Lease or Lend?

(1)

0

- An analysis of how operating leases

effect cost of debt

Master’s Thesis 30 credits

Department of Business Studies

Uppsala University

Spring Semester of 2019

Date of Submission: 2019-05-29

Rasmus Eriksson

Tim Thran

(2)

(3)

Acknowledgements

First, we would like to take the opportunity to thank our supervisor Jan Lindvall for excellent guidance and support during the process of this paper. Secondly, we thank our student colleagues for their wise input during the seminar discussions. Last but certainly not least, we would like to thank our friends and family for providing valuable feedback throughout the semester.

(4)

ABSTRACT

Title Lease or Lend? - An analysis of how operating leases effect cost of debt

Date of Submission 2019-05-29

Authors Rasmus Eriksson, Tim Thran

Supervisor Jan Lindvall

Course Master’s Thesis 30 HP 2FE840

Five key words IFRS 16, Operating lease, Financial lease, Debt, Capital structure

Purpose The purpose of this study is to examine how the change in disclosure of operating leases due to the mandated IFRS 16 standard will influence entities cost of debt. Therefore, this paper investigate whether an increase in operating leases has an equal effect on the cost of debt as a corresponding increase in debt.

Methodology This paper utilizes secondary quantitative data collected from Eikon. The main method used in this paper is a regression model. Further, the study applies a deductive approach.

Theoretical perspectives The field of IFRS 16 implication on capital structure and cost of capital is weakly researched as prior literature has been more focused on how it effects operational key figures.

Empirical foundation The data consist of observations on 213 Swedish public companies during the time-period 2006-2017, resulting in a total sample of 1549 observations.

(5)

1. Introduction

“One of my great ambitions before I die is to fly in an aircraft that is on the airline’s balance sheet”

-Sir David Tweedie, Former Chairman of the IASB (ACCA, 2018, p.16)

As of 2005, listed enterprises within the European Economic Area (EEA) are mandated to adopt and disclose their financial statements in accordance with the International Financial Reporting Standards (IFRS). The reason for this mandatory harmonization process is to increase transparency and comparability of annual reports. Harmonization is set out to reduce information asymmetry and enhance risk assessment, thereby, enabling a reduction in cost of capital both stemming from equity and debt. (Moscariello et al., 2014)

The International Accounting Standards Board (IASB) is constantly updating financial reporting standards to improve disclosure comparability and transparency. The implementation conducted by IASB and its accounting standards has according to Söderström & Jun (2007) increased the accounting quality. However, there are still further improvements needed to enhance transparency of disclosed accounting information. One issue of reporting standards that has been debated over the years is the separation between operating and financial leases. Operating leases are according to the previous International Accounting Standard (IAS) 17

Leases not disclosed on the lessee’s balance sheet, enabling the concept of off-balance sheet

financing. The debate consequently arising highlights the issue of non-disclosed assets and liabilities, as the former chairman of the United States Securities and Exchange Commission (SEC) Arthur Levitt Jr asked in 2003:

"Should companies still be allowed to leave billions of off-balance sheet debt, such as lease

financing, out of a company’s reported liabilities? Off-balance sheet debt persists, distorting the financial picture investors have been given in companies in many sectors. Markets will

discipline themselves and their participants but only if they have accurate information." (WSJ, 2003)

(7)

view of firms’ debt related obligations. This is due to the accounting treatment of financial leases, as they are capitalized and disclosed as any other asset with a corresponding liability. The classification of whether a lease is to be considered operating or financial is based on the risk and return related to the asset, as both must be transferred from the hands of the lessor to the lessee for a financial classification to be accurately imposed. However, effective as of January 2019, listed firms within the EEA are mandated to report financial information in accordance with IFRS 16 Leases which eliminates the distinction between operating and financial leases. The purpose is to enhance transparency of financial disclosure and comparability between firms, as all leases, except short-term leases and leases with low value assets, now are to be disclosed at their discounted value on the balance sheet. (Săcărin, 2017) As IAS 17 previously provided companies with an opportunity to off-balance sheet finance its operations it created incentives for companies to increase their level of operating leases. However, after the transition to IFRS 16 financial leverage ratios such as to-equity or debt-to-assets will change and most likely increase (Lim et al., 2017). Hence, if no adjustments of operating leases are made and if it is currently neglected or inaccurately accounted for as comparable to interest bearing debt, entities will be faced with an enhanced risk of having their firm credit rating downgraded and, perhaps even more importantly, their cost of debt increased (ibid.). On the contrary, one could argue the opposite, as explained by Florou and Kosi (2015), the increased transparency brought by IFRS 16 could be seen as an improvement of the harmonization process, thereby, reducing information asymmetries and actually lower companies cost of debt. As these contradicting propositions exist, this paper will enrich current literature in this field by further enlightening how the implementation of IFRS 16 will affect companies cost of debt.

1.1 Background

(8)

which derives the overall lease development towards operating leases. The use of operating leases has increased by 745% during 1980-2007 and amounted in 2014 to $3.3 trillion USD in operating lease commitments worldwide of companies applying IFRS or US GAAP (Săcărin, 2017). Operating lease commitment is accordingly to IAS 17 only disclosed in the notes and at a non-discounted value, thereby, not recognized as an asset under control, nor as the unavoidable related liability, which creates the phenomenon of off-balance sheet funding (Morales-Díaz & Zamora-Ramírez, 2018). Consequently, the disclosure in the financial statements does not provide a complete and comprehensive picture of leased assets and its associated liabilities.

On the other hand, financial leases are recognized and disclosed as any other asset along with its financial counterpart, the liability, both disclosed at their discounted value (Morales-Díaz & Zamora-Ramírez, 2018). The implementation of IFRS 16 is launched with the objective to eliminate the classification between operating and financial leases in order to put an end to off-balance sheet funding of leases by disclosing the lease debt (Săcărin, 2017). Thus, as according to IFRS 16, all leases except short-term leases and leases with assets of low value will be disclosed as what IAS 17 previously has defined as financial leases (IASB, 2016). Although, the consequences of the transition from IAS 17 to IFRS 16 are several, the scope of this paper will focus on leverage-ratios and cost of debt. Furthermore, as IFRS 16 requires firms to disclose operating lease obligations on the balance sheet, it is of interest to examine if the lessees’ cost of debt will be affected by the change in accounting treatment of leases as IFRS 16 explicitly will influence firms’ capital structure.

1.2 Purpose of study

The purpose of this paper is to examine and analyse how the transition from IAS 17 to IFRS 16 will influence the cost of debt for publicly listed Swedish firms, thus this study aims to add a Swedish perspective concerning the effect of capitalizing what IAS 17 previously defined as operating leases.

(9)

debt. The topic is of interest as previous research has not yet reached any consensus regarding creditors treatment of operating lease expenses and as implicitly imposed by IFRS 16, operating leases should be comparable to interest bearing debt.

