Value relevance of IFRS 16 - A study of the stock market reaction

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Value relevance of IFRS 16 -

A study of the stock market reaction

Master’s Thesis 15 credits

Department of Business Studies Uppsala University

Spring Semester of 2019

Date of Submission: 2019-06-05

Sebastian Björklund

Supervisor: David Andersson

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Abstract

From the 1^st of January companies will start to use the new accounting standard IFRS 16, and by this all leases that companies have will be recognized in the balance sheet. The implementation will have several effects on the balance sheet and the income statement and

therefore change key ratios that investors use. This studies purpose is the examine how the Swedish stock market reacts during the implementation of IFRS 16.

The study uses the event study method for 319 different stock on the Nasdaq OMX Stockholm stock market and uses a regression to see if the changes from IFRS 16 have an effect on the abnormal return. Although, the study finds no significant abnormal return and the implementation has low impact. Hence the study concludes that the operating leases were

already incorporated during before the implementation.

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1. Introduction

Accounting are supposed to show a true picture of how a company's financial status is with the data presented in the financial statements. Leasing of assets are one of the things that are presented in the financial statements. Leasing is an important tool for companies, because through leasing they are able to use leased plants and equipment that otherwise would demand a high amount of cash to obtain (PWC, 2016; Marton, Lundqvist & Pettersson, 2016). Although, leasing has been a controversial and well discussed topic lately since the current standard can be unclear and abused through the classification between operating and financial leases, by structuring the terms so that it can be classified as an operating lease (Hales, Venkatraman &

Wilks, 2012). Around 85 % of all the leasing in the leasing market consists of operating leasing, which means that the exposure to these leases are not shown in the balance sheets (Donkersley, Ravelli and Buchanan (2018). One of the downsides in the past with leasing has been that companies are able to hide how exposed they are to the leasing contract through restructuring of leases, and because of that having low debt in the balance sheet (Hillier, 2013). Companies use operating leasing because it shows a more positive picture of the company through important key ratios connected to the balance sheet, such as return on invested capital (Marton, Lundqvist & Pettersson, 2016, s. 253).

From the 1st of January 2019 companies that follow the International Financial Reporting Standards (IFRS) will have to implement the new accounting standard IFRS 16. This standard will replace the earlier accounting standard International Accounting Standards (IAS) 17. The development of the standard has been driven by both the FASB and IASB and has been going on since 2005, when they started a project where all leases would be recognized in the balance sheet (Marton, Lundqvist & Pettersson, 2016, s. 251). In the earlier IAS 17, companies had a choice to use both operating leasing and financial leasing, where the first only meant that you treated a lease as a cost and the second that you recognized the lease in the balance sheet. With the new IFRS 16 most of the leases will be treated the same and all will be reported in the balance sheet. Studies have earlier shown that there are proof that introduction of new standards in IFRS and IAS have increased the value relevance through higher transparency, effectivity and accountability (Gürarda, 2013; Horton & Sarafeim, 2009). The purpose with IFRS 16 is to better show how much the leases are worth by putting them in the balance sheet and therefore also increase the value relevance. Investors do capitalize the operating leases in their analysis, but assumptions still have to be made to do that.

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2 With the new leasing standard this invisible debt will impact the balance sheet of the companies.

A report by PWC (2018) shows that the average solidity of companies worldwide will decrease from 35 % to 32 % due to the implementation of IFRS 16. IFRS (2016) also released an effect analysis where they mention that companies affected by the implementation of IFRS 16 will have e.g. increased leverage and increased EBITDA. Grefberg (2018) says that multiples will be significantly affected by the implementation, and that it’s important to check if multiples are taken after or before the implementation of IFRS 16 since it could significantly change the outcome of the analysis from the multiples. Demirakos, Strong & Walker (2004) shows that a majority of the analysts uses multiples for their stock analysis, and this is partly because multiples are easy to use and understandable to clients and while looking at multiples information in the notes could be overlooked (Schreiner, 2007). When investors look closely on multiples for analysing their equity investments, the effects from IFRS 16 can have effects in how the investors looks at the company through these multiples.

Companies and countries are differently exposed to the change of accounting standards, and the change of IFRS 16 is no exception. Sweden has the third highest private sector debt as % of GPD in Europe (Eurostat, 2017), and in 2017 Sweden also had the 10^th largest annual volume of leasing in the world and also that Sweden’s leasing penetration, the percentage of investment financed by leasing and hire purchase, was 27.1 % in 2017 (White Clarke Group, 2019;

Leaseeurope, 2017). Due to the fact that earlier studies regarding capitalization shows that if companies with high amount of operating leasing will be affected by IFRS 16 in their financial statements and the fact that Sweden has a significant portion of leases the effects in Swedish companies could be significant. The first reports that uses IFRS 16 was published during the first quarter of 2019. And connecting the use of leases in Sweden to investors only looking at multiples an abnormal return could be found on the day of implementation of IFRS 16 and therefore an event study would be of interest.

1.1 Purpose

To see how the Swedish stock market reacts to the publishing of accounting with the new IFRS 16 Leasing by looking how the implementation affects the price of the stock.

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3 1.2 Disposition

The continuing structure of this study is dispositioned as following. Chapter 2 consists of background and information about the accounting standards where both IAS 17 and IFRS 16 are presented. Chapter 3 consists of the theoretical framework and presents the efficient market hypothesis and debt overhang theory and how it is connected to the introduction of IFRS 16.

