Implications of IFRS 16 adoption Evidence from Swedish publicly listed firms
Master’s Thesis 15 credits Department of Business Studies Uppsala University Spring Semester of 2020 Date of Submission: 2020-06-27
Jonathan Spånberger Momtahina Rista
Supervisor: Derya Vural-Meijer Abstract
In this study, we investigate how the implementation of IFRS 16 is affecting the financial statements of Swedish publicly listed firms, and what implications there are for financial statement users. These effects are analyzed by looking at transitional effects on total assets, total liabilities and EBITDA and by comparing different sectors, following estimations of sectoral differences in prior studies (e.g. Fülbier et al., 2008; Morales-Díaz & Zamora-Ramírez, 2018a). As a way of approximating the practical implications of IFRS 16, this study is analyzing changes in the key financial ratios: D/E and EV/EBITDA.
We find significant median increases in total assets, total liabilities and EBITDA in the full sample, as well as within each sector group. Further, we confirm the existence of sectoral differences, finding the largest median increases in the Consumer Services sector and the smallest in the Financials sector. We also confirm that IFRS 16 bring new implications for financial statement users, since important and commonly used financial ratios are significantly changed: we observe a significant median increase in the D/E ratio and a significant median decrease in the EV/EBITDA multiple.
Keywords: IFRS 16, lease accounting, off-balance sheet financing, impact assessment, transitional effects Table of Contents
1. Introduction ...... 1 2. About IFRS 16 ...... 4 3. Theory and literature review ...... 6 3.1 Asymmetric information and IFRS ...... 6 3.2 Asymmetric information and lease accounting ...... 7 3.3 Effects of capitalizing operating leases ...... 9 3.3.1 Balance sheet effects ...... 9 3.3.2 Profit and loss statement effects ...... 11 3.4 Hypotheses development ...... 12 4. Methodology ...... 14 4.1 Research design ...... 14 4.2 Data and sample ...... 15 4.3 Statistical methods of hypothesis testing ...... 16 4.4 Methodological considerations ...... 21 5. Results ...... 23 5.1 Results from the full sample ...... 23 5.1.1 Effects on financial statements ...... 23 5.1.2 Effects on key financial ratios ...... 24 5.2 Results by sector ...... 25 5.2.1 Effects on financial statements ...... 27 5.2.2 Effects on key financial ratios ...... 29 6. Discussion ...... 31 6.1 Effects on financial statements ...... 31 6.1.1 Results from the full sample ...... 31 6.1.2 Results by sector ...... 32 6.2 Implications for financial statement users ...... 34 6.2.1 Effects on key financial ratios ...... 34 6.2.2 Impact on information asymmetries ...... 36 6.2.3 The implementation process ...... 37 7. Conclusions ...... 39 References ...... 41 Appendix A – Examples of differences in reporting of transitional effects ...... 45
1. Introduction The aim of this study is to investigate how the implementation of IFRS 16 – the new accounting standard on leasing – is affecting the financial statements of Swedish publicly listed firms, and what implications there are for financial statement users. IFRS 16 is the most recently implemented standard within the International Financial Reporting Standards (IFRS): a series of global, principle-based, accounting standards aimed at facilitating cross-border capital movement by eliminating national differences in accounting regulations (IFRS Foundation, 2019a). The standards are used widely all over the world, including Sweden where IFRS has been mandatory for publicly listed firms since 2005 (European Commission, 2019).
IFRS 16 was implemented in fiscal years started January 1st, 2019 or later. The main impact is that future payments of operating leases need to be capitalized. IFRS 16 replaced IAS 17, under which finance leases were capitalized but fees from operating leases were reported as operating expenses, complemented with information disclosed in notes. According to the IFRS Foundation, the aim of IFRS 16 is to “faithfully represent lease transactions” and to “provide a basis for users of financial statements to assess the amount, timing and uncertainty of cash flows arising from leases” (IFRS Foundation, 2019b). Hence, an implicit aim is to decrease the information asymmetry between the firms and their stakeholders (Hoogervorst, 2016). During the last decades, there seems to have been an increase in the usage of operating leases, creating a suspicion of companies deliberately using operating leases as a way of keeping assets and liabilities out of the financial statements, so called “off-balance sheet financing” (Abdel-Khalik, 1981; Imhoff & Thomas, 1988; Imhoff et al., 1991; Reason, 2005; Duke et al., 2009). Previous studies such as Imhoff et al. (1993) are pointing out how disclosed, but not recognized, information about operating leases in the annual reports (as was the case under IAS 17) seem to not be utilized to the same extent by all stakeholders. Hence, the results point to a need of including such information in the financial statements, i.e. capitalizing the operating lease expenses. In similar studies, although not explicitly about leasing, Ahmed et al. (2006) and Davis-Friday et al. (1999) also provide evidence of differences in utilization of the information in recognized and disclosed amounts.
There has, however, been a lot of criticism of the IFRS 16 and its counterparty within US GAAP: FASB ASC 842 (Accounting Today, 2013; Tysiac, 2013; Bratten et al., 2013; Altamuro et al., 2014). The criticism is not mainly regarding whether or not more information is needed, but rather regarding the complexity of the suggested model of using capitalization and the
1 proportion between benefits and negative economic consequences that could be caused by the new standard (Accounting Today, 2013; Tysiac, 2013; Kabureck, 2015). Some business representatives claim that capitalizing operating leases is a complex and time-demanding activity, which does not have proportionate benefits for stakeholders (Accounting Today, 2013). Further, Bratten et al. (2013), Altamuro et al. (2014) and Giner & Pardo (2018) point to evidence indicating that a lot of stakeholders are in fact utilizing disclosed and recognized information about operating leases in a similar way. Hence, a possibility of achieving similar results with a less complex accounting standard than IFRS 16 is indicated.
Concluding the debate on lease capitalization, there exist evidence pointing in different directions regarding the most appropriate way to decrease the information asymmetries that seem to exist under IAS 17 and US GAAP (e.g. Imhoff et al., 1993; Bratten et al., 2013; Altamuro et al., 2014). However, rather than the studies showing explicitly contradicting evidence, the opposite arguments of the debate stem from conflicting assessments of what level of benefit is proportionate to the costs of increased accounting input. For instance, regarding if it is necessary for all, or just most, stakeholders to be able to utilize the information, and to what extent external parties can be required to make their own valuations of the information.
Previous studies are commonly using slightly varying estimation models based on the constructive capitalization model (Imhoff et al., 1991) to estimate the effects from capitalization of operating leases. Prior studies, investigating markets in North America (Mulford & Gram, 2007; Durocher, 2008; Duke et al., 2009), Europe (Fülbier et al., 2008; Branswijck et al., 2011; Morales-Díaz & Zamora-Ramírez, 2018a, 2018b) and Australia (Wong & Joshi, 2015), all show significant effects on the financial statements, primarily increases in total asset and total liabilities. These effects have implications also on key financial ratios such as ROA and D/E and are in many studies not uniformly distributed across industries and sectors. For instance, Fülbier et al. (2008) identify retail and fashion as lease intensive sectors being particularly affected by lease capitalization. Similarly, Branswijck et al. (2011) identify manufacturing, and Morales-Díaz & Zamora-Ramírez (2018a) identify retail, hotels and transportation. Our study focuses on transitional effects in three measures in the financial statements: total assets, total liabilities and EBITDA. This is due to the main changes of the IFRS 16 adoption being an addition of right-of-use assets and lease liabilities, together with the replacement of operating lease expenses with depreciation and interest expenses. As a way of approximating the implications to practitioners, the study is also examining two additional measures: the leverage ratio D/E and the valuation multiple EV/EBITDA.
2 Firms are often experiencing the process of implementing IFRS standards as complex and quite burdensome (Jermakowicz & Gornik-Tomaszewski, 2006). The presence of the preceding discussions and criticism (e.g. Accounting Today, 2013; Kabureck, 2015) suggests that many firms expect this to be true for IFRS 16 as well. Further, prior studies show that regulation aimed at harmonizing accounting standards internationally is not guaranteed to achieve its goal, just because the standards are uniform (Soderstrom & Sun, 2007; Holthausen, 2009). Differences in outcome can still occur due to country-specific institutional and economic factors (ibid.). In this wider context, comparisons of country-specific settings and differences in implications of accounting standards are of great relevance in understanding the challenges of international harmonizing of financial reporting. In such comparisons, Sweden is an interesting country to analyze, due to its ability to be an appropriate representative for more than one group of countries. For instance, by having a relatively small economy, by being part of the European Union, and by being one of the top 10 most competitive countries in the world (IMD World Competitiveness Center, 2019; Schwab, 2019), it can be used in multiple relevant comparisons.
This study is relevant to users of financial statements due to its analysis of IFRS 16-caused changes in key financial ratios, and of sectoral differences. The findings can help these users to assess the appropriateness of different multiples and ratios in comparison across sectors and in retroactive analysis of firms. It also contributes to the academic fields of lease accounting and harmonization of financial reporting. Due to the complexity of IFRS 16 (Accounting Today, 2013; Tysiac, 2013; Altamuro et al., 2014) and the general difficulties of implementing new accounting standards (Jermakowicz & Gornik-Tomaszewski, 2006), our study contributes by examining the actual, reported numbers calculated by the companies themselves during the transition to IFRS 16. Due to the need of understanding implementation of new accounting standards in different countries and settings, our study also contributes both by analyzing the case of Sweden, and by making sectoral comparisons. Finally, this study adds to the debate on the usefulness and informational content of lease capitalization (e.g. Duke et al., 2009; Bratten et al., 2013) by investigating key financial ratios that are important to firms’ external stakeholders. In the study, two research questions are answered:
1. What effects did the implementation of IFRS 16 have on the financial statements of Swedish publicly listed firms?
2. Did the effects cause implications for financial statement users?
3 2. About IFRS 16
The IFRS are issued by the International Accounting Standards Board (IASB), overseen by the IFRS Foundation (IFRS Foundation, 2019c). The standards are used all around the world and firms listed on stock exchanges are in most countries required or permitted to use the IFRS standards. One important example is the EU where, since 2005, all publicly listed firms are required to use IFRS for the group consolidated numbers (European Commission, 2019). One important exception, still using national GAAP, is the USA. However, the US GAAP and the IFRS are ensured to be conformed through the “Norwalk Agreement” (FASB, 2002), hence IFRS should be seen as globally influential, even if not implemented in all major capital markets.
New IFRS standards are issued on a regular basis to replace previously used International Accounting Standards (IAS). IFRS 16 is the most recently implemented standard, issued to replace IAS 17, with an objective of ensuring high quality and transparency of lease accounting in financial statements (Hoogervorst, 2016). IFRS 16 was issued in January 2016 and the standard applies to fiscal years starting January 1st, 2019 or later.