1.3 Research Question

v How will the change in disclosure of operating leases due to the mandated IFRS 16

standard influence cost of debt?

1.4 Research paradigm & Research design

This paper performs a quantitative study, meaning that the data and the empirical testing is of quantitative characteristics. Further, by using a deductive approach the research question is tested based on existing theory. This study will conduct a longitudinal research design meaning that data is collected on at least two occasions, observing the same variable, something normally referred to as longitudinal or panel data. Further, this is a cohort study indicating that the data has similar characteristics as the sample contains firms that operate within the same economic and regulatory environment.

1.5 Delimitations

(10)

2. Theoretical approach & Literature review

This chapter presents previous literature along with a continuous discussion of their contributions to the field. The chapter is structured as containing of two parts. First, the relevant accounting standards are presented and discussed together with previous research and its implications for this study. Second, previous empirical results are discussed in order to generate a solid foundation for the hypotheses development.

2.1 Theoretical approach

There is an ongoing debate of whether accounting should focus on being balance sheet or income statement oriented. The focus has previously been on matching the opposite parts of the income statement, hence, revenues and costs. Thus, accounting has been less focused on presenting a picture of the companies’ fair value based on its net assets. However, since 1970 there has been a shift in the focus of accounting, as entities nowadays put more emphasis on fair value balance sheet disclosure. This is due to the fact that the income statement consists of figures based on assumptions that are more subjective while the balance sheet is considered more objective in nature. (Dichev, 2008)

The adaptation of IFRS 16 can be seen as a part of this ongoing transition by switching focus from income statement-oriented accounting towards an increased emphasis on fair value accounting. This is because IAS 17 has allowed entities to use operating leases as a source of off-balance-sheet funding. On the contrary, IFRS 16 will bring the operating lease onto the balance sheet.

2.1.1 IAS 17

According to the IAS 17 standard a lease is defined as “...an agreement whereby the lessor

conveys to the lessee in return for a payment or series of payments the right to use an asset for an agreed period of time” (Deloitte Global Services Limited, 1997). Hence, a lease is defined

(11)

“A lease is classified as a finance lease if it transfers substantially all the risks and rewards incident to ownership. All other leases are classified as operating leases...” (Deloitte Global

Services Limited, 1997; IAS 17.4)

Hence, in order to classify a lease as financial the lessee should obtain the same financial advantages and disadvantages as if the asset had been purchased. The distinction between the two types of leases becomes more evident when taking the different accounting and disclosure treatment into consideration. Financial leases are to be accounted for as if the asset would have been purchased by the entity, thus the leased asset should be included in the financial statement together with the corresponding liability, depreciation, tax and interest expense. The leased asset should be valued as the discounted value of all future cash flows related to the lease contract and the asset/liability should be depreciated in accordance with IAS 16 or IAS 38, depending on whether the asset is tangible or intangible. In contrast, agreements that are not classified as financial leases shall be classified as operating leases. This implies that during the lease term, the majority of the asset's financial advantages and disadvantages cannot be considered attributable to the lessee and consequently the lessee is not mandated to disclose the discounted value of the lease. The company instead carries the burden of the profit-related effect of the agreement, the lease expense, which IAS 17 requires the lessee to disclose the minimum lease commitments within one year, between two to five years, and beyond five years. (Deloitte Global Services Limited, 1997)

(12)

In an Effect Analysis performed by IASB in January 2016, examining 1500 companies worldwide, it was found that the transition from IAS 17 to IFRS 16 does not affect all sectors equally. The largest effect was to be found in the aviation sector, where the effect of IFRS 16 adoption is estimated to result in an average increase of 22% in liabilities. The need for a solution to the current accounting treatment of operating leases was justified as a 2014 IASB survey revealed that 3.3 trillion dollars of liabilities were not disclosed when examining companies that report in accordance with IFRS or US GAAP. (Săcărin, 2017)

2.1.2 IFRS 16

Effective as of January 2019 IFRS 16 Leases supersedes the previous standard IAS 17 Leases as the regulatory framework for IFRS compliant firms in regards to lease accounting and disclosure, however, earlier adaptation of the standard is allowed if IFRS 15 Revenues From

Contracts With Customers is applied (IASB, 2016). The origin of the new framework is traced

back to July 2006, as a result of a convergence project carried out by the IASB and FASB in order to deal with the critique and issues surrounding off-balance sheet leases (Săcărin, 2017). Thus, a project debate was initiated to solve the transparency issues concerning lease disclosure that consequently led to the presentation of a new standard proposition in 2009 that has acted as the foundation for IFRS 16 to be built upon (Henraat et al., 2013). In comparison to IAS 17, IFRS 16 introduces a single lessee accounting model that requires the lessee to recognise assets and liabilities for all leases with a term of more than 12 months, unless the underlying asset is of low value. Hence, the new accounting standard removes the separation of operating and financial lease classifications and determines that all lease agreements are to be considered what IAS 17 defines as a financial lease. Ergo, the IFRS 16 definition of leases is slightly modified in contrast to the previous accounting standard, and follow as “the right to control the use of

an identified asset for a period of time in exchange for consideration.” This, consequently,

implies that firms are obligated to report lease agreement as a "right-of-use" asset and liability that is capitalized to its present value and disclosed on the balance sheet. (IASB, 2016)

2.2 IFRS Harmonization

(13)

the information asymmetry towards external stakeholders and market participants (Hlaciuc et al., 2010). As this paper focus on IFRS 16’s impact on cost of debt this section will contain previous research related to the harmonization process and its effect on firms cost of capital. Previous research has foremost been concerned with the implications on firms’ cost of equity, Lee, Walker and Christensen (2008), for instance, find that there is a significant decrease in cost of equity for mandatory IFRS firms in countries with greater financial reporting incentives. The authors are also able to provide evidence that the cost of equity reduction is more pronounced among companies with demand for foreign capital, hence they are able to derive the reduction from improved cross-border comparability. This is in line with the results of Hlaciuc et al. (2010) who examine the mandatory adoption of the IAS/IFRS principles by European companies and conclude that it is a vital step towards full integration of European financial markets. The findings of Hlaciuc et al. (2010) show that the revision of the existing European Union directives on annual and consolidated accounts of European firms has increased the GAAP harmonization. Especially, the revision of the 4th and 7th company law directives, which concerns collective board member responsibility of financial statements and annual reports, increased transparency in off-balance sheet arrangements and related parties’ transactions as well as a requirement for public firms to present a corporate governance statement. The pros and cons from financial reporting harmonization are, however, not allocated in a uniform manner for all firms, as explained by Christensen et al. (2007) who examine the mandatory IFRS adoption in the UK. The authors present evidence that the total effect of a forced IFRS adoption is beneficial in both short-term market reactions and in long-term effects on cost of equity. The benefits caused by the adoption of IFRS are to be divided into two sources according to Christensen et al. (2007). First, companies will have to adopt new accounting-measurement principles that will influence earnings and balance sheet figures depending on firm specifics. Second, the amount of required disclosure will increase for a large part of the firms, mitigating information asymmetry and thus increasing the information quality to other stakeholders.