This is also the chapter where the hypothesis build-up is done. Further, chapter 4 presents the chosen method to test the hypothesis. In chapter 5 the data and results from the study is presented and finally chapters 6 consists of analysis and conclusion are presented.

2. Accounting standards

In this chapter information about the accounting standards are presented together with the differences of them. Further studies about the implementation of lease capitalization are presented.

The International Accounting Standards board (IASB) and Financial Accounting Standards board are the two biggest organs that are responsible of accounting standards that are released.

IASB gives out accounting standards in the International Financial Reporting Standards, IFRS, and FASB gives out accounting standards in US Generally Accepted Accounting, or US GAAP.

Generally, their mission is to give out standards that improve effectivity, transparency and accountability (IFRS, 2018). Furthermore, their standards are supposed to support long-term investment and financial stability (IFRS, 2018), and lead to more relevant, neutral and consequent accounting without being too costly for the companies using the standards (Marton, 2016). Ball (2006) states that the advantages of adopting IFRS for investors and companies are (a) more comprehensive, accurate and timely financial statements compared to the standard that are used nationally, (b) improved financial reporting quality, (c) more comparable financial reports by reducing the differences, (d) reduced cost for financial reporting and (e) reduced national differences.

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4 2.1 IAS 17

The first kind of lease that is present in the IAS 17 is financial leases (Marton, Lundqvist &

Pettersson, 2016). Financial leases are defined as an agreement where risks and advantages with ownage are transferred to the leaser. This means that the company that is the leaser will show the leased asset in their balance sheet., and the lessee will not show anything in their balance sheet. To determine the value of the leased asset you use the leasing amount that you pay together with an implicit rate r and the time t shown in equation 1 below (Marton, Lundqvist &

Pettersson, 2016).

𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑙𝑒𝑎𝑠𝑒 = ∑ 𝐿𝑒𝑎𝑠𝑖𝑛𝑔 𝑝𝑎𝑦𝑚𝑒𝑛𝑡 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡 (1+r)^𝑡

𝑛𝑡=1 (1)

Operating leases are instead an agreement where risks and advantage of ownage is not transferred to the leaser, but has stayed with the lessee (Marton, Lundqvist & Pettersson, 2016).

Here the leaser does not activate the lease in the balance sheet, but the lessee instead has the asset in their balance sheet.

One of the problems with IAS 17, as earlier stated, was that you had to classify the leasing agreement, if the agreement is of financial or operational nature (Marton, Lundqvist &

Pettersson, 2016). This was hard to regulate and if a lease was close defined close enough to the below definition, it could actually be defined as both operating and financial. The IAS 17 states what the properties of a leasing agreement is below in table 1.

Table 1: Properties for leases

1. the lease transfers ownership of the asset to the lessee by the end of the lease term

2. the lessee has the option to purchase the asset at a price which is expected to be sufficiently lower than fair value at the date the option becomes exercisable that, at the inception of the lease, it is reasonably certain that the option will be exercised

3. the lease term is for the major part of the economic life of the asset, even if title is not transferred

4. at the inception of the lease, the present value of the minimum lease payments amounts to at least substantially all of the fair value of the leased asset

5. the lease assets are of a specialised nature such that only the lessee can use them without major modifications being made

Taken from IAS 17.10

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5 Further, the users argued that IAS 17 lead to lack of comparability since all leases aren’t recognized in the balance sheet (Beattie, Goodacre & Thomson, 2006)

2.2 IFRS 16

The new standard IFRS 16 has now replaced the old standard IAS 17 from the 1st of January.

The purpose with the new IFRS 16 is, as earlier stated, to increase the relevance of the financial reports and clearer showing the leased assets in the balance sheet, making them equal to bought assets (IFRS, 2016b). The new standard will erase the two different distinctions and all leases will now be introduced in the balance sheet, like the earlier financial leases, on both the asset- side and liability-side (IFRS, 2016b). The leases will be shown as “right of use” asset or together with property, plant and equipment in the balance sheet, and as a liability there will be a “lease liability”. The question if a certain contract is considered as a lease is also stated, and to identify a lease you look at following three requirements: If a contract is to be considered as a lease it must: relate to a specific asset, give the leaser the right to control the use of the asset during a period of time and give the leaser economic advantages (IFRS, 2016a; Marton, Lundqvist &

Pettersson 2016). Important here is that service contracts are not considered as leases and should not be presented in the balance sheet (IFRS, 2016b). Service contracts do not fulfill the three requirements stated.

Although there are some circumstances were lease contracts not will be recognized in the balance sheet, which are (1) when the lease agreement is short-term and the period of time is 12 months or under and (2) when the leased assets are of low-value (IFRS, 2016b). In these circumstances the leases will be accounted for the same way as operating leases in IAS 17, where you recognize the expense in the income statement and disclose them if they are material (IFRS, 2016b). The new standard is mainly changing the definition of a lease and what classifications there are. Before there were two different kinds of leases, operating and financial, and you had to classify to which group the lease belonged (IFRS, 2016b). But now, the only focus instead is on whether a lease is a lease (IFRS, 2016b).

