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Risk disclosures & Cost of Capital

Risky business: Are firm risk disclosures worth it?

Master Thesis A&C

B. Sijbom S2044560 06-46138797 b.sijbom@student.rug.nl

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1 | A b s t r a c t B a r t S i j b o m

Risk disclosures & Cost of Capital.

Abstract: This study investigates the effect of the assurance & risk disclosure part of the IFRS 7 disclosures on a company’s cost of capital. In particular whether assurance disclosure is treated differently from risk disclosure. The sample consists of data from 243 firms in the UK for the period 2007-2016. Panel data regression models with industry and year effects are used to investigate the effect of the assurance & risk disclosures on cost of capital, and the moderating effect of information asymmetry. This study is unable to find significant results for the relationship between IFRS 7 disclosure quality and cost of capital. Also no evidence is found for a moderating effect of ex ante information asymmetry. The investigation of the different disclosure aspects (assurance/risk) within IFRS 7, as well as the moderating effect of ex ante information asymmetry is the main contribution of this study. The evidence of the effect on cost of capital is relevant for practitioners because it shows how companies have an incentive to overstate their assurance, or downplay risk, disclosure.

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U n i v e r s i t y o f G r o n i n g e n 2 | T a b l e o f c o n t e n t Table of Content

1. Introduction 3

2. Theoretical development 5

2.1 IFRS 7 Financial Instruments 5

2.2 Hypothesis development 6

2.2.1 IFRS 7 disclosure: Risk versus Assurance 6

2.2.2 Information asymmetry 7

2.2.3 Conceptual model 9

3. Research Methodology 10

3.1 Research method, data collection and sample 10

3.2 Research Model 11

3.3 Measurements of the variables 12

4. Results 15 4.1 Descriptive statistics 15 4.2 Hypothesis 1 17 4.3 Hypothesis 2 19 4.4 Additional analyses 21 4.4.1 Endogeneity concerns. 21

4.4.2 Alternative cost of capital 22

5. Discussion and conclusion 25

5.1 Findings 25

5.2 Theoretical and practical implications 26

5.3 Limitations and further research 27

References 28

Appendices 30

Appendix A1: Construction of the Disclosure index – Nature and Extent of risk 30

Appendix A2: Summary of test variables 33

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3 | I n t r o d u c t i o n B a r t S i j b o m 1. Introduction

One of the significant initiatives taken by the International Accounting Standards Board (IASB) to improve disclosures in the annual reports is the introduction of IFRS 7 Financial Instruments. According to this standard, the IASB mandates that all firms with financial instruments would provide disclosures on “(1) the significance of financial instruments for the entity’s financial position and performance and (2) the nature and extent of risks arising from financial instruments to which the entity is exposed during the period and at the

reporting date, and how the entity manages those risks.” (IFRS 7, p.2).

The content of the IFRS 7 disclosures therefore consists of both negative information regarding risk exposure, in which companies have to disclose the significance, nature and extent of the companies risk exposure and positive information related to assurance, in which companies explain the hedging activities utilized to address risks arising from the use of financial instruments.

The aim of the regulator is to provide greater transparency about the risks that companies face form using financial instruments, which in turns provides investors and other users of

financial statements with better information in order to make informed judgements about risk and return (IASB 2005). The goal of IFRS 7 is to provide higher transparency, and therefore to lower information asymmetry, which in theory means managers should want to disclose information in order to signal good risk management practices and thereby reducing the cost of capital (Abraham et al., 2014).

In the last decade, regulators aim at improving the quantity and quality in firms’ risk reporting (Elshandidy, Shrives, Bamber, and Abraham, 2018). According to Campbell et all (2014) critics have two main arguments for explaining why risk disclosures are unlikely to be informative. Firstly, companies are not required to estimate the likelihood that a risk they report on in the disclosure will actually manifest. Second, firms do not have to quantify the impact that a disclosed risk might have on their current and future financial statements. Therefore managers simply disclose all possible risks and uncertainties, regardless of the likelihood that they will ultimately affect the firm. Therefore the disclosure surrounding each of these risks and uncertainties is likely to be vague and boilerplate in nature (Reuters, 2005).

A start could be made by testing the main benefits mentioned to support increased disclosure. Managers should trade off the benefits of expanded disclosure against the costs of disclosing potentially damaging information (Abraham and Shrives, 2014). Therefore it might be of

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U n i v e r s i t y o f G r o n i n g e n 4 | I n t r o d u c t i o n

importance to understand more about the actual benefits in practice of detailed risk disclosure. Although theoretical work suggests that increased disclosure reduces the cost of capital, empirical studies have found mixed evidence. Kothari et al. (2009a) argue that the reason prior empirical findings are mixed is because disclosure tone affects the relationship between disclosure and the cost of capital. While they predict that negative/pessimistic disclosures increase the cost of capital, they are unable to find evidence of this association when the source of the disclosure is the company itself.

In the case of IFRS 7 disclosures companies have a significant influence over the tone of the disclosure. As mentioned the IFRS 7 disclosures consists of risk and assurance information. As such the main research gap this research attempts to address is the effect of the

information content of the disclosure on cost of capital. In the literature the main argument for increased disclosure is to signal good risk management practices and thereby reducing the cost of capital (Abraham et al., 2014). By providing empirical evidence on whether or not risk disclosure leads to a lower cost of capital, it could help the literature that focuses on

improving the quality of risk disclosure. This research can provide future research with a basis to build on to persuade companies to start disclosing more detailed information. Or it could signal to legislators that something has to be changed in the way risk disclosures have to be presented because it does not have the desired effect.

The research question for this proposal is therefore:

Have the IFRS 7 disclosures led to benefits for companies in terms of lower cost of capital because of increased disclosure?

The rest of this research proposal is structured as follows. First the theoretical framework describes IFRS 7 in detail. Next the hypothesis development describes how I’ve come to my hypothesis. Afterwards the research methodology will be explained.

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5 | T h e o r e t i c a l d e v e l o p m e n t B a r t S i j b o m 2. Theoretical development

In this chapter I will explain the details of the risk disclosure as mentioned in IFRS 7.

2.1 IFRS 7 Financial Instruments

IFRS 7 is a standard introduced by the IASB and is effective since 2007. It’s main goal is to increase the transparency of financial instruments risks and to show how these risks are managed. According to the IASB, increased transparency will lead to better understanding of these risks for investors, and in turn will enhance their decision-making process. In turn the disclosure should be beneficiary for companies because of a lower cost of capital. The risk information may increase cost of capital whereas the explanations regarding how the risks are managed may decrease cost of capital.

The standard is divided into two sections on which the company’s management has to

disclose information. The first is information on “the significance of financial instruments for the entity’s financial position and performance” (IFRS 7, 2005). Secondly, information on “the nature and extent of risks arising from financial instruments to which the entity is exposed during the period and at the end of the reporting period, and how the entity manages those risks”. (IFRS 7, 2005).

