dissertation draft
TRANSCRIPT
Dissertation Supervisor: Dr Santhosh Abraham
Word Count: 15,395
Dissertation submitted in partial fulfillmentof the degree of MA (Hons) in
Accountancy and Finance
at
School of Management and LanguagesHeriot-Watt University
Edinburgh
1st April 2015
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Board Determinants of Voluntary Risk Disclosure in UK FTSE 100 Non-Financial Companies: Boilerplate or Value-Relevant
Incremental Information?
by
Christopher Edward StoutH00099984
Abstract
This dissertation provides empirical evidence, within an agency theoretical framework, of the
association between the quantity/quality of voluntary risk disclosures with corporate board
monitoring variables within 72 UK FTSE 100 non-financial companies in the year 2013.
Voluntary risk disclosures were examined within the ‘Principal Risks and Uncertainties’
section of the Annual Report using content analysis. A normalised composite quality index
was constructed measuring the incremental quality value associated with the semantic
properties of the risk information. It was found that as risk disclosure quantity increases, the
quality of risk disclosure decreases. Companies disclose greater quantities of risk information
which is of low incremental quality value with a non-time orientation, qualitative as opposed
to quantitative and neutral in content. This provides evidence of boilerplate risk reporting.
These risk disclosures provide limited usefulness to professional users. It was found after
controlling for firm size, industry, liquidity and leverage, that independent non-executive
directors are significantly positively associated with quantity of risk disclosure, yet non-
significantly related to quality of risk disclosure. If increasing the quantity of risk disclosure
leads to greater and greater generic non-useful information, then the results indicate that
independent non-executive directors are contributing to a greater quantity of boilerplate risk
disclosure but are not enhancing the quality or incremental value of the risk information.
Additionally, no association was found between board size and either quantity or quality of
risk disclosure, suggesting that board size, another influencing factor of a board’s monitoring
effectiveness, is not a significant determinant of risk disclosure. It is proposed that the UK
board’s effectiveness in performing a monitoring function, may not be sufficient to the extent
that information asymmetries are only partially reduced through quantity dimensions but not
via quality dimensions between managers and shareholders. This research uncovers valuable
insights from an agency theory perspective, as to the voluntary risk disclosure practices
influenced by managers and implemented by boards in corporate narratives.
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Acknowledgements
I would like to thank Dr Santhosh Abraham for his continual interest and helpful remarks
throughout 4th year in completing my dissertation in partial fulfilment of the degree of MA
(Hons) in Accountancy and Finance. Additionally, I would also like to thank my family and
friends for their support throughout writing this dissertation.
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Personal Statement
I, Christopher Edward Stout, confirm that I have read and understood the SML
Undergraduate Dissertation Courses: Regulations and Procedures. I understand that the
university considers plagiarism a form of cheating and is taken very seriously. To provide
assurance that all the materials presented in this dissertation are my own effort, I ensure that
all work used as well as ideas taken from other researchers is properly referenced in
accordance with the Harvard system of referencing. I further provide confirmation that my
dissertation has gained ethical approval.
Signature:
Date:
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Table of Contents
Abstract.........................................................................................................................................1
Acknowledgements........................................................................................................................2
Personal Statement.......................................................................................................................3
List of Tables.................................................................................................................................6
List of Figures...............................................................................................................................7
1. Introduction..............................................................................................................................8
1.1 Introduction.................................................................................................................................8
1.2 Motivation to Investigate Corporate Boards................................................................................9
2. Literature Review....................................................................................................................11
2.1 Defining Risk.............................................................................................................................11
2.2 Benefits and Costs of Risk Disclosure.......................................................................................12
2.2.1 Benefits.................................................................................................................................12
2.2.2 Costs.....................................................................................................................................13
2.3 Disclosure Vehicles...................................................................................................................14
2.4 UK Risk Reporting Regulations................................................................................................15
2.4.1 Legislation and Listing Requirements..................................................................................16
2.4.2 Accounting Standards...........................................................................................................16
2.4.3 Voluntary Corporate Governance Codes..............................................................................17
2.5 Risk Disclosure Theories...........................................................................................................17
2.6 Agency Theory, The Board of Directors and Information Asymmetries...................................18
2.6.1 Agency Theory.....................................................................................................................19
2.6.2 Information Asymmetries and the Board of Directors.........................................................21
2.7 Control Variables.......................................................................................................................24
2.7.1 Firm Size...............................................................................................................................24
2.7.2 Leverage...............................................................................................................................24
2.7.3 Liquidity................................................................................................................................25
2.7.4 Industry.................................................................................................................................25
2.8 Hypotheses Development..........................................................................................................25
2.8.1 Independent Non-Executive Directors (INED’s) and the Quantity/Quality of Risk Disclosures.....................................................................................................................................25