2.3 Effects from IFRS 16

Several studies have been made about the effects that would occur if and when IFRS 16 will be implemented. The method that has been used for determining that is either a constructive method (Imhoff, Lipe & Wright, 1991) or a factor method (Moody’s, 2015). The first method

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6 is more widely used in the accounting literature and is the same method for determining the present value as in the accounting standard, meanwhile the second method is more used by credit rating agencies where they multiple the operating lease with a multiple to estimate the present value of the lease (Morales-Diaz & Zamora-Ramirez, 2018).

By first looking at the balance sheet items we can see that there are some significant changes for the companies that is using operating leases in the IAS 17. The exact results differ between different studies, since they are done in different point of times and in different areas, different sectors and the amount of operating leasing can differ (Morales-Diaz & Zamora-Ramirez, 2018). The effects for the balance sheet are summarized in figure 1. The total liabilities will increase between 4.34%-26.4% depending on which study that is examined (Mulford & Gram, 2007; Fulbier et al., 2008; Duke et al., 2009; Wong & Joshi, 2015). Further the total asset will be affected similarly since the lease liability is connected to a leased asset in the balance sheet.

Source: IFRS 16 – Effect analysis (2016)

When examining the significance of the effects on the income statements is similar in the balance sheet (Morales-Diaz & Zamora-Ramirez, 2018). The main reason for the effects is that the leased asset now recognized in the balance sheet leads to a depreciation cost. As shown in figure 2, the earlier cost for the operating lease was seen directly in the EBITDA, but since the cost is now considered as depreciation the EBITDA will be heavily affected in a positive way.

(Mulford & Gram, 2007; Duke et al., 2009). Further, the leased liability comes with an interest expense, and as shown in figure 2 it will affect the EBIT or the operating profit in a positive way (Fülbier et al., 2008).

Figure 1: IFRS 16 effects on the balance sheet

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7 Figure 2: IFRS 16 effects on income statement

Source: IFRS 16 – Effect analysis (2016)

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3. Theoretical framework

In this chapter the effective market hypothesis is explained and the debt overhang theory that will be the theoretical framework for this study. Further, the theory is connected to studies regarding IFRS 16 and the hypothesises are developed.

3.1 The efficient market hypothesis

The efficient market framework says that prices in the market are fully incorporated with the information that is available to the market, which leads to that no investors should be able to receive any additional returns that normal (Fama, 1970; Ross, Westerfeld & Jaffe, 2005). Fama (1970) states that for the market to be effective information must be available for free to everybody in the market, it’s agreed that the current and future price reflects the current information available and that there are no extra transaction fees.

The efficient market hypothesis comes in three forms, weak, semi-strong and strong form (Fama 1970). The weak form states that the market uses all available information but doesn’t use historical data for determining the price, cause all price changes has been random. The semi- strong form says that the market both uses available information and look at historical data. The strong form says that the market instead has all the data and incorporate it into the price, even insider information (Ross, Westerfeld & Jaffe, 2005). Applying this to the implementation of IFRS 16, if a reaction occurs due to the implementation the market could be inefficient since there is basically no new information presented, but it is only presented in another way.

Although, even if analysts are capitalizing operating leases already in their analysis, some assumptions must be made in their equations and the reaction could be related to that.

3.2 Value relevance

When looking at studies done for the value relevance of accounting standards, Barth et al.

(2008) studies the application of IAS in 327 firms in 21 different countries and how that affected the accounting quality in these firms. They also compared firms that used the IAS and firms that didn’t use IAS. They found that the companies that used IAS had higher value relevance and better accounting quality in their financial statement with less earnings management, more

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9 timely loss recognition and higher correlation between accounting figures with share prices and returns.

Ball and Brown (1968) showed that there is a correlation between the accounting information and the capital market where they examine annual income reports and their impact on stock returns. Beaver (1968) also studied the relationship between accounting information and the capital market. He finds a positive correlation between the reported earnings and reaction of volume and stock price. Furthermore, Fama and French (1992) studies the correlation between size and book-to-market ratio the find significant explanatory power on companies’ stock returns. When the accounting is relevant for investors and is correctly presented, then there is a significantly correlation with the stock price (Barth et al. 2001, Hung 2001).

Imhoff et al (1991) studies the importance of long-term operating lease commitments on measures used for determining risk and performance. Managers were able to reconstruct their leases and record their leases as operating leases to avoid capitalization of them. By using the earlier mentioned constructive method for capitalization Imhoff et al. (1997) investigate the effects of unrecorded assets and leverage ratios and return on assets. They show that by capitalizing leases you get a significant change in return on assets and debt-to-equity ratios.

These are ratios often used by investors to determine the financial value of the firm, and also to compare firms within one sector or compare different sectors with one and another. They say that it’s crucial to capitalize operating leases to better show the differences between firms and sectors.

Although, Giner & Pardo (2018) studies how investors value recognised debts versus operating lease liabilities ,from the information in the notes, in the retail sector in Spain. The study recognises the criticism, and the fear from managers of leasing-exposed companies, regarding recognising off balance sheet leases. Their results show that investors equally value recognized debt and operating leases, so they see operating leases as property rights. The study says that their result can give some relief to the managers, since the effects on the investors are insignificant. The conclusion from this could therefore lead to minimized market reaction from the implementation of IFRS 16.

