How Capital Leases Affect Firm Performance: An Analysis in the Shipping Industry

Abstract

This study examines the effects of capital lease arrangements on the operating performance of shipping firms as proxied by Return on Assets (ROA). The maritime industry is highly capital-intensive, often requiring substantial investments in fleet acquisition and maintenance, making ROA particularly relevant as it captures the effectiveness of firms in utilizing their leased and owned assets to generate operating income. As such, many firms rely on lease arrangements to access necessary resources while preserving liquidity and financial flexibility. Using an international sample of 209 shipping firms, we estimate fixed effects regressions to assess the relationship between lease intensity and performance of the shipping firms. The findings reveal that capital lease intensity is positively associated with operating performance, indicating that leasing can be a value-enhancing financing strategy in this sector. However, the performance benefits of capital leases diminish under IFRS 16 reporting, particularly for firms with higher leverage. These findings offer important implications for investors, regulators, and managers evaluating capital structure decisions and financial reporting strategies in capital-intensive industries post-IFRS 16 implementation.

1. Introduction

The maritime industry is one of the most important sectors in the global economy, facilitating the transportation of goods and raw materials, and supporting international trade and commerce. In fact, according to UNCTAD (2024), 80% of the world’s trade volume is carried by sea, and global maritime trade grew by 2.4% in 2023, recovering from a 2022 contraction. However, the recovery remains ongoing. UNCTAD (2024) also underscores the importance of developing sustainable and resilient infrastructure to accelerate the transition to low-carbon shipping. In this respect, gaining a deeper understanding of the factors affecting the operating performance of maritime firms is crucial for assessing economic uncertainties and market fluctuations, which is of paramount importance to stakeholders, investors, and policymakers alike.Prior studies have examined leasing as a strategic financial tool enabling firms to optimize their capital structures and maintain operational flexibility (Rampini & Viswanathan, 2013; Chowdhury et al., 2021). Leasing serves as secured financing, particularly benefiting firms with limited access to traditional credit markets, by enhancing liquidity and operational agility (Lin, 2016). Maritime firms have seen an important change in accounting and financial reporting with the implementation of new lease accounting standards, notably IFRS 16 and ASC 842 (PwC, 2016; FASB, 2016). One of the most important changes in leasing arrangements, stemming from those accounting standards, is that almost all leases (with some exceptions) are recognized as capital leases. This change bears significant effects for financial statements and capital structure for firms. In response, it is essential to examine how maritime firms’ leasing policies affect their financial reporting and operating performance. However, despite extensive research on general leasing implications, empirical studies specifically addressing the performance implications of capital leases in the maritime industry remain limited, particularly following the recent introduction of IFRS 16.

This study examines the relationship between maritime firms’ operating performance and their use of lease arrangements. The research motivation of the study derives from the unique characteristics of the shipping industry. Specifically, maritime firms’ extensive use of leases and their unique operational characteristics provide an excellent research setting. The purpose is to assess factors related to lease usage and operating performance, providing valuable insights into how leases are treated in financial statements under the new standards. IFRS 16, implemented in January 2019, represents a significant regulatory shift by mandating the capitalization of nearly all lease obligations on firms’ Statement of Financial Position, effectively removing the previous distinction between operating and finance leases under IAS 17. This standard significantly increases transparency by eliminating off-balance sheet financing practices previously common in capital-intensive industries such as shipping (Morales-Díaz & Zamora-Ramírez, 2018; PwC, 2016). Hence, this study aims to assess the implications of lease arrangements for maritime firms’ performance, offering valuable insights into their financial health, risk management strategies, and overall market competitiveness. Additionally, the study aims to unravel the implications of lease accounting reforms on key financial metrics, such as liquidity, leverage, and profitability, providing a comprehensive understanding of the sector’s financial dynamics. A number of theories may explain the results of the study, and previous studies provide multiple theoretical frameworks to explain the link between leasing arrangements and firm performance, including the trade-off theory of capital structure, the resource-based view (RBV), and agency theory. However, due to the effects that IFRS 16 may have on a firm and the associated choices that must be made by the firm’s management, we base the study on agency theory. Agency theory suggests that transparently reported and effectively structured capital leases can improve firm performance through enhanced monitoring and capital discipline. Therefore, we examine how IFRS implementation has affected the relationship between leasing arrangements and firm performance.

The results of the empirical analysis seem to support our research hypotheses. First, capital expenditures are positively related to Return on Assets (ROA), and this result indicates that shipping firms using lease arrangements may have higher performance in relation to shipping firms that do not. Moreover, firms with capital lease arrangements that implement IFRS have lower operating performance. Overall, our results provide direct support for the importance of lease arrangements for firms in the shipping sector.