2.8.2 Board Size and the Quantity/Quality of Risk Disclosures....................................................26
3. Sample Collection and Research Methodology.......................................................................28
3.1 Sample Collection......................................................................................................................28
3.2 Research Methodology..............................................................................................................29
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3.2.1 Dependent Variable: Quantitative Dimension......................................................................29
3.2.2 Dependent Variable: Quality Dimension..............................................................................32
3.2.3 Model Specification..............................................................................................................38
4. Empirical Results....................................................................................................................40
4.1 Content Analysis........................................................................................................................40
4.1.1 Descriptive Statistics and Total Risk Disclosure Quantity...................................................40
4.1.2 Financial versus Non-financial Risks...................................................................................41
4.1.3 Semantic Properties..............................................................................................................42
4.2 Normalized Composite Quality Index.......................................................................................44
4.2.1 Descriptive Statistics............................................................................................................44
4.2.2 TOM Index...........................................................................................................................44
4.2.3 ES and OP Index...................................................................................................................45
4.2.4 Incremental Quality Value....................................................................................................46
4.2.5 Comparison of RDQ with NCQI..........................................................................................47
4.3 Cross-sectional Regression Analysis.........................................................................................48
4.3.1 Independent Variables..........................................................................................................48
4.3.2 Gauss-Markov Assumptions.................................................................................................49
4.3.3 Cross-sectional Analysis.......................................................................................................54
5. Discussion and Conclusions.....................................................................................................57
5.1 Risk Reporting Practices............................................................................................................57
5.2 Monitoring Environment...........................................................................................................58
5.3 Information Asymmetries..........................................................................................................61
5.4 Limitations.................................................................................................................................62
5.5 Further Research........................................................................................................................63
References...................................................................................................................................65
Appendix A: Decision Rules, Coding Template and Checklist....................................................73
Appendix B: Risk Sentence Examples and Sample Companies..................................................76
Appendix C: Regression Models (SPSS Output).........................................................................80
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List of Tables
Table 1 - Sample Size Calculation
Table 2 - Descriptive Statistics for Risk Sentences and Semantic Properties
Table 3 - Descriptive Statistics for Indices Measuring Quality of Risk Disclosure
Table 4 - Descriptive Statistics for Continuous Independent Variables
Table 5 - Sample Companies Industry Classification
Table 6 - Correlation Matrix
Table 7 - RDQ Multiple Regression Model
Table 8 - NCQI Multiple Regression Model
Table 9 - Hypotheses Testing and Key Outcomes
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List of Figures
Figure 1 - Proposed Solution to Boilerplate Risk Reporting
Figure 2 - Risk Reporting Regulatory Landscape
Figure 3 - Information Asymmetries
Figure 4 - Proposed Agency Theoretical Framework
Figure 5 - Coding Structure
Figure 6 - Types of Risks Disclosed
Figure 7 - Type of Measure Index
Figure 8 - Economic Sign Index and Outlook Profile Index
Figure 9 - Normalized Composite Quality Index
Figure 10 - RDQ Comparison with NCQI
Figure 11 - RDQ Model Residuals versus Predicted Values
Figure 12 - NCQI Model Residuals versus Predicted Values
Figure 13 - Summary of Empirical Results within Agency Theoretical Framework
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1. Introduction
1.1 Introduction
Risk management has received increased attention in recent years, as it is the commonly held
belief among investors, that the business environment has become increasingly more volatile
and uncertain with the greater complexity of commercial dealings (CIMA, 2011). There
exists a growing demand for improved risk reporting of the principal risks and uncertainties
that a firm is exposed to in both its day-to-day operations and future activities (ICAEW,
2011). In light of the 2007/2008 financial crisis, the quality of risk information has been a
major concern, not restricted to the financial industry (ICAEW, 2011; Dobler, 2008). Despite
the implementation of internal control guidance through voluntary corporate governance
codes, the corporate board voluntarily discloses risk information based on its own risk
appetite (ICAEW 1999). The challenge for corporate boards is the alignment of their own
risk appetite with investors, without jeopardizing future business strategies, corporate
reputation or internal risk management systems. Boards are conscious of the negative
consequences imposed by shareholders if the risk information is deemed to negatively impact
the firm (Miihkinen, 2013). Hence, risk reporting is found by many users, analysts in
particular, as too vague in nature to be considered useful (Campbell and Slack, 2008). More
generally, company Annual Reports have increased in size obscuring high quality risk
disclosures and undermining useful information. This has prompted the UK initiative ‘cutting
clutter’ with the intended aim of improving narrative reporting (FRC, 2011). In the context of
risk disclosure, boilerplate1 risk information is considered not to be totally void of quality
value, nevertheless, these safe risk disclosures serve limited use for professional users.
(Abraham and Shrives, 2014).
Professional Bodies and the Accounting Standards Board (ASB) have highlighted
deficiencies, identified challenges and conclude a general lack of improvement in risk
reporting practices (ICAEW, 2011; FRC, 2009; Campbell and Slack, 2008). A number of
studies have investigated the extent of compliance with mandatory risk disclosure regulation
and the impact of adoption on risk reporting practices (Greco, 2011; Bamber and
McMeeking, 2010; Bhamornsiri and Schroeder, 2004). In the absence of comprehensive
mandatory regulation such as in the UK, voluntary risk disclosure determinant studies have
1 Boilerplate risk disclosures are defined as “writing that says nothing new, informative or interesting” (Rooney, 2001, p.155).