IFRS 16 is an accounting standard on leasing. Two forms of leasing exist: operating and finance leasing, which were treated differently under IAS 17. An operating lease is a contract that allows a lessee use of an asset without transferring ownership or risks related to the asset. A finance lease is a contract that allows a lessee use of an asset without transferring ownership, but where the risks and rewards are transferred so that the transaction is very close to a purchase transaction. To achieve the aim of IFRS 16 – to “faithfully represents lease transactions” and “provide a basis for users of financial statements to assess the amount, timing and uncertainty of cash flows arising from leases” (IFRS Foundation, 2019b) – lessees are now required to treat operating and finance leasing similarly. This is performed through the recognition of a lease liability and a right-of-use asset in the balance sheet. Thereby, IFRS 16 eliminates the difference between operating leases and finance leases, requiring lessees to recognize assets and liabilities for all lease contracts. However, the standard is voluntarily applied for those contracts where the lease term is less than 12 months or the contract has a low value (IFRS Foundation, 2019b).
Under IAS 17, information about operating lease expenses, including future expenses from non- cancellable contracts, was required to be disclosed in notes in the annual reports. Under IFRS 16, firms are instead required to recognize a right-of-use asset and a lease liability in the balance sheet when they enter into a lease contract. The right-of-use asset is initially determined at the
4 amount of the lease liability and other payments that include the lessee’s initial direct cost, prepayments and estimated restoration obligations (IASB, 2016). The lease liability is determined at the present value of the lease payments payable over the lease term. The present value is calculated as the non-cancellable lease contracts, discounted with a discount rate. The discount rate used should be the “…interest rate implicit in the lease, if that rate can be readily determined. Otherwise the lessee shall use its incremental borrowing rate…” (IASB, 2016, p. 26). Due to this regulation, different discount rates can be used in different companies, although sectoral and industrial similarities could be expected, following that companies within an industry could be facing similar risks and financing situations.
Following the changes in the balance sheet, the profit and loss statement is adjusted by replacing the operating lease expenses with depreciation of the right-of-use asset and interest expenses based on the size of the lease liabilities. This implies that P&L changes exist primarily in EBITDA. Cash flow statements are adjusted as well, since the lease payments are now not comprising operating expenses but are classified as repayments of the liability and interest (i.e. financial) expenses (IFRS Foundation, 2019b). In contrast to lessee accounting, lessor accounting remains the same under IFRS 16. Lessors are able to classify a lease as an operating lease or a finance lease. When a lease allows transferring all risks and rewards of the ownership of an underlying asset, it is classified as a finance lease. Otherwise, it will be classified as an operating lease.
5 3. Theory and literature review
3.1 Asymmetric information and IFRS Information plays an important role in all financial decision-making. In neo-classical economics, a situation of perfect information is often assumed. In contrast to this theoretical assumption, a common situation in different kinds of markets seems to be that all participants do not, in fact, have access to equal information, i.e. a situation of asymmetric information (Akerlof, 1970; Spence, 1973; Stiglitz, 1975). In financial markets, there is a general fear of asymmetric information since it can cause problems such as adverse selection or moral hazard (Akerlof, 1970). The effects on trade and transactions caused by information asymmetries are assessed to be harmful for capital markets and society in general and are therefore counteracted with regulation such as accounting standards (Assidi & Omri, 2012; Kao & Wei, 2014).
Accounting standards in particular are aimed at addressing the asymmetric information in the relationship of company management and shareholders, often referred to as the principal-agent problem (Jensen & Meckling, 1976), where neither information nor interests are automatically aligned between parties. Accounting standards, however, are just one aspect of dealing with asymmetric information, since it is also affected by a firm’s corporate governance model, the work performed by auditors, country-specific legal aspects etc. (Jensen & Meckling, 1976).
Previous studies suggest that adoption of a uniform set of accounting standards, such as the IFRS, can mitigate information asymmetry and improve accounting quality (Barth et al., 2008; Daske et al., 2008; Muller et al., 2011). Positive effects such as increased market liquidity and decreased cost of capital (Daske et al., 2008) as well as decreased earnings management and increased value relevance of accounting (Barth et al., 2008) have been shown using large international samples. In more specific areas, positive effects have also been shown in aspects such as reduced underpricing of IPO: s1 (Leone et al., 2007; Hong et al., 2014) and by decreased information asymmetry in the case of the European real estate industry (Muller et al., 2011).
New accounting standards, such as IFRS, are in general aimed at increasing accounting quality and are expected to minimize barriers to cross-border trading in securities. This can in turn increase market efficiency and reduce the cost of capital (Daske et al., 2008). Improved transparency, comparability and quality of financial reporting have positive effects such as helping investors to make more efficient investment decisions. However, as previous studies
1 Initial public offerings, i.e. the process of issuing new shares to the public.
6 show (Jermakowicz & Gornik-Tomaszewski, 2006; Soderstrom & Sun, 2007; Holthausen, 2009), it is not always the case that these kinds of expectations are met when implementing new accounting standards. Jermakowicz & Gornik-Tomaszewski (2006) examine the difficulties in implementing IFRS from the perspective of EU publicly traded companies. The results indicate that most companies identify some limitations regarding the process of implementing IFRS, e.g. it being a costly, complex and burdensome process. Also, these preparers of financial statements claim to face problems in the implementation process due to a lack of implementation guidance and uniform interpretations (Jermakowicz & Gornik-Tomaszewski, 2006).
The argument of increased market efficiency is based on the assumption that a uniform set of accounting and reporting standards will work in the same way wherever it might be implemented. However, some prior studies (Soderstrom & Sun, 2007; Holthausen, 2009) question whether it is sufficient to implement uniform standards to achieve comparability of financial statements between different countries. For instance, Soderstrom & Sun (2007) show that accounting quality differ across countries, which is explained due to institutional differences, and thus conclude that differences in accounting quality are likely to remain after IFRS adoption. Holthausen (2009) similarly suggests that country-specific institutional settings might have an even more important impact on the financial reporting outcome than the reporting standards themselves.
3.2 Asymmetric information and lease accounting Companies across different industries have different strategies of lease usage. Certain industries might experience larger benefits from lease usage than others, which causes sectoral differences in lease intensity (Smith & Wakeman, 1985; Finucane, 1988; Adams & Hardwick, 1998). According to Finucane (1988), companies in air transport and retail industries use more lease financing than others. Adams & Hardwick (1998) find that companies in service and utilities sectors use more leases, while for example construction companies have a low tendency to lease.
For a long time, there has been a trend of increased use of operating leases, while the same development cannot be seen in finance leasing (Abdel-Khalik, 1981; Imhoff & Thomas, 1988; Duke et al., 2009). Due to the different accounting treatment of finance and operating leasing, the trend of increased operating leasing has been commonly interpreted as companies deliberately using operating leases to hide assets and liabilities from the balance sheet, so called
7 “off-balance sheet financing” (Abdel-Khalik, 1981; Reither, 1998; Duke et al., 2009). Off- balance sheet financing can have a favorable effect on different financial measures, e.g. debt- to-equity leverage (D/E), enterprise value (EV) and return on assets (ROA). Hence, a strategy of off-balance sheet financing has the potential to create large information asymmetries between company management and different stakeholders, why the arguments for similar accounting treatment of all leasing have grown stronger (Imhoff et al., 1993; Duke et al., 2009).
Previous studies investigate off-balance sheet financing by estimating the effects from lease capitalization and by analyzing how information disclosed in notes respectively recognized in the balance sheet is used by stakeholders (e.g. Imhoff et al., 1993; Duke et al., 2009; Giner & Pardo, 2018). Imhoff et al. (1991, 1997) introduce a model for estimating the effects on the financial statements called the constructive capitalization model2. Using this model, they examine how investors respectively executive compensation committees in the USA seem to utilize disclosed information differently (Imhoff et al., 1993), which points towards an existence of information asymmetries. Their findings suggest that investors are utilizing the disclosed information of operating leases when assessing the riskiness of a firm’s shares, but no evidence is found of executive compensation committees adjusting reported numbers with disclosed information about operating leases when establishing CEO compensation (Imhoff et al., 1993). Duke et al. (2009) also argue that companies, by using operating leases, can hide large amounts of liabilities and assets from investors, as well as report more favorable net income, hence creating large information asymmetries.
Further, Ahmed et al. (2006) and Davis-Friday et al. (1999) also show evidence of that disclosed information is not necessarily utilized to the same extent as recognized information, however not specifically examining information on leasing. Analyzing information on fair value of derivative financial instruments in US companies’ reporting, Ahmed et al. (2006) present evidence of significant valuation coefficients on recognized derivatives, but not on disclosed derivatives. Their conclusion is therefore that recognized information contains more utilizable information than disclosed information, thereby suggesting that disclosure and recognition is not to be seen as substitutes. Same conclusions are drawn by Davis-Friday et al. (1999) using information on anticipated liabilities for retiree benefits in US companies.
2 A model for estimating the size of the effects on assets, liabilities and net income from capitalization of the noncancelable commitments embodied in a firm's operating leases. The model was developed by Imhoff et al. (1991) to investigate the effects of unrecorded assets and liabilities on profitability ratios and leverage ratios.
8 However, there are also prior studies showing somewhat contradicting findings, indicating that information in disclosed amounts is not per se less utilized than in recognized amounts. Bratten et al. (2013) suggest that market participants in the US capital markets do in fact utilize disclosed and recognized information on operating leases similarly, as long as the information in disclosed items is reliable and not based on management estimates. Similar results are found by Giner & Pardo (2018) for Spanish firms in the retail sector. Further evidence on similar treatment of recognized and disclosed information is provided by Altamuro et al. (2014) in a study of US bank loans, showing that credit agencies are utilizing the disclosed information on operating leases. This implies that, for companies with credit ratings, banks deciding on whether to issue a loan or not can access the information on operating leases without it being recognized (Altamuro et al., 2014).
Concluding this debate on whether or not capitalization of operating lease expenses is needed and valuable, there is some contradicting evidence regarding how different stakeholders are utilizing disclosed information (e.g. Davis-Friday et al., 1999; Ahmed et al., 2006; Bratten et al., 2013; Altamuro et al., 2014). Even so, there are indications of an existence of some information asymmetries following evidence of not all stakeholders being able to utilize the disclosed information uniformly (Imhoff et al., 1993). However, the level of information asymmetry of course also depends on aspects such as the specific firm’s governance model, and legal and institutional factors (Jensen & Meckling, 1976).
3.3 Effects of capitalizing operating leases Even though IFRS 16 was first implemented in 2019, there are a multitude of prior studies estimating the effects from capitalization of operating lease expenses (e.g. Imhoff et al., 1991, 1997; Durocher, 2008; Fülbier et al., 2008; Duke et al., 2009; Wong & Joshi, 2015; Morales- Díaz & Zamora-Ramírez, 2018a, 2018b). These studies are performed in different national settings and are based on information disclosed in notes in the financial statements, showing significant effects on financial statements and different financial ratios. The following sections summarize the findings of recent studies on North American, Australian and European markets, compiled by balance sheet effects and profit and loss statement effects.
3.3.1 Balance sheet effects Due to the aim of lease capitalization being to reduce off-balance sheet financing, the primary anticipated effects are on balance sheet items. This is estimated in studies using adjusted
9 versions of the constructive capitalization model (Imhoff et al., 1991) on North American, Australian as well as European capital markets.