This study contributes to the growing literature on IFRS 16 and lease accounting in three key ways. First, it provides empirical evidence on the relationship between capital leases and firm performance in the shipping industry, a sector that remains under-represented in IFRS 16 impact studies despite its high lease dependence. Second, by introducing a profitability metric adjusted for lease capitalization and employing interaction terms, the study demonstrates how financial leverage intensifies the effect of lease obligations on operating outcomes. This methodological design allows for a better understanding of how lease capitalization interacts with firm capital structure. Third, the findings contextualize agency and trade-off theories within a post-IFRS 16 capital-intensive setting, thereby extending their application to the evolving regulatory environment and providing insights for regulators, analysts, and investors evaluating leasing strategies under the new reporting regime.

The remainder of this study is structured as follows: Section 2 reviews the literature and develops the research hypotheses; Section 3 develops the research methodology; Section 4 describes the sample and describes the empirical findings; and last, Section 5 concludes the study.

2. Literature Review and Hypotheses Development

The shipping industry is highly capital-intensive, requiring significant investments in vessels and operational infrastructure. As noted in both academic and industry studies, the scale and cost of vessel acquisition often necessitate alternative financing strategies (Alexandridis et al., 2018; UNCTAD, 2023). Consequently, many shipping firms use long-term leasing as an alternative to outright ownership, with capital leases (or finance leases) serving as a key financing strategy (Y. Li, 2006; OECD, 2019). These lease arrangements impose fixed obligations and require recognition of both the leased asset and the corresponding liability in the Statement of Financial Position. Understanding the implications of such arrangements for firm performance is essential, as they intersect directly with decisions about capital structure, operational strategy, and financial transparency. Previous studies provide multiple theoretical frameworks to explain this link, including the trade-off theory of capital structure, the resource-based view (RBV), and agency theory. These are complemented by emerging empirical studies from both the finance and accounting domains. Importantly, recent reforms in lease accounting, particularly the introduction of IFRS 16, further shape how these lease performance dynamics affect firms, particularly by enhancing transparency and comparability. This section reviews the literature and formulates the research hypotheses of the study.

Traditional capital structure theories, especially the trade-off theory, assert that firms aim to optimize their mix of debt and equity to balance the tax benefits of debt with the costs of financial distress (Myers, 1984, 2001; Myers & Majluf, 1984;). Within this framework, lease obligations, functionally similar to debt, can offer firms tax shields while preserving liquidity, thereby supporting stronger performance. Leasing also enables firms to finance operations without immediate equity dilution. Empirical evidence from Chowdhury et al. (2021) shows that firms strategically adjust lease intensity to align with target leverage levels. This finding is particularly important for under-leveraged or financially constrained firms, which often rely on leasing as a form of shadow debt. Rampini and Viswanathan (2013) reinforce this view by modeling leasing as collateral-backed financing that increases firm-level borrowing capacity. Their findings confirm that firms with greater financing frictions are more reliant on leasing to expand operations. In this sense, leasing is not just a tactical financing tool but a strategic capital structuring device with performance implications. Lin (2016) also supports this by demonstrating that asset tangibility plays a key role in determining a firm’s reliance on secured debt, with higher collateral encouraging bank loans.

Leasing additionally offers strategic flexibility, especially in volatile industries. While asset ownership can reduce long-term cost, leasing provides the adaptability needed to scale operations up or down. Bourjade et al. (2017) show that there exists an optimal level of leasing in the airline industry beyond which profitability begins to decline, suggesting that the performance–leasing relationship is nonlinear. In shipping, which shares similar volatility and capital needs, leasing through bareboat or time charter arrangements may also enable firms to better manage capacity and respond to market conditions.

From a strategic management perspective, the resource-based view (RBV) provides a complementary explanation. RBV posits that performance advantages arise from firms’ control over valuable and rare resources (Barney, 1991). In shipping, leased vessels and maritime infrastructure represent such strategic assets. Leasing enables firms to access modern fleets without prohibitive capital outlays, enhancing their ability to respond to market shifts and demand volatility. This aligns with findings from Parola et al. (2015), who identify fleet flexibility and strategic logistics alignment as key determinants of shipping firm profitability. By pursuing an “asset-light” model through leasing, firms can focus on core competencies, like network optimization and freight strategy, while externally securing the physical resources needed for operations. These arguments underscore how leasing enhances agility and competitiveness, both of which are drivers of improved performance.

Agency theory adds a management dimension to this discussion. Leasing commitments, like debt, impose external discipline on managerial decision making. Jensen and Meckling (1976) and Jensen (1986) argue that financial obligations can reduce agency costs by limiting discretionary spending. Leasing can thus serve as a constraint on free cash flow, causing management to prioritize efficient capital use. However, prior to reforms such as IFRS 16 and ASC 842, many leases were treated as operating leases and excluded from the balance sheet. This created opportunities for managerial opportunism; managers could obscure leverage levels and avoid scrutiny from creditors and investors. The strategic use of off-balance sheet leasing was especially pronounced in industries like shipping, where operating leases (e.g., time charters) were standard. Nonetheless, even in this context, leasing could enhance governance: because leased assets typically remain under lessor ownership, the risk of repossession provides a natural check against default and moral hazard. Thus, agency theory suggests that capital leases, when transparently reported and effectively structured, can improve firm performance through better monitoring and capital discipline. This leads to our first research hypothesis:

H1: 

The performance of shipping firms is directly and positively related to their use of capital leases.