When instead looking at the income statement, there are reactions to adjustments in the financial statements. In one of Gürardas (2013) studies he examines the reaction on the stock market

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10 when companies start to report according to IAS or IFRS for the first time. The study uses an event study on the Turkish market during the reporting period 2005. The studies hypothesis is that the adjustment of the earning will be associated with abnormal return, and the studies result shows that when earnings numbers are adjusted negatively the abnormal return is negative and vice versa for positive adjustments.

Another multiple that analysts widely use is the enterprise multiple (Loughran & Wellman, 2011). The enterprise multiple consists of the enterprise value (equity value + debt + preferred stock – cash) divided by EBITDA. Their findings shows that firms with high enterprise multiple is associated with higher stock returns than firms with low multiple. Although, the study has not stated how they have considered operating leases and since IFRS 16, like earlier stated, affects both debt and EBITDA significantly there could be a market reaction because of the changed valuation of this multiple.

3.3 Debt overhang

Myers (1977) studied the determinants of corporate borrowing and how the amount of debt is dependant on the debt overhang theory. He states that there is a gap in the modern finance theory. If firms generate tax savings from debt borrowing, they should borrow as much as possible to maximize the savings. Meanwhile if the debt is maximized, the default risk is higher and the company and due to this companies will have a harder time to get new profitable loans for new profitable projects and therefore lower the growth of the companies(ibid).

Furthermore, studies have proven the debt overhang theory. Cai & Zhang (2010) studies the US market during a period between 1975 to 2002 where the examine the association between quarterly change of leverage, future investments and monthly stock returns. Firstly, their findings say that an increase in leverage ratio is associated with lower real investment in the future. Results show that an increase of 10 % in leverage ratio is associated with a reduction of 6.23 % in the investment rate and 7.5 % capital expenditures for the next year. The correlation between change in leverage and stock return is negative and significant. Dimitrov and Jain (2008) finds similar findings where they examine change in leverage and the current and next- year stock returns in the US stock market and concludes that change in leverage is value relevant for future stock returns. Although, criticism has been given to these kinds of studies because the measurement for leverage can be biased (Gu, 2008). The studies are only using book value of liabilities and are therefore excluding the off-balance sheet financings items. These off-

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11 balance sheet items are for example operating leases, that is going to be recognised in the new IFRS 16.

Another ratio that credit institutes and analysts are watching is the EBITDA to interest coverage ratio (EBITDA / Interest expense) which shows how well a company can pay their interest expenses from the generated cash flow (Morales-Diaz & Zamora-Ramirez, 2018). As stated earlier in section 2.3, both the EBITDA and the interest expense will increase from the implementation of IFRS 16. Although, Morales-Diaz & Zamora-Ramirez (2018) finds that the interest expense is increasing more relatively than EBITDA, and therefore the EBITTDA to interest coverage ratio will decrease with 13.6 %. This further emphasises that companies earlier exposed to operating leases could have a decreased creditworthiness as stated in the financial statements with the new IFRS 16.

3.4 Hypothesis development

Earlier studies are quite agreed that IFRS 16 will have an impact on both the balance sheet and the income statement and from the implementation key ratios will be adjusted. The balance sheet will be impacted both on the asset and liability-side where the leverage will increase, while the income statement will mostly be positively affected on EBITDA, but also EBIT-level.

Studies show that an increased leverage could lead to negative stock returns, due to decreased capacity of getting new loans for new profitable investments. Meanwhile, when the EBITDA and EBIT is rising, studies show that earnings figures and stock price is positively correlated.

So the increase of EBITDA and EBIT suggests that there could be an increase of valuation in the stock price. But in the same time the increase of leverage and EBITDA to interest coverage ratio could lead to lower valuation of the stock. Meanwhile the enterprise value multiple is affected by both the debt and the EBITDA. Because of the all the changing factors that is pulling the stock price in different directions an abnormal return could be developed.

Hypothesis 1: Adjustments from IFRS 16 is associated with abnormal stock returns.

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4. Method

In this chapter the event study is presented together with the multiple regression analysis for how the result can be statistically tested. Further the data gathering process is presented.

4.1 Event study

This study wants to see how investors are reacting to changes in the balance and income statements, and to do that we look at the change in share price. To see how they react the study follow the method of MacKinlays (1997) to examine eventual abnormal return at the time the changes in the statements are announced. The method is also in line with Woolridge and Snow’s (1990) proposals, that event studies are well suited for quantifying the effects of abnormal return announcements from companies. Marginal information content studies often use event studies to determine the effects of implementation of standards (Holthausen & Watts, 2001).

4.2 Event and event window

The accounting with IFRS 16 started the 1st of January 2019 (IFRS, 2016a). The first reports that will include this standard is therefore the 1st quarterly reports of 2019. There are two different approaches for the transition to IFRS 16, the retrospective and modified retrospective approach (KPMG, 2016). In the retrospective approach you apply IFRS 16 to all financial statements for 2018 after the implementation, but in the modified retrospective you only change the entry book value in the first quarterly report of 2019. In both approaches the first quarter that IFRS 16 will be used is the official statement of the Q1 report of 2019 even though some companies will inform about the effects before that.

Brown and Warner (1985) says that if you use an exact date and day for the event you get higher statistical validity in the event study. Although, Glascock et. al (1987) says that there could be leakage of private information prior to an event which can lead to misleading results. In this case, the effects of the introduction of IFRS 16 are not private and are well known by analysts.