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been useful to determine which firm specific and corporate governance variables are
influencing the quantity of risk disclosure. These studies have incorporated disclosure theory,
including incentives (Dobler, 2008), to provide motivations for risk disclosure from a
preparers perspective and have found conflicting results (Linsley and Shrives, 2006, Oliveria
et al, 2011; Abraham and Cox, 2007; Amran et al, 2009; Rajab and Schachler, 2009). The
focus of investigation in these studies was on the quantity dimension of risk information, with
very few exceptions investigating risk disclosure quality dimensions (Berretta and Bozzolan,
2004; Mokhtar and Mellett, 2013; Miihkinen, 2012; Abraham and Shrives, 2014). From an
investors or users perspective, there have been studies exploring the value relevance of risk
disclosures, predominately market risk disclosures (Ahmed et al, 2004; Linsmeier et al, 2002;
Jorion, 2002). More recently, a number of comprehensive US studies have looked at the
informativeness of mandatory risk factor disclosures (Campbell et al, 2011), a new
methodological approach to quantifying risk disclosures using a multi-label text classification
algorithm (Huang, 2011) and the value relevance of textual risk disclosures in 10-K filings
(Kravet and Muslu, 2011). The broad consensus from the most recent US studies seems to
suggest that investors surprisingly perceive risk disclosures as informative, despite the
reporting deficiencies that exist in annual reports (Miihkinen, 2013).
1.2 Motivation to Investigate Corporate Boards
The focus on board determinants in this study, allows for investigation into the corporate
boards chosen governance arrangements which influence risk disclosure policy or strategy. In
the context of the principal-agent framework (Abraham and Cox, 2007; Oliveria et al, 2011),
the board of directors act as a corporate governance mechanism enhancing the monitoring
environment and decreasing agency problems stemming partly from information asymmetry.
Indeed, for managers2, the board acts as the top internal disciplining court of appeals (Fama
and Jensen, 1983).
Standard setters face significant challenges with the regulation of risk reporting. Extensive
disclosure requirements will impose disclosure costs on companies, leading to boilerplate risk
disclosures of little value-relevance to professional users (Jorgensen and Kirschenheiter,
2 The term ‘managers’ refers to the internal agents and decision makers of an organisation. The Chief Executive Officer (CEO) is considered the individual top manager or “highest ranking executive officer within a company or corporation, who has responsibility for overall management of its day-to-day affairs under the supervision of the board of directors” (Rooney, 2001, p.246).
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2003). Alternatively, piecemeal regulations will also lead to boilerplate risk disclosures as
boards are more inclined to disclose nothing (Dobler, 2008). This is not to say that external
risk reporting requirements are ineffectual but perhaps moderate external reporting
requirements would be most influential on risk disclosure. Thus, Corporate Boards are
suggested to provide an internal solution to boilerplate risk disclosures where an extensive
monitoring environment decreases incentives for mangers to act opportunistically, improving
risk reporting information flows3.
Figure 1: Proposed Solution to Boilerplate Risk Reporting
Extensive Endogenous Monitoring Environment Provided by Boards
and Moderate Exogenous Risk Disclosure Requirements
Boilerplate Risk Disclosures
Boilerplate Risk Disclosures
High Quality Risk Disclosures
Excessively High Exogenous Risk Disclosure Requirements
Piecemeal Exogenous Risk Disclosure Requirements
This study offers a solution to uninformative risk reporting practices to be tested empirically
in accordance with an agency theoretical framework. This provides a tentative first step to
understanding board determinants, or more importantly, non-board determinants of risk
disclosure quality and quantity. This will determine the extent to which boards provide a
monitoring function to prevent managers from opportunistically concealing risk information
at board level intended to maintain the status quo, where risk disclosures do not convey real
meaning (Abraham and Shrives, 2014).
3 The CEO is situated on the board of directors and it is therefore expected that the board uses internal risk information provided by the CEO, along with lower ranking managers, in determining the quality/quantity of risk information disclosed to the outside world (Fama and Jensen, 1983).