The risks identified within IFRS 7 are credit, liquidity and market risks. Credit risk is the risk that another party related to a financial instrument fails to meet their obligation, which creates a financial loss for the other party. Liquidity risk is failing to meet obligations associated with financial liabilities, which are settled by the delivery of an asset. Market risk is composed of interest, currency and other price risk and, it is the risk of fluctuation in financial instruments’ future cash flow because of changing interest rates, exchange rates or other market prices.

For all risks, managers have to report the extent of risk exposure and the strategies to manage and measure the risks. The management of the company has to describe from their viewpoint how the company controls and manages its risks.

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U n i v e r s i t y o f G r o n i n g e n 6 | T h e o r e t i c a l d e v e l o p m e n t 2.2 Hypothesis development

In this chapter I will explain how risk disclosure can lead to lower cost of capital. In the last section I provide an overview of how the hypotheses are connected in a conceptual model.

2.2.1 IFRS 7 disclosure: Risk versus Assurance

A company's cost of equity capital is fundamental to a wide variety of corporate decisions, asset-pricing issues, as well as to financial reporting regulation. Recently, there has been considerable interest in the relationship between company’s informational environment and their cost of equity capital in finance and accounting research as well as in discussions of financial reporting regulation. A company's cost of equity capital is the riskless interest rate plus a risk premium. Releasing more information and, in particular, more public information through financial reports and other public disclosures by firms reduces the uncertainty about the size and the timing of future cash flows and, therefore, also the risk premium (Easley and O'Hara 2004). This idea has significant implications for financial reporting regulation and company's voluntary disclosure policies: more informative public disclosures reduce the company’s capital costs.

In the same line of reasoning risk disclosure is said to reduce the cost of capital. The cost of capital of a company is impacted by the investor’s risk perceptions. Risk disclosures can therefore reduce investor uncertainty, thereby reducing the risk premium required from the company (Semper & Beltran, 2014). Semper & Beltran (2014) are also one of the few studies that have actually studied the relationship between specifically risk disclosure and cost of equity. Previous studies focus on the relationship between disclosure and cost of capital, considering disclosure as a whole. They don’t distinguish between different types of

information company’s disclose. However, not all information company’s disclose is equally relevant (Semper & Beltran, 2014).

Within the risk disclosure of IFRS 7 we can differentiate between disclosure on risks and disclosure on hedging of the risks, i.e. assurance. Both of these disclose information about the company that should reduce the uncertainty. However both are probably not valued the same. The first one is prodromal a negative type of information and can be seen as a form of bad news: the company faces certain risks that might impact their profitability. The second one is about how the company deals with the risks, this is a more positive type of information because company’s can signal how well they’re dealing with the risks they face.

So in general, it's said that improving information disclosure reduces cost of capital (He, Plumlee, & Wen, 2019), however there could be a distinction between information on risks

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7 | T h e o r e t i c a l d e v e l o p m e n t B a r t S i j b o m

and information on assurance. A lot of disclosure on risks might reduce uncertainty but a company may still be perceived as somewhat risky. Whereas a lot of disclosure on assurance could also reduce uncertainty but also might signal that the company is less risky because of how well they deal with the risks. Therefore the first hypothesis is:

H1: Higher level of “assurance” relative to “risk” disclosure about financial instruments leads to a lower cost of capital.

2.2.2 Moderating effect of information asymmetry

A second line of research regarding disclosure and cost of equity capital is based on the contribution that disclosure makes towards reducing transaction costs, which is associated with information asymmetry (Semper & Beltran, 2014). Research state that companies that disclosure more information should have lower cost of capital that arise from information asymmetries (Leuz & Verrechia, 2000). The economic theory states that information asymmetries will lead to costs by introducing adverse selection into transactions between buyers and sellers of company’s shares. In real institutional settings, adverse selection is typically manifested in reduced levels of liquidity for the company's shares. To overcome the reluctance of potential investors to hold company’s shares in illiquid markets, the companies have to issue capital at a discount. Discounting results in fewer proceeds to the company and therefore a higher cost of capital (Leuz & Verrechia, 2000).

In this kind of research it’s important to not only look at the direct aftermath of publication but look at it from an ex-ante perspective on information and the cost of capital (Christensen et al., 2010). Financial reporting regulation and voluntary disclosure policies are information system choices and must be evaluated as such, and not only by what happens subsequent to the release of the signals from these systems. As for the ex-ante asymmetric information, Garlier & Renou (2006) suppose that a lender and a borrower have different and privately known opinions about the possible returns of the risky investment. Differences in opinions might be explained by (un-modelled) differences in private information or, more simply, differences in subjective beliefs. So, by increasing the level of disclosure a company can reduce the possibility of differences in opinion, because both parties have the same information to base their opinion from.

The publication of additional information will probably more influential in areas where obligatory and standardized accounting information has the greatest gaps (Semper & Beltran,

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U n i v e r s i t y o f G r o n i n g e n 8 | T h e o r e t i c a l d e v e l o p m e n t

2014). In our case, in company’s where there is a high level of information asymmetry, the disclosure of more detailed risk information will have a greater impact.

Campbell et al (2014)’s find evidence in their research that suggests that, investors

incorporate the unexpected portion of these disclosures into their assessments of firm risk and value. They find a positive association between the unexpected portion of risk factor

disclosures and the post-disclosure level of market beta and stock return volatility, suggesting that the disclosures are positively associated with investors’ assessment of firms’ fundamental risk. That is, investors revise their estimate of the level of the risk parameters in the

distribution function of future cash flows. In other words: unexpected information in disclosures (suggesting information asymmetry) has an effect on investors risk perception. Campbell et al (2014) find support for this by finding a negative association between the unexpected portion of risk factor disclosures and the post-disclosure level of information asymmetry among investors (I.e. bid-ask spread). This suggests that, after controlling for the fact that risk factor disclosure increases the market’s assessment of a firm’s risk, the public availability of risk factor disclosure decreases information asymmetry among that same firm’s shareholders.

So therefore, the proposed effect of hypothesis is moderated by the level of ex ante information asymmetry. If all the information in the disclosure was already known then it only confirms it, thus the impact of the information is lower. Because Campbell et al (2014) have already shown that investors do react to unexpected information in the disclosure, the argument could be made that where prior to disclosure there is a lot of information asymmetry the disclosure should have more impact, because investors are presented with a unexpected information that they have to in cooperate in their assessments. Therefore the second hypothesis is:

H2: Higher level of “assurance” relative to “risk” disclosure about financial instruments leads to an incrementally lower cost of capital for firms with higher ex ante information.

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9 | T h e o r e t i c a l d e v e l o p m e n t B a r t S i j b o m 2.2.3 Conceptual model

The hypotheses as proposed in this chapter are summed up in the following figure. It gives a graphical image of the proposed relationship between the variables.

Figure 1 Conceptual Model

Level of

assurance Cost of Capital

Level of ex ante information asymmetry

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U n i v e r s i t y o f G r o n i n g e n 10 | R e s e a r c h m e t h o d o l o g y 3. Research Methodology

The first section provides a description of the data collection method, and the sample used in this research. The second section describes the measurements for the variables. The last section of this chapter presents the statistical model.