Furthermore, Rao & Sreejith (2014) shows that event studies that have used an exact date has received better results than event studies with a vague period.

MacKinley (1997) states that it’s not only the event date that is interesting in the study. Also the days prior and after the event are of interest. This day span captures if there are offsets in

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13 the time that the investors receive the news from the event, meaning that either the investors has received information in beforehand, or that the investors take a long time to capture the published information. The amount of days that are examined for the event is three, cause no longer than three days are needed (Runesson, 2015; MacKinlay, 1997), and a short period of time is preferable in event studies because it reduces the risk for time series dependence (Binder, 1998). The event window is shown in figure 3 below.

Figure 3: Estimation and event window

4.3 Estimation

The return that would occur if there would be no external influence is called the normal rate of return (MacKinlay, 1997). To determine the normal rate of return you can use the market model (Runesson, 2015; MacKinlay 1997). MacKinlay (1997) look at the differences between the market model and the constant mean return model, and concludes that the market model is better suited for event studies since it better includes the variances of the market. If the regression model get a high R^2 model is stronger for determining abnormal return. With the help of the regression model you estimate the normal return from a window of estimation consisting of 220 days of stock prices the day before the event window shown in figure 3 above.

The model uses a market index to then determine the normal rate of return during the event window. The equation for the regression is presented below in equation 1. Brown & Warner (1985) emphasizes the importance of using a robust index that is well represented by the total exposure of the market, to get a good result. This study will use the OMXSGI, partly because all the stocks that are examined is in that index, and also because it is an accessible index for investors and can be said to replicate a diversified investor on the Swedish stock market.

rt = total return at time t

α = intercept

β = systematic market index risk

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14 r^m,t= return of market index at time t

ε^t = residual, expected to be zero

4.4 Cumulative abnormal return

After determining the normal return, you look at the residual presented in equation 2, which is created during the event window. The abnormal return (AR), shown in equation 3, is the return the stock generates that is abnormal from the expected normal return from the market model (MacKinlay, 1997).

𝑒̂

_𝑡

= 𝑟

_𝑡

−(𝛼̂ + 𝛽̂𝑟

_𝑚𝑡

)

When looking at the abnormal returns, this study looks at it over a certain time and for several different companies. The better grasp both time and quantity, MacKinlay (1997) says that, to get a better indicator of the effect at the event the cumulative abnormal return(CAR) is used which is shown below in equation 4 (Strong, 1992).

Lastly, the definition of return that MacKinlay (1997) states is used, where the return R for a stock is determined by the difference in stock price between two days, divided by the first days stock price. The equation for that is shown below in equation 5.

4.5 Regression model

To test the hypothesis an OLS-regression is done with equation 6.

𝐶𝐴𝑅 = 𝛼 + 𝛽

₁

𝐿𝐸𝑉

_{𝐼𝐹𝑅𝑆16}

+ 𝛽

₂

𝐿𝐸𝑉

_𝑄1

+ 𝛽

₃

𝐿𝐸𝑉

_𝑈𝑁𝐴

(3)

(4)

(5)

(6) (2)

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15

+𝛽

₄

𝐸𝐵𝐼𝑇𝐷𝐴 + 𝛽

₅

𝐿𝑜𝑔𝑇𝐴 + 𝜖

Where,

CAR = cumulative abnormal return

𝛼

= intercept

𝐿𝐸𝑉

_{𝐼𝐹𝑅𝑆16} = adjustment of leverage from IFRS 16

𝑆𝑂𝐿

_𝑄1 = the solidity after IFRS 16, in Q1 2019.

𝐸𝐵𝐼𝑇𝐷𝐴

= the change of EBITDA between the quarters

𝐿𝐸𝑉

_𝑈𝑁𝐴 = the change of leverage, exclusive IFRS 16

𝐿𝑜𝑔𝑇𝐴

= the logarithm of total assets in Q1 2019

Below, in table 2, further description is presented of the variables from the multiple regression analysis are found.

Table 2

Variable Description

Dependant variable

CAR The cumulative abnormal return is calculated through equations xx-xx in sections 4.2-4.4.

Independent variables

𝐿𝐸𝑉

_{𝐼𝐹𝑅𝑆16} The adjustment made in leverage due to the implementation of IFRS 16. Used as the key ratio for how much IFRS 16 affected a company. Gathered from the quarterly report if the first quarter in 2019 where the effects were stated. Ratio is calculated as Debt acquired from IFRS 16/Equity.

𝑆𝑂𝐿

_𝑄1 The solidity of the company after the implementation of IFRS 16 in Q1 2019. This variable is used to test if the debt overhang theory is applicable on the effects.

𝐸𝐵𝐼𝑇𝐷𝐴

The change of EBITDA between Q4 2018 and Q1 2019 divided by the equity of Q4 2018. Not all companies present the effects from IFRS 16 on the EBITDA, therefore the whole change in EBITDA will be used.

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16 Control variables

𝐿𝐸𝑉

_𝑈𝑁𝐴 The change of leverage between Q4 2018 and Q1 2019, exclusive the adjustments made from IFRS 16.

LogTA

The logarithm of total assets in Q1 2019. Widely used as a control variable in regression models.

4.6 Data

This study used several ways of obtaining the data and information that is used. The time period for this study is the when the financial statements are released for the first quarter of 2019. This is the first report that is using the IFRS 16 and therefore it’s natural to have this time period.