(14)

indicate that credit ratings of both voluntary and mandatory IFRS adopters are more sensitive for accounting-based default risk measures post the IFRS adoption. This implies an increased credit relevance in accounting information and as credit rating is strongly linked with cost of debt, this implicitly should incentivize firms to improve reporting quality and convey more relevant information to stakeholders. Florou and Kosi (2015) examine the direct effect on cost of debt for entities that during the transitional period of IFRS were subject to mandatory adoption. An interesting result from their paper indicates that mandatory firms post the IFRS adoption more frequently access debt markets, in terms of the bond market, and are able to decrease their bond yield. The authors are able to provide empirical evidence that mandatory IFRS issuers are able to lower their cost of bonds significantly compared to non-IFRS adopting firms. However, Florou and Kosi (2015) are not able to present a similar relationship between IFRS adoption and loan rates, indicating that the relevance of public accounting information is of greater importance in public markets.

Another study within the area was performed by Moscariello et al. (2014) who examined the impact of mandatory IFRS adoption on cost of debt on firms in Italy and the U.K.. Contrary to Florou and Kosi (2015), Moscariello et al. (2014) fail to provide empirical evidence for a direct impact on debt rates within both countries. There is, however, an interesting result from the Moscariello et al. (2014) study as they are able to report a general improvement in accounting quality by measuring and reporting an increase of accrual quality in Italy. The authors identify an impact on cost of debt in Italy due to an increase in quality and relevance of accounting information, which is derived from accounting measures such as interest coverage. Thus, Moscariello et al. (2014) and Wu and Zhang (2009) are able to identify benefits from harmonization of international accounting standards.

(15)

2.3 Impact on cost of debt determinants

The transition from IAS 17 to IFRS 16 will, due to the nature of the accounting standard, affect financial ratios and several cost of debt determinants such as profitability, operational and financial leverage. According to Săcărin (2017), the adoption will lead to an illusionary improvement of operational key figures as the operating lease expense will be replaced by depreciation of the leased asset and a financial expense connected to its financing liability. As depreciation and the financial expense are excluded when analysing operating performance in terms of e.g. EBIT, EBITDA and valuation multiples such as EV/EBIT, multiples will increase, only as a result of IFRS 16 (Ibid.). Thus, adjustments have to be made in order to reach accurate operational key figures. However, due to the scope of this paper the following section will consider the impact on cost of debt determinants such as capital structure, credit ratings and leverage ratios as they are of interest for the purpose of this study.

2.4 Impact on Capital Structure

Theories of capital structure stem back to Modigliani and Miller’s (1958) irrelevance theorem proving that the choice of equity or debt financing does not have any material effect on firm value or on the cost of capital. The irrelevance theorem of Modigliani and Miller (1958) is, however, based on assumptions of efficient markets and no frictions, even though the logic of the theorem is widely accepted, the circumstances of which it is built upon are not reflected in reality. Recent research on traditional capital structure theories such as the trade-off, pecking order and free cash flow theories, nevertheless, state that the choice of financing clearly can matter due to reasons such as taxes, differences in information and agency costs (Myers, 2001). The adoption of IFRS 16 raises the question of the choice of capital structure as the standard will consequently affect issues regarding taxes, information and agency concerns. First, Cornaggia et al. (2013) investigates the effect on financial leverage by capitalizing operating lease expenses, thus bringing the leased assets onto the balance sheet. By reviewing 855 companies during the period 1980-2007 their results indicate that the average operating lease expense as percentage of total debt has increased from 0.84 to 7.12 during the period, the change reflects an increase of 745%.

(16)

fact, during the investigated period the unadjusted debt-to-asset ratios have not changed significantly, implying that operating leases actually increase debt capacity. In contrast to previous literature within the scope of corporate finance this is somewhat contradicting, for instance Myers, Dill and Bautista (1976) theorize that lease payments displace debt as lease obligations are fixed, whilst Ross, Westerfield and Jaffe (1990) state that a firm will not use as much debt if lease financing is applied. The result of these papers is that lease expenses tend to reduce the debt capacity of firms and that debt financing and lease obligations are to be viewed as substitutes.

Cornaggia et al. (2013) rather identify the two financing options as complements and present results similar to Lewis and Schallheim (1992) who also emphasize that the two sources of financing are to be viewed as complements as they assume that the optimal lease and capital structure is determined endogenously. Lewis and Schallheim (1992) emphasize on the trade-off between tax benefits of debt and the potential redundancy of other tax shield such as depreciation. Based on these assumptions the authors are able to derive a benefit for lessee. The implementation of IFRS 16 does however, raise the question of capital structure importance in managing decisions along with the question of its significance for the determination of credit ratings, thus implicitly influencing cost of debt. This is something that has been examined by Park and Na (2018) as they explain that firms’ capital structure is a main determiner used by creditors and credit rating institutions when determining credit health and cost of debt.

The importance and influence of capital structure in managing decisions is something that also is referred to as the “credit rating - capital structure hypothesis” (“CR-CS”). Implying that companies that face an upgrade or downgrade of their credit rating tend to issue less debt, something that goes along with both the trade-off and pecking order theory (Kisgen, 2006). The findings made by Kigsen (2006) are consistent with Lim et al. (2017) as there seems to be a current consensus regarding the tendency to not issue debt when firms face the border of having their credit rating changed, as both papers find that there is less debt issuance under these prevailing circumstances.

(17)

On the contrary, distress caused by financial leverage are derived from associated financial costs, namely, interest expenses related to the issued debt. Therefore, as explained by Dogan (2016), high levels of non-cancellable operating lease expenses are to be seen as the main driver of operating leverage. However, the adaptation of IFRS 16 will cause a shift of classification. Hence, what previously was considered as operating leverage will now will be recognized as financial. Therefore, one can question how the change from operating to financial leverage will affect firms’ credit rating and cost of debt?

2.5 Implication on Credit rating & Cost of debt

Altamuro et al. (2014) examine to which degree operating leases are taken into consideration by creditors when determining the loan spread, which is defined as the premium above the London interbank offered rate (LIBOR) determined by a credit health assessment of the company. Their findings indicate that operating leases are included in the determination of companies’ credit ratings. Hence, the researchers are able to draw the conclusion that sophisticated creditors consider operating leases as comparable to other interest bearing liabilities when determining cost of debt. The findings made by Altamuro et al. (2014) are of interest to this study as their results suggest that the capitalization caused by IFRS 16 of operating leases neither will have a significant impact on the company's credit rating nor cost of debt as sophisticated creditors already makes these considerations.

(18)

However, off-balance sheet funding cannot be used to fool the market as bond yields are accurately reflecting the increase of operating leases, thus, you can run but you cannot hide (Lim et al., 2003).