3.1 Sample

This study uses a UK sample. The UK was chosen because of the availability of data required for conducting this research. The study considers data from 2007-2016, because the IFRS 7 standard is effective since 2007 (IASB, 2005). Furthermore the databases used in this study provide sufficient and appropriate data until 2016, whereas more recent information is usually not complete.

Originally all listed premium UK firms were selected. This resulted in 538 unique companies and 2699 observations to be collected for the time period. A total of 740 observations haven’t been collected yet, therefore these were removed from the sample. This resulted in a sample size of 1959 firm-year observations on the IFRS 7 reporting quality. This sample size contains 502 unique companies and covers the entire time period of 2007-2016. The sample size was further reduced by the availability of the other variables. Table III in the appendix gives an overview of the amount of observations for each variable before the data was worked on. From the collected data, observations with incomplete data are deleted. The main reduction in sample size was caused by availability of the cost of capital data. Eventually the final sample consisted of 599 observations from 243 different companies.

3.2 Data collection

This study is based on archival data, because this is the most appropriate data method because of the large amount of information available over multiple years. Several sources were used to retrieve all the data for this study. The required information on IFRS 7 disclosures is hand collected from the company’s annual reports. The data on cost of capital of the companies is retrieved from the summary forecast files of I/B/E/S within the database EIKON1. The required information for the control variables is extracted from Compustat global, which contains all sorts of company specific information and financial ratio’s.

By using these databases I’m able to generate a large sample, which makes it’s possible to achieve a high validity for this study. When conducting research of this kind it’s often advised to hand collect at least one variable, because this increases the reliability of the study

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11 | R e s e a r c h m e t h o d o l o g y B a r t S i j b o m

(Fogarty, 2006). In this study this recommendation is fulfilled because the IFRS 7 disclosure variable is hand collected.

The following paragraphs describe the research model and how the different variables used within the model are being measured in this study.

3.3 Research Model

The following research models are tested in this thesis:

Hypothesis 1:

𝑪𝑶𝑪𝒊,𝒕= 𝜷𝟎+ 𝜷𝟏𝑨/𝑹𝒊,𝒕+ 𝜷𝟐𝑺𝑰𝒁𝑬𝒊,𝒕+ 𝜷𝟑𝑳𝑬𝑽𝑬𝑹𝑨𝑮𝑬𝒊,𝒕+ 𝜷𝟒𝑩𝑻𝑴𝒊,𝒕+ 𝜷𝟓𝒀𝑬𝑨𝑹 + 𝜷𝟔𝑰𝑵𝑫𝑼𝑺𝑻𝑹𝒀 + 𝜺

Where: 𝑪𝑶𝑪𝒊,𝒕 = the cost of capital at the end of the fiscal year;

𝑨/𝑹𝒊,𝒕 = assurance index relative to the risk index;

𝑺𝑰𝒁𝑬𝒊,𝒕 = the natural logarithm of total assets of the company;

𝑳𝑬𝑽𝑬𝑹𝑨𝑮𝑬𝒊,𝒕 = the total debt to total assets ratio; and

𝑩𝑻𝑴𝒊,𝒕 = the book to market ratio.

Hypothesis 2:

𝑪𝑶𝑪𝒊,𝒕= 𝜷𝟎+ 𝜷𝟏𝑨/𝑹𝒊,𝒕+ 𝜷𝟐(𝑰𝑨𝒊,𝒕∗ 𝑨/𝑹𝒊,𝒕) + 𝜷𝟑𝑺𝑰𝒁𝑬𝒊,𝒕+ 𝜷𝟒𝑳𝑬𝑽𝑬𝑹𝑨𝑮𝑬𝒊,𝒕+

𝜷𝟓𝑩𝑻𝑴𝒊,𝒕+ 𝜷𝟔𝒀𝑬𝑨𝑹 + 𝜷𝟕𝑰𝑵𝑫𝑼𝑺𝑻𝑹𝒀 + 𝜺

Where 𝑪𝑶𝑪𝒊,𝒕 = the cost of capital at the end of the fiscal year;

𝑨/𝑹𝒊,𝒕 = assurance index relative to the risk index;

𝑰𝑨𝒊,𝒕 = level of information asymmetry;

𝑺𝑰𝒁𝑬𝒊,𝒕 = the natural logarithm of total assets of the company;

𝑳𝑬𝑽𝑬𝑹𝑨𝑮𝑬𝒊,𝒕 = the total debt to total assets ratio; and

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U n i v e r s i t y o f G r o n i n g e n 12 | R e s e a r c h m e t h o d o l o g y 3.4 Measurements of the variables

Measuring cost of capital

The dependent variable in this study is the cost of capital. There are several ways of measuring cost of capital. For example He et al. (2019) use three implied cost of equity measures (MPEG, DIV and STPEG) consistent with prior literature. MPEG is based on the modified price-earnings-growth ratio method, following the procedure detailed in Easton (2004). DIV is estimated based on the target price method as mentioned in several studies (Botosan & Plumlee, 2013; Botosan, Pumlee & Wen, 2011). They also state that literature so far has failed to reach consensus on the best measure of cost of capital. According to these authors, the calculation suggested by Easton (2004) is a robust measure of firm-specific cost of equity capital.This estimate is based on the price/earnings to growth (PEG) ratio, as the square root is related to the inverse of PEG ratio, which is important from a theoretical point of view. Many financial websites often report PEG ratios and rely on these ratios as a primary basis for stock recommendations (Easton, 2004). The formula requires data about earnings per share forecasts and prices combined in the following way: where eps2 and eps1 refer to earnings per share forecasts 2 and 1 year ahead, P0 is current price and COC is the proxy used for cost of capital.

𝑪𝑶𝑪 = 𝑨 + √𝑨𝟐+𝑭𝒆𝒑𝒔𝟐 − 𝑭𝒆𝒑𝒔𝟏

𝑷𝟎

𝐴 = 𝑭𝒅𝒑𝒔𝟏

𝟐𝑷𝟎

Where:

𝑷𝟎 = stock price (from I/B/E/S) the day before I/B/E/S releases monthly forecasts

𝑭𝒆𝒑𝒔𝟏= the I/B/E/S forecasted earnings per share for year t+1

𝑭𝒆𝒑𝒔𝟐= the I/B/E/S forecasted earnings per share for year t+2

𝑭𝒅𝒑𝒔𝟏= the I/B/E/S forecasted dividends per share for year t+1

The measure is estimated by using monthly mean forecasts from the IBES summary file. An annual value is obtained by using the average of the monthly estimates for the fiscal year.

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13 | R e s e a r c h m e t h o d o l o g y B a r t S i j b o m

Measuring level of assurance

This research uses hand-collected IFRS 7 data which measures the extent as to which specific hedging terms are mentioned in the risk disclosures sections in the annual reports. The data is gathered from Karaibrahimoglu and Porumb (2019) which is hand-collected within the research project funded by IAAER and IASB.

For each company in the sample, financial risk disclosure mandated by IFRS 7 was collected from the notes of the financial reports. These IFRS 7 disclosures concerned quantitative disclosures (IFRS 7.34) of summary quantitative data about exposure to each risk at the reporting date, disclosures about credit risk, liquidity risk, and market risk, how these risks are managed, and concentrations of risk.