The study will use all the listed companies on OMX Stockholm Large, Mid and Small cap in Sweden since these companies are using IFRS as accounting standard. The study is examining all share classes of each company. Some dropout will although occur in this study. Firstly, since the IFRS 16 is implemented in the accounting from 1^st January 2019 some companies will not have to implement it until a time further on. These are companies that have broken fiscal years and they do not have to apply the new standard in the accounting of 2018/2019, but can wait until 2019/2020 to implement it, meaning that it can take up a year from at the time of this study for them to implement it.

The full list of companies listed on Nasdaq OMX Stockholm, together to which sector they are assigned, was gathered from Nasdaq’s official site. The study used the companies that was listed on OMX Stockholm at the point of time when the first quarterly report was released. The date for the quarterly report was gathered from the press release that was released on Nasdaq’s web page. If the market was closed during the release, the next point of time when the market was open was used as date. And if the stock market was open, the same date was used. Further, the accounting data that was used was gathered from Bloomberg consisting of figures from the balance sheet and income statement. Although, the adjustments made from IFRS 16 was gathered manually directly from the quarterly reports that the companies released. Some companies had informed about the effects from IFRS 16 in beforehand in the annual report of 2018 and some in a separate release exclusively for IFRS 16, but the study still used the same date as the quarterly report was released. This is because of the studies assumption of investors

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17 looking more at key ratios, and not doing a comprehensive conclusion from the notes. Lastly the stock price data was gathered from Bloomberg, consisting of the total 220 days of historical prices for each stock and the market index.

4.6.1 Dropout

In the table 3 the dropouts of the study are presented. Bryman & Bell (2013) states that it’s important to examine why there are dropouts in a study and how they can affect the results. A part of the beginning sample dropped out because of the broken fiscal year, cause they don’t need to use IFRS 16 until the broken fiscal year 2019/2020. There were also 6.7 % of the companies that partly didn’t have enough historical accounting data, and partly not enough historical stock price data. There were also I minor portion of companies that hadn’t released their quarterly report at the time of this study. That last quarterly report that was used was released on 29^th of May 2019.

Table 3

Category N %

Sample 319 85.1 %

Broken fiscal year 25 6.7 %

No Q1 report yet 6 1.6 %

Not enough historical data 25 6.7 %

Total 375 100%

4.6.2 Sectors

In the table 4 below the sectors for the used sample is presented. Since the implemententation of IFRS 16 can have different effects on different companies, and therefore also different sector, because of their different exposure against operating leases, it is important to show how the sectors are distributed in this sample. The sectors with the highest representation in the sample are industrials (25,4%) and financials (21,9 %), meanwhile utilities (0,3 %), oil & gas (1,3 %) and telecommunication (1,6 %) have a low representation.

Table 4

Sector N %

Basic Materials 22 6.9 %

Consumer Goods 32 10.0 %

Consumer Services 22 6.9 %

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18

Financials 70 21.9 %

Health Care 48 15.0 %

Industrials 81 25.4 %

Oil & Gas 4 1.3 %

Technology 34 10.7 %

Telecommunications 5 1.6 %

Utilities 1 0.3 %

Total 319 100%

4.7 Pearson’s correlation

To check for multicollinearity between the variables a Pearson correlation test was done. This is to see if independent variables are correlated and therefore could lead to a shifting result. The test looks at the coefficient of the covariance between two variables divided by the standard deviation between the two variables and can be between -1 to 1. If the correlation is 1 the correlation is perfectly positive between the variables and negative if it is -1. The results from the correlation test is presented in table 5. Some coefficients how a significant correlation but are not higher than (-)0.562. Collis och Hussey (2003) states that a correlation below 0.7 is not considered too high. To further investigate how the variables is linked, the study conducts a test of multicollinearity by looking at the mean variance factors (VIF). The results from this is presented in the Appendix, and the VIF for all variables are within the range 1.16-1.80 which is considered low and therefore the variables can be used.

Table 5

CAR LEVIFRS LEVUNA EBITDA SOLQ1 LogTA

CAR Sig

1

LEVIFRS

Sig

0,089 0.115

1

LEVUNA

Sig

-0.074 0.187

-0.038 0.497

1

EBITDA Sig

0.195**

0.000

0.253**

0.000

-0.341**

0.000

1

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19 SOLQ1

Sig

-0.078 0.164

0.330**

0.000

-0.199**

0.000

0.120*

0.032

1

LogTA Sig

0.011 0.844

-0.111*

0.047

0.224**

0.000

-0.176**

0.002

0.327**

0.000

1

’ 5%-significance, ’1%-significance

4.8 Credibility

If a study has validity the study is measuring what actually is supposed to be measured (Bryman

& Bell, 2013). This studies purpose is to study how the implementation of IFRS 16 affects the stock market, and more specifically if there is abnormal return on a company’s stock when the new accounting standard is implemented. Variables that were used are the variables that the implementation of IFRS 16 is affecting and the event study method by MacKinlay (1997) that is used is well known and widely used for these kinds of studies and gives a valid quantitative result for abnormal return. Through this the study can be considered to have a validity.

Furthermore, if a study has good generalisability the results from the study can be transferred to other samples (Bryman & Bell 2013). The sample that is used does not consider the whole Swedish stock market. Firstly, not all companies have released their first report with IFRS 16 because of that they didn’t have to implement it yet cause of their broken fiscal year. Secondly, there are several stock exchanges in Sweden, but this study only used the Nasdaq OMX Stockholm exchange. These two factors could endanger the generalisability of the results.