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2. Literature Review
2.1 Defining Risk
This study with the intention of identifying risk disclosures, must first fundamentally,
establish a definition of risk. At present, ideas of risk include an uncertainty-based and target-
based conceptualization (Dobler, 2008). The uncertainty based view may be further
deconstructed into unmeasurable subjective uncertainty and supposedly objective measurable
uncertainty, in the form of probability (Holton, 2004). The latter constitutes a decision
situation and Knight’s (1921) definition considers risk as the uncertainty of a distribution of
future outcomes which can be expressed numerically using probabilities. Basically, risk is
considered quantitatively measurable. In portfolio theory, risk is measured as the variance of
returns and expected returns are computed using probability inputs (Markowitz, 1952). Non-
systematic risk may be reduced through diversification strategies where as non-diversifiable
or systematic risk is unaffected by the principles of diversification. Probability, however, is a
measure of the likelihood of an events occurrence. A difficulty, aptly identified by Hansson
(2004), is that probabilities are dependent on the observer’s knowledge or lack of knowledge
more precisely. If probabilities were known with certainty, the observer would possess
knowledge which could predict future outcomes. In reality, nearly all decisions are decisions
under uncertainty. Subscribers to the unmeasurable view of uncertainty claim that human
beings can only operationally define their subjective perception of risk, not actual risk itself,
which is inherently unpredictable. This is a criticism of Bayesian decision theory where it
does not consider the cognitive limitations of humans (Hansson, 2004). Examples of risks
where quantitative probabilities are extremely difficult to apply, are in natural disasters and
potential political instability to name a few. The common element associated with either
measurable or unmeasurable uncertainties adopted in this study, is that all risks will relate to
a distribution of future outcomes. Therefore, risk reporting is considered to document the
risks that influence a firm’s distribution of future cash flows, as it provides shareholders with
the knowledge of potential impacts on future economic performance (Dobler, 2008).
The target-based view of risk provides additional conceptual insight. Risk is defined as the
potential deviation from a particular focal point (Borch, 1968). The distinction is made
between the potential loss or gain, also known as upside or downside risk (Borch, 1968;
ICAEW, 2002). In terms of a company’s cash flows, it is ludicrous to expect only negative
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outcomes to occur. All companies would become insolvent in the long-run and a business
enterprise would cease to exist. A definition of risk disclosure which incorporates both the
uncertainty and target-based views of risk is as follows:
“if the reader is informed of any opportunity or prospect or of any hazard, danger, harm,
threat, or exposure, which has already impacted upon the company or may impact upon the
company in the future or of the management of any such opportunity prospect, hazard, harm,
threat or exposure” (Linsley and Shrives, 2006, p.389).
This all-encompassing and broad definition of risk disclosure tested in prior empirical
literature will be used for the purpose of identifying risk disclosures. The main risks
categorised and analysed in this study consisted of operational risks, financial risks,
empowerment risks, information processing and technology risks, strategic risks and integrity
and governance risks (see appendix A).
2.2 Benefits and Costs of Risk Disclosure
2.2.1 Benefits
If the costs of risk disclosure exceeds the benefits, it is argued that risk reporting becomes a
symbolic practice (ICAEW, 2011). Information asymmetry in the context of corporate
disclosure refers to the situation when one party holds superior information which cannot be
accessed by another party. This information problem or lemon problem is conceptualised in
the Akerlof (1970) paper. It uses the American automobiles market for illustration purposes.
A buyer is unable to distinguish between a good car and a bad car at the time of purchase.
This enables the seller to make the claim that a bad car is equivalent to a good car. The
quality of the car is unknown at this time. Only after a period of time will the buyer have a
better understanding of whether or not a lemon has been sold to them. The lemons therefore
drive out the good cars in this model. A degree of information asymmetry therefore exists
between the buyer and the seller. In the context of risk disclosure, the preparer (i.e. the
manager influenced by boards) holds greater risk information than users (i.e. investors).
Healy & Palepu (2001) suggest that corporate disclosure is the primary mechanism for
reducing information asymmetries, improving economic decision making and allowing for
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the functioning of an efficient capital market. Hence, moral hazard and adverse selection
problems will be minimized (Jensen and Meckling, 1976).
The communication of a company’s risk management polices is considered to reduce the cost
of capital (Linsley and Shrives, 2006; Solomon et al, 2000; Abraham and Cox, 2007; Dobler
2008; Mohobbot, 2005). This rationale stems from portfolio theory where investors require a
higher return for a more risky investment. If future returns were known with certainty then
surely investors would choose the securities which offer the highest returns. In reality, future
returns are to a large degree uncertain. Therefore, disclosure of risk information to interested
users such as investors will improve their decision making and will increase their chances of
picking high return securities (ICAEW, 2011; Markowitz, 1991). Disclosure of corporate risk
information will reduce information asymmetries and therefore decrease the cost of capital as
investors perceive the company as less risky. This is assuming of course that the disclosure of
risk information is interpreted by users as good news and therefore a perception of a less
risky company. Negatively interpreted risk information still reduce information asymmetries
but the bad news may influence users to perceive the company as a more risky investment
hence a rise in the cost of capital (Healy and Palepu, 2001). Recently, Campbell et al (2011)
found disclosure tone or the economic sign impacts the cost of capital in a directional
manner. The cost of capital decreases with the disclosure of good news but increases with the
disclosure of bad news.