IFRS_7_Disclosure_Quality is defined as a quality index measuring the extent to which firms’ disclosures are in line with the best practices as described by IFRS 7. For selecting the items to be used in the construction of this index, the items that are most relevant to financial statement users were identified. IFRS_7_Disclosure_Quality was constructed by using the normalize value of the equal weighted average of the 13 items presented in Appendix A1. The overall IFRS 7 quality index was split in order to obtain a more refined measure of its

components of risk exposure and assurance. The disclosure quality index was split into three different variables: IFRS 7 Disclosure Quality Assurance, IFRS 7 Disclosure Quality Risk and IFRS 7 Disclosure Quality Other. See Appendix A2 for the categorization of the items used in the creation of each variable.

To answer the research questions I have to create a new variable. The two variables regarding the quality of the risk and assurance part, IFRS 7 Disclosure Quality Assurance and IFRS 7 Disclosure Quality Risk, were combined into a single variable. This new variable IFRS 7 Disclosure Quality Assurance Over Risk was constructed by subtracting the Risk index from the Assurance index and multiplying by 2. A higher value for this index indicates that the IFRS 7 disclosure is more focused on assurance disclosure relative to risk disclosure.

Measuring information asymmetry

In the literature there are several proxies for measuring the information asymmetry. In line with Leuz & Verrechia (2000) this research uses the bid-ask spread. The bid-ask spread is often thought to measure information asymmetry explicitly. The bid-ask spread addresses the adverse selection problem that arises from transacting in company’s shares in the presences of

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U n i v e r s i t y o f G r o n i n g e n 14 | R e s e a r c h m e t h o d o l o g y

asymmetrically informed investors. Less information asymmetry implies less adverse selection, which in turn, implies a smaller bid-ask spread (Leuz & Verrechia, 2000).

Control variables

This study focused on the relationship between risk disclosure and cost of capital. Cost of capital is however also related to a number of other factors. Similar to research of Semper & Beltran (2014), the control variables used are size, leverage and book-to-market.

Size

Semper & Beltran (2014) argue that larger companies need more financing and therefore provide more information in order to reduce information asymmetry on perceived risk. Therefore it’s likely that company’s size is negatively related to cost of capital. For example Hail and Leuz (2006), and Rakow (2010) have already showed this relationship. In line with the research of Semper & Beltran (2014) the variable used to measure the size of a company is the natural logarithm of total assets.

Leverage

The amount of leverage is associated with a company’s risk and cost of equity. High levels of leverage increase a company’s risk(Semper & Beltran, 2014). So therefore it’s likely that level of leverage is positively related to cost of capital. Evidence has been provided by a number of studies, such as Gebhardt et al. (2001). In line with Semper & Beltran (2014) the debt-to-assets ratio has been used as a proxy for leverage.

Book-to-market ratio

The book-to-market ratio can be used as a proxy measure of a company’s growth opportunities (Gebhardt et al. (2001). A high book-to-market ratio indicates lower

opportunities for growth. Lower opportunities for growth are associated with a higher cost of equity.Therefore the book-to-market ratio is positively related to cost of capital. Evidence is provided by for example Hail and Leuz (2006). The book-to-market ratio is calculated by dividing the common shareholder’s equity by the market capitalization.

Year and industry dummy

Within panel data there could be differences in the years and industry. Analysis of the years 2007-2016 had shown differences between years (appendix A2). To control for these time and industry effects a YEAR and INDUSTRY dummy is included.

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15 | R e s u l t s B a r t S i j b o m 4. Results

In this chapter the survey results will be discussed. The first section presents the descriptive statistics that were performed on the data. The second section provides the linear regression that is performed to test the hypotheses.

4.1 Descriptive statistics

Figure 2 shows the development of the IFRS 7 quality variables and the COC throughout the years.

Figure 2 Disclosure quality over the years

The following table shows the descriptive on all the items that are used to calculate the quality indexes. On several items there was some information missing. In total there were 1958 observations, however some items had missing data. These missing datapoints were removed from the sample. Most of the missing items were on item number 19.

Table 1

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VARIABLES N Mean sd min max

item4 1,958 0.598 0.491 0 1 item7 1,957 0.458 0.498 0 1 item10 1,958 0.642 0.480 0 1 item13 1,958 0.139 0.346 0 1 item16 1,958 0.0409 0.198 0 1 item22 1,958 0.0235 0.152 0 1 item18 1,958 0.991 0.761 0 2 item19 1,941 0.291 0.646 0 2 0 0,1 0,2 0,3 0,4 0,5 0,6 year 2007 2008 2009 2010 2011 2012 2013 2014 2015

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U n i v e r s i t y o f G r o n i n g e n 16 | R e s u l t s item15 1,958 1.031 0.859 0 2 item6a 1,958 0.00613 0.0781 0 1 item6b 1,958 0.583 0.493 0 1 item9 1,958 0.591 0.492 0 1 item12 1,958 0.108 0.311 0 1 item20 1,958 0.837 0.858 0 2 item21 1,955 0.462 0.941 0 3

The items were then loaded into the indexes as described in chapter 3. The following table shows the results in terms for mean and standard deviations for the different indexes:

Table 2

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VARIABLES N mean sd min max

index_quality_assurance 1,940 0.241 0.178 0 1

index_quality_risk 1,940 0.356 0.255 0 1

index_quality_all 1,940 0.332 0.159 0 0.846

index_quality_other 1,940 0.450 0.289 0 1

Regarding IFRS 7 disclosure quality the mean value was 0,33. This indicates that on average company’s comply with 33% of the disclosures. When split into the different disclosure aspects it shows that companies have a higher score for the risks and other aspects than the assurance aspects. On average company’s only comply with 24% of the disclosures on assurance.

In table 3 the descriptive statistics concerning the variables used in our final sample are presented. COC represents the proxy used for the Cost of Equity Capital of the company’s. The mean value of this variable is 0,304 which indicates that the company’s in this sample have an average cost of capital of 30,4% throughout the years.

The outliers in the variables are eliminated by standardizing them. This adjusts the value of the 1st and 99th percentile of these variables. The distribution of the sample size over the years

and different industries can be found in Appendix A2. It seems fairly even distributed. The calculated indexes regarding the IFRS 7 quality have quite similar means and standard

deviations than the total collected data on IFRS 7 quality as shown in table 2. So it seems that the variables for IFRS 7 quality in our sample size are quite representive for the total data collected.

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17 | R e s u l t s B a r t S i j b o m Table 3

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VARIABLES N mean sd Min max

Size 599 14.89 1.465 11.92 19.42 Leverage 599 0.236 0.183 0 1.666 Coc 599 0.304 0.428 0 5.673 btm 599 2.943 9.279 -174.99 77.86 Spread 599 2.687 4.124 0.05 66.54 index_quality_assurance 599 0.286 0.198 0 1 index_quality_risk 599 0.353 0.266 0 1 index_quality_all 599 0.359 0.165 0 0.846 index_quality_other 599 0.500 0.287 0 1 index_quality_AssuranceOverRisk 599 -0.171 0.600 -1.667 1.333

Table 4 shows the Pearson correlation matrix. This test is conducted to determine if the variables are positively or negatively correlated and if variables are possibly influenced by multicollinearity. According to this test, the variable LEV (0.1402*) is significantly positively correlated with the cost of equity capital. This correlation is in line with the expectation that companies with a lot of leverage are perceived as riskier and therefore have a higher cost of capital. The variable Size (-0,0386) shows a negative correlation which is in line with

expectation, because bigger companies often find it easier to attract capital. Book to market is negatively correlated to cost of capital which is according to expectations.