In a large study of US companies from the S&P index, Duke et al. (2009) show a significant mean increase in total liabilities (11.13%) and total assets (3.97%). The study also analyzes ratios such as D/E, showing an increase in the mean value of approximately 13% (Duke et al., 2009). Durocher (2008) shows similar results in a study on large Canadian companies, with median increases in total liabilities (6.0%) and in total assets (2.6%). Durocher (2008) further shows findings such as increased D/A ratio and decreased current ratio, following the balance sheet effects (Durocher, 2008), and show indications of some sectoral differences. In an industry specific study, using a lease intensive sample of US retail companies, Mulford & Gram (2007) show significant increases, larger than the studies with broader samples (e.g. Duke et al., 2009; Durocher, 2008), with median increases in total liabilities (26.4%) and total assets (14.6%). A median increase in the D/E ratio (26.4%) is also emphasized, as well as a decrease in ROA (-1.7%) (Mulford & Gram, 2007).
In a study of the Australian capital market, using a sample of 107 companies, Wong & Joshi (2015) identify significant effects in the form of a mean increase in total liabilities (4.34%), a mean increase in total assets (3.47%) and a mean decrease in total equity (-0.27%). Financial ratios such as D/E and ROA are analyzed as well, showing a relative increase in the mean value of D/E (31.69%), and a relative decrease in the mean value of ROA (-15.35%).
In the European context, Fülbier et al. (2008) investigate a German sample of 90 companies from three major German indices, showing a median increase in total liabilities of 17.3% and in non-current assets of 8.5%. Financial ratios such as D/E and ROA are further analyzed, with a median change in relative terms of 8.0% respectively -0.3%. The study further identifies retail and fashion industries as particularly affected by the lease capitalization, and for example natural resources and energy as quite unaffected (Fülbier et al., 2008). Compared to other studies from the same period of time, Fülbier et al. (2008) seem to present effects of a rather large magnitude, something that is explained by the usage of individual discount rates for each company, lower than the uniform rates used by many other studies (e.g. Duke et al., 2009; Branswijck et al., 2011).
Branswijck et al. (2011) investigate country-specific differences in a study of lease capitalization in Belgium and the Netherlands. For the total sample, including companies from
10 both countries, mean increases in total liabilities of 5.80% and in total assets of 3.00% are identified, as well as an increase in the mean value of D/E ratio of 8.4%, a decrease in the mean value of current ratio of -3.5%, and no material changes in ROA (Branswijck et al., 2011). In order to test country-specific differences, Branswijck et al. (2011) perform a regression analysis with the estimated lease liability as the dependent variable and country and industry as two of the independent variables. They are able to show significant impact from both industry and country (Branswijck et al., 2011).
Morales-Díaz & Zamora-Ramírez (2018a, 2018b) are investigating lease capitalization effects on a sample of European respectively Spanish companies, using a model based on the final version of IFRS 16 instead of a more general constructive capitalization model. In a study of more than 600 European companies (2018a), Morales-Díaz & Zamora-Ramírez show effects of a median increase of 11.2% in total liabilities and of 5.2% in total assets. In a similar study of Spanish companies (2018b), median increases in total liabilities respectively total assets are shown of 6.5% respectively 3.8%
Morales-Díaz & Zamora-Ramírez (2018a) also show a median increase in the D/E ratio of 14.9%, a median increase in the D/A ratio of 4.9% and actually a median increase in ROA of 0.7%. Morales-Díaz & Zamora-Ramírez (2018b) show both similar and different effects for the Spanish sample: a median increase in D/A of 2.5% but a median decrease in ROA of -1.7% (however the mean value is an increase). Both studies (2018a, 2018b) identify significant differences between sectors. Balance sheet effects are particularly intense in Retail and Hotels (2018a, 2018b) as well as Foods and Transportation (2018a), while Financial and Real Estate companies experience the smallest changes (2018a, 2018b).
3.3.2 Profit and loss statement effects Due to the balance sheet effects of increased assets and liabilities, effects on EBITDA follow from the replacement of operating lease expenses with depreciation and interest expenses. Recent studies (e.g. Mulford & Gram, 2007; Fülbier et al., 2008; Duke et al., 2009; Morales- Díaz & Zamora-Ramírez, 2018a, 2018b) investigate effects on the profit and loss statements following the example of Imhoff et al. (1997), but what measures being analyzed (EBITDA, EBIT, Net income, or included in ratios such as ROA) vary between studies. In this section, findings from studies presenting profit and loss statement effects, together with literature pointing to practical implications, are presented.
11 In the US setting, Duke et al. (2009) report P&L effects divided by positive and negative income subgroups: a mean increase of Net income for the negative income subgroup of 3.59% and a mean decrease for the positive income subgroup of -5.12%. Mulford & Gram (2007), in their study of US retail companies, do not report effects on Net income, but are instead reporting a median increase of EBITDA of 22.5%. In the European setting, Fülbier et al. (2008) investigate the P&L effects on EBIT in German companies, reporting a median increase of 2.9%, and investigate effects on the earnings-related valuation multiple P/E, without observing any significant effects. The studies on European and Spanish samples (Morales-Díaz & Zamora- Ramírez, 2018a, 2018b) are using both EBIT and EBITDA in the calculations of the ratios ROA and Financial expenses coverage, mentioning increases of EBITDA but not reporting the magnitude of the profit and loss statement effects separately.
Following the estimated effects in the profit and loss statement, as well as the effects in the balance sheet mentioned earlier, financial ratios and multiples using these measures as input would be also affected by the IFRS 16 adoption. One such multiple is the valuation multiple EV/EBITDA, affected by changes in EBITDA and total liabilities. According to Pinto et al. (2019), performing a large global scientific survey on professional equity analysts to cover equity valuation practices, EV/EBITDA is together with P/E the most commonly used valuation multiple among practitioners. Loughran & Wellman (2011) further stress the practical importance of the EV/EBITDA multiple by showing empirical evidence of the multiple being able to explain realized stock returns, thus arguing that the common use of the multiple by practitioners is justified.
3.4 Hypotheses development Based on the theory of information asymmetry and agency problems (Akerlof, 1970; Spence, 1973; Stiglitz, 1975; Jensen & Meckling, 1976), new accounting standards such as the IFRS are issued to decrease information asymmetries (Hoogervorst, 2016; IFRS Foundation, 2019a, 2019b). Following the attention that lease accounting and off-balance sheet financing have drawn in the last decades, IFRS 16 was, even though heavily criticized, implemented in 2019, requiring operating leases to be recognized in the balance sheet. Recent literature on North American, European and Australian markets (e.g. Duke et al., 2009; Fülbier, 2008; Wong & Joshi, 2015) estimate increases in total liabilities, total assets and EBITDA following the capitalization of operating leases. Some studies also indicate that the effects from the final IFRS 16 standard might be even larger than the effects estimated using older capitalization models
12 (Morales-Díaz & Zamora-Ramírez, 2018a, 2018b). In order to analyze the effects on the financial statements of Swedish publicly listed companies, we formulate our first hypothesis:
H1: The implementation of IFRS 16 increased total liabilities, total assets and EBITDA of Swedish publicly listed firms.
Prior research shows differences in the usage of leasing in different sectors (Smith & Wakeman, 1985; Finucane, 1988; Adams & Hardwick, 1998). Recent literature also shows that different sectors exhibit largely different lease intensity when it comes to operating leases, and estimate larger effects from capitalization in some sectors than others; retail and hotels being the most commonly identified lease intensive industries (Mulford & Gram, 2007; Fülbier, 2008; Morales-Díaz & Zamora-Ramírez, 2018a, 2018b). In this context, we formulate a second hypothesis:
H2: The implementation of IFRS 16 affected total liabilities, total assets and EBITDA of Swedish publicly listed firms differently across sectors.
It has been debated among practitioners (e.g. Accounting Today, 2013) as well as in academia (e.g. Duke et al., 2009) whether or not lease capitalization is contributing to external users of financial statements by enabling utilization of previously hidden information. While there is evidence of some financial statement users in fact being able to utilize information disclosed in notes similarly to information being recognized (Bratten et al., 2013; Altamuro et al., 2014; Giner & Pardo, 2018), there is also evidence indicating that this is not the case (Ahmed et al., 2006; Davis-Friday et al., 1999), as well as research showing that not all stakeholders are able to utilize disclosed information about leasing uniformly (Imhoff et al., 1993). Following that the effects investigated in hypotheses 1-2 would cause changes to the input in key financial ratios, known to be commonly used by practitioners in risk assessment (Imhoff et al., 1993; Fülbier et al., 2008) and company valuation (Loughran & Wellman, 2011; Pinto et al., 2019), we formulate a third hypothesis:
H3: The implementation of IFRS 16 caused changes in the D/E ratio and EV/EBITDA multiple of Swedish publicly listed firms.
13 4. Methodology
This study investigates the IFRS 16 effects on financial statements and key financial ratios of Swedish publicly listed firms. IFRS 16 was implemented in fiscal years starting in 2019, meaning that companies that do not use calendar year as fiscal year are in some cases not presenting their first full year of implementing IFRS 16 until late 2020 (i.e. after this paper is presented). However, in the last annual reports using IAS 17, all companies are required to disclose the transitional numbers of going into IFRS 16 reporting: the effects caused by addition of a right-of-use asset and a lease liability in the opening balances of the next fiscal year. In order to achieve the aim of the paper, we analyze the effects from IFRS 16 by examining the moment of transition from IAS 17 to IFRS 16.
4.1 Research design Based on the underlying aim of IFRS 16 to reduce off-balance sheet financing, and through analysis of recent literature on lease capitalization (e.g. Duke et al., 2009; Morales-Díaz & Zamora-Ramírez, 2018a), we have identified three primary financial measures that capture the IFRS 16 effects on the financial statements: total liabilities, total assets and EBITDA. Total assets and liabilities are directly affected by the inclusion of a right-to-use asset and a lease liability, and EBITDA is affected from the replacement of the prior operating lease expenses, included in EBITDA, with the depreciation of right-of-use assets and the interest expenses from the lease liability, both excluded from EBITDA. Analyzing the effects on these three measures in the full sample respectively by sector enable us to answer the first and third hypothesis of the study. Thus, these measures imply a valid method of achieving the aim of the study.
In order to approximate the implications to financial statement users, we will also calculate effects on a leverage ratio (D/E) and a valuation multiple (EV/EBITDA), two ratios directly affected by the changes in the financial statements. Due to its relevance in credit ratings and risk assessments, measuring companies’ ability to cover outstanding debt with shareholder equity, the effect on the D/E ratio is analyzed in prior research on lease capitalization (Durocher, 2008; Fülbier et al., 2008; Duke et al., 2009; Wong & Joshi, 2015; Morales-Díaz & Zamora- Ramírez, 2018a). The EV/EBITDA multiple is, according to Pinto et al. (2019), together with P/E the most commonly used multiple valuation models. Since the IFRS 16 adoption is expected to increase both EV (following increased liabilities) and EBITDA, this valuation multiple would also be affected (PwC, 2019). By using one measure commonly used in prior studies together with one additional measure identified to be practically relevant and likely to
14 be affected, we assess our method of approximating the implications to financial statement users to be valid and contributing to the field of lease accounting research.