Grammenos et al. (2007) further highlight the risk sensitivity of public debt markets in shipping, where capital costs fluctuate significantly with perceived risk and market cycles. Drobetz et al. (2017) also explore the governance dimension through disclosure practices. Their study on global shipping IPOs finds that earnings forecasts tend to be conservative, especially when insider ownership remains high, suggesting that governance structures and disclosure incentives are tightly linked in capital market access. Such agency considerations extend to leasing practices, particularly when lease obligations are substantial yet opaque.

Yeo (2016) examines the unexpected coexistence of high leverage and high liquidity in shipping, interpreting it as a response to market volatility. Firms simultaneously carry debt and maintain liquidity buffers to hedge against downturns, revealing a nuanced capital management strategy in which leasing may play a role by conserving liquidity while still enabling asset access. Taken together, these results imply that capital lease commitments affect performance through multiple channels: financial optimization, strategic asset access, and improved governance.

Capital leases under IFRS 16 are treated as debt-like obligations, affecting both the financial structure and reported performance of capital-intensive firms. This treatment is particularly relevant in the shipping industry, where firms typically engage in long-term lease contracts for vessels and equipment. Existing literature indicates that the performance effects of lease capitalization are not uniform across firms but are amplified in those with higher financial leverage.

Chowdhury et al. (2021) provide evidence that firms with high deviations from their target capital structure increasingly rely on leasing as an alternative to traditional debt, particularly when under financial constraint. Their findings support the substitutive role of lease obligations for firms facing limited access to credit. Similarly, Altamuro et al. (2014) demonstrate that lease obligations, although previously off balance sheets, are recognized by credit analysts and rating agencies as equivalent to debt when assessing credit risk. These findings reinforce the notion that in high-leverage environments, the capitalization of leases intensifies perceived risk and affects borrowing costs. Empirical work by Chen et al. (2023) further supports this view. Their results show that mandatory lease capitalization, such as that required under IFRS 16, significantly constrains investment behavior in firms with high lease intensity, especially when financial leverage is high. In these cases, lease-related obligations reduce financial flexibility, limiting the firm’s capacity to invest and thereby impacting operating performance.

From a theoretical perspective, these findings are consistent with trade-off theory and agency cost theory. According to Kraus and Litzenberger (1973), optimal leverage balances the tax benefits of debt with bankruptcy costs. As lease capitalization increases reported leverage, highly leveraged firms experience higher marginal costs, reinforcing the impact of leases on profitability. Furthermore, Jensen and Meckling (1976) argue that increasing debt-like obligations heightens agency conflicts, particularly in firms with already significant debt exposure. Therefore, capital leases are more likely to reduce operating performance in high-leverage firms due to rising agency and distress costs. This leads to our second research hypothesis:

H2: 

The higher the leverage, the stronger the relationship between capital leases and shipping firm performance.

Financial reporting standards also influence this relationship by determining the accounting treatment and disclosure of leases. In 2019, IFRS 16 was implemented to standardize lease accounting and improve transparency. Unlike its predecessor, IAS 17, which allowed many leases to remain off-balance sheets (see Dhaliwal et al., 2011 on the use of such lease arrangements), IFRS 16 mandates that virtually all long-term leases be recognized as liabilities with corresponding right-of-use assets. These changes have been particularly impactful in shipping, where charter contracts such as bareboat and time charters often qualified as operating leases. PwC (2016) and Lopes and Penela (2025) report that lease capitalization under IFRS 16 significantly increased reported debt and EBITDA in transport-related sectors. These changes affect financial ratios used by analysts and creditors, such as return on assets, debt-to-equity, and interest coverage, thereby altering stakeholders’ assessments of firm performance.

Global comparability also depends on whether firms adopt IFRS 16 or equivalent standards. While ASC 842 in the United States shares IFRS 16’s emphasis on lease capitalization, it retains dual expense models (straight-line for operating leases), leading to differences in income statement presentation. Other jurisdictions, such as India (Ind AS 116) (ICAI, 2020), China (CAS 21), and Russia (IFRS for listed entities) (IFRS Foundation, 2019), have adopted similar frameworks with localized adjustments. By contrast, countries like Japan, where many firms still report under J-GAAP, allow continued use of off-balance-sheet leasing, while Vietnam maintains local GAAP differences (PwC Vietnam, n.d.). As Xu and Rahman (2021) and Kim and Koga (2020) note, such discrepancies reduce cross-border comparability and obscure the financial implications of leasing. This matters in capital markets where investors increasingly operate globally. IFRS 16’s impact on interpreting lease performance can also be analyzed through signaling theory and institutional theory. Signaling theory (Spence, 1973; Ross, 1977) posits that transparent financial disclosures convey credibility to investors. By mandating lease capitalization, IFRS 16 removes ambiguity about a firm’s financial commitments, sending a clear signal of transparency and reducing information asymmetry.Institutional theory (DiMaggio & Powell, 1983) adds that firms conform to regulatory and normative expectations in their environment. Adoption of IFRS reflects institutional pressures toward standardization and legitimacy, and firms operating in these environments are likely to emphasize accurate lease reporting. As noted by Hoogervorst (2018), IFRS fosters consistent lease treatment globally, particularly when reinforced by enforcement mechanisms and governance cultures. Meanwhile, in transitional contexts like Vietnam, or in firms subject to dual GAAP reporting, inconsistencies persist (PwC Vietnam, n.d.), affecting how leasing influences perceived performance.