In addition, Morales-Díaz and Zamora-Ramírez (2018) discuss the implications of funding operations with operating leases. The authors emphasize that the degree of lease financing should be decreased in order to mitigate the risk of experiencing an increased cost of debt due to imposed leverage ratios beyond optimal levels caused by IFRS 16 adaptation. Thereby, the findings of Morales-Díaz and Zamora-Ramírez (2018), Lim et al. (2017) and Lim et al. (2003) put light on the issue that acts as the foundation for this study. The findings made by Lim et al. (2017) and Morales-Díaz and Zamora-Ramírez (2018) are consistent with Park and Na’s (2018) examination of Korean companies when investigating listed and non-listed firms. Park and Na (2018) are able to conclude that cost of debt differs based on the choice of financial or operating leases. However, this result is only significant when reviewing private firms and Park and Na’s (2018) research further indicates that there is no significant relationship found between a firm's credit rating and the choice of financing source. Park and Na (2018) rather explain the difference in cost of debt as a result of disclosure quality, indicating that such is lower in private firms compared to public firms. Park and Na’s (2018) results are thus consistent with the conclusions made by Florou and Kosi (2015) that a reduction in information asymmetry should benefit the firms in terms of a reduction in cost of capital.

(19)

of IFRS 16 on cost of debt, neither its effect on credit health assessment, and as imposed by IFRS 16 operating leases should be equal to financial, this dilemma will be further examined throughout this paper.

2.6 Hypothesis Development

Based on the previous discussion of the literature one could argue that the findings of Lim et al. (2017), Lewis and Schallheim (1992) and Cornaggia et al. (2013) are consistent, as their results present a consensus that the use of operating leases acts as a complement rather than a substitute to debt financing due to the separation of operating and financial leverage. Therefore, by increasing the use of operating lease contracts when a certain level of financial leverage is met entities are able expand their debt capacity without facing the same increase in cost of borrowing as would have been imposed by an equal increase of interest-bearing liabilities. However, these results are contradicting to the findings of Altamuro et al. (2014) when stating that, as also being imposed by IFRS 16, operating lease contracts are treated equal to those of a financial character, through the use of capitalization. Thus, based on these mentioned contradictions, the hypothesis is stated as below:

v !_": An increase in operating leases has the same impact on the cost of borrowing as

a corresponding increase of debt.

However, one could also argue in consistency with Florou and Kosi (2015), Park and Na’s (2018) and Musaki (2018). Thus, stating that the issuance of IFRS 16 is launched to improve disclosure transparency and thereby mitigate concerns caused by information asymmetries. Hence, the application of IFRS 16 might lower entities’ cost of borrowing as uncertainties regarding the capitalization of operating leases will be standardized and financial disclosure transparency will increase. Therefore, as uncertainties regarding the implications of IFRS 16 still prevails it brings us to the alternative hypothesis of this paper, which stands in opposition to the one previously stated:

v !_$: An increase in operating leases does not have the same impact on the cost of

(20)

3 Methodology

This section elaborates and structures the methodology used in this paper. The section begins with an introduction regarding the sample selection and data collection. Next, we present previous models used within the research area before presenting our main regression model. Finally, a discussion of ethical implications for the paper is presented.

3.1 Sample selection

The sample in this paper is restricted to Swedish firms listed on Nasdaq OMX Stockholm main markets during the period 2006-2017. As the majority of previous research on IFRS 16 and its impact on cost of capital has been undertaken in a public setting we find it appropriate to evaluate our models in this context. In order to ensure that all firms within the sample are subject to similar accounting standards the time period is limited to the years following the mandatory implementation of IFRS. These limitations result in a sample of 319 firms, however, in order to conduct our empirical work further adjustments are made in order to derive the final sample. To start, 91 firms are excluded due to lack of interest-bearing liabilities and data required to capitalize the lease liability. Thus, firm observations without operating lease expenses are removed together with observations that lack financial leverage.

Next, 7 financial firms such as banks and insurance companies are excluded from the sample. Financial firms are excluded because their capital structure is driven by other factors compared to non-financial firms (Rajan & Zingales, 1995). Furthermore, banks are able to lend directly from the Riksbank, the Swedish central bank, this decreases the relevance in cost of debt comparison to non-financial firms which motivates the delimitation.

(21)

The data examined in this study has been treated by models used in previous studies, thereby, increasing their legitimacy and, hence, validity. However, when it comes to external validity, as discussing the generalizability of the study, one could question the delimitation of only including Swedish companies. Therefore, it is difficult to generate results that can be claimed to be of maximum external validity. (Bryman, 2012)

Table 1

Sample Selection

Notes: shows adjustments made in order to derive the main

sample as part of the sample selection process * Listed on Nasdaq OMX Stockholm 2007-2017

Criteria Adj. # of obs.

Delimitation* 319

1. Data available in Eikon -91 228

2. Commercial and Investment Banks -7 221 3. Listed for two consecutive years or more -8 213

Total main sample -106 213

Firm year observations 1549

3.2 Data collection

(22)

3.3 Empirical framework

In this section, the empirical framework of this paper is presented together with previously used methods from research within the same field.

3.3.1 Credit Rating Determinants

Musaki (2018) performs a multivariable regression, see Formula 1, in order to examine the relationship between firms’ credit rating and the impact of operating leases. The dependent variable is specified as the credit rating converted to numerical values from 1 (AAA) to 21 (D). The independent variables used are Debt, defined as interest bearing liabilities divided by equity; FLO, defined as finance lease obligations divided by the book value of equity; OLO, defined as operating lease obligations divided by the book value of equity; Size, defined as the natural logarithm of sales; ROA, defined as net income divided by total assets; Tangibility, defined as tangible assets divided by total assets and DMATURE defined as operating cash flows divided by debt. In addition, two dummy variables are included, one for industry and the other for year.

(1) )*+,-._/01 = 3₄+ 3₁678+ + 3₉:;< + 3₌<;< + 3_>?,@7 + 3_A)<B + 3_CD*-.,8,E,+F +

3_G6HBDI)J K7*L6MNNF + O-PMQ+LF6MNNF + R₁ 3.3.2 Cost of Borrowing Determinants

Lim et al. (2017) use Formula 2 as shown below when looking at explanatory variables determining the cost of debt. The explanatory variables used are balance sheet debt (D) put in relation to total assets (TA), followed by the present value of off-balance sheet lease obligations (L) also put in relation to TA. An additional “catch all” variable has been added, (Z), which includes credit rating determinant factors other than debt and leases. This multiple regression model is used to examine their hypotheses that the first derivative of f(D/TA) should equal

g(L/TA) implying that if off-balance sheet funding is a substitute for interest bearing debt, then f’(D/TA)=g’(L/TA). Hence, implicitly suggesting that a change in operating leases should have

the same effect as a change in interest bearing liabilities when determining entities’ cost of debt.

(2) TUQ+ UV WULLUX,-. = V 6 DB + .