4.2 Data and sample The sample of this study is taken out of the total population of firms publicly listed on the Nasdaq OMX Stockholm main market – including Large Cap, Mid Cap and Small Cap – as of 2019-12-31. The population as of 2019-12-31 is identified to be the most relevant population due to that most companies are reporting full year effects of IFRS 16 in fiscal years ending at that date.
Out of a total population of 338 companies, we identify 59 companies needed to be excluded in order to get a sample where all companies share important institutional settings that respond to the aim of this study. These 59 companies are excluded out of six different aspects: being delisted or declared bankruptcy in early 2020 (thus losing future relevance to analysts), not being listed at the date of transition into IFRS 16, not being required to implement IFRS 16 due to not reporting consolidated numbers, having a non-Swedish parent companies, or being an investment company classified to the Financials sector (these companies can consolidate the numbers of group companies from a multiple of sectors, hence making analysis of sectoral differences blurry). Following these exclusions, the full sample consists of 279 companies and is distributed according to table 1.
Table 1: Sample exclusions
Large Cap Mid Cap Small Cap Total Total population 100 138 100 338 Exclusion1-4 2 7 5 14 Exclusion5 14 13 3 30 Exclusion6 7 6 2 15 Full sample 77 112 90 279
Notes: 1: Companies delisted in January 2020 (before publishing annual report 2019), 2: Companies declaring bankruptcy early 2020, 3: Companies not listed 2019-01-01 (the date of the IFRS 16 implementation), 4: Companies not implementing IFRS 16 due to no consolidated reporting, 5: Groups having a non-Swedish parent company, 6: Investment companies categorized as Financials
This study is investigating effects from IFRS 16 and its implications to practitioners, as well as sector-specific effects. In order to analyze sectoral differences, the sample is categorized into 10 sectors using the Global Industry Classification Standard (GICS); the classifications are retrieved from Nasdaq. GICS is a globally used system for classification of companies into sectors and industries using a total of 11 sectors (MSCI, 2020), of which 10 are used by Nasdaq
15 Nordic as the Real Estate sector is grouped into the Financials sector (Nasdaq Group, 2020). The usage of this global system in the study facilitates comparisons with studies in other countries. Since four of the sectors, however, only contained a few companies in our sample, we modified the GICS classifications by reclassifying the four smallest sectors into one larger sector group called Others (see table 2). After the reclassification, the smallest sector consists of 18 companies, which is assessed to be a sufficient sample size for the non-parametric tests performed in this study (Pallant, 2016, p. 183).
Table 2: Sector classification
Sector (GICS) Sector reclassification Large Cap Mid Cap Small Cap Total
Consumer Goods Consumer Goods 8 12 5 25 Consumer Services Consumer Services 7 14 8 29 Financials Financials 21 16 4 41 Health Care Health Care 6 23 22 51 Industrials Industrials 24 31 28 83 Technology Technology 2 15 15 32 Basic Materials 6 0 5 11 Oil & Gas 1 1 1 3 Others Telecommunications 2 0 1 3 Utilities 0 0 1 1 Sum of all sectors 77 112 90 279
Note: The table consists of the number of firms in the sample divided by sector (before and after reclassification) and by market segment.
To retrieve the data needed to perform the calculations of the IFRS 16 effects, data is manually collected from annual and quarterly reports, the Nasdaq website (www.nasdaqomx nordic.com) and the database Retriever. Retriever is used for reported balance sheet and income statement items. To retrieve the data of transitional balance sheet items, as well future non- cancellable operating leasing fees, annual reports of 2018 or 2018/2019 for each company are used, complemented by annual or Q1 reports from 2019 in cases where sufficient information is missing in the annual reports of 2018 (2018/2019). Information of market capitalization, as
of the year-end, is collected from the annual reports or from Nasdaq.
4.3 Statistical methods of hypothesis testing To be able to properly measure the effects from the IFRS 16 transition, we make some important assumptions described in the following section. First, when calculating the change in total liabilities and assets (i.e. the total liabilities and assets post-implementation), we are using the transitional effects from the addition of right-of-use assets and lease liabilities reported by the
16 companies in the annual reports of 2018 (2018/2019). The reporting of transitional effects is similar, but not exactly uniform, among the firms in the sample. Some differences can exist between, for example, the reported right-of-use asset and the effect on total assets, following that prepayments and deferred tax assets etc. can also be affected by the IFRS 16 adoption (see examples in Appendix A). In many cases, companies provide information explicitly stating the effects on total assets and liabilities. In some cases, however, companies do not explicitly clarify if the reported right-of-use assets and lease liabilities differ from the effects on total assets and liabilities (Appendix A). If such differences exist, they are likely to be small, why the amount of the reported right-of-use assets and lease liabilities are in such cases treated as effects on total assets and liabilities. A small degree of uncertainty thereby exists, due to this lack of information. However, we assess this to be the most valid and reliable method of measuring the effects, since the alternative to using reported numbers would be to use a model of estimation for the full sample. By using the firms’ reported numbers, based on actual discount rates and similar assumptions, a contribution is made to the field of research by measuring more precise effects than prior studies (e.g. Fülbier et al., 2008; Durocher, 2008; Duke et al., 2009).
Second, since transitional numbers are only available for balance sheet items, another approach needs to be taken regarding EBITDA. The aim is to analyze the effects in 2019 (2019/2020) – using information available for the fiscal year ending at the date of transition – and the effect is assessed to be mainly the removal of operating lease expenses (Mulford & Gram, 2007). Some companies do, in the annual reports of 2018 (2018/2019), report their own estimations of the IFRS 16 effect on the upcoming year’s EBITDA. In these cases, the reported numbers are used in the estimation of EBITDA post-implementation. In most annual reports, however, estimated effects on EBITDA are not reported and due to this, we estimate the EBITDA post- implementation by removing the non-cancellable operating lease expenses, reported in the annual reports of 2018 (2018/2019), to be paid within the following year (see examples in Appendix A). Since the removal of these fees from the operating expenses are the main regulatory changes in EBITDA, inflicted by IFRS 16, this method of estimation is assessed to be very similar to the company’s own estimations in the cases where these are reported.
If a company would enter into a new lease contract after the publication of the annual report, this amount’s effect on EBITDA would of course not be covered by our estimation. However, since the numbers used are from the year-end closest to the IFRS 16 implementation, we assess these reported amounts to be the most valid proxies for estimating the effect. By making the estimation of EBITDA post-implementation from EBITDA pre-implementation, adjusted only
17 for the regulatorily mandated removal of non-cancellable operating leasing fees, we isolate the effect caused by IFRS 16.
Following the procedures described above, the effects (%) on total liabilities, total assets and EBITDA are calculated according to the following equations:
Total assets post implementation : (%) = 1 Total assets pre implementation 𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄 𝟏𝟏 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 − Total liabilities post implementation : (%) = 1 Total liabilities pre implementation 𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄 𝟐𝟐 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 − Estimated EBITDA post implementation : (%) = 1 EBITDA pre implementation 𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄 𝟑𝟑 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖𝑖𝑖 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 − When calculating the change in the D/E ratio, potential transitional effects on equity also need to be regarded. Such effects exist due to the IFRS 16 standard enabling different methods of calculating the transitional balance sheet effects, of which some are making retroactive enforcement, causing transitional effects also to the opening balance of equity. In some cases, effects on equity can also follow from making provisions for uncertain tax positions. Because of these reasons, reported effects on the opening balance of equity is included in the calculation of changes in D/E:
/ : / (%) = 1 / Total liabilities post implementation Equity post implementation 𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄 𝟒𝟒 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖𝑖𝑖 𝐷𝐷 𝐸𝐸 Total liabilities pre implementation Equity pre implementation − Enterprise value (EV) is commonly defined as Total liabilities + Market capitalization - Cash & Cash equivalents (Loughran & Wellman, 2011). The effect on EV/EBITDA is calculated as following, in line with equation 1-4:
: / (%) =
( & )/ 𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄 𝟓𝟓 𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖𝑖𝑖 𝐸𝐸𝐸𝐸 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 1 ( & )/ Total liabilities post implementation + Market capitalization − Cash Cash Equivalents 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
Total liabilities pre implementation + Market capitalization − Cash Cash Equivalents 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑝𝑝𝑝𝑝𝑝𝑝 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 − Using these equations, the effects are calculated as relative changes in percentages for each firm in the sample, in line Morales-Díaz & Ramírez-Zamora (2018a, 2018b). Using SPSS, the data is examined as a full sample and divided per sector, arriving at a mean and a median change for each type of change, along with minimum and maximum values etc. for each group.
18 In order to test the hypotheses of this study, statistical testing is needed. When concluding if a significant difference exists between two populations, such as before and after a change, the Student’s t-test is commonly used. However, this test requires the tested samples to be normally distributed, an assumption needed to be examined before performing any statistical testing. Whether or not our full sample of percentage changes in total assets, total liabilities, EBITDA, EV/EBITDA and D/E is normally distributed is examined using the Test of Normality in SPSS (table 3). For a sample to be assessed as normally distributed, it needs to have a significance (p) value above 0.05 according to either the Kolmogorov-Smirnov or Shapiro-Wilk test (Pallant, 2016, p. 65), depending on sample size. Since our sample consist of 279 (>50) cases in each calculation, the Kolmogorov-Smirnov test is used for interpretation. To assess normality, skewness and kurtosis also need to be examined. According to Pallant (2016, p. 58) skewness and kurtosis values should be close to 0 if the data is normally distributed. If being larger than the absolute value of 1, the data is generally assessed not to meet the assumptions of a normal distribution.
Table 3: Tests of normality for the full sample of firms
Tests of Normality Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig. Skewness Kurtosis Change in total assets % 0.299 279 0.000 0.462 279 0.000 5.476 39.286 Change in total liabilities % 0.305 279 0.000 0.437 279 0.000 6.801 64.57 Change in EBITDA 2018 % 0.396 279 0.000 0.202 279 0.000 11.348 147.586 Change in EV/EBITDA % 0.326 279 0.000 0.356 279 0.000 8.045 121.772 Change in D/E % 0.31 279 0.000 0.427 279 0.000 6.599 58.163
Note: The table consists test statistics for of all five measures analyzed in the study. It presents the test statistic, the degrees of freedom and the significance level of the tests of normality of the respective measure. In this study, it is only the Kolmogorov-Smirnov test of normality that is used for interpretation. The table also presents values of skewness and kurtosis.
From table 3, it is evident that our sample of percentage changes is clearly non-normally distributed. For all five measures, the Kolmogorov-Smirnov significance value is 0.000 (<0.05) and the sample is largely skewed (all measures >5) and has a high kurtosis value (all measures >30). These findings are in line with our expectations, based on prior studies (Fülbier et al, 2008; Morales-Díaz & Ramírez-Zamora 2018a, 2018b) concluding that changes in total assets, liabilities etc. due to lease capitalization are not generally normally distributed. Following that our sample is skewed for all measures, this study mainly focuses on interpretation of the median values in our findings, rather than the mean changes. Median is a non-parametric statistic in opposite to the parametric mean, which can be heavily distorted by skewness (Pallant, 2016, p.