Empirical research supports the importance of lease transparency. Ma and Thomas (2023) found that after ASC 842’s implementation, U.S. firms adjusted leasing strategies and capital expenditures but experienced no decline in performance or valuation. Morales-Díaz and Zamora-Ramírez (2018) similarly documented that lease capitalization affected key ratios but enhanced alignment with economic substance. Moreover, Altamuro et al. (2025) examine firms that reported under both the U.S. GAAP and IFRS that had concurrently high use of operating leases. Their results show that adopting the revised lease standards led to a greater increase in accounting comparability for firms with high lease usage compared to those with low lease usage, with improvements arising primarily from balance sheet changes rather than income statement adjustments. These findings suggest that transparent lease reporting under IFRS-like regimes can strengthen the information value of lease data for performance assessment. Thus, the third hypothesis is formulated as follows:

H3: 

The lease arrangements of firms reporting under IFRS after the implementation of IFRS 16 have incremental information content for operating performance.

3. Methodology

3.1. Sample Selection

This study used firm-level panel data from Refinitiv Eikon, focusing on deep-sea shipping companies worldwide. The initial sample comprised firms classified under the Global Industry Classification Standard (GICS) as Marine Transportation. Through manual screening, we retained only firms that own or lease vessels, using detailed disclosures on leasing activity, capital structure, and asset utilization. We excluded firms with negative book values of equity, missing key financial variables, or continuous variables falling in the top or bottom 1% of the distribution, following standard winsorization practices to minimize the influence of outliers. The final sample consisted of 209 shipping firms, resulting in 2829 firm-year observations for the period of 1986–2024. The firms spanned multiple jurisdictions and were publicly listed across a wide range of major capital markets. Country coverage included, but was not limited to, the United States, Greece, China, Japan, South Korea, Germany, and Singapore.

3.2. Research Methodology

The research methodology of the study was based on a model expressing a proxy for operating efficiency as a function of related firm characteristics and a lease arrangements proxy. We chose to use ROA as the main performance proxy. However, due to being affected by financial lease payments, we added back those payments to ROA and estimated our main outcome, denoted as ROAPRERENT. We then estimated the following regression:

$$\begin{array}{l}ROAPREREN{T}_{i,t}={\alpha }_1+{\alpha }_2{\mathrm{LEVERAGE}}_{i,t}+{\alpha }_3{\mathrm{MTB}}_{i,t}+{\alpha }_4{\mathrm{TAN}\mathrm{GIBILITY}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\alpha }_5{\mathrm{LIQUIDITY}}_{i,t}+{\alpha }_6{\mathrm{SIZE}}_{i,t}+{\alpha }_7{\mathrm{CAP}\_\mathrm{RENT}}_{i,t}{+\mathrm{u}}_{i,t}\end{array}$$

(1)

and

$$\begin{array}{l}ROAPREREN{T}_{i,t}={\alpha }_1+{\alpha }_2{\mathrm{LEVERAGE}}_{i,t}+{\alpha }_3{\mathrm{MTB}}_{i,t}+{\alpha }_4{\mathrm{TAN}\mathrm{GIBILITY}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\alpha }_5{\mathrm{LIQUIDITY}}_{i,t}+{\alpha }_6{\mathrm{SIZE}}_{i,t}+{\alpha }_7{\mathrm{CAP}\_\mathrm{RENT}}_{i,t}+{\alpha }_8{\mathrm{IFRS}}_{i,t}{+\mathrm{u}}_{i,t}\end{array}$$

(2)

The model helped in assessing the operating performance of shipping firms in relation to the determinants, where ROAPRERENT is the earnings of the firm increased by the rental expense to exclude this kind of information from the outcome variable and deflated by total assets; LEVERAGE is the sum of short-term and long-term debt to total assets and is used as a leverage ratio; MTB is the Book-to-Market ratio; TANGIBILITY is a proxy of the fraction of tangible assets in the Statement of Financial Position and is estimated as the ratio of PPE to the lag of total assets; LIQUIDITY is a proxy of liquidity estimated as cash and cash equivalents deflated by total assets; SIZE is total assets of the firm in a logarithmic form; IFRS is a dummy variable that takes the value of 1 if the firm implements IFRS; and CAP_RENT is the estimate of capitalized leases. We followed Lin (2016) and estimated the proxy of capital lease expenditures as the rental expense multiplied by ten and divided by the lag of total assets. Equation (1) was used to assess the validity of research hypothesis H₁. Specifically, if coefficient a₇ was positive and statistically significant, then it supported research hypothesis H₁. We used panel fixed effects by firm and year, as well as clustered standard errors by firm and year.