In terms of the main variable we’re interested in whether we find a negative correlation between the quality of disclosure and the cost of equity. This tests shows a positive

correlation between the total quality of the IFRS 7 disclosure and the cost of capital. This is against expectations because it’s often expected that more disclosure actually lowers cost of capital. Regarding our hypothesis we expect a negative correlation between our

Assurance/Risk variable, the results are in line with this expectation (-0.0788). However the correlation is not significant. Regarding the independent indexes, assurance is negatively correlated with cost of capital and quality of risk is positively correlated with cost of capital. Both are in line with expectations, positive information should decrease cost of capital, whereas negative information is expected to increase cost of capital as it increase the perceived risk of the company.

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U n i v e r s i t y o f G r o n i n g e n 18 | R e s u l t s Table 4 Variables (1) (2) (3) (4) (5) (6) (7) (8) (1) coc 1.000 (2) Quality Assurance/risk -0.0788 1.000 (3) Quality all 0.0395 -0.093* 1.000 (4) Quality risk 0.0612 -0.802* 0.590* 1.000 (5) Quality assurance -0.0405 0.478* 0.713* 0.143* 1.000 (6) size -0.0386 0.095* 0.229* 0.0317 0.203* 1.000 (7) leverage 0.1402* -0.0610 0.0495 0.0437 -0.0367 0.259* 1.000 (8) btm -0.1098* -0.0078 -0.0143 0.0018 -0.0104 -0.0286 -0.0024 1.000 * shows significance at the .05 level

4.2 Hypothesis 1

The next step was to test the hypotheses from chapter 2.

Table 5 presents the results of the regression analyses. The three columns each represent a regression model regarding the first hypothesis. For the hypothesis only model 3 is relevant, however the two quality indexes are also tested independently. The table shows both the coefficients and the P-values of the different variables. The R-squared values indicate the explanatory power of the regression model.

Table 5

Dependent variable: Cost of Capital Model 1 Model 2 Model 3

index_quality_assurance -0.030 (0.084) index_quality_risk 0.057 (0.092) index_quality_AssuranceOverRisk -0.028 (0.042) Size -0.040*** -0.040*** -0.040*** (0.015) (0.015) (0.014) Leverage 0.450*** 0.404*** 0.417** (0.164) (0.164) (0.166) Mtb -0.005 -0.005 -0.005 (0.007) (0.007) (0.007) Constant 0.877*** 0.847*** 0.841*** (0.232) (0.212) (0.210) Observations 599 599 599 R-squared 0.097 0.098 0.098

Year fixed-effect Yes Yes Yes

Industry fixed-effect Yes Yes Yes

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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19 | R e s u l t s B a r t S i j b o m

The model aims to find the effect of the quality of the IFRS 7 disclosures on the cost of capital. The first and second model show a regression model independently for the risk and assurance quality measures. The results of the first regression indicate a non-significant negative coefficient for the variable index_quality_assurance (-0,030). This indicates that increasing the quality of the “assurance” part of the disclosure has a negative effect

(decreases) the cost of capital. According to the P-value this relation is not significant at the 10% level.

The results of the second regression indicate a non-significant positive coefficient for the variable index_quality_risk (0,057). This indicates that increasing the quality of the “risk” part of the disclosure has a positive effect (increases) the cost of capital. According to the P-value this relation is not significant at the 10% level.

The control variables show varying results. The results show a significant and positive relationship between leverage and cost of equity, as expected. For Size the relationship with cost of capital is negative, which is also confirm expectations. The last control variable, btm is however not significantly related to cost of equity.

The R-squared value of 0,098, indicates that 9.8% of the variance is explained by the variables in this regression model.

The third regression model is the one that actually tests our hypothesis. It aims to find the effect of a higher level of assurance relative to risk disclosure on the cost of capital. The combination variable uses both the risk and assurance variable. The results indicate a non-significant negative coefficient for the interacting variable index_quality_AssuranceOverRisk (-0,028). This indicates that a higher level of disclosure on assurance relative to risk in the disclosure has a negative effect (decreases) the cost of capital, which is in line with the

expected negative relation. However, according to the P-value this relation is not significant at the 10% level.

4.3 Hypothesis 2

For the second hypothesis I’m testing the proposed moderating effect of information asymmetry on the relation from hypothesis 1.

Table 4 shows the Pearson correlation matrix. This test is conducted to determine if the variables are positively or negatively correlated and if variables are possibly influenced by multicollinearity. Most of the variables were already tested in table 4. These results haven’t changed. For the information asymmetry component, the variable SPREAD is added.

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U n i v e r s i t y o f G r o n i n g e n 20 | R e s u l t s

According to this test, the variable SPREAD (0.009) is not-significantly positively correlated with the cost of equity capital. This correlation is in line with the expectation that higher information asymmetry leads to a higher cost of capital. However the correlation is very small.

In terms of the main variable we’re interested in we find a negative correlation between the quality of disclosure and the spread. This correlation is in line with expectations as more disclosure should decrease the information asymmetry.

Table 6 Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (1) coc 1.000 (2) Quality Assurance/risk -0.0788 1.000 (3) Spread 0.0009 -0.0316 1.000 (4) Quality all 0.0395 -0.093* -0.0632 1.000 (5) Quality risk 0.0612 -0.802* -0.0516 0.590* 1.000 (6) Quality assurance -0.0405 0.478* 0.0005 0.713* 0.143* 1.000 (7) size -0.0386 0.095* -0.104* 0.229* 0.0317 0.203* 1.000 (8) leverage 0.1402* -0.0610 -0.084* 0.0495 0.0437 -0.0367 0.259* 1.000 (9) btm -0.1098* -0.0078 0.0704 -0.0143 0.0018 -0.0104 -0.0286 -0.0024 1.000 * shows significance at the .05 level

Table 7 presents the results of the regression analyses. The columns represent a regression model regarding the hypothesis. The table shows both the coefficients and the P-values of the different variables. The R-squared values indicate the explanatory power of the regression model.

The model aims to find the effect of information asymmetry on the relationship between the quality of the IFRS 7 disclosures and the cost of capital. The results show a significant and positive relationship between leverage and cost of equity.

The control variables show varying results. The R-squared value of 0,098, indicates that 9,8% of the variance is explained by the variables in this regression model.

Again there are no significant results regarding the hypothesis. Our variable of interest from hypothesis 1, Assurance Over Risk is not significantly related to cost of capital. By including spread as a moderator the results change, however no significant results are found.