;

(23)

3.3.3 Estimating the operating lease value

The value of operating lease obligations are estimated by applying the LMM approach, developed by Lim et al. (2003), which capitalizes lease expenses thru a multiple based on estimated interest and depreciation in order to estimate the perpetuity value of the lease obligations (V). The perpetuity estimate (V) is calculated using the average of current year rental expense and the next year rental expense as a proxy for lease payments (P). The depreciation of the leased assets is expressed as V/N, N represents the average useful life of assets that is estimated by dividing the firms’ property, plant and equipment by its annual depreciation expense.

Furthermore, the LMM approach addresses the tax benefit to the lessor by reducing the cost of the depreciation charge with its statutory tax rate (τ). Cost of debt is represented as the interest rate (K) and by combining the depreciation and interest components Lim et al. (2003) formulate Equation 1 as the following expression:

(J[. 1) ] = ^_ + ( _ / a ) (1 − c) = _ [^ + (1/a) (1 − c) ]

Lim et al. (2003) rearrange the expression to Equation 2, which enables us to estimate the value of the lease obligation:

(J[. 2) _ = ] / (^ + (1/a)(1 − c)

(24)

and even more so to estimate. In addition, the Moody’s multiple method has been excluded, as it is not sophisticated to use if not first compared to the S&P method.

3.3.4 Regression model

As the objective of this paper is to examine whether operating lease obligations are considered an explanatory variable equal to debt when determining entities cost of borrowing, we find a linear ordinary least square (OLS) regression model to be the appropriate statistical method when performing the empirical work.

The model used in this paper is based on previous work by Lim et al. (2017) with some justified adjustments in order to increase the relevance of the model in relation to this paper. The main regression model in this study will include six explanatory variables and cost of debt as the dependent variable.

(3) TUQ+UV678+/01 = 34+ 31678+ + 39<]; + 3=)<B + 3>D*-.,8,E,+F + 3A?,@7 +

K7*L6MNNF + R₁

The dependent variable, CostofDebt, is defined as the realized interest expense divided by the average interest-bearing debt outstanding during years’ t and t-1. This measurement is consistently used in prior research by Lim et al. (2017) and Moscariello et al. (2014) and will be further explored in this paper.

(25)

Third, the variable ROA, is defined as net income divided by TA and is included as a profitability variable based on a modified version of the one used by Lim et al. (2017). ROA is expected to have a negative relationship towards cost of debt since, according to findings made by Musaki (2018), higher profitability also has a negative relationship towards cost of debt. Fourth, a variable that measures the degree of tangible assets in relation to total assets is included in the regression model. The variable Tangibility is thus defined as the book value of tangible assets such as property, plant and equipment divided by total assets. Previous research by Lim et al. (2017) and Musaki (2018) found a negative relationship between tangibility and cost of debt, thus, this is also expected to be the case in this paper.

All four independent variables presented so far, Debt, OPL, ROA and Tangibility are affected by the capitalization of the operating lease expense, as the variables are fractions of total assets. Fifth, as presented by Musaki (2018), the variable Size is defined as the natural logarithm of sales and is included as previous research provides empirical evidence that the variable has a significant effect on cost of debt and firms credit rating. Research by Musaki (2018) expects a negative relationship for Size towards credit rating. This paper expects credit rating to have a negative relationship towards cost of debt, hence, this relationship is also expected for Size. At last a dummy variable, YearDummy, is included in order to capture any time-related fixed effects such as shifting macroeconomic circumstances during the examined period. Furthermore, all variables, except the dummy variables, in the regression models are winsorized at the 2nd and 98th percentiles in order to mitigate the impact of outliers. (Kothari et al., 2005) The hypotheses of this study are examined by testing if the regression coefficient of OPL is equal to the coefficient of Debt, thus, β1 = β9, if the two could be considered substitutes. As

(26)

3.5 Ethical Considerations

As the study uses secondary data it does not directly encounter any problems concerning ethical considerations. However, since the paper aims to study managerial decisions by creditors when determining entities’ cost of debt the study might implicitly claim that creditors tend to use inaccurate procedures when accounting for operating leases.

4. Empirics

In this part of the paper, descriptive statistics are presented together with relevant testing in order to certify that the empirical assumptions hold.

4.1 Descriptive statistics Table 2

Descriptive statistics

Notes: This table reports mean, standard deviation, range and number of firm-year observations for the

variables included in the regression analysis together with a comparison against variables prior adjustments for the capitalized lease liability. CostofDebt is defined as interest expenses divided by total debt (TD), OPL as the capitalized lease liability divided by total assets (TA), Debt as TD divided by TA, ROA as net income divided by TA and Tangibility as property, plant and equipment to TA. Size is measured in SEK and is presented in MSEK. After defining the variables, we winsorize each variable except Size at the lower and upper 0.02 percentiles. In the regression models, we use the natural logarithm of the variables OPL, Debt and Size. Prior adjust. Total sample Std. Dev. Min Max Large _cap Mid _cap Small _cap CostofDebt 0,0574 0,0574 0,0491 0,0097 0,2613 0,0452 0,0542 0,0704 OPL - 0,0967 0,0979 0,0035 0,4070 0,6967 0,1115 0,1049 Debt 0,2506 0,2238 0,1530 0,0056 0,6048 0,2468 0,2340 0,1955 ROA 0,0418 0,0329 0,0923 -0,3138 0,1982 0,0627 0,0474 -0,0048 Tangibility 0,2036 0,2824 0,2318 0,0147 0,9420 0,2798 0,3031 0,2651 Size 16 578 38343139 60 335 748 47 093 6 325 1 316 Observations 1,549 1,549 1,549 1,549 1,549 459 525 565 Distr. % 100 29.63 33.89 36.48

Table 2 displays descriptive statistics for the dependent and independent variables in this paper.

(27)

Reviewing the independent variables there are some noteworthy observations. To start, the OPL variable, which is of greatest interest, presents an average of operating lease obligations in relation to total assets of 9.67% for the population. From Table 2 there are indications that large cap firms with an average of 7% OPL tend to use lease financing to a lesser degree than mid and small cap firms who demonstrate an average OPL of 10-11%. In comparison to previous research by Lim et al. (2017), Swedish entities during 2007-2017 tend to use less lease financing than North American firms during 1995-2011 with an average of 21.2%.

In contrast, the financial leverage variable, Debt, illustrates an average of interest-bearing liabilities to total assets of 22.37%, where large cap firms tend to bear a larger degree of financial leverage compared to Small and Mid-cap firms. Based on these observations firms in different size categories tend to use different levels of debt and lease financing.

The Debt variable is derived from the capitalization of the lease liability, which is also included in total assets thus making the two variables Debt and OPL comparable. Furthermore, the profitability, ROA, and the Tangibility variables are put in relation to the adjusted total assets due to the same reason as stated for the Debt variable.