19 58). Differences between the two values do, however, tell something about the distribution in our sample and because of this, these differences are also discussed in chapter 5.
Since the sample is not normally distributed, non-parametric tests are needed to address our hypotheses. For the purpose of testing differences between pre- and post-implementation numbers (hypotheses 1 and 3), the Wilcoxon signed-rank test is used, in line with previous studies (Fülbier et al., 2008; Morales-Díaz & Zamora-Ramírez, 2018a, 2018b). This test is considered to be the non-parametric alternative to the repeated measures t-test, and it is designed to measure the same participants or data under two different conditions, i.e. using dependent samples (Pallant, 2016, p. 197). The Wilcoxon test converts scores to ranks and compares them at Time 1 and Time 2. In the case of this study, a sample of 279 pairs of pre- and post-implementation values in percentages is used. This test is used for determining if the change in total assets, total liabilities, EBITDA, EV/EBITDA and D/E is significantly different from zero (i.e. the pre-implementation period), in the full sample and for each sector.
Through the Wilcoxon test, a z-value is calculated. However, the z-value is based on the sum of signed ranks and not on the mean values of observed changes – as the repeated measures t- test is – hence, the Wilcoxon z-value only helps explain if the median change is significantly positive, negative, or not significant. Following our procedure of calculating differences in the sample (post-implementation minus pre-implementation), a significantly positive Wilcoxon z- value implies a median increase in the post-implementation data, and a significantly negative z-value implies a median decrease. Important assumptions for using the Wilcoxon signed-rank test are that the observations are paired and consist of the same participants or companies being re-tested on different occasions (i.e. dependent samples), that the pairs are independent, and that the data is continuous and measured on at least an interval scale (Pallant, 2016). Our data meet these assumptions. Hence, the Wilcoxon signed-rank test can be used for testing our first and third hypothesis.
For the purpose of testing sectoral differences in the post-implementation data (hypothesis 2), the Kruskal-Wallis test is used. The Kruskal-Wallis test is a non-parametric statistical test for comparing scores between three or more groups. Similar to the Wilcoxon signed-rank test, the Kruskal-Wallis test does this by converting the scores to ranks. By comparing the mean ranks of the different groups, the test is able to determine if the groups are significantly different from each other (Pallant, 2016, p. 199). The Kruskal-Wallis test statistics do not by themselves explain anything about which groups are significantly different (Pallant, 2016). However, SPSS
20 have a built-in function of post hoc-testing in the form of pairwise comparisons, available when performing a Kruskal-Wallis test. This test is used in this study (table 7 in chapter 5). Post-hoc testing of differences between two independent samples can also be performed in a separate function, using the Mann-Whitney u test (Pallant, 2016).
When performing multiple pairwise tests, like this study does, the alpha values need to be adjusted using the Bonferroni adjustment. The adjustment is made by dividing the alpha value (alternatively multiplying the p-value) with the number of pairwise tests performed (Pallant, 2016, p. 202). In the built-in pairwise comparison of the Kruskal-Wallis test samples that is used in this study, SPSS is automatically making Bonferroni adjustments to all p-values. Following that the Kruskal-Wallis test is a non-parametric test, the test assumptions are similar to the Wilcoxon signed-rank test, however, requiring independent samples (e.g. post- implementation effects in different sectors) opposite to the Wilcoxon signed-rank test.
4.4 Methodological considerations A common criticism of studies on effects from regulation is that it can be difficult to isolate the effect from the investigated variable when time is an issue. When observations are made in different time periods, other variables than the identified are likely to also affect the financial statement measures between period 1 and period 2. Hence, it is uncertain to what extent the measured change is an effect of the investigated variable. In this study, this problem is managed through analysis of the moment of transition, i.e. measuring the difference between for example 2018-12-31 and 2019-01-01. In normal circumstances, there are no differences between the closing balances of year 1 and the opening balances of year 2. Hence, when differences between the two periods are observed, the effect from IFRS 16 is isolated quite precisely.
Concerning the relative change in EBITDA, the above problem is more applicable. By using the moment of transition for collecting information on the effects on EBITDA in the upcoming year, as well as using the most recently reported EBITDA pre-implementation, we assess this problem being sufficiently managed. However, criticism could be expressed regarding that the EBITDA of the last year of IAS 17 might not be fully representative for the hypothetical EBITDA according to IAS 17 in the first year after the implementation of IFRS 16. It could also be criticized that new lease contracts could be entered or pro-longed during the year post- implementation. Hence, when using the non-cancellable contracts as of the transition date, this could underestimate the effect in the full year.
21 Following that our sample is quite small when divided into sectors, problems could occur in the significance testing. The first action taken into consideration is the reclassification of the four smallest sectors (described in section 4.2). Even so, the sample sizes of some sectors could still be criticized for being problematically small (<30) for performing significance testing (Pallant, 2016). However, this is mainly the case when performing parametric tests, e.g. the Student’s t- test. By using non-parametric tests, e.g. the Wilcoxon signed-rank test and the Kruskal-Wallis test, smaller sample sizes can be used due to that a normal distribution is not a required assumption.
22 5. Results
In the following sections, our findings regarding the effects from the IFRS 16 implementation in Swedish publicly listed firms are described and the three hypotheses are answered.
5.1 Results from the full sample The full sample results, following the equations of change in total assets, total liabilities, EBITDA, EV/EBITDA and D/E described in the methods section, are presented in table 4 below. Presented are the mean and median values, together with standard deviation, minimum and maximum values, as well as the Wilcoxon z-values.
Table 4: Descriptive statistics of the full sample of firms Measure Δ Tot. assets Δ Tot. liabilities Δ EBITDA Δ EV/EBITDA Δ D/E N 279 279 279 279 279 Median 4.7% 9.5% 11.2% -6.4% 9.6% Mean 10.1% 21.7% 39.6% -9.9% 22.3% Wilcoxon z 14.478** 14.478** 14.478** -11.466** 14.478** Min 0.1% 0.1% 0.1% -206.8% 0.1% Max 190.5% 499.5% 2133.3% 587.6% 499.5% Sd 19.1% 42.2% 149.6% 44.3% 44.6%
Notes: The table consists of statistics for all five measures analyzed in this study. It presents the number of cases (N), the median and mean values, the Z-value of the Wilcoxon signed-rank test, the minimum and maximum values observed, and the standard deviation (Sd). * Significant on the 5% level ** Significant on the 1% level
5.1.1 Effects on financial statements For the three measures of change in financial statements – total assets, total liabilities and EBITDA (table 4) – there are major effects following the implementation of IFRS 16. The median effect of relative increase is larger for total liabilities than for total assets but are both smaller in magnitude than the observed median increase in EBITDA. Further, all three measures show larger mean increases than median increases, a difference caused by the existence of high maximum values in all three measures. The maximum values, ranging between 190.5% and 2133.3% for the three measures, deviate much more extensively from the median values than the minimum values of 0.1% do. This indicates that, for a number of firms, the implementation of IFRS 16 did completely change the structure of the financial statements, more than doubling the size of the liabilities. These findings are interesting from the perspective of Duke et al.s (2009) argument of information previously being withheld from stakeholders, a topic further discussed in chapter 6. However, the hypothesis testing in this study is performed by interpreting the median values only.
23 The differences between the relative increases in total assets and total liabilities are mainly attributable to the assets, pre-implementation, being larger than the liabilities. The actual amount of the new assets (mainly right-of-use assets) added through the IFRS 16 adoption were in most cases the exact same as the amount of the added liabilities (mainly lease liabilities), following that most companies have chosen the most simplified method of transition. A small number of companies in the sample did report small differences between added assets and liabilities, often due to them using methods of transition including retroactive calculations, causing some effects also on the opening balances of equity. The observed differences between the percentage change of total assets and liabilities are, however, still mainly attributable to the larger size of the pre-implementation assets.
The relative change in EBITDA is largely depending on the pre-implementation size of EBITDA. Following that some companies such as growth companies or companies in financial distress might report very low EBITDA in relation to expenses, such as from leases, the wide range of results is not surprising. Indeed, the maximum 5 values of relative changes in EBITDA all consist of companies reporting a positive or negative EBITDA close to zero. Even though a wide range is evident, the full sample consists only of positive changes (i.e. increases compared to pre-implementation). Hence, the findings are in line with our expectations.
Considering the results of the Wilcoxon signed-rank test, the median changes in total assets, total liabilities and EBITDA are all positive and significant on the 1% level. The reason behind the Wilcoxon z-values being the same in all three measures (table 4) is because it is based on ranks, and 14.478 is the largest possible z-value in a sample of 279 pairs of cases. This result follows from all cases showing the same sign (i.e. an increase). The results enable us to confirm our first hypothesis and conclude that the implementation of IFRS 16 caused a significant median increase in total liabilities, total assets and EBITDA of Swedish publicly listed firms.
5.1.2 Effects on key financial ratios For the measures of change in key financial ratios, there is a negative median change in the EV/EBITDA multiple and a positive median change in the D/E ratio (table 4). As with the effects on the financial statement measures, there are wide ranges in the data and following this, there are distinct differences between mean and median values. The change in the D/E ratio is in all respects (median, mean and range) very similar to the change in total liabilities, following that equity is in most cases not affected on the day of transition to IFRS 16. As briefly discussed above, effects (mainly decreases) on equity do exist in a number of companies, following a
24 choice of a less simplified method of transition, and this is the cause of the small differences that do exist between changes in the D/E ratio and in total liabilities.
The observed median change in the EV/EBITDA multiple is negative, in contrast to the four other measures. As previously shown, the median relative increase in EBITDA is larger than the relative increase in total liabilities (affecting EV), which gives a first explanation to the negative change in the multiple. Further, since the nominator (EV) is affected not only by total liabilities, but also of market capitalization and cash and cash equivalents, the relative change of EV is smaller than the relative change of total liabilities. Since such a mitigating effect does not exist in the denominator (EBITDA), this further explains the observed decrease in the EV/EBITDA multiple. Another specific feature for the EV/EBITDA multiple is that the range of data consists of both positive and negative observations. However, the relation between the mean and median change is still in line with the other measures; the mean change being larger than the median change, which brings further clarity to the distribution of the sample and the variations in effects between firms. However, only the median change is used for hypothesis testing.
Considering the results of the Wilcoxon signed-rank test of the changes in the EV/EBITDA multiple and the D/E ratio, they are both significant on the 1% level. The change in EV/EBITDA has a negative z-value and the change in D/E has a positive z-value, thus confirming a significant median decrease in EV/EBITDA and a significant median increase in D/E. These results enable us to confirm our third hypothesis and conclude that the implementation of IFRS 16 caused significant median changes in the D/E ratio and EV/EBITDA multiple of Swedish publicly listed firms.
5.2 Results by sector The results are presented by sector in table 5 below, following the same procedures as the calculations presented from the full sample in table 4. Presented are the mean and median values, minimum and maximum values, as well as the Wilcoxon z-value. Examining the different median values in the sectors gives an indication of an existence of sectoral differences, and these differences are statistically tested using the Kruskal-Wallis test (table 6). In table 5, the sectors are sorted by the size of the median change in total liabilities, sorted largest to smallest. The largest and the smallest observed median effects for all five measures are highlighted in bold in the table.