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21 | R e s u l t s B a r t S i j b o m Table 7

Dependent variable: Cost of Capital Model 1 Model 2 Model 3

index_quality_assurance -0.006 (0.100) Spread 0.0038* 0.006 0.003 (0.002) (0.007) (0.005) c.index_quality_assurance#c.spread - 0.008 (0.015) index_quality_risk 0.079 (0.092) c.index_quality_risk#c.spread -0.008 (0.015) index_quality_AssuranceOverRisk -0.029 (0.041) c.index_quality_AssuranceOverRisk#c.spread 0.001 (0.006) Size -0.039*** -0.039*** -0.038*** (0.015) (0.015) (0.014) Leverage 0.453*** 0.449*** 0.445*** (0.165) (0.164) (0.166) Mtb -0.005 -0.005 -0.005 (0.007) (0.007) (0.007) Constant 0.805*** 0.749*** 0.803*** (0.221) (0.205) (0.233) Observations 599 599 599 R-squared 0.097 0.098 0.098

Year fixed-effect Yes Yes Yes

Industry fixed-effect Yes Yes Yes

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

4.4 Additional analyses

To test for the robustness of the main results, some additional analyses are conducted. First endogeneity concerns are addressed and explained, all models are re-estimated with lagged independent variables and with using fixed effects. Thereafter, the potential multicollinearity issues are tested and described. Finally the models are re-estimated utilizing two alternative cost of capital measures.

4.4.1 Endogeneity concerns.

A potential problem with panel data lies in endogeneity. Results can show a negative a negative influence of disclosure quality and the moderating effect, however it’s possible that the direction of causality is reversed. The constructed hypotheses state that IFRS 7 disclosure negatively influences (decreases) a company’s cost of capital. However it could be possible

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U n i v e r s i t y o f G r o n i n g e n 22 | R e s u l t s

that the direction of causality is quite the opposite, whereby a company’s cost of capital determines the extent of IFRS 7 disclosure. There are several options to control for this effect.

Lagged variables. Lagged variables can be used to ensure results are not driven by a potential endogenous relation. In the main analysis the independent and control variables are not lagged. To check if this influences the findings and conclusion, the models are re-estimated using lagged variables. The results can be found in the appendix. The results are similar to the main analysis, therefore the use of non-lagged variables did not influence the conclusions of this study.

Fixed effects. The second way to control for endogeneity concerns, and in particular omitted variables, is through the use of fixed effects. It’s quite possible that certain variables may be correlated with one or more explanatory variables while they are not observed and included in the model. Fixed effects can control for these variables because it allows for correlation between observed and unobserved variables. Fixed effects can also be used to control for potential influence of firm fixed effects. In the main analysis dummy variables are used for industry and year. Appendix shows the results of the re-estimated analysis with the use of fixed effects instead of dummy variables. These results are similar to the main results.

4.4.2 Alternative cost of capital

As explained before there is no consensus within the literature regarding the measurement of cost of capital. There are only certain proxies. Previous studies regarding disclosures and cost of capital have shown mixed results. One potential explanation given in the literature (Semper & Beltran, 2014) is that these results are dependent on the measurement of cost of capital used. Therefore this studies regression models are re-estimated using two alternative cost of capital measures. In line with He et al (2019), the measurement used is another of the implied cost of capital measures. The calculation for the cost of capital in this case is as follows:

𝑪𝑶𝑪 = √𝑭𝒆𝒑𝒔𝟐− 𝑭𝒆𝒑𝒔𝟏

𝑷𝟎

Where:

𝑷𝟎 = stock price (from I/B/E/S) the day before I/B/E/S releases monthly forecasts

𝑭𝒆𝒑𝒔𝟏= the I/B/E/S forecasted earnings per share for year t+1

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23 | R e s u l t s B a r t S i j b o m

The measure is estimated by using monthly mean forecasts from the IBES summary file. An annual value is obtained by using the average of the monthly estimates for the fiscal year.

The results for the re-estimation of hypothesis 1 are shown in the following table:

Table 8

Dependent variable: Cost of Capital 2 Model 1 Model 2 Model 3

index_quality_assurance -0.017 (0.043) index_quality_risk 0.049 (0.038) index_quality_AssuranceOverRisk -0.023 (0.017) Size -0.025*** -0.025*** -0.024*** (0.007) (0.006) (0.006) Leverage 0.272*** 0.269*** 0.265*** (0.093) (0.092) (0.090) Mtb -0.001 -0.001 -0.001 (0.003) (0.003) (0.003) Constant 0.693*** 0.666*** 0.663*** (0.111) (0.103) (0.104) Observations 599 599 599 R-squared 0.186 0.191 0.191

Year fixed-effect Yes Yes Yes

Industry fixed-effect Yes Yes Yes

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

The results are similar to those of the main analysis. Of the control variables only Size and Leverage are significantly related to cost of capital. In this case however the R-squared is 0,191, indicating that 19,1% of the variation in cost of capital is explained by the model. This is a lot higher than our main results.

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U n i v e r s i t y o f G r o n i n g e n 24 | R e s u l t s

The results for the re-estimation of hypothesis 2 are shown in the following table:

Table 9

Dependent variable: Cost of Capital 2 Model 1 Model 2 Model 3

index_quality_assurance 0.014 (0.052) Spread 0.003** 0.005 0.001 (0.001) (0.003) (0.002) c.index_quality_assurance#c.spread -0.012* (0.007) index_quality_risk 0.075 (0.049) c.index_quality_risk#c.spread -0.010 (0.008) index_quality_AssuranceOverRisk -0.023 (0.021) c.index_quality_AssuranceOverRisk#c.spread 0.001 (0.003) Size -0.025*** -0.025*** -0.024*** (0.007) (0.006) (0.006) Leverage 0.273*** 0.268*** 0.266*** (0.094) (0.091) (0.090) Mtb -0.001 -0.001 -0.001 (0.003) (0.003) (0.003) Constant 0.697*** 0.654*** 0.702*** (0.112) (0.102) (0.123) Observations 599 599 599 R-squared 0.189 0.193 0.191

Year fixed-effect Yes Yes Yes

Industry fixed-effect Yes Yes Yes

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

The results are similar to those of the main analysis. Of the control variables only Size and Leverage are significantly related to cost of capital. In this case however the R-squared is 0,191, indicating that 19,1% of the variation in cost of capital is explained by the model. This is a lot higher than our main results.

The results of the re-estimates for both hypotheses are similar to the main analysis. Therefore in this study the choice of measurement for cost of capital didn’t have an effect on the results of this study.

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25 | D i s c u s s i o n a n d c o n c l u s i o n B a r t S i j b o m 5. Discussion and conclusion

In this chapter the main findings of this study are presented, which results in an answer of the main research question. Secondly, the theoretical and practical implications based on these findings are described. After that the limitations of this study and thoughts for future research are given. And at last follows the overall conclusion of this study.