The average return on assets, ROA, are 3.29% whilst tangible assets in relation to total assets amounts to 28.24%. Reflecting upon Table 2 it is evident that the population contains a range of firms with different characteristics, for instance financial leverage (Debt) varies from 0.6% to 60%, whilst OPL ranges from 0.3% to 41%. Furthermore, profitability (ROA) ranges from -31% to 19.8%, thus the sample includes firms that are highly profitable and non-profitable whilst also including firms with both high and low debt burden.

Table 3 illustrates a correlation matrix presenting both the dependent and independent

variables. As expected there are some influence of correlations identified where the financial leverage variable (Debt) and Size tend to exhibit the largest correlation towards the dependent variable. Furthermore, we are able to observe a fairly large positive correlation between

Tangibility and the OPL variable. According to Pallant (2010), the presence of multicollinearity

(28)

Table 3

Correlation matrix

Notes: The table presents a correlation matrix for the dependent and independent variables used in this

paper. The table presents a rather high correlation between the dependent variable, CostofDebt, and the independent variable Debt. The independent variables Size and ROA also exhibits a stronger correlation in comparison to other variables. In order to ensure the non-existence of multicollinearity the variation inflation factors has been calculated and presented in Appendix 1.

CostofDebt OPL Debt ROA Size Tangibility Year CostofDebt 1.0000 OPL -0.1993 1.0000 Debt -0.4467 0.0292 1.0000 ROA -0.2252 0.0146 0.0140 1.0000 Size -0.2486 0.0875 0.2064 0.3688 1.0000 Tangibility -0.2082 0.1733 0.2837 0.0005 0.0190 1.0000 Year -0.1595 0.0593 0.0469 0.0617 0.0709 0.0258 1.0000

However, in order to ensure there is no presence of multicollinearity, the variance inflation factors (VIF) have been calculated and are presented in Appendix 1. A rule of thumb is that a variable with a VIF exceeding 4 motivates further investigation (O’Brien, 2007). As displayed in Appendix 1, the VIF’s are above and around 1 for each variable, the result thus strengthens the indication of no multicollinearity among the independent variables.

Furthermore, as the empirical work is based on an OLS regression model the assumption of homoscedasticity needs to be addressed in order to test whether the variance of the data residuals is consistent. If this is the case, the data is said to be homoscedastic. This is of importance as if not, the variance of the dependent variable will be affected by the movement of the independent variable. (Newbold et al., 2013)

(29)

4.2 Industry Descriptive

The sample consists of entities collected from nine different industries as presented in Table 4. The Energy industry exhibits the highest average Debt with a mean of 39.28% and a corresponding OPL of 1.91%. On second place is the Financial industry with a Debt of 31.88% and an OPL of 3.64% followed by the Basic Material industry with an average Debt of 23.78% and an OPL of 4.73%. Further, in terms of OPL the three industries that possess the largest average operating leases in relation to total assets are Consumer Services followed by Industrials and lastly Consumer Goods. Consumer Services exhibit an average OPL of 18.93% and a corresponding Debt of 16.03%. Second, Industrials has an average OPL of 11.02% and a

Debt of 22.76%. Lastly, the Consumer Goods industry has an average OPL of 9.46% and a

corresponding Debt of 22.87%.

Table 4

Industry Descriptive

Notes: Table 4 illustrates changes in capital structure due to capitalization of operating lease

expenses on an industry level. Debt prior Opl adj. is defined as total debt in relation to total asset. OPL is defined as the capitalized lease liability in relation to adjusted total assets (TA + lease liability). Debt is defined as total debt in relation to adjusted total assets and Debt incl. Opl is defined as total debt plus the lease liability in relation to adjusted total assets. From the table it is evident that the industry with the largest degree of operating lease financing are Consumer services, Industrials and Consumer goods.

Industry

Number of obs.

Debt prior Opl

adj. OPL Debt Debt incl. Opl

(30)

5. Results and analysis

This section contains the empirical results together with an analysis and discussion of the empirical output. First, the empirical results are interpreted with a profound interpretation and explanation of this paper’s main findings. The implications of the answering to the hypotheses of this study will be put into relevant context. In addition, the paper’s shortcomings and ideas for further research are presented.

5.1 Effect on financial leverage

The first matter investigated in this paper is whether the non-separation of operating and financial leases will influence firms’ leverage ratios. In order to address the issue, we perform a two-sample t-test between an unadjusted and adjusted debt-to-asset ratio, the unadjusted variable is defined as total debt divided by total assets (see Table 2 Descriptive Statistics) whilst the adjusted measurement includes the value of capitalized lease liability in both total debt and total assets. As presented in Table 5 the average debt-to-asset ratio prior to the operating lease adjustments amounts to an average of 25.06%. Put in contrast to the adjusted leverage ratio of 32.55% a significant increase of financial leverage is observed within the population as the test exhibits an t-statistic of -12.206. The interpretation of the t-test is thus that the IFRS 16 implementation will have a significant effect on financial leverage of public Swedish firms, the implication of the effect is further investigated in the coming sections.

Table 5

Two-sample t test with equal variances

Notes: Table 5 illustrates a Two-sample t test between the financial leverage variables Debt prior Opl adj.

and Debt incl. Opl. Debt prior Opl adj. is defined as total debt in relation to total assets, while Debt incl. Opl is defines as total debt plus the capitalized lease liability in relation to total assets plus the capitalized lease liability. The Two-sample t test present a significant increase in financial leverage of Swedish companies after adjusting for operating lease liabilities.

Variable Obs Mean Std. Err.

Std.

Dev. [95% Conf Interval] Debt prior Opl

adj. 1,549 .2506167 .00432 .170022 .2421431 .2590903

Debt incl Opl 1,549 .3254614 .00448 .176474 .3166663 .3342565

Combined 3,098 .2880391 .00318 .177247 .2817952 .294283

Diff -.0748447 .0062263 -.0870529 -.0626365

Diff = mean(Debt prior Opl adj.) - mean(Debt incl Opl) t = -12.206

Ho: diff = 0 Degrees of freedom = 3096

Ha: diff

(31)

5.2 Empirical results

Table 6

Regression output

Notes: The asterisk represents the significance level of the coefficients. One asterisk (*) represents

significance at the 10% significance level, two asterisk (**) represent the 5% level and three asterisks (***) represent the 1% level. All regressions performed are significant. Model 1 and 3 was performed in order to test the hypothesis of β1 = β2, while Model 2 and 4 was performed in order to control for differences due to market capitalization.