Next, we examined the effects of high leverage and high size on the relationship between capital leases and firm performance. Our intuition was that highly leveraged firms may use leasing as an alternative form of leverage to reduce debt frictions, while bigger firms may be able to negotiate better leasing terms and thus be able to enjoy higher performance when leasing. For the task at hand, we created two dummy variables. The first was HIGH_LEVERAGE, which took the value of 1 if the firm was in the upper group of firms based on the median of LEVERAGE and zero otherwise, and the second was HIGH_SIZE, which took the value of 1 if the firm was in the upper group of firms based on the median of SIZE and zero otherwise. We also measured the effects of the implementation of IFRS 16 and created a dummy variable (denoted as IFRS16), which was assigned the value of 1 for firms that implemented IFRS 16 after 1 January 2019 (fiscal year 2019). The relevant models are as follows:

$$\begin{array}{l}ROAPREREN{T}_{i,t}={\beta }_1+{\beta }_2{\mathrm{LEVERAGE}}_{i,t}+{\beta }_3{\mathrm{MTB}}_{i,t}+{\beta }_4{\mathrm{TAN}\mathrm{GIBILITY}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\beta }_5{\mathrm{LIQUIDITY}}_{i,t}+{\beta }_6{\mathrm{SIZE}}_{i,t}+{\beta }_7{\mathrm{CAP}\_\mathrm{RENT}}_{i,t}+{\beta }_8{\mathrm{IFRS}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\beta }_9{\mathrm{HIGH}\_\mathrm{SIZE}}_{i,t}+{\beta }_{10}{\mathrm{HIGH}\_\mathrm{SIZE}}_{i,t}\times {\mathrm{CAP}\_\mathrm{RENT}}_{i,t}{+\mathrm{u}}_{i,t}\end{array}$$

(3)

and

$$\begin{array}{l}ROAPREREN{T}_{i,t}={\beta }_1+{\beta }_2{\mathrm{LEVERAGE}}_{i,t}+{\beta }_3{\mathrm{MTB}}_{i,t}+{\beta }_4{\mathrm{TAN}\mathrm{GIBILITY}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\beta }_5{\mathrm{LIQUIDITY}}_{i,t}+{\beta }_6{\mathrm{SIZE}}_{i,t}+{\beta }_7{\mathrm{CAP}\_\mathrm{RENT}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\beta }_8{\mathrm{IFRS}}_{i,t}+{\beta }_9{\mathrm{HIGH}\_\mathrm{LEVERAGE}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\beta }_{10}{\mathrm{HIGH}\_\mathrm{LEVERAGE}}_{i,t}\times {\mathrm{CAP}\_\mathrm{RENT}}_{i,t}{+\mathrm{u}}_{i,t}\end{array}$$

(4)

The model was used to examine the validity of research hypothesis H₂ through coefficient β₁₀. As in the previous models, we used panel fixed effects by firm and year, as well as clustered standard errors by firm and year. Last, we examined the contemporaneous effects of the implementation of IFRS 16 and high size (high leverage) on the relationship between capital leases and firm performance using the following models:

$$\begin{array}{l}ROAPREREN{T}_{i,t}={\gamma }_1+{\gamma }_2{\mathrm{LEVERAGE}}_{i,t}+{\gamma }_3{\mathrm{MTB}}_{i,t}+{\gamma }_4{\mathrm{TAN}\mathrm{GIBILITY}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\gamma }_5{\mathrm{LIQUIDITY}}_{i,t}+{\gamma }_6{\mathrm{SIZE}}_{i,t}+{\gamma }_7{\mathrm{CAP}\_\mathrm{RENT}}_{i,t}+{\gamma }_8\mathrm{IFRS}{16}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\gamma }_9{\mathrm{HIGH}\_\mathrm{SIZE}}_{i,t}+{\gamma }_{10}\mathrm{IFRS}{16}_{i,t}\times {\mathrm{HIGH}\_\mathrm{SIZE}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\gamma }_{11}\mathrm{IFRS}{16}_{i,t}\times {\mathrm{CAP}\_\mathrm{RENT}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\gamma }_{11}{\mathrm{HIGH}\_\mathrm{SIZE}}_{i,t}\times {\mathrm{CAP}\_\mathrm{RENT}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\gamma }_{12}\mathrm{IFRS}{16}_{i,t}\times {\mathrm{HIGH}\_\mathrm{SIZE}}_{i,t}\times {\mathrm{CAP}\_\mathrm{REN}\mathsf{{\rm T}}}_{i,t}{+\mathrm{u}}_{i,t}\end{array}$$