25 Table 5: Descriptive statistics by sector Δ Tot. Sector Measure Δ Tot. assets liabilities Δ EBITDA Δ EV/EBITDA Δ D/E Median 20.2% 39.6% 67.0% -22.5% 39.6% Mean 40.2% 73.9% 124.1% -36.0% 78.5% Consumer Services (n=29) Wilcoxon Z -4.703** -4.703** -4.703** -4.681** -4.703** Minimum 1.7% 3.9% 3.6% -201.2% 3.9% Maximum 190.5% 499.5% 1100.4% 0.6% 499.5% Median 5.0% 16.5% 6.9% -2.2% 16.5% Mean 9.2% 29.2% 21.4% -9.7% 29.6% Health Care (n=51) Wilcoxon Z -6.215** -6.215** -6.215** -3.009** -6.215** Minimum 0.1% 0.3% 0.3% -206.8% 0.3% Maximum 54.2% 232.0% 194.7% 25.7% 232.0% Median 5.5% 13.0% 18.7% -10.6% 13.0% Mean 6.7% 15.9% 34.9% 3.1% 16.0% Technology (n=32) Wilcoxon Z -4.937** -4.937** -4.937** -3.852** -4.937** Minimum 1.6% 4.0% 0.7% -68.0% 3.1% Maximum 19.7% 47.2% 264.9% 587.6% 47.2% Median 5.9% 11.2% 16.9% -10.1% 11.3% Mean 7.7% 14.9% 53.1% -12.8% 15.1% Industrials (n=83) Wilcoxon Z -7.913** -7.913** -7.913** -7.114** -7.913** Minimum 0.2% 0.7% 0.2% -95.3% 0.7% Maximum 25.2% 98.2% 2133.3% 80.4% 98.2% Median 4.3% 9.8% 12.8% -7.0% 9.8% Mean 8.6% 17.2% 19.8% -5.9% 17.3% Consumer Goods (n=25) Wilcoxon Z -4.372** -4.372** -4.372** -3.700** -4.372** Minimum 1.0% 1.4% 1.6% -39.4% 1.4% Maximum 34.8% 69.8% 76.0% 88.2% 69.8% Median 0.9% 2.4% 3.1% -1.7% 2.4% Mean 3.4% 6.4% 8.8% -2.1% 6.4% Others (n=18) Wilcoxon Z -3.724** -3.724** -3.724** -2.243* -3.724** Minimum 0.2% 0.3% 0.3% -17.5% 0.3% Maximum 26.7% 46.1% 53.5% 22.8% 46.1% Median 1.0% 1.8% 1.8% -0.5% 1.8% Mean 1.6% 2.8% 4.1% -1.9% 2.9% Financials (n=41) Wilcoxon Z -5.579** -5.579** -5.579** -3.427** -5.579** Minimum 0.1% 0.1% 0.1% -13.8% 0.1% Maximum 9.1% 16.4% 26.7% 4.6% 16.4%
Notes: The table consists of statistics for all five measures analyzed in this study. The statistics are calculated from all 279 firms in the sample, which is divided into 7 sector groups. The largest and smallest sector median values for each measure are highlighted in bold. * Significant on the 5% level ** Significant on the 1% level
26 5.2.1 Effects on financial statements For the three measures of effects on financial statements (total assets, total liabilities and EBITDA), the observed effects per sector are similar to the effects observed in the full sample. The relative change in EBITDA is in most sectors larger than the change in total liabilities, in turn being larger than the change in total assets. The mean change is also, for most sectors, larger than the median change. Further, the observed effects are for all sectors significantly different from zero on the 1% level according to the Wilcoxon signed-rank test. As is evident from the median values in table 5, the sectors show differences in observed effects. When comparing the sector most affected in relative terms, Consumer Services, with the sector that in most respects is least affected, Financials, the observed differences are large. In Consumer Services, the observed median change in total liabilities is 39.6% and in Financials, the same effect amount to 1.8%. Further, a median value of 16.5% in total liabilities indicate that Health Care could be the sector secondly most affected, but sectors such as Technology and Industrials also show material changes, with median increases in total liabilities of 13.0% and 11.2%.
In the Consumer Services sector, there are companies operating in for example retail, hotels, foods and education. These companies all have extensive rental contracts in common, such as for stores, hotels and schools. Examining a couple of examples, the retail company Hennes & Mauritz can be found with a relative increase in total liabilities and EBITDA of 121.4% respectively 57.2% and the education company Internationella Engelska Skolan with relative increases of 499.5% respectively 131.9%. On the other end of the spectrum, there is the Financials sector, consisting mainly of real estate companies and banks. These two groups of companies have large pre-implementation assets and liabilities in common, as well as few lease contracts as lessees. Examining a couple of examples, the real estate company Hufvudstaden can be found with relative increases in total liabilities and EBITDA of 3.6% respectively 1.6% and Swedbank with relative increases of 0.2% respectively 3.0%.
Although indications of sectoral differences are evident from the varying median effects in table 5, statistical testing of the differences is needed to answer the second hypothesis. In order to confirm such differences, a Kruskal-Wallis test is performed on the seven sectors. The results are presented in table 6 below. What is indicated by the sectoral differences in median values, presented in table 5, is statistically confirmed by the Kruskal Wallis test, presented in table 6.
27 Table 6: Kruskal-Wallis test statistics of sectoral differences
Δ Tot. Measure Δ Tot. assets liabilities Δ EBITDA N 279 279 279 Test statistic 96.466** 98.548** 92.254** Degrees of freedom 6 6 6
Notes: The table consists of the three measures used to analyze the effects on financial statements. It presents the number of cases (N), the test statistic of the Kruskal-Wallis test, and the degrees of freedom. * Significant on the 5% level ** Significant on the 1% level
Sectoral differences exist in the effects on all three measures and are significant on the 1% level. These results enable us to confirm our second hypothesis and conclude that the implementation of IFRS 16 affected total liabilities, total assets and EBITDA of Swedish publicly listed firms significantly different across sectors. Following that the test statistics in table 6 do not explain which sectors are significantly different from other sectors, post-hoc testing of pairwise sectoral comparisons is performed, as described in chapter 4.3. The findings are presented in table 7.
Table 7: Pairwise comparison of sectors
Consumer Health Consumer Sectors Services Care Technology Industrials Goods Others Financials Consumer Δ A: Sign. Δ A: Sign. Δ A: Sign. Δ A: Sign. Δ A: Sign. Δ A: Sign. #N/A Services Δ L: Δ L: Δ L: Sign. Δ L: Sign. Δ L: Sign. Δ L: Sign. Δ E: Sign. Δ E: Δ E: Sign. Δ E: Sign. Δ E: Sign. Δ E: Sign. Health Δ A: Sign. Δ A: Δ A: Δ A: Δ A: Δ A: Sign. #N/A Care Δ L: Δ L: Δ L: Δ L: Δ L: Sign. Δ L: Sign. Δ E: Sign. Δ E: Δ E: Sign. Δ E: Δ E: Δ E: Sign. Δ A: Sign. Δ A: Δ A: Δ A: Δ A: Sign. Δ A: Sign. Technology Δ L: Δ L: #N/A Δ L: Δ L: Δ L: Sign. Δ L: Sign. Δ E: Δ E: Δ E: Δ E: Δ E: Sign. Δ E: Sign. Δ A: Sign. Δ A: Δ A: Δ A: Δ A: Sign. Δ A: Sign. Industrials Δ L: Sign. Δ L: Δ L: #N/A Δ L: Δ L: Sign. Δ L: Sign. Δ E: Sign. Δ E: Sign. Δ E: Δ E: Δ E: Sign. Δ E: Sign. Consumer Δ A: Sign. Δ A: Δ A: Δ A: Δ A: Δ A: Sign. #N/A Goods Δ L: Sign. Δ L: Δ L: Δ L: Δ L: Δ L: Sign. Δ E: Sign. Δ E: Δ E: Δ E: Δ E: Δ E: Sign. Δ A: Sign. Δ A: Δ A: Sign. Δ A: Sign. Δ A: Δ A: Others Δ L: Sign. Δ L: Sign. Δ L: Sign. Δ L: Sign. Δ L: #N/A Δ L: Δ E: Sign. Δ E: Δ E: Sign. Δ E: Sign. Δ E: Δ E: Δ A: Sign. Δ A: Sign. Δ A: Sign. Δ A: Sign. Δ A: Sign. Δ A: Financials Δ L: Sign. Δ L: Sign. Δ L: Sign. Δ L: Sign. Δ L: Sign. Δ L: #N/A Δ E: Sign. Δ E: Sign. Δ E: Sign. Δ E: Sign. Δ E: Sign. Δ E:
Notes: The table presents a summary of the pairwise comparison of sectors and shows whether a difference between two sectors is significant or not. The three measures used to analyze the effects on financial statements are presented in the table. Δ A: Change (%) in total assets, Δ L: Change (%) in total liabilities, Δ E: Change (%) in EBITDA Sign. = Significant difference on at least the (Bonferroni adjusted) 5% level.
28 In the pairwise comparison of sectors, significant differences are observed between multiple sectors. This provides further insights to the already confirmed existence of sectoral differences. In at least one of the three observed measures, the Consumer Services sector significantly differs from all other sectors. Similarly, the Health Care sector is significantly different from four of six other sectors, but not significantly different from the Technology and Consumer Goods sectors in any observed measure. Further, the Technology sector is significantly different from three other sectors, the Industrials sector from four other sectors, the Others sector from four other sectors, and the Financials sector is significantly different from all sectors except the sector called Others. The Consumer Goods sector, being the sector significantly different from the lowest number of other sectors, is only significantly different from the two most affected sectors: Consumer Services and Financials.
5.2.2 Effects on key financial ratios The observed effects per sector on the D/E ratio are in general similar to the pattern found in the full sample, with observed median and mean effects in D/E being very similar to the corresponding effects in total liabilities (table 5). One interesting exception however exists, the Consumer Services sector, showing an absolute difference of 4.6% between mean changes in D/E and total liabilities (78.5% respectively 73.9%). This finding indicates that the companies in the Consumer Services sector have been more likely than companies in other sectors to use the transitional method of retroactive enforcement, causing a negative effect on the opening balance of equity (6 of 29 companies).
The observed effects on the EV/EBITDA multiple show some important sectoral divergences. While all seven sectors show negative median effects, in line with the median effect observed in the full sample, one sector (Technology) show a positive mean effect and one sector (Consumer Goods) show a larger median decrease than mean decrease, opposite to the relation observed in the full sample. This is explained by the existence of positive values (i.e. increases), observed in all sectors but apparently especially influential in the two sectors mentioned. Examining the observations in more detail, there is an occurrence of increases in EV/EBITDA (i.e. higher multiple values post-implementation) in 43 cases, mainly concentrated to the Financials and Health Care sectors (table 8).