Dependent variable: CostofDebt

Independent variables Model 1 Model 2 Model 3 Model 4 OPL -.0079317*** -.0031108* -.0067834*** -.002452 (-6.91) (-1.74) (-7.36) (-1.39) Debt -.0206907*** -.0120243*** -.0189432*** -.0120223*** (-11.42) (-3.61) (-17.78) (-3.80) ROA -.0959753*** -.0977742*** (-7.86) (-4.99) Size -.0019175*** (-3.28) Tangibility -.01319** -.0110213*** (-2.77) (-3.13) OPL#List Midcap -.0034811** -.0033642** (-2.02) (-2.15) Smallcap -.0087045*** -.008201*** (-3.12) (-2.97) Debt#List Midcap -.0065533* -.0070404** (-1.94) (-2.15) Smallcap -.0093655** -.0085697** (-2.11) (-1.99) List -.0120667** -.0144557** (-1.92) (-2.32) Constant -.0038945 .0276295* .0502623*** .0553132*** (-0.77) (1.89) (4.61) (3.81) Observations 1,549 1,549 1,549 1,549 Number of years 10 10 10 10 R-squared 0.2342 0.2667 0.3061 0.3239 F-statistic 68.42 27.05 46.53 15.45

Year FE NO NO YES YES

(32)

The regression results indicate that operating lease expenses are not an equivalent factor to debt financing when determining entities’ cost of borrowing. For all models presented in Table 6 the beta coefficient for OPL and Debt are significantly different from each other while continuously displaying a negative relationship towards the dependent variable. The initial model, Model 1, includes OPL and Debt as explanatory variables for the cost of borrowing. The model as a whole is significant on a 5% significance level with an R-squared of 23.42% and a corresponding F-value of 68.42. The F-test conducted to examine the hypotheses, shows that there is a significant difference between the OPL variable of -0.0079 and the Debt coefficient of -0.0207. Interpreting the results from Model 1 indicates that firms experience a greater effect on their cost of borrowing when financing thru debt rather than lease contracts.

In Model 2, the impact of market capitalization is examined, taking into consideration whether firms are listed on Small, Medium or Large cap. Model 2 includes two interaction variables with Large cap firms as the base. The interaction variables are based on the independent variables from Model 1 together with a factor variable for market capitalization, thus controlling for which specific market list the observations are referred to. The interaction variables enable a comparison based on firm size and from Table 6 it is evident that Large cap firms, which are set at as base scenario, has an OPL coefficient of -0.00311, yet, only significant on a 10% level. However, the Debt coefficient of -0.0120 is still significant on a 5% level. In contrast to Small and Mid cap firms the model implies that Large cap firms are less sensitive to changes in capital and funding structure. With coefficients of -0.0087 and -0.0094 for the OPL and Debt interaction variables Small cap firms exhibit to gain more in terms of decreased interest rates when increasing debt or lease financing. Ceteris paribus, the impact on cost of debt when increasing financial leverage or operating leases for Mid and Small cap firms is larger than the base scenario. Hence, the results imply that companies that are not listed on Large cap experience a larger, hence more beneficial, marginal effect on their cost of debt when increasing the level of debt or operating lease. The coefficients of OPL and Debt are still significantly separated from one another on a 5% level.

(33)

R-squared of 30.61% with a corresponding F-value of 46.53, thus the model is still significant on a 5% level. The results still reject the null hypothesis as OPL is not an explanatory variable comparable to Debt. Thus, 3₁ ≠ 3₉ on a 5% significance level according to the performed F-test. All control variables in the model are significant on a 5% level and consistent with prior expectations the coefficients of ROA, Tangibility and Size indicate a negative relationship towards the dependent variable cost of debt. However, the Debt coefficient of -0.019 and the

OPL of -0.068 could not be foreseen, as it implies that an increase of 1% in Debt or OPL results

in a decrease of -0.0019 or -0.0068 percentage units in the cost of debt. The findings of Debt and OPL having weak, yet, negative coefficients and thus a negative relationship towards the cost of debt variable are intriguing. Hence, it implies that an increase in debt or operating lease, when put in relation to TA, actually have a weak lowering effect on the cost of borrowing. In order to examine if any difference in impact caused by listing characteristics still prevails Model 2 and 3 are combined into Model 4. Thus, once again the sample is divided into three categories based on market capitalization, furthermore the variable Size is removed as the List variable consider fixed effects due to market value. The results are consistent with Model 2, as the largest marginal impact on cost of debt due to an increase in OPL or Debt is to be found among small cap firms whilst large cap firms still presents a non-significant OPL variable.

5.2 Implications in different industries

(34)

operating leases. Thus, including companies such as SAS AB, Hennes & Mauritz AB, Clas Ohlson AB and Kappahl AB to name a few.

5.3 Implication of IFRS 16 harmonization on cost of debt

As presented in the two-sample t-test, the transition from IAS 17 to IFRS 16 will have a significant impact on the entities’ leverage ratios. Thus, one has to acknowledge its practical implications in a real-world setting. The revealed debt will influence the debt ratios of the companies included in this study. Hence, one can claim that the increased harmonization brought by IFRS 16 is mainly beneficial for improving the comparability between firms. The transition from IAS 17 to IFRS 16 contributes to the IFRS transparency aim. However, in terms of yearly internal comparisons of the entities’ capital structure, the transition will require suitable adjustments in order to account for the lease expense.

The results of this paper establish that the beta coefficient of OPL is significantly different from the beta coefficient of Debt. As the marginal effect of the variables differs, we are able to conclude that firms should view the two financing alternatives as complements. Hence, the results are consistent with previous findings by Lim et al. (2017), Lewis and Schallheim (1992) and Cornaggia (2016) who state that operating lease financing should be viewed as a complement rather than a substitute to debt financing. The findings of this paper thus question Altamuro et al. (2014) and Ross et al. (1990) whom state that an increase of operating leases or a corresponding increase of debt have an equal effect on the entities’ cost of borrowing. Rather, this study agrees with the conclusion made by Lim et al. (2017), that an increase in debt does not correspond to an equal increase of operating leases, hence, 3₁ ≠ 3₉. Thus, one can reject the null hypothesis of the two being equal on a 5% significance level.

(35)

is due to the fact of the uncertainties regarding the capitalization procedure as there is a lack of transparency in terms of operating lease disclosure.

Therefore, the findings of this study imply that the transition from IAS 17 to IFRS 16 contributes to IFRS’s transparency aim and increases financial disclosure harmonization. The findings of this paper are highly intriguing and should be thoughtfully interpreted. The study expected to find positive coefficients for the OPL and Debt variables. However, this was not the case and even if the negative relationship towards cost of debt is weak, one has to recognize its implication in a real-world setting. Hence, contrary to previously discussed research this study finds that an increase in debt or operating leases when put in relation to total assets actually has a marginal lowering effect on the cost of borrowing. Thus, these findings are rather contradicting when discussed out of a capital structure perspective, as one would imagine that an increase in leverage and, hence, implicitly a higher level of credit risk would have an increasing effect on the cost of debt.