(5)

and

$$\begin{array}{l}ROAPREREN{T}_{i,t}={\gamma }_1+{\gamma }_2{\mathrm{LEVERAGE}}_{i,t}+{\gamma }_3{\mathrm{MTB}}_{i,t}+{\gamma }_4{\mathrm{TAN}\mathrm{GIBILITY}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\gamma }_5{\mathrm{LIQUIDITY}}_{i,t}+{\gamma }_6{\mathrm{SIZE}}_{i,t}+{\gamma }_7{\mathrm{CAP}\_\mathrm{RENT}}_{i,t}+{\gamma }_8\mathrm{IFRS}{16}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\gamma }_9{\mathrm{HIGH}\_\mathrm{SIZE}}_{i,t}+{\gamma }_{10}\mathrm{IFRS}{16}_{i,t}\times {\mathrm{HIGH}\_\mathrm{LEVERAGE}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\gamma }_{11}\mathrm{IFRS}{16}_{i,t}\times {\mathrm{CAP}\_\mathrm{RENT}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\gamma }_{11}{\mathrm{HIGH}\_\mathrm{LEVERAGE}}_{i,t}\times {\mathrm{CAP}\_\mathrm{RENT}}_{i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} +{\gamma }_{12}\mathrm{IFRS}{16}_{i,t}\times {\mathrm{HIGH}\_\mathrm{LEVERAGE}}_{i,t}\times {\mathrm{CAP}\_\mathrm{RENT}}_{i,t}{+\mathrm{u}}_{i,t}\end{array}$$

(6)

The model was used to examine the validity of research hypothesis H₂ through coefficient γ₁₂. As in the previous equation models, we used panel fixed effects by firm and year, as well as clustered standard errors by firm and year. A summary of all variables and their definitions is provided in the Appendix A.

4. Results and Discussion

4.1. Descriptive Statistics

Table 1. The sample.

Panel A: Descriptive Statistics
	Mean	Median	Q1	Q3	Stand. Dev.
ROAPRERENT	0.040	0.038	0.003	0.087	0.123
LEVERAGE	0.347	0.347	0.200	0.482	0.194
MTB	1.710	0.813	0.458	1.440	3.840
LIQUIDITY	0.109	0.073	0.031	0.145	0.112
TANGIBILITY	0.595	0.625	0.431	0.784	0.282
SIZE	19.700	19.800	18.500	21.000	1.860
CAP_RENT	0.005	0.000	0.000	0.000	0.023
IFRS	0.179	0.000	0.000	0.000	0.383
Panel B: Countries in the Sample
Japan	Norway	Brazil	Canada	Bangladesh
Taiwan	India	Germany	Pakistan	Israel
Indonesia	Denmark	Croatia	Egypt
Hong Kong	Thailand	Bermuda	Poland
China	Chile	Russia	Bulgaria
Greece	USA	Philippines	Estonia
Korea (Republic of S. Korea)	Romania	Sweden	United Arab Emirates
Vietnam	Monaco	Switzerland	Cyprus
Malaysia	Turkey	Ireland	Iceland
Singapore	Jordan	Qatar	Cayman Islands

Notes: The table presents the descriptive statistics for the sample. The sample is drawn from Eikon Refinitiv.

presents the descriptive statistics for the key variables used in the empirical analysis. The variation in ROAPRERENT, with a relatively low mean, but high standard deviation, indicates considerable dispersion in profitability across firms. LEVERAGE shows a moderately high average, suggesting that a significant portion of firms operate with substantial financial obligations. The CAP_RENT variable displays a low mean and highly skewed distribution, reflecting the fact that only a subset of firms recognize substantial capital lease intensity. The observed heterogeneity across variables supports the need for a firm-level analytical framework and reinforces the relevance of testing interaction effects, particularly the moderating role of leverage on the lease–performance relationship.

It appears that the variables remain unaffected by extreme observations.

Table 2. Correlation matrix.

	1.	2.	3.	4.	5.	6.	7.
1. ROAPRERENT	1.000
2. LEVERAGE	−0.258 ***	1.000
3. MTB	0.029 *	−0.056 ***	1.000
4. LIQUIDITY	0.226 ***	−0.322 ***	−0.004	1.000
5. TANGIBILITY	−0.066 ***	0.421 ***	−0.006	−0.386 ***	1.000
6. SIZE	0.113 ***	0.188 ***	−0.118 ***	−0.093 ***	0.165 ***	1.000
7. CAP_RENT	0.058 ***	−0.019	0.098 ***	0.144 ***	−0.109 ***	−0.056 ***	1.000

Notes: The table presents the descriptive statistics for the sample. The sample is drawn from Eikon Refinitiv. *, **, and *** refer to statistical significance at the 10%, 5%, and 1% levels, respectively.

presents the correlation coefficients among the key variables in the study. As expected, LEVERAGE is negatively correlated with ROAPRERENT, indicating that higher financial leverage is associated with lower adjusted profitability. This supports the underlying hypothesis that leverage may interact with lease intensity in shaping firm performance. LIQUIDITY is positively associated with profitability, while TANGIBILITY shows a weakly negative correlation. CAP_RENT shows a low but significantly positive correlation with profitability, suggesting that lease intensity may not have a uniform linear effect but might vary depending on firm characteristics, supporting the use of interaction terms in the regressions. Correlations between independent variables remain generally low to moderate, suggesting limited risk of multicollinearity in the main model specifications. Moreover, multicollinearity does not seem to be present in our data based on the results of Table 2.