Additionally, due to the fact that different companies are affected differently by the implementation of IFRS 16 because they don’t use operating leases the same a sample of other companies could have different results. Different sectors are, as earlier mentioned, differently exposed to operating leases, and therefore a different allocation of sectors in the sample could give other results.

Reliability is to which extent a studies results are reliable and if the result would be the same if the study was done again (Bryman & Bell, 2013). This study mainly uses official data, partly from the Nasdaq OMX Stockholm stock exchange, from Bloomberg and directly from the financial statements. All these sources are constantly maintained, checked and reviewed for eventual flaws and can therefore be seen as reliable sources. Both stock prices and figures from the financial statements were automatically gathered from the Bloomberg add-in in Excel, and after that copied to SPSS for analysis, and are therefore not exposed to very little manual handling. Although, all the adjustments for IFRS 16 were gathered manually and there is always

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20 a risk connected to that. But through careful handling, checking if the data was reasonable and a thorough description of the method the reliability of this study can still be considered high.

5. Results and analysis

In this chapter the results from the data gathering and regression analysis will be presented.

Further the analysis of the results will be connected to the theory.

5.1 Descriptive statistics

Table 6 shows the descriptive statistics for the variables used in the regression for the sample of 320 stocks. The results show that on average the cumulative abnormal return was -0.14 % during the implementation of IFRS 16. The table also shows the minimum, maximum, median and standard deviation for all the variables. This table shows that there is no indication that the implementation has made a cumulative abnormal return whether in a single positive or negative way, but instead the cumulative abnormal return seems to be both positive and negative. In the control variable for unadjusted leverage we see both a low minimum of -6.7028 and a high maximum of 22.0587, which was observed in companies with very low equity which gives a significant change in leverage when the debt changes. For the control variable SOLQ1 there were also two companies with negative solidity which is quite unusual. The analysis has also been done without these outliers without any significant change in the results from the regression.

Table 6

Variable N Min Max Mean Median Std. Dev

CAR 319 -0.1305 0.1556 -0.0014 -0.0024 0.0314

LEVIFRS16 319 0.0000 4.2809 0.1920 0.0744 0.3395

LEVUNA 319 -6.7030 22.0587 0.0907 -0.0148 1.5432

EBITDA 319 -0.0609 0.3585 0.0025 0.0000 0.0288

SOLQ1 319 -0.3174 0.9987 0.4608 0.4300 0.2203

LogTA 319 3.4904 15.6519 8.6693 8.3526 2.2169

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21 The highest recorded change of leverage due to IFRS 16 was 4.2809. Further we see that the median for the change of leverage due to IFRS 16 is a bit lower than the mean, which indicates that the variable can be skewed to the right, although only a small amount. The same skewness can also be seen in the cumulative abnormal return. Figure 4 visualizes how the cumulative abnormal return is spread throughout the spectra, and it can be seen that there is a higher frequency of returns on the negative side. Similar features are shown on the variable LEVIFRS16

in table 6 where the median 0.0744 is lower than the mean 0.1920. This indicates that there are more companies that are affected very little by IFRS 16 than there are companies that are highly affected.

To further test if there is a significant CAR during the implementation of IFRS 16 a one sample t-test is done for the dependant variable. The results for the t-test are shown in table 7 and clearly shows that there is no significant mean for CAR for the sample during the implementation. To further test if the sample actually was affected by the implementation of IFRS 16 a one sample t-test is also done for the change of leverage due to IFRS 16. The result shows that the leverage had a significant change with a mean of 0.1791, so the debt from the operating leases had a significant increase of 17.91% of the company’s equity and 95 % of the sample is within 14.17 % - 21.65 % change of debt of the company’s equity.

-0,15 -0,1 -0,05 0 0,05 0,1 0,15 0,2

1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 253 262 271 280 289 298 307 316

CAR

Figure 4 Spread of CAR

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22 Table 7

N Mean Std.dev p-value 95% CI

Lower

95% CI Higher

CAR 319 -0.0014 0.03144 0.443 -0.0048 0.0021

LEVIFRS16 319 0.1791 0.33949 0.000** 0.1417 0.2165

5.2 Regression

The results from the regression analysis are found in table 8. The results show that the majority of the variables have low coefficients and high p-values. But it has to be noted that all variables are presented in hundredths, so for example for every 1 % change in SOLQ1 a -0.4 % CAR would occur. The strongest coefficient is the EBITDA which has a coefficient of 20.9 % and is the only variable that is significant on the 99% level. Although this variable both includes the actual change of EBITDA in the company and the change from IFRS 16, and therefore it is difficult to draw any conclusions from this variable.

Even though all the variables aren’t significant, it is still of interest to examine the results of the regression and how the coefficients are presented. We see that the coefficients LEVIFRS, LEVUNA, EBITDA and LogTA are positive which indicates that an increase of these variables affects CAR positively. Meanwhile SOLQ1 has a negative impact on CAR. The most interesting variable to examine is the LEVIFRS which indicates how much the implementation of IFRS 16 affected CAR. Although this variable is insignificant and therefore the null- hypothesis can’t be rejected.

Table 8

Beta Std.err Sig.