However, one must consider that the prevailing macroeconomic factors during the majority of the period examined consisted of low interest rates, as a long-lasting result of the aftermath caused by the US subprime crisis (Goddard et al., 2009). These macroeconomic conditions have increased liquidity and, thus, made debt an appealing and easily accessible source of financing (Ibid.). Therefore, one can imagine that part of the explanation for the findings of Debt and

OPL having weak negative coefficients when used as explanatory variables for cost of debt, is

due to the surrounding low levels of interest rates. In addition, one has to consider the maturity of the companies included in the sample. As these are all public companies one could argue that their size makes them more stable and therefore consisting of less credit risk compared to smaller private entities.

(36)

and transparency brought by improved IFRS quality actually lowers entities’ cost of capital. As operating leases exhibit a less beneficial marginal impact on the cost of borrowing compared to debt, one can imagine that the increased transparency of financial disclosure brought by IFRS 16 and its aligning effect of operating leases and interest bearing liabilities could lower entities cost of borrowing.

5.4 Ideas for future research

As this study only considers the implication caused by using operating leases on the overall cost of borrowing it would be interesting to make a comparison of its impact on private versus public debt. As this paper focuses on public companies, it would be of interest to examine the difference between public and private firms, in order to evaluate the importance of financial disclosure transparency, when determining cost of debt.

6. Conclusion

The overall findings of this paper indicates that an increase of operating leases do not have the same impact as a corresponding increase of debt on entities’ cost of debt, thus, the results enables a rejection of the null hypothesis on a 5% significance level. Therefore, the results of this study are in contrast to previous findings made by Ross et al. (1990) and Altamuro et al. (2014). However, the results are consistent with Lim et al. (2017), Lewis and Schallheim (1992) and Cornaggia (2016), when providing evidence that operating leases are to be viewed as a complement rather than a substitute to debt. The interpretation somewhat differs from the one made by Lim et al., (2017) as an increase of operating lease financing reveals a less beneficial effect compared to a corresponding increase of debt on the cost of borrowing. Therefore, operating leases does enable an expansion of entities’ credit capacity but not at a cost that is lower than what would have been imposed by increasing debt financing.

(37)

making a rejection of the null hypothesis. In order to examine the impact of market capitalization, Model 2 and 4 included two interactive variables, enabling a categorisation of the sample based on market capitalization. The result showed that an increase in debt or operating lease financing had the most beneficial effect on small cap entities and the least beneficial, yet not significant for the operating lease variable, on large cap firms.

This study presents negative coefficients for the operating lease and financial leverage variables in all performed regressions. Thus, implying that there is a negative relationship between these variables and the cost of debt. Therefore, findings indicate that an increase of debt or operating leases, to a weak extent, actually has a lowering effect on the cost of borrowing. However, when interpreting the results, one should bear in mind the impact of the prevailing macro environment, as low interest rates have been imposed during the majority of the examined time-period. Nevertheless, findings are consistent with Florou and Kosi (2015) and Wu and Zhang (2009) showing that the increased harmonization effect and the reduction of information asymmetries caused by the alignment of interest bearing liabilities and operating leases, could be beneficial in terms of decreasing entities cost of capital.

7. Limitations

One should bear in mind that the conclusions made in this paper are based on a single capitalization method. Thus, the study implicitly assumes that this is the model conducted by creditors when making these sorts of credit assessments. However, it is justified by the fact that other models, as previously discussed, require increased transparency in terms of operating lease disclosure and therefore contain a higher degree of uncertainty. Thus, this paper has chosen the model that was found to be best suited for this study.

(38)

References

Acca Global (2018) IFRS 16. Retrieved 7 mars 2019 from:

http://www.acca.ee/lk/en/member/discover/cpd-articles/corporate-reporting/ifrs16-dellernovcpd.html

Altamuro, J., Johnston, R., Pandit, S.. and Zhang, H., 2014. Operating Leases and Credit Assessments. Contemp Account Res, 31: 551-580.

Ball, R., Robin, A. & Sadka, G. (2008). Rev Acc Stud, 13: 168.

Beattie, V., Edwards, K., & Goodacre, A. (1998). The impact of constructive operating lease capitalization on key accounting ratios. Accounting and Business Research, 28(4), 233-254.

Cornaggia, K. J., Franzen, L. A., Simin, T. T., 2013. Bringing leased assets onto the balance sheet. Journal of Corporate Finance, 22(2), pp. 345-360.

Christensen, H, B., Lee, E., Walker, M., 2007. Cross-sectional variation in the economic consequences of international accounting harmonization: The case of mandatory IFRS adoption in the UK, The International Journal of Accounting, Volume 42, Issue 4, pp. 341-379.

Daske, H., Hail, L., Leuz, C and Verdi, R., 2013. Adopting a Label: Heterogeneity in the Economic Consequences Around IAS/IFRS Adoptions. Journal of Accounting Research, 51: pp. 495-547.

Deloitte Global Services Limited (1997). IAS 17 Leases. Retrieved 5 Februari 2019 from: https://www.iasplus.com/en/standards/ias/ias17

Dogan, F. G., 2016. Non-cancellable Operating Leases and Operating Leverage. European

Financial Management, 22(4), pp. 576–612.

Florou A, Kosi U., 2015. Does mandatory IFRS adoption facilitate debt financing? Review of

(39)

Goddard, J., Molyneux, P., Wilson, J O.S., 2009. The financial crisis in Europe: evolution, policy responses and lessons for the future. Journal of Financial Regulation and Compliance, Vol.17 Issue: 4, pp.362-380.

Goodacre, A. (2003). Assessing the potential impact of lease accounting reform: a review of the empirical evidence. Journal of Property Research, 20(1), 49-66

Henraat, D., Georgakopoulos, G., Kalantonis, P and Rodosthenous, M., 2013. A Comparative Empirical Study for the Different Approaches of the Operational Leases Capitalization. Journal

of Computational Optimization in Economics and Finance, 5(1), ss. 51-68.

International Accounting Standards Board [IASB] (2016). IFRS 16 Leases. Retrieved 29 January 2019 from: http://eifrs.ifrs.org/eifrs/bnstandards/en/IFRS16.pdf

Kisgen, D., 2006. Credit Ratings and Capital Structure. The Journal of Finance, 61(3), 1035-1072.

Karagiannis, S., Panagopoulos, Y., Vlamis, P., 2010. Interest rate pass-through in Europe and the US: Monetary policy after the financial crisis, Journal of Policy Modeling, Volume 32, Issue 3, Pages 323-338

Lee, E., Christensen, H., Walker, M., 2008. Mandating IFRS: Its Impact on the Cost of Equity Capital in Europe. Association of Chartered Certified Accountants, Report No. 105.

Levitt, A., 1998. The Importance of High-Quality Accounting Standards, Accounting Horizons, 12: pp. 79–82.

Levitt, A., 2003. The SEC’s Repair Job, Wall Street Journal. From: https://www.wsj.com/articles/SB1044843331451016783

Lewis, C and Schallheim, J., 1992. Are Debt and Leases Substitutes? Journal of Financial and

Lease or Lend? - An analysis of how operating leases effect cost of debt