4.2. Regression Results

Table 3. Main Results.

	Main Model		IFRS
Variable	Coef	Tstat	Coef	Tstat
INTERCEPT	−0.027	−0.145	−0.029	−0.155
LEVERAGE	−0.160 ***	−4.946	−0.161 ***	−5.016
MTB	0.001	0.381	0.001	0.378
LIQUIDITY	0.310 ***	4.789	0.311 ***	4.825
TANGIBILITY	0.038 *	1.872	0.038 *	1.883
SIZE	0.024 **	2.500	0.024 **	2.522
CAP_RENT	0.337 **	2.147	0.336 **	2.138
IFRS			0.013	0.878
Adj. R2	0.364		0.364
Firms	209		209
Observations	2829		2829
Fixed Effects	Firm and year		Firm and year
Clustered Standard Errors	Firm and year		Firm and year

Notes: The table presents the descriptive statistics for the sample. The sample is drawn from Eikon Refinitiv. *, **, and *** refer to statistical significance at the 10%, 5%, and 1% levels, respectively.

presents the results of estimating Equations (1) and (2). The model of Equation (1) is the base specification, aimed at examining the effect of capital lease arrangements on the operating performance of shipping firms. The results show that CAP_RENT, the proxy for capitalized lease intensity, is positive and statistically significant, thereby supporting Hypothesis H₁. This finding aligns with the trade-off theory (Myers, 1984; Rampini & Viswanathan, 2013), suggesting that leasing may provide liquidity and tax benefits without immediate equity dilution. It also supports the resource-based view (Barney, 1991) by highlighting how leased assets enhance access to strategically critical resources like vessels. The insignificance of the IFRS dummy suggests that the standalone adoption of IFRS does not systematically differentiate firm performance.

Regarding control variables, the results show that LEVERAGE is negatively associated with firm performance, consistent with agency theory (Jensen & Meckling, 1976), which posits that high leverage increases monitoring and distress costs. LIQUIDITY, TANGIBILITY, and SIZE are all positively associated with performance, reflecting improved access to internal funding, operational stability, and economies of scale. These findings align with prior work by Parola et al. (2015), emphasizing the performance relevance of firm-level characteristics in asset-intensive sectors like shipping.

Table 4. High size and high leverage.

	High Size		High Leverage
Variable	Coef	Tstat	Coef	Tstat
INTERCEPT	−0.027	−0.123	−0.004	−0.073
LEVERAGE	−0.159 ***	−4.989	−0.126 ***	−4.449
MTB	0.000	0.326	0.001	0.561
LIQUIDITY	0.311 ***	4.822	0.223 ***	4.574
TANGIBILITY	0.037 *	1.851	0.044 ***	3.127
SIZE	0.026 **	2.270	0.010 ***	3.916
CAP_RENT	0.577 **	2.171	0.441 ***	3.942
IFRS	0.010	0.710	−0.003	−0.335
HIGH_SIZE	−0.006	−0.347
HIGH_SIZExCAP_RENT	−0.532 *	−1.991
HIGH_LEVERAGE			−0.002	−0.234
HIGH_LEVERAGExCAP_RENT			−0.308 *	−1.902
Adj. R2	0.365		0.229
Firms	209		209
Observations	2829		2829
Fixed Effects	Firm and year		Firm and year
Clustered Standard Errors	Firm and year		Firm and year

Notes: The table presents the descriptive statistics for the sample. The sample is drawn from Eikon Refinitiv. *, **, and *** refer to statistical significance at the 10%, 5%, and 1% levels, respectively.

reports the results of the regression models testing the moderating effect of financial leverage on the relationship between capital lease intensity and firm performance. The interaction term HIGH_LEVERAGE x CAP_RENT is negative and statistically significant, offering empirical support for Hypothesis H₂. This finding is consistent with the theoretical framework established by the trade-off theory (Kraus & Litzenberger, 1973), which suggests that highly levered firms face higher marginal costs when adding lease-related obligations. Similarly, it reflects the logic of agency theory (Jensen & Meckling, 1976), whereby additional debt-like obligations such as capitalized leases increase agency costs in already constrained capital structures. The results align with prior empirical studies by Chowdhury et al. (2021) and Chen et al. (2023), who demonstrate that leases impose stronger financial performance effects in firms that are more heavily leveraged. These findings underscore the amplifying role of leverage in shaping the profitability implications of IFRS 16 lease capitalization in capital-intensive industries such as shipping. Regarding control variables, the results show consistent patterns with economic intuition and prior literature. SIZE and TANGIBILITY are positively associated with performance, reflecting economies of scale and operational stability in asset-intensive firms. LEVERAGE maintains a negative main effect, while LIQUIDITY remains a positive predictor, consistent with agency-based arguments that firms with greater internal funds face fewer financing frictions.

To further explore the interaction between IFRS 16 implementation, leverage, and lease intensity,

Table 5. High size and high leverage effects after the implementation of IFRS 16.