2.2.2 Costs
A firm which chooses to disclose risk information will increase agency costs, in the form of
higher bonding costs. There are also reputational costs, political costs, legal costs and
proprietary costs which may be incurred. In relation to proprietary costs, the decision to
disclose or not to disclose will depend on what the implications are for the price of a risky
asset (Verrecchia, 1983). Rational users of undisclosed risk information are conscious of the
existence of this information, however, are unable to distinguish whether the information is
good news or bad news and as a result firm value is discounted to the point whereby it was
more appropriate to have disclosed the information in the first place (Verrecchia, 1983). The
firm must weigh up the cost associated with disclosing risk information and the discount in
firm value if risk information is withheld. According to Verrecchia (1983), if proprietary
14
costs fell to zero, companies would disclose full information. This seems unlikely with the
consideration of the other costs to disclosure.
With regards to litigation costs, risk information which is ambiguous or misleading may lead
users to interpret or make economic decisions based on this risk information which
negatively affects their investment choices. Firms may be legally liable, who perhaps did not
intend to be deceitful, but disclosed inaccurate risk information nevertheless (Deumes, 2008).
Jorgensen and Kirschenheiter (2003) proposed a model of voluntary risk disclosure which
suggested that when managers choosing to disclose discretionary risk information, considered
the impact of fixed disclosure costs. A manager4 will only disclose risk information if the
variance of a company’s future cash flows is low. Otherwise, the manager will withhold the
risk information. It was also suggested, that the implementation of mandatory risk reporting
requirements, which forces firm’s to disclose risk information that they would have not
disclosed voluntarily, reduces a firm’s share price or value.
Exogenous enforcement of risk regulation in isolation, is unlikely to promote informative risk
disclosures if risk reporting is an endogenous system negatively impacted by external
disclosure costs (Dobler, 2008). Due to the unpredictable nature of the information disclosed
in risk reporting, disclosure incentives, perhaps implemented by boards, is possibly the
endogenous solution to minimizing perceived disclosure costs and potentially enhancing the
quality and benefits of risk disclosure.
2.3 Disclosure Vehicles
An Annual Report may be considered as the primary disclosure vehicle used by a company to
disclose risk information to shareholders and stakeholders exterior to the firm. Companies
are, however, increasingly utilizing alternative voluntary mechanisms to disclose risk
information including press releases, conference calls, corporate websites, interim reports and
meetings with management (Abraham et al, 2012). It is important to note that exclusive focus
on just the Annual Reports of companies may portray a fragmented representation of risk
disclosure practices (Unerman, 2000). Thus in this study, if a company is deemed to disclose
low quality risk information within the Annual Report, this does not necessarily mean that a
4 When managers are referred to as disclosing risk information, this is in accordance with the board of directors as the annual report is prepared by the board (Abraham and Cox, 2007).
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company across all disclose vehicles is disclosing comparatively uninformative risk
information in relation to other firms.
2.4 UK Risk Reporting Regulations
A piecemeal regulatory approach is adopted in the UK (Dobler, 2008). Unlike GAS 5 in
Germany, no comprehensive standard yet exists in the UK. It was found, however, that
exogenous implementation of GAS 5 did not improve the quality of risk reporting practices
overall in Germany and evidence of non-compliance existed (Linsley and Shrives, 2006).
Further evidence of the inadequacy of mandatory risk reporting regulations include
significant differences in the reporting of financial risks relating to financial instruments
(Woods and Marginson, 2004). Pérignon and Smith (2010) found that value-at-risk (VAR)
disclosures were uninformative and were not considered high quality estimates of future
volatility. Woods et al (2008) investigated the extent of de facto harmonization relating to
market risk disclosures in different banks across the globe. Risk reporting practices varied
extensively. A common element of mandatory regulation is the focus on financial risks as
opposed to non-financial risks. This possibly may reflect pressure on standard-setters to
prevent future financial crises from occurring. The ICAEW (2011) considers risk reporting as
limited and should not be considered as a mechanism for the prediction of future financial
failure.
Despite this piecemeal approach and lack of compliance with mandatory requirements,
Miihkinen (2013) considers the UK to be the most advanced in terms of mandatory risk
reporting requirements along with the US, Germany, Finland and Canada. The focus of this
study is on voluntary risk disclosure (i.e. greater than the mandated minimum), hence it is
vitally important to ensure that disclosures made by companies are actually driven voluntarily
rather than by rules (Beretta & Bozzolan, 2004; Core, 2001). A hierarchical depiction of the
risk reporting regulatory landscape is as follows:
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Figure 2: Risk Reporting Regulatory Landscape
2.4.1 Legislation and Listing Requirements
The Companies Act (2006) statutory instrument s414C (2) (b) specifies that a Strategic
Report must be prepared within the Annual Report and contain a section including the
Principal Risks and Uncertainties to which companies are exposed. The content and quality
of risk information contained within this section are unregulated and voluntary. It is argued
that a section of the Annual Report dedicated to risk disclosure will allow investors to easily
interpret a company’s risk profile and make investment decisions (Beretta and Bozzolan,
2004). Companies listed on the FTSE 100 are required to disclose key investment risks (LSE,
2010) for investors to make financial decisions.