Constant -0.005 0.011 0.208

LEVIFRS 0.003 0.006 0.592

LEVUNA 0.000 0.000 0.249

EBITDA 0.209** 0.064 0.001

SOLQ1 -0.004 0.009 0.632

LogTa 0.001 0.001 0.546

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23

Model summary F-value R R^2 Std.err

2.814*

(0.017)

0.207 0.043 0.03095

Table 8 also shows the that the model has R-value of 20.7 % and R^2 of 4.3 %. These are both quite low values and therefore it can be said that the model doesn’t explain well how CAR is affected. Although, the model has a F-value of 2.814 which makes the model significant on a 95 % level.

6. Analysis

In this chapter the results are analysed and connected to earlier studies.

The study’s hypothesis is that the implementation of IFRS 16 will generate an abnormal return when the first report that uses IFRS 16 is released. This is due to the changed way of accounting for operating leases, where both the balance sheet and income statement is affected. To test this hypothesis a regression is done, and this shows that the coefficient for change of leverage due to IFRS 16 is positive. This implies that the more IFRS 16 affected a company, the more positive the cumulative abnormal return would be. Although the variable was insignificance so the null- hypothesis can’t be rejected.

The results do not seem to be in line with Myers (1977) debt overhang theory, which would mean that the stock return would be negative when the leverage rises. Although, the studies made by Cai & Zhang (2010) and also Dimitrov & Jain (2008) studied a longer time period for stock returns when the leverage was increased, and this study only examined the day, the day prior and the day after the increase of leverage was announced. This could mean that return more in line with the theory could occur in the future. Moreover, the effect on the change of leverage is more significant in companies with already high leverage (Cai & Zhang, 2010)., and therefore the results in this study maybe not is applicable to the theory. Instead, to explain the positive abnormal return due to the implementation of IFRS 16 it is better to look at how the EBITDA affected the stock return, and how the variable achieved a significant positive coefficient. This is in line with several studies regarding value relevance such as Ball and Brown (1968), Beaver (1968) and Fama & French (1992). The problem when analysing the EBITDA:s impact on the cumulative abnormal return is that we can’t differentiate if the effect is done by

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24 the implementation of IFRS 16, or if the unadjusted EBITDA actually is the driver of the abnormal return, since only a minority of the companies report how much the implementation affected their income statement for the first quarterly report. Therefore, all the conclusions from this study must be made from the change of leverage, and the assumption of an increasing leverage due to IFRS 16 mean that also the EBITDA has increased due to the connected depreciation.

Further, as the t-test suggests, a cumulative abnormal return has not significantly occurred. This is not in line with Gürardas (2013) study where the implementation of new accounting standards under IFRS and IAS is positively associated with abnormal return if the earnings figures are adjusted. One explanation to this could be that the investors already have incorporated the operating leases as a leasing debt already and therefore there is no significant reaction. The semi-strong form of the efficient market hypothesis states that investors incorporate all available information, and if the investors already has incorporated the operating leases then the market would be efficient in the semi-strong form (Fama, 1970; Ross, Westerfeld & Jaffe, 2005). Giner (2018) also finds that investors similarly values debt in the balance sheet and off- balance sheet debt, so the investors would therefore value the companies similarly as before since the off-balance sheet debt already was incorporated. From this result the study also could suggest that investors not only look at multiples when doing a stock analysis as Demirakos, Strong & Walker (2004) stated. Finally, Barth et al. (2008) and Horton & Sarafeim (2009) states that the value relevance should increase when accounting standards are introduced.

Although, since no reaction happened during the implementation the results could suggest that the value relevance of the accounting standard didn’t change significantly.

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25

7. Conclusion

In the last chapter the study the conclusions from the study is presented and also proposed further studies in the area

The purpose of the study was to examine of investors reacts from the changes done in the financial statements due to the implementation of IFRS 16. The implementation of IFRS 16 affects the leverage positively in the balance sheet, and EBITDA and EBIT positively in the income statement. Myers (1997) debt overhang studies suggests that increased leverage is associated with negative stock returns, meanwhile studies in value relevance suggest that increased earnings figures are associated with positive stock returns. Since the effects on the stock price from the implementation is unclear the hypothesis that an abnormal return will occur is developed.

The results show that there is no significant cumulative abnormal return during the release of the first report with the new IFRS 16 implemented. The reason for this is possibly because the investors already incorporated the off-balance sheet debt from the operating leases and not only looking at the multiples when doing the stock analysis. Therefore, the results could suggest that the investors incorporate the available information which is in line with the semi-strong form of the efficient market hypothesis.

7.1 Further studies

Firstly, as the studies states several times, it would be of interest to study other markets since different markets are differently exposed to operating leases and could therefore be differently affected. Earlier studies regarding the capitalisation of operating leases have also focused on particular sectors that are more affected and it could be interesting to focus more on them.

Furthermore, the earlier studies regarding debt overhang theory suggest that the long-term stock return can get lower due to the increased leverage. When more time has passed the companies that are significantly affected by IFRS 16 can get in line with the results suggested from these studied, but here more time is needed before. A study using more of value relevance methodology, from for example Francis & Shipper (1999) is also suggested to further expand the knowledge of how this implementation of IFRS 16 affected the value relevance of the accounting standard.

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26

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Value relevance of IFRS 16 - A study of the stock market reaction