	High Size and IFRS 16		High Leverage and IFRS 16
Variable	Coef	Tstat	Coef	Tstat
INTERCEPT	−0.027	−0.126	−0.027	−0.146
LEVERAGE	−0.159 ***	−4.878	−0.163 ***	−4.806
MTB	0.000	0.315	0.000	0.331
LIQUIDITY	0.313 ***	4.766	0.312 ***	4.896
TANGIBILITY	0.038 *	1.845	0.038 *	1.909
SIZE	0.026 **	2.299	0.024 **	2.549
CAP_RENT	0.567 **	2.132	0.696 ***	2.927
IFRS16	0.007	0.386	0.018	1.020
HIGH_SIZE	−0.007	−0.357
IFRS16xHIGH_SIZE	0.005	0.238
IFRS16xCAP_RENT	0.183	0.705	−0.200	−0.673
HIGH_SIZExCAP_RENT	−0.606 **	−2.364
IFRS16xHIGH_SIZExCAP_RENT	0.406	1.012
HIGH_LEVERAGE			0.007	0.680
IFRS16xHIGH_LEVERAGE			−0.017	−1.025
HIGH_LEVERAGExCAP_RENT			−0.487 **	−2.519
IFRS16xHIGH_LEVERAGExCAP_RENT			0.709 *	1.929
Adj. R2	0.365		0.365
Firms	209		209
Observations	2829		2829
Fixed Effects	Firm and year		Firm and year
Clustered Standard Errors	Firm and year		Firm and year

Notes: The table presents the descriptive statistics for the sample. The sample is drawn from Eikon Refinitiv. *, **, and *** refer to statistical significance at the 10%, 5%, and 1% levels, respectively.

presents the results of estimating Equations (5) and (6). The triple interaction term IFRS16 × HIGH_LEVERAGE × CAP_RENT is positive and statistically significant, suggesting that the effect of lease capitalization on operating performance is stronger in highly leveraged firms that implement IFRS 16.

This finding implies that these firms may strategically benefit from lease recognition under the revised accounting framework, possibly due to better lease structuring, transparency advantages, or increased managerial discipline. Interestingly, the corresponding interaction with HIGH_SIZE (IFRS16 × HIGH_SIZE × CAP_RENT) does not yield a significant result, suggesting that firm size alone does not systematically moderate the lease–performance relationship under IFRS 16. These results provide further support for Hypothesis H₂ and suggest partial support for a conditional effect moderated by leverage but not size.

As in prior models, control variables continue to display expected signs. LEVERAGE exerts a negative effect on performance, while TANGIBILITY, SIZE, and LIQUIDITY retain positive and statistically significant coefficients. This consistency reinforces the robustness of the main findings.

4.3. Discusion of the Results

The results of the study provide evidence that capital lease arrangements affect the operating performance of shipping firms. The positive association between capital lease intensity and operating performance reveals that leasing serves as more than a financing tool. Specifically, shipping firms use lease arrangements to gain access to critical assets for their operations while maintaining operational flexibility.The practical implications of these findings are useful for shipping market participants, as they show that lease arrangements should be considered integral components of operational strategy. B. Li and Venkatachalam (2024) also support the importance of lease arrangements, showing how changes in lease policy can significantly affect firms operating in asset-heavy industries like airlines. Moreover, for investors and analysts, the positive performance effects suggest that lease intensity should not automatically be seen negatively, but rather as a potentially value-enhancing strategic choice.In this respect, the IFRS 16 findings are particularly relevant, as enhanced transparency appears to be related to firm characteristics affecting performance interpretation and suggests the need for adjusted analytical frameworks.

5. Conclusions

This study examines the effects of capital lease arrangements on the operating performance of shipping firms under the IFRS 16 framework. Using profitability metrics adjusted for lease capitalization and firm-level data from an international sample, we show that higher levels of capitalized leases are associated with improved operating performance. These findings provide empirical support for the view that leasing enhances asset utilization and strategic resource access in capital-intensive industries. Moreover, our results reveal that the interaction between capital lease expenditures and firm characteristics, particularly financial leverage, plays a significant role in moderating this relationship. In particular, while capital leases generally enhance performance, this positive effect diminishes for highly leveraged firms, highlighting the importance of capital structure considerations. Additionally, firms operating under IFRS 16 with high leverage appear to benefit more from lease recognition, suggesting that institutional frameworks and financial discipline influence performance outcomes.

The findings of this study offer valuable insights with broad implications for academics, practitioners, and regulators. First, the study contributes to the literature by providing new evidence on how lease capitalization interacts with capital structure within the under-researched context of the shipping industry. Second, from a managerial perspective, the results provide actionable insights for CFOs and financial planners in capital-intensive firms, particularly regarding optimal lease design and its interaction with leverage. Third, the results underscore the role of regulatory environments, such as IFRS 16, in shaping financial reporting outcomes and suggest that standard setters should account for sector-specific financial dynamics when assessing the economic consequences of accounting change.