2.4.2 Accounting Standards
International Financial Reporting Standards (IFRS) implemented by the IASB focus
predominantly on financial risks associated with derivatives such as IAS 32, IAS 39 and
IFRS 7 (Mokhtar & Mellett, 2013). This accounts for a small proportion of the total risks
faced by a company (Cabedo and Tirado, 2004). IAS 1 and IAS 37 refer to risk disclosure
through the presentation of financial statements and provisions (Thuélin et al, 2006). The
Financial Reporting Council (FRC) issued a number of national accounting standards such as
FRS 4, FRS 5, FRS 8 and most significantly FRS 13. FRS 13 focuses on the disclosure of
financial instruments and their implications for business risk. Principally four types of risks
have been identified as significant in relation to financial instruments and are market risk,
credit risk, cash flow risk and liquidity risk (Mohobbot and Konishi, 2005). Financial risk
17
disclosures relating to IFRS or FRS are more likely to be found in the notes to the financial
statements (Abraham and Cox, 2007).
2.4.3 Voluntary Corporate Governance Codes
Voluntary codes have played a crucial part in linking the overall agenda for corporate
governance reform to improvements in the risk reporting practices of companies, enhancing
communications between investee firms and their investors (Solomon, 2013). In relation to
internal control reporting requirements, The Cadbury Report (1992) highlighted risk
disclosure as a central issue for corporate governance reform (Solomon et al, 2000). It
identified impediments to the disclosure of relevant risk information to outsiders as market
imperfections. The Turnbull Report (1999) built on the Combined Code (1998) by linking
corporate risk disclosure to internal control (Abraham and Cox, 2007) and established a
conceptual framework for the disclosure of risk (Solomon et al, 2000; ICAEW, 1999). In
2003, the Combined Code was again revised to include recommendations on board
composition and audit committees, forming both the Higgs Report (2003) and the Smith
Report (2003) respectively (Abraham an Cox, 2007). More recently, the Stewardship Code
(2012) introduced a framework designed for institutional investor commitment, transparency
and accountability with companies. Informative risk reporting should facilitate smooth
functioning of this relationship to enhance long-term shareholder value. Most recently, the
Combined Code (2003) was replaced by the UK Corporate Governance Code in 2012, which
included a number of additional recommendations for enhanced disclosure of risk
information to investors (Solomon, 2013).
In the absence of risk disclosure regulations, firms can seek guidance from professional
bodies and academia (Beretta & Bozzolan, 2004). Otherwise, it is up to the board’s own
discretion as to how to voluntarily disclose risk information, which is dependent upon their
own risk appetite (ICAEW, 1999).
2.5 Risk Disclosure Theories
Mathews and Perera (1996) consider a theory to be composed of a body of knowledge,
internally consistent, provide solutions and explain or predict phenomena. There is, however,
18
no universally accepted theory of discretionary risk disclosure which identifies determinants
of voluntary risk disclosure (Abraham and Shrives, 2014). A wide range of disclosure
theories exist instead, including agency, signalling, capital needs, proprietary costs,
attribution, legitimacy, stakeholder and resource-dependence (Abraham and Shrives, 2014).
A large number of studies make no attempt to incorporate disclosure theory at all (Abraham
& Shrives, 2014). A theoretical dichotomy appears to exist in the voluntary risk disclosure
literature (Oliveria et al, 2011). A distinction is made between self-interested, profit
maximising behaviour and consideration of wider social and political objectives where
systems oriented theories are used to describe disclosure practices.
With review of the risk reporting literature, it is evident that there are two ways in which
theory is currently constructed for empirical investigation. Firstly, one particular theory is
selected based on its suitability in relation to design of a particular hypothetical argument
(Rajab and Schachler 2009; Amran et al 2009). This approach has been criticized as unable to
fully explain the complex motivations influencing risk disclosure (Ntim et al 2013).
Secondly, a multi-theoretical approach is adopted when no single theory is appropriate in
explaining risk reporting practices. Although it is not always possible to identify how specific
theories relate to determinant variables (Abraham & Shrives, 2014), along with a number of
incompatibility problems associated with combination (Ntim et al, 2013).
The current state of descriptive theorizing seems to lack consensus. A pioneering normative
approach has recently been suggested by Abraham and Shrives (2014) who developed a
model of determining risk disclosure quality, with the intended aim of improving risk
reporting practices. Be that as it may, in this study it was opted to utilise a descriptive theory,
agency theory specifically, as the interest of this study was in board motivations, influenced
by managers, to disclose risk information. The agency perspective, especially in the context
of boards, is considered by many researchers as the dominant research paradigm although
does suffer from a number of limitations (Wright et al, 2001).