29 Table 8: Increases in EV/EBITDA Observations of increases Sector in EV/EBITDA Consumer Goods 1 Consumer Services 1 Financials 10 Health Care 18 Industrials 5 Technology 4 Others 4 Total 43
Note: The table consists of the amount of observations in each sector where EV/EBITDA is larger post-implementation than pre-implementation, i.e. where an increase is observed.
Increases in the EV/EBITDA multiple, caused by the IFRS 16 implementation, are observed in cases where the relative increase of total liabilities widely exceeds the relative increase in EBITDA. Such a situation would mainly be explained by one of two situations: companies reporting a very high EBITDA pre-implementation, or companies having lease contracts extending over such a long period of time that the IFRS 16 effect on EBITDA in a single year becomes a small or insignificant portion of the lease liability.
Having identified these significant effects on key financial ratios, but with sectoral differences in median effects as well as indications of some sectoral differences in methods of transition to IFRS 16, we are able to provide a wider perspective to the effects from which the second and third hypotheses were confirmed.
30 6. Discussion
In the following sections, the findings of this study are discussed in relation to prior research on lease accounting, information asymmetries and implementation of accounting standards, presented in chapter 3.
6.1 Effects on financial statements
6.1.1 Results from the full sample From our full sample of Swedish publicly listed firms, we are able to confirm our hypothesis of significant increases in total assets, total liabilities and EBITDA due to the capitalization of operating leases under IFRS 16. These results are in line with our expectations, based on the implicit purpose of IFRS 16 being to add off-balance sheet amounts to the balance sheet, as well as what prior studies have predicted (e.g. Mulford & Gram, 2007; Fülbier et al., 2008; Duke et al., 2009; Morales-Díaz & Zamora-Ramírez, 2018a, 2018b). Our results from the full sample of firms are a median (mean) increase in total assets of 4.7% (10.1%), in total liabilities of 9.5% (21.7%) and in EBITDA of 11.2% (39.6%).
The magnitude of effects on total assets and liabilities in our study can be compared with most prior studies (Fülbier et al., 2008; Durocher, 2008; Duke et al., 2009; Branswijck et al., 2011; Wong & Joshi, 2015; Morales-Díaz & Ramírez-Zamora 2018a, 2018b). As described in chapter 3.3.1 though, not all studies use the exact same model for capitalization, and different studies have somewhat varying assumptions regarding aspects such as discount rates, following different samples with different country-specific settings. Of course, the economic settings also change over time, thus further distorting the comparison. With this in mind, the magnitude of effects in different studies is interesting to compare, so that the reasons behind such differences can be discussed and understood.
Compared with most prior studies (Durocher, 2008; Duke et al., 2009; Branswijck et al., 2011; Wong & Joshi, 2015), the balance sheet effects in this study (table 4 in chapter 5) are of a larger magnitude. The German study performed by Fülbier et al. (2008) deviates from the previously mentioned studies by not using a uniform discount rate for all firms in the study, and is reporting median increases in non-current assets and total liabilities larger in magnitude than what is observed in our study – however, not using the exact same measure for assets. The most recently performed studies discussed in chapter 3, those by Morales-Díaz & Zamora-Ramírez (2018a,
31 2018b) on European respectively Spanish companies, report effects quite closely aligned to our results.
As discussed by Branswijck et al. (2011) and Morales-Díaz & Zamora-Ramírez (2018a, 2018b), country-specific settings can lead to differences in the magnitude of effects. Hence, this is one likely explanation to the quantitative differences between our results and the results in prior studies (e.g. Durocher, 2008; Fülbier et al., 2008; Duke et al., 2009; Branswijck et al., 2011; Wong & Joshi, 2015). However, as pointed out by Morales-Díaz & Zamora-Ramírez (2018a), modelling differences between earlier studies and the methods suggested in IFRS 16 can be significant. This is therefore a likely explanation to our results, consisting of actual effects from the IFRS 16 adoption, being specifically similar to the studies using a model aimed at being aligned to the final IFRS 16 model in the aspects of lease term estimations and levels of discount rates (Morales-Díaz & Zamora-Ramírez, 2018a, 2018b). What is assessed to be a reasonable level of discount rate is of course something that changes over time, along with economic settings in general. Hence, it is not surprising that the studies performed most recently (ibid.) are the ones presenting results most closely aligned with ours.
Increases in EBITDA are quite inevitable, following that IFRS 16 require reclassification of the leasing fees reported as operating expenses under IAS 17. Although recent literature on lease capitalization is not always presenting the magnitude of effects on EBITDA (e.g. Durocher, 2008; Fülbier et al., 2008; Duke et al., 2009; Branswijck et al., 2011; Wong & Joshi, 2015), our results (table 4 in chapter 5) should not be seen as contradicting these findings. Mulford & Gram (2007) present a median EBITDA increase of 22.5% among retail companies and Morales-Díaz & Zamora-Ramírez (2018a, 2018b) discuss increases in EBITDA as an important reason to the observed changes in the key financial ratios they present. Even though the study by Mulford & Gram (2007) might not be representable to a wider range of studies, focusing only on retail companies, it is still an interesting comparison that leads into our findings of sectoral differences. The median increase observed by Mulford & Gram (2007) is significantly larger than the median increase in our full sample, a relation that is in line with our results of sectoral differences. Retail companies are included in the Consumer Services sector in our study, which is the sector experiencing the largest effects on EBITDA.
6.1.2 Results by sector By testing the effects on firms grouped by sectors, we are able to confirm our hypothesis of companies being affected differently across sectors (table 6 in chapter 5). The two sectors that
32 are most respectively least affected in most respects are Consumer Services respectively Financials, with for example median increases in total liabilities of 39.6% respectively 1.8%. The existence of sectoral differences, as well as these two sectors being the ones experiencing the largest respectively smallest magnitude of effects, are results in line with prior studies (Mulford & Gram, 2007; Fülbier et al., 2008; Morales-Díaz & Zamora-Ramírez (2018a, 2018b). In our study, Consumer Services primarily include retail, hotels, foods and education firms. Retail is also identified as particularly affected by Mulford & Gram (2007) and by Fülbier et al. (2008). It is also identified as a particularly affected industry by Morales-Díaz & Zamora- Ramírez (2018a, 2018b), together with hotels, foods and transportation firms. In our study, the Financials sector consists mainly of real estate companies and banks, which is also the groups of companies identified by Morales-Díaz & Zamora-Ramírez (2018a, 2018b) to be the least affected. Fülbier et al. (2008) present natural resources and energy as particularly unaffected, and although these are not companies included in the Financials sector, the Swedish counterparts are included in the Others sector of this study, a sector also identified as experiencing small effects (table 5 in chapter 5).
The reasons for the sectoral differences discussed above can of course first and foremost be attributed to the lease intensity of the operations. For Consumer Services firms, rental of stores is often a central part of the business. Companies in the Financials sector, however, do not do business that similarly require having the role of a lessor. These explanations are in line with Smith & Wakeman (1985) who point out larger benefits from leasing in some sectors than others, and with Finucane (1988) who points out retail as an industry particularly dependent on lease financing.
Apart from these different operational business requirements, affecting the number of lease contracts and the lease fee amounts, the length of the contract periods and the level of discount rates are also affecting what amounts are to be capitalized. Following that firms within a sector are commonly experiencing somewhat similar needs and benefits of leasing (Smith & Wakeman, 1985), it is likely they end up with quite similar lease contracts. Following this logic, a comparison of the attributes of different sectors would likely imply different average contract periods. Examining two examples: The Consumer Services company Internationella Engelska Skolan probably wants a longer contract period for the lease of a school facility, than the Financials company Swedbank wants for the lease of cars and offices. Similarly, if aspects such as alternatives of financing are more uniform within a sector than across sectors, this would imply that the level of discount rates to be used in capitalization of leases would also be
33 somewhat harmonized sector-wise. This is something that could enhance sectoral differences and could therefore help explain the underlying aspects of the sectoral differences observed in this study.
6.2 Implications for financial statement users
6.2.1 Effects on key financial ratios As described in chapter 3, recent literature on lease capitalization presents significant effects on key financial ratios. Which exact ratios that are investigated (e.g. D/E, D/A and ROA), and how these are defined and measured, differ somewhat between studies (e.g. Fülbier et al., 2008; Duke et al., 2009; Wong & Joshi, 2015; Morales-Díaz & Zamora-Ramírez, 2018a). However, being aware of these differences, it is interesting to compare the findings of our study with the findings of these prior studies (ibid.), to enable further understanding of the implications for financial statement users. Our results from the full sample show of a median (mean) increase in the D/E ratio of 9.6% (22.3%) compared to a median (mean) increase in total liabilities of 9.5% (21.7%). This effect on the D/E ratio is larger than that observed by Fülbier et al. (2008), Duke et al. (2009) and Branswijck et al. (2011). However, our observed effects on the D/E ratio are smaller than the median (mean) increases of 14.9% (32.1%) observed by Morales-Díaz & Zamora-Ramírez (2018a). The reported D/E effect in our study is also smaller than the reported effect in Wong & Joshi (2015). However, Wong & Joshi’s (2015) presented increase in D/E of 31.7% is the relative increase in the mean value of the full sample and not the mean/median increase, as measured in our study and in most other prior studies (Mulford & Gram, 2007; Durocher, 2008; Fülbier et al., 2008; Morales-Díaz & Zamora-Ramírez, 2018a, 2018b). Hence, these are not truly comparable effects.
The observed relation between the change in D/E and in total liabilities, following effects on equity, is an interesting object of comparison. The results of our study are in this aspect similar to the result of most prior studies (Fülbier et al., 2008; Duke et al., 2009; Branswijck et al., 2011; Morales-Díaz & Zamora-Ramírez, 2018a, 2018b), showing a larger increase in D/E than in total liabilities, due to a median decrease in equity being observed. However, the relation between the change in D/E and in total liabilities differs somewhat between our study and prior studies (ibid.). Taking the median (mean) effect on D/E in our study divided by the effect on total liabilities results in a relation of approximately 1.01 (1.03). Duke et al. (2009), Branswijck et al. (2011) and Morales-Díaz & Zamora-Ramírez (2018a) show corresponding relations of approximately 1.2-1.5, while Fülbier et al. (2008) show a relation above 2. These prior studies
34 assume some differences between the amounts of the right-of-use asset and the lease liability, thereby presenting changes also on equity. However, as is seen in our sample, many companies chose a simplified method of transition into IFRS 16, valuing the right-of-use asset and the lease liability to similar amounts, thus not causing any effects on equity.
The relation between the change in D/E and total liabilities, observed in our study, indicate that the effects on equity might be overestimated in prior studies (e.g. Fülbier et al., 2008; Duke et al., 2009; Branswijck et al., 2011; Morales-Díaz & Zamora-Ramírez, 2018a, 2018b), at least when isolated to the moment of transition. Of course, effects on equity do later on occur as the relation between right-of-use assets and lease liabilities change over the contract period, as well as when the P&L measures are being affected, hence any conclusions of prior studies overestimating effects of equity must be limited to the moment of transition. Also, the choice of transitional method could be something that varies between countries.
