircraft leasing companies over the period
2013-2023 using panel data analysis.
In this study, where the financial criteria affecting the
capital structure decisions of aircraft leasing companies are examined, five
different models are established in order to determine the determinants of the
capital structure. In the models created by utilizing similar studies in the
literature, variables measuring the leverage level of the companies are used.
In this context, Total debt/total assets (Model 1), Long-term debt/total assets
(Model 2), Total debt/total capital (Model 3), Short-term debt & current
port/total capital (Model 4) and long-term debt/total capital (Model 5)
indicators are used as dependent variables. In the study, EBIT/total assets,
Property, plant & equipment /total assets, Depreciation and depletion/total
assets, Log (total assets), Cash/total capital and Operating income/total
assets are used as independent variables. In this direction, it is aimed to
reveal the factors affecting the short- and long-term financing decisions of
aircraft leasing companies.
The findings of this study indicate that the total,
long-term, and short-term financing behaviors of aircraft leasing companies are
quite similar. It was found that operating profitability has a positive impact
on the total debt ratio. This suggests that aircraft leasing companies may
prefer to utilize external financing rather than internal resources,
potentially yielding more favorable outcomes. When examining the relationship
between asset structure and debt structure, a positive correlation was observed
between the asset structure and the long-term use of external funds. This
implies that tangible fixed assets of aircraft leasing companies can serve as
collateral, enabling them to borrow at a lower cost. The results concerning the
tax shield effect were mixed. Specifically, the tax shield was found to have a
positive effect on the ratio of total debt to capital and on the ratio of
short-term debt to capital, while exhibiting a negative effect on the ratio of
long-term debt to total assets. These findings suggest that higher depreciation
levels are preferred as a substitute for long-term borrowing, likely due to the
associated tax shield benefits. Furthermore, a positive relationship between
firm size and debt structure was identified, indicating that larger aircraft
leasing companies tend to borrow more in proportion to their total assets.
Additionally, cash flow was found to positively influence firm leverage,
suggesting that companies with higher cash flows can access more external
resources. Lastly, a negative relationship between operating income and debt
structure was observed, implying that aircraft leasing companies tend to rely
more on equity financing for their investments.
When the findings related to aircraft leasing companies
are evaluated from a theoretical perspective, it is observed that firm size,
profitability, asset structure, and cash flow variables yield results
consistent with the trade-off theory. Conversely, the tax shield variable
aligns with the trade-off theory only in terms of the ratio of long-term debt
to total assets. Meanwhile, the operational income variable produces results
that support the pecking order theory.
This study, which investigates the factors determining
the capital structure of aircraft leasing companies, has certain limitations.
First, the analysis was conducted on 12 aircraft leasing companies. However,
the number of firms operating in the aircraft leasing sector is considerably
larger. This study focused on the largest leasing companies for which
uninterrupted financial data were available. Therefore, the findings are
limited to the specific sample of aircraft leasing companies examined. Second,
the financial data analyzed covers the period from 2013 to 2023. This timeframe
was selected to optimize both the number of companies and the number of
observations. Nevertheless, it should be noted that different results may
emerge when alternative time periods or combinations are analyzed.
For future research, it is recommended that scholars
focus on the strategies influencing the capital structure of aircraft leasing
companies. The literature on this sector is still in its developmental stages.
Therefore, conducting detailed analyses of these companies from various
financial perspectives is crucial. Such efforts will not only contribute
significantly to the existing body of knowledge but also highlight the
importance of aircraft leasing companies, which are often overlooked.
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Received 30.06.2025; accepted in revised form 12.10.2025
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