2.6 Agency Theory, The Board of Directors and Information Asymmetries
A theoretical link exists between corporate governance and voluntary disclosure (Cheng and
Courtenay, 2006; Eng and Mak, 2003). The board’s major roles are to provide both oversight
and advisory functions as well as critically the ability to fire, hire and compensate managers
19
(Larcker and Tayan, 2011; Fama and Jensen, 1983). The board plays an extremely important
role in the risk management of a firm (ICAEW, 1999). The disclosure of risk information is
integral for a board’s overall risk management strategy and corporate governance (Solomon
et al, 2000). As an integral corporate governance mechanism, the board is responsible for
producing the Annual Report and therefore it is expected that risk disclosure policy emanates
from the board (Abraham and Cox, 2007). The board of directors may be considered at the
heart of an organisations control and decision making system (Fama and Jensen, 1983). It is
therefore expected that the board play’s an integral role in the provision of a framework of
accountability and governance, limiting discretion on the top manager’s part, in terms of
decision making.
2.6.1 Agency Theory
The positivist approach to the application of agency theory5 links disclosure behaviour to
corporate governance (Ho and Wong, 2001). Agency theory identifies scenarios where the
goals of the principal(s) and the agent(s)6 conflict and predicts those governance mechanisms
which mitigate the agent’s self-interested behaviour, control the agency problem and reduce
information asymmetries (Eisenhardt, 1989). In the context of this study, agency theory is
considered most appropriate with consideration of the large diffused equity ownership of
FTSE 100 non-financial firms. Corporate governance mechanisms exist to decrease
manager’s incentives to engage in opportunistic behaviour including the misallocation of
company funds, concealment of risk information from shareholders and the expropriation of
investors (Shleifer and Vishny, 1997).
An agency relationship is considered a contract, a metaphor, whereby the principal(s) plagued
by the free rider problem must appoint an agent(s) to carry out activities on their behalf.
Agents will acquire extensive decision making authority and control over the firm’s non-
human assets (Eisenhardt, 1989; Ross 1973), also known as decision management and control
functions (Fama and Jensen, 1983). Agency theory assumes that the goals of the agent and
the principal conflict. Humans are self-interested and pursue their own objectives where
managers do not act in the principal(s) interests. Both the principal and the agent also exhibit
different attitudes towards risk (Eisenhardt, 1989). Differential risk aversion or moral hazard
5 All individuals are assumed to behave opportunistically (maximization of utility) and rationally in making decisions.6 Manager(s).
20
is one reason for the conflict of interests that exists and stands as a major impediment for the
disclosure of high quality risk information by agents (Lambert, 2001). This is where greater
monitoring, exerted by boards, should play a major role.
A firm is considered a nexus of contacts and the behaviour of the firm is the outcome of a
complex equilibrium process. The solution to agency problems seems to be the establishment
of a nexus of optimal contracts between the agent and the principal. Finding this optimal
structure of contacts is costly. Parties will write an incomplete contract which allows for
agency costs (Hart, 1995). Agency costs are incurred by the principal in the form of
monitoring costs, the agent in the form of bonding costs and the divergence or reduction in
welfare that could not have been avoided in the form of residual loss stemming from the
separation of ownership and control (Jensen and Meckling, 1976):
Agencycosts=monitoring costs+bonding costs+residual loss
Examples of monitoring costs incurred by the principal are incentive compensation schemes
and budget restrictions which alter the mangers potential to capture both pecuniary and non-
pecuniary benefits. This will overall increase firm value if the overall agency costs incurred
to limit the divergent activities of the agent are less than the reduction in firm value that
would have occurred if managers were left to their own devices (Jensen and Meckling, 1976).
Bonding costs are also incurred from the agent’s side, which are meant to guarantee that the
agent will not undertake actions which will harm the principal. Solomon (2013) suggests that
an example of ex-ante bonding costs is the disclosure of additional risk information in the
Annual Report. Managers however will have discretion as to how to present this risk
information at board meetings. They will need to determine whether the bonding costs
incurred are less than the benefits received from the reduction in information asymmetries
and a resulting decrease in the cost of capital assuming investors perceive the risk
information as lowering the overall risk of the firm. Theoretically, risk disclosure therefore
actually increases agency costs in the form of greater ex ante bonding costs (Solomon, 2013)
and gives risk adverse managers the incentive not to improve risk information flows at board
level, to minimize specifically bonding costs or agency costs more generally. This may
overall reduce agency problems and information asymmetries if the sum of agency costs,
including bonding costs, are less than the savings made in establishing a near optimal
contract.
21
2.6.2 Information Asymmetries and the Board of Directors
In the above conceptual development it was assumed that risk disclosure as a fully
encompassing term reduces information asymmetries between managers and shareholders. It
is argued in this study, that risk disclosure is far more complex than a simple quantitative
dimension can explain. Information asymmetries are therefore reduced through both a higher
quantity of risk disclosure and a higher quality of risk disclosure (see figure 3).
Figure 3: Information Asymmetries
Hence, if there is an increase in quantity but quality decreases or remains constant,
information asymmetries will only partially decrease. The same is true, if there is an increase
in quality but not in quantity. If both an increase in quantity and quality is induced by boards
through a more comprehensive monitoring environment, information asymmetries will
decrease at maximum potential between managers and shareholders.