Prior studies on lease capitalization do in most cases not investigate changes in EV/EBITDA explicitly (Mulford & Gram, 2007; Durocher, 2008; Fülbier et al., 2008; Duke et al., 2009; Branswijck et al., 2011; Wong & Joshi, 2015; Morales-Díaz & Zamora-Ramírez, 2018a, 2018b). However, following that these studies are confirming changes in the multiple’s inputs of balance sheet and P&L measures, such effects can be assumed to be, to some extent, present also in these samples. The lack of presented numbers does, however, make a comparison of observed magnitude impossible. A comparison that is possible, however, is with the findings of the P/E multiple not being significantly changed (Fülbier et al., 2008). This is an interesting comparison, following Pinto et al.s (2019) presentation of EV/EBITDA and P/E being the most commonly used valuation multiples. Our findings of significant effects on EV/EBITDA suggest that, in order to utilize the benefits of the two multiples in the way it was done before IFRS 16, and to be able to analyze companies retroactively, practitioners would have to make new adjustments following that the multiples seem to be affected differently.
The observed median effect on EV/EBITDA multiple is a decrease of -6,4%. However, within the sample, 43 cases of increases in EV/EBITDA are also identified, primarily concentrated to the Health Care and Financials sectors (table 8 in chapter 5). The reasons behind the existence of increases in the EV/EBITDA multiple, as well as the sectoral concentration, are interesting to analyze in order to understand the attributes of specific sectors and the Swedish market. As described in chapter 5.2.2, increases in the EV/EBITDA are likely caused by one of two situations: companies reporting a very high EBITDA pre-implementation, or companies having
35 lease contracts extending over such a long period of time that the IFRS 16 effect on EBITDA in a single year becomes a small or insignificant portion of the lease liability. If such features were not easily identified in the disclosed information prior to IFRS 16, they might have been causing some information asymmetries that would now have been mitigated by the new reporting standard.
In the Health Care sector, we find two possible explanations to the increases in EV/EBITDA, from analyzing the annual reports. One explanation is the existence of long-term rental contracts (>5 years) and inclusion of pro-longing options in the capitalization calculations. Another important feature, more specific for the Health Care sector, is that all companies showing increases in EV/EBITDA are running with large losses and negative EBITDA. Because the calculation of change in EBITDA is based on absolute numbers, this has the same effect as when a company reports a very high EBITDA.
In the Financials sector, we find one important, sector-specific, explanation to the increases in EV/EBITDA. A majority of the companies in the Financials sector are real estate companies, mainly involved in lease contracts as a lessor. They are, however, involved in some lease contracts as a lessee, mainly so-called leasehold agreements (i.e. ground rents). Leaseholds have an ‘infinite’ contract period, for which the fees are renegotiated every 10 or 20 years, between the lessee and the municipality owning the leased piece of land. These leasehold agreements are thus capitalized using a much longer lease period than most lease contracts in other sectors, hence causing the relative increase in total liabilities to widely exceed the relative increase in EBITDA. Using Large Cap company Wallenstam as an example: Future, non-cancellable operating lease payments are disclosed in the annual report of 2018 as 27 MSEK per year in 2019-2022, and as 1005 MSEK in the period “2023 and later”, implying usage of a contract period of approximately 40 years (Wallenstam, 2019).
6.2.2 Impact on information asymmetries Providing further insights to the debate on the informational content in disclosed versus recognized information and different stakeholders’ ability to utilize these different kinds of information in financial reports (Imhoff et al., 1993; Davis-Friday et al., 1999; Ahmed et al., 2006; Duke et al., 2009; Bratten et al., 2013; Altamuro et al., 2014; Giner & Pardo, 2018), our findings suggest some important implications for the financial statement users. By clarifying that key financial ratios such as the D/E ratio and the EV/EBITDA multiple – indeed important for many stakeholders (Loughran & Wellman, 2011; Pinto et al., 2019; PwC, 2019) – are
36 significantly changed by the implementation of IFRS 16, and by following the argument of information about operating lease expenses being important to stakeholders such as investors and banks (Bratten et al., 2013; Altamuro et al., 2014; Giner & Pardo, 2018), we are able to make the following assessments:
The implementation of IFRS 16 seems to have either provided some stakeholders with additional information (regarding discount rates, pro-longing options etc.) or, if all stakeholders were already able to retrieve and utilize such information, decreased the need for manual company- or sector-specific adjustments of key financial ratios. Hence, even though it cannot be concluded whether IFRS 16 have been necessary or reasonable from a cost-benefit perspective, we do suggest it to be likely that the information asymmetry regarding operating leasing, between company management and their external stakeholders, is mitigated by the IFRS 16 reporting. These implications are quite similar to what is predicted by IASB Chairman Hans Hoogervorst (Hoogervorst, 2016), anticipating both additional information for some stakeholders, and a reduced need for manual adjustments for others. Hence, from the perspective of the standard-setter, it is likely that IFRS 16 is so far considered successful, reaching the aim of the standard, mitigating information asymmetries and enabling more uniform interpretations by external stakeholders. However, there might be too early yet to conclude the level of success of the uniformity in implementation of IFRS 16 across countries and sectors, relating to the overall purpose of IFRS.
6.2.3 The implementation process As described in chapter 3, differences across countries in financial reporting is often the case, even though a uniform set of standards is used (Jermakowicz & Gornik-Tomaszewski, 2006; Soderstrom & Sun, 2007; Holthausen, 2009). Similarly, as estimated by Branswijck et al. (2011), the effects from adopting a new accounting standard such as IFRS 16 are affected by the industry in which a firm is operating. Following Soderstrom & Suns (2007) and Holthausens (2009) arguments, differences in lease accounting across countries are likely to remain, even once IFRS 16 has settled. Also, as presented in chapter 5.2 of this study, and in line with Branswijck et al. (2011) among others, there are more industry- and sector-specific features than just different amounts of operating leasing fees and number of leasing contracts causing these differences in effects from the IFRS 16 adoption. There are also aspects such as variations in transitional methods, assessments regarding discount rates and contract lengths to consider.
37 An interesting observation regarding such differences are the different forms of transitional methods, such as including retroactive implementation to a varying extent, used by companies and suggested by IASB (2016). The existence of such differences in transition could of course be seen as decreasing comparability and quality of the financial reports. However, the fact that IASB allows such different simplification methods should maybe be seen as a way of addressing problems in implementation of prior IFRS standards, being too complex and burdensome, as emphasized by Jermakowicz & Gornik-Tomaszewski (2006). As these method differences are primarily attributable to the moment of transition, impairment of reporting quality and comparability might be mitigated following that the implementation might have gone smoother than with prior standards. Of this, one can of course not be certain without qualitative investigation. Following prior criticism (Accounting Today, 2013) and the fact that IFRS 16 implies larger changes than many prior IFRS standards, it could however also be the case that firms experience this implementation of IFRS 16 as particularly complex and burdensome.
38 7. Conclusions
The aim of this study is to investigate how the implementation of IFRS 16 is affecting the financial statements of Swedish publicly listed firms, and what implications there are for financial statement users. By measuring transitional effects, we are able to conclude that the implementation of IFRS 16 did increase total assets, total liabilities and EBITDA for all firms in our sample, however, with large variations in magnitude. By comparing different groups of sectors, we show that significantly different effects in the financial statement measures are caused by the implementation of IFRS 16, the Consumer Services sector being most affected and the Financials sector being least affected.
We also show that there are implications for financial statement users, following that key financial ratios are significantly changed, thus requiring an altered use of such measures. The D/E ratio increased for all companies, with a median effect larger than the median effect observed on total liabilities, following negative effects on equity in a number of firms. Further, we show that there are both firms whose implementation of IFRS 16 caused increases and decreases in the EV/EBITDA multiple, although the median effect being a decrease. These findings add to the debate on the usefulness and informational content of lease capitalization and suggest that the changes might have been beneficial to users of financial statements such as analysts, either by increasing the utilizable information or reducing the need for manual adjustments. Our results are thereby relevant to standard-setters evaluating the implementation process and planning for future standards, as well as for practitioners using such ratios in comparisons of different firms and sectors.
In many aspects, our study shows larger effects than many older studies, performed around 10 years prior to our study or earlier (e.g. Fülbier et al, 2008; Duke et al., 2009; Branswijck et al., 2011). This suggests that the changes in institutional settings over time, such as what levels of discount rates are assessed to be reasonable, could be affecting the magnitude of the effects. This would be in line with the argument used by Morales-Díaz & Zamora-Ramírez (2018a) of older models of constructive lease capitalization using discount rates being quite differently from what is proposed in the actual IFRS 16 standard. However, since studies such as Branswijck et al. (2011) present evidence also of country-specific differences, we cannot with our study claim to have isolated any variable causing the main differences in the magnitude of effects from lease capitalization. This is a limitation of our study and something needed to be
39 investigated further. However, our study facilitates future research by bringing light to leasing features specific for Sweden and different sectors.
Both future qualitative and quantitative research on lease accounting would be beneficial. Qualitative studies, aimed at understanding firms’ choices and assessments, such as regarding lease agreements and contract periods, calculation of discount rates and choice of transitional methods, would be beneficial. The same applies to quantitative research establishing the significance of such variables in models aimed at explaining the magnitude of the effects or establishing what factors that create common institutional settings in a country, industry or sector, relevant to accounting for leases. The indications in this study of sectoral differences in choice of transitional method of going into the new accounting standard, where firms in sectors largely affected by IFRS 16 seem somewhat overrepresented in choosing a less simplified method of transition, points to an interesting topic of future studies. Whether such a correlation is truly significant or not needs to be tested properly. Thus, future research investigating this would be beneficial, perhaps with comparisons also with similar transitions into other accounting standards.
Another limitation of this study is that it only confirms the existence of implications for financial statement users. It does not attempt to explain how practitioners experience these implications or what strategies that exist to manage the changes. Thus, further research taking a qualitative approach is needed to fully understand the practical implications of IFRS 16.
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44 Appendix A – Examples of differences in reporting of transitional effects
Below is an excerpt from the Mid Cap firm Swedol’s annual report of 2018, note 1 (Accounting Principles). The note includes reporting of the difference between the amount of the added right-of-use assets (537.2 MSEK) and the effect on total assets (505.9 MSEK), due to reclassification of prepayments (-31.3 MSEK) that were included in the balance sheet before IFRS 16.
The note also includes reporting of the company’s own estimation of the IFRS 16 effects on EBITDA in the full year of 2019. Hence, this amount is used in our calculation of effects on EBITDA.
45 Below are excerpts from Large Cap firm AAK’s annual report of 2018, note 2 (Summary of significant accounting policies). The note includes reporting of the amount of the added right- of-use assets (900 MSEK) but does not explicitly report the effect on total assets. 900 MSEK is therefore treated as the effect on total assets.
Note 2 does not include any reporting of the company’s own estimation of the IFRS 16 effects on EBITDA in the full year of 2019. Therefore, information disclosed in note 28 (attached below) is used for estimation of the effects on EBITDA: 110 MSEK in minimum non- cancellable operating leasing fees to be paid within one year from 2018-12-31.
46