5.1 Findings

The main goal of this research is to understand the different effects of different aspects of the IFRS 7 disclosures on cost of capital. More specific, I investigated whether assurance

disclosure, being a more positive kind of information, leads to a lower cost of capital than risk disclosure, being a more negative kind of information. According to the literature risk

disclosure is negatively related to cost of capital. However empirical results have seen mixed results. Explanations are often sought in the measurement of cost of capital. However Kothari et al. (2009) argue that the relation is influenced by the tone of the disclosure. I.e. positively phrased risk disclosure follows the argumentation from literature and has a negative relation with cost of capital. Whereas more negatively phrased risk disclosure is positively related to cost of capital. Therefore it’s expected that the assurance aspect of a disclosure has a more negative relation with cost of capital than the risk aspect of the disclosure.

The first hypothesis focusses on the effect of different contents, i.e. positive and negative information, of the disclosure on the cost of capital. Although the results show a negative coefficient for the Assurance/Risk disclosure, as well as difference in coefficient between the quality of the assurance part and quality of the risk part. It is not significant enough to draw inferences based on usual significance levels, therefore I find no support for hypothesis 1. One explanation for the non-significant results is that the measure used in this study differs from the measures used in other studies Campbell et al, 2014; Christensen et al., 2010; He et al., 2019). This study focusses on IFRS 7 disclosure and especially on the assurance and risk part of the disclosure, which is unlike previous studies. Another explanation could be that the sample is too small or unrepresentative of the population.

The second hypothesis focusses on the moderating effect of information asymmetry on the proposed relation of hypothesis 1. The results show how the moderator changes the

coefficient for the Assurance/Risk disclosure effect on cost of capital. It is not significant enough to draw inferences based on usual significance levels, therefore I find no support for hypothesis 2. One explanation for the non-significant results could be that since there is no significant relationship found in hypothesis 1 it’s unlikely that testing a moderator on this relation will find significant results.

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U n i v e r s i t y o f G r o n i n g e n 26 | D i s c u s s i o n a n d c o n c l u s i o n

One alternative cost of capital measures is used to test the robustness of these findings. The results found are mainly similar to the original measure.

Summarizing this paragraph the research question can be answered: “Have the IFRS 7 disclosures led to benefits for companies in terms of lower cost of capital because of increased disclosure?” The results show no significant evidence that a company’s cost of capital is influenced by the level of IFRS 7 disclosure. Furthermore the results show no significant evidence that information asymmetry influences the potential relation between IFRS 7 disclosure and a companies cost of capital. Concluding this paragraph this study does not succeed in providing support for the benefits for companies of IFRS 7 disclosure.

5.2 Theoretical and practical implications

The main theoretical implication of this study is investigation the effect of IFRS 7 disclosure, specially the distinction between assurance and risk, on the cost of capital of companies. To the best of my knowledge, this study is among the first considering this distinction between the different parts in the disclosure. Therefore, this study adds to the existing literature about the relationship between level of disclosure and cost of capital. Most studies focus on the entire disclosure and don’t differentiate between the different tones and content of the disclosures.

A second theoretical implication is the investigating into the moderating effect of information asymmetry on the relation between disclosure and cost of capital. Most studies focusing on information asymmetry and cost of capital assume a direct relation between the two. These studies show mixed results. This study is among the first to not assume a direct relation but a moderating effect.

A policy implication for the IASB is that companies should be encouraged to focus on the improving the quality of disclosure regarding their hedging activities. This paper is therefore in line with the study of He et al. (2019) who find that investors are likely to misinterpret the meaning of risk disclosure components.

A practical implication of this study could be for the interpreters of the IFRS 7 disclosures of the companies. By showing that disclosing more on assurance over risks leads to a lower cost of capital. Companies have an incentive to downplay the risk part and promote the assurance part. The results show that the companies can also actually benefit from this because it leads to a lower cost of capital for them.

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27 | D i s c u s s i o n a n d c o n c l u s i o n B a r t S i j b o m

Investors should be aware of the incentive that companies have to downplay their risk. They should be critical when a company discloses substantially more on assurance than on risks.

The results of this study might also be off interest for the legislators, in this case the IASB. From the requirements of the disclosure speaks a desire of transparency. However the

disclosure does not align with the desires of the companies disclosing the information. It also seems that companies significantly benefit more from disclosing assurance than from risks. Asking companies to disclose risks is already quite challenging of its own. This study shows that it’s even more challenging because it’s more favorable for companies to disclosure more assurance relative to risks in the IFRS 7 disclosures.

Overall, this study complements existing literature and provides further evidence that is of interest to both standard setters and academics.

5.3 Limitations and further research

The results of this study are obviously subject to some limitations. One of the main problems regarding these types of studies is regarding the measurement techniques used for cost of capital and information asymmetry. Within the literature there is no one single agreed upon measure for these variables. It might be possible that the measured cost of capital is different from the actual cost of capital.

A second limitation is the data set used in this study. The results of this research are based on data between 2007 and 2016. Out of all the observations, 2699, a total of 599 made it into the sample size. Because data was combined from several sources, all observations with no complete data available were deleted, therefore a selection bias could exist. Furthermore, this study only focusses on the UK, which means the results of this study may not be generalizable for other European countries.

This study also provides some possibilities for future research. In line with the limitations of this study, future research might focus on different countries. As Cormier et al. (2005) have shown, there might be differences in the way investors perceive risk disclosures between countries. Another avenue of further research lies in following Kothari et al. (2009) and no longer research disclosures as a whole but incorporate tone and content of the disclosure within future research.

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U n i v e r s i t y o f G r o n i n g e n 28 | R e f e r e n c e s References

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Campbell, J. L., Chen, H., Dhaliwall, D. S., Lu, H.-m., & Steele, L. B. (2014). The information content of mandatory risk factor disclosures in corporate filings. Rev Account Stud, 19, 396-455.

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29 | R e f e r e n c e s B a r t S i j b o m

Miihkinen, A. (2012). What drives quality of firm risk disclosure? The impact of a national disclosure standard and reporting incentives under IFRS. The International Journal of Accounting, 47, 4317-468.

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U n i v e r s i t y o f G r o n i n g e n 30 | A p p e n d i c e s Appendices

Appendix A1: Construction of the Disclosure index – Nature and Extent of risk

IFRS 7 Disclosure Quality Index

Item# Name of the item Explanation

1

Explicit and detailed disclosure of the monetary amount of hedge activity - Interest rate risk

If the company states explicitly hedging related to interest rate risk and discloses detailed monetary amount, it is coded as 1. Otherwise "0". If it is crossreferenced it will be considered as “0” as well.

If the company discloses the non-existence of hedging it is coded as 1 as well.

IFRS7.2 1A

2

Explicit and detailed disclosure of the monetary amount of hedge activity - Hedge activity - Currency Risk

If the company states explicitly hedging related to currency risk and discloses detailed monetary amount, it is coded as 1. Otherwise "0". If it is crossreferenced it will be considered as “0” as well.

If the company discloses the non-existence of hedging it is coded as 1 as well.

IFRS7.2 1A

3

Explicit and detailed disclosure of the monetary amount of hedge activity - Hedge activity - Other Price Risk (commodity and equity price risks)

If the company states explicitly hedging related to other price risk (risks for equity or commodity prices) and discloses detailed monetary amount, it is coded as 1. Otherwise "0". If it is cross-referenced it will be considered as “0” as well.

If the company discloses the non-existence of hedging it is coded as 1 as well.

IFRS7.2 1A

4

Explicit and detailed disclosure of the monetary amount of hedge activity - Hedge activity - Liquidity Risk

If the company states explicitly hedging related to liquidity risk and discloses detailed monetary amount, it is coded as 1. Otherwise "0". If it is crossreferenced it will be considered as “0” as well.

If the company discloses the non-existence of hedging it is coded as 1 as well.

IFRS7.2 1A

5

Explicit and detailed disclosure of the monetary amount of hedge activity - Hedge activity - Credit Risk

If the company states explicitly hedging related to credit risk and discloses detailed monetary amount, it is coded as 1. Otherwise "0". If it is crossreferenced it will be considered as “0” as well.

If the company discloses the non-existence of hedging it is coded as 1 as well.

IFRS7.2 1A

6 Disclosure of the existence of collateral for credit risk exposure

If the company does not disclose the level of collateral, then the company will get a score “0”.

If the company makes narrative disclosures on the level of collateral in the risk disclosure, then the company will get a score “1”.

If the company discloses tables containing quantitative data regarding the level of collateral in the risk disclosure, then the company will get a score “2”. If the company disclose about the non-existence of collateral it is coded as 2 as well.

IFRS7.3 8a

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31 | A p p e n d i c e s B a r t S i j b o m

7 Explicit disclosure of the amount of maximum exposure to credit risk

If the company does not disclose the amount of credit risk exposure explicitly, the company will get a score “0”. If the company gives a cross reference to any other notes or financial statement information other than the notes on financial instrument risk disclosure, then the company will get a score “1”.

If the amount is disclosed in the financial instrument risk disclosure, then the company will get a score “2”.

IFRS7.3 6a

8 Explicit and Detailed Explanation of credit risk concentration

If the company does not disclose credit risk concentration, then the company will get a score “0”.

If the company makes narrative disclosure on credit risk concentration, then the company will get a score “1”. If the company gives a cross reference to any other notes or financial statement information, it will be coded as “1”. If the company has an explicit and detailed explanation of credit risk concentration, then the company will get a score “2”. If the company discloses non-existence of concentration, the company will get a score “2”.

IFRS7.35b

9 The existence of Impairment

If the company does not disclose impairment of financial assets, then the company will get a score “0”.

If the company gives a cross reference to any other notes or financial statement information, then the company will get a score “1”.

If the company makes narrative disclosures on impairment in the risk disclosure, then the company will get a score “2”.

If the company has an explicit and detailed explanation of impairment, then the company will get a score “3”. If the company discloses non-existence of impairment it is coded as 3 as well.

IFRS7.35a

10

Sensitivity Analysis (VAR)

If the company discloses a detailed analysis with monetary amounts, then code it as “1”. Otherwise "0". IFRS7.40a and IFRS7.41 Explicit and detailed disclosure of the

amount in the Sensitivity Analysis - Interest rate risk

If the company has no VAR analysis, but the company has an explicit and detailed disclosure of the amount in the sensitivity analysis to manage interest rate risk, then the company will get a score “1”. Otherwise "0".

If the company has VAR analysis, the company will be coded directly as “1”.

IFRS7.40a and IFRS7.41

11

Explicit and detailed disclosure of the amount in the Sensitivity Analysis - Sensitivity Analysis - Currency Risk

If the company has no VAR analysis, but the company has an explicit and detailed disclosure of the amount in the sensitivity analysis to manage currency risk, then the company will get a score “1”. Otherwise "0".

If the company has VAR analysis, the company will be coded directly as “1”.

IFRS7.40a and IFRS7.41

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U n i v e r s i t y o f G r o n i n g e n 32 | A p p e n d i c e s

12

Explicit and detailed disclosure of the amount in the Sensitivity Analysis - Sensitivity Analysis - Other Price Risk (commodity and equity price risk)

If the company has no VAR analysis, but the company has an explicit and detailed disclosure of the amount in the sensitivity analysis to manage other Price

Risk (commodity and equity price risk), then the company will get a score “1”. Otherwise "0". If the company has VAR analysis, the company will be coded directly as “1”. Otherwise "0".

IFRS7.40a and IFRS7.41

13

Existence of a maturity analysis for derivative and non-derivative financial assets/liabilities

If the company does not have any maturity analysis, the company will get a score of “0”.

If the company has maturity analysis of only for non-derivative financial liabilities or derivative financial liabilities, then the company will get a score “1”. If the company has maturity analysis of both for non-derivative financial liabilities and derivative financial liabilities, then the company will get a score “2”.

IFRS7.39a-b

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33 | A p p e n d i c e s B a r t S i j b o m Appendix A2: Summary of test variables

Summary of test variables – average per year

Table I

Year N Percentage CoC Spread Assurance/

Risk Of total N 2008 82 13,7% 0.31 2.69 -0.09 2009 121 20,2% 0.29 2.60 -0.11 2010 50 8,3% 0.31 3.75 -0.21 2011 58 9,7% 0.33 2.18 -0.18 2012 53 8,8% 0.25 2.36 -0.29 2013 45 7,5% 0.30 2.47 -0.07 2014 62 10,4% 0.28 3.68 -0.17 2015 67 11,2% 0.40 2.69 -0.27 2016 61 10,2% 0.25 1.90 -0.23

Summary of test variables – average per industry

Table II

Industry N Percentage CoC Spread Assurance/ Risk Of total N Basic Materials 41 6.8% 0.39 1.35 -0.58 Consumer Goods 112 18.7% 0.29 3.10 -0.27 Consumer Service 162 27.0% 0.29 2.72 0.01 Health Care 17 2.8% 0.24 3.34 -0.05 Industrials 203 33.9% 0.29 2.70 -0.19

Oil & Gas 33 5.5% 0.45 2.08 -0.11

Technology 14 2.3% 0.19 3.73 -0.42

Telecommunicati on

3 0.5% 1.19 0.15 -0.04

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U n i v e r s i t y o f G r o n i n g e n 34 | A p p e n d i c e s Appendix A3: List of Variables

IFRS_7_Disclosure_Quality

An index variable computed as the equal weighted average of the normalized value of 13 items presented in the Table A1.

IFRS_7_Disclosure_Quality-Assurance An index variable computed as the equal weighted average of the normalized value of 6 items on hedging and collateral presented in the Table A2.

IFRS_7_Disclosure_Quality-Risk An index variable computed as the equal weighted average of the normalized value of 3 items on risk exposure, risk concentration and impairment presented in the Table A2.

IFRS_7_Disclosure_Quality-Other An index variable computed as the equal weighted average of the normalized value of 4 items on sensitivity and maturity analysis presented in the Table A2.

IFRS_7_Disclosure_AssuranceOverRisk Calculated from the IFRS_7_Disclosure_Quality -Assurance and – Risk indices.

Size The natural logarithm of total assets of a company.

Leverage The debt tot total assets ratio of a company.

Book to market Book to market ratio

Cost of Equity Capital Cost of capital of a company.

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