The board of directors is considered a formal information system which monitors manager’s
behaviour (Ho and Wong 2001; Eisenhardt, 1989). Assuming governance mechanisms are
complementary and not substitutive (Bathala and Rao, 1995), the existence of a
comprehensive monitoring environment provided by boards is expected to increase
informative risk disclosures both in terms of quantity and quality therefore decreasing
information asymmetries. Managers have less of an incentive to withhold risk information for
their own benefit. Mangers would be penalised by shareholders, enforced through the board
of directors. John and Senbet (1998) considered three determinants of the effectiveness of
boards in providing an extensive monitoring environment. These determinants included board
size, composition and independence. This study investigates both board size and
independence. Composition is not examined as it is considered to be closely related to
22
independence (John and Senbet, 1998). Board characteristics which are therefore expected to
lead to increased monitoring abilities of manager’s, will have a positive association with both
risk disclosure quantity and quality (see figure 4).
23
Figure 4: Proposed Agency Theoretical Framework
Corporate Board’s Provide an Extensive Monitoring Environment
High Quality of Risk Disclosure
High Quantity of Risk Disclosure
Significant Positive Association with Independent Non-Executive Directors
Significant Positive Association with Board Size
Significant Positive Association with Independent Non-Executive Directors
Significant Positive Association with Board Size
Maximal Reduction of Information Asymmetries between Managers and Shareholders
24
2.7 Control Variables
A number of firm specific variables have been identified in the literature as significant
empirical or theoretical drivers of disclosure (Ahmed and Courtis, 1999). These variables
were included within the regression models as control variables. Firm profitability and audit
firm size were excluded as they were found to be non-significant drivers of disclosure
(Ahmed and Courtis, 1999). CEO/Chairman duality (i.e. dual leadership) was also excluded
as all the companies within the sample separated these roles, hence no variation. Duality is
also considered not to be a major determinant of a board’s monitoring effectiveness (John and
Senbet, 1998).
2.7.1 Firm Size
An important influencing factor on disclosure is the size of the firm (Cooke, 1992; Mokhtar
and Mellett, 2013). Larger companies tend to have a greater proportion of outside capital and
therefore agency costs in the form of monitoring costs will increase (Rajab and Schachler,
2009). Ahmed and Courtis (1999) conducted a meta-analysis and found that small firms
disclose less information compared with large firms. A large number of risk disclosure
studies have found a significant positive association (Mohobbot 2005; Oliveria et al 2011;
Vandemaele et al, 2009; Amran et al, 2009; Lajili and Zeghal, 2007), although, conflicting
evidence has also been found (Hassan 2009; Beretta and Bozzolan 2004; Mokhtar and
Mellett 2013).
2.7.2 Leverage
Prior empirical literature has found a significant positive association between risk disclosure
and leverage (Oliveria et al, 2011; Deumes and Knechel, 2008). Other researchers have found
no association (Rajab and Schachler, 2009; Linsley and Shrives, 2006). Those firms with a
higher debt ratio (i.e. highly geared firms) are perceived as riskier investments by investors
with greater agency costs. Hence, companies disclose risk information to lower the cost of
capital, satisfy investors and reduce agency problems.
25
2.7.3 Liquidity
Liquidity has been found to be a non-significant factor influencing risk disclosure (Elzahar &
Hussainey, 2012; Mokhtar and Mellett, 2013; Hassan, 2009). Those firms with a lower
liquidity ratio are deemed less financially stable than firms with a greater liquidity ratio and
would have an incentive to disclose informative risk information to increase investor
confidence. There is thus strong theoretical impetus to include liquidity within the regression
model.
2.7.4 Industry
Beretta and Bozzolan (2004) argue the environment to which the company is exposed (i.e.
industry) should influence risk disclosure practices. Substantial variation should be observed
for company’s occupying different industries. Beretta and Bozzolan (2004) find no
association, along with Amran et al (2009). Nevertheless, industry is considered an important
influencing variable in the corporate financial reporting literature (Ball and Foster, 1982). A
few empirical risk disclosure studies have found a significant association (Hassan, 2009;
Rajab and Schachler, 2009).
2.8 Hypotheses Development
2.8.1 Independent Non-Executive Directors (INED’s) and the Quantity/Quality of Risk Disclosures
It is assumed in this study that both quality and quantity of risk disclosure are intrinsically
linked. The board is composed of both corporate insiders (executive directors) and outsiders
(non-executive directors) (Hermalin and Weisbach, 2003). Agency theory predicts that not all
classes of non-executive directors will improve accountability and disclosure of information
to shareholders (Haniffa and Cooke, 2002). A distinction should be made between dependent
and truly independent non-executive directors (Abraham and Cox, 2007). Truly independent
non-executive directors are expected to monitor and control the actions of management. Both
Ntim et al (2013) and Oliveria et al (2011) found a statistically significant positive
26
relationship between independent non-executive directors and the quantity of risk disclosure.
Abraham and Cox (2007) also found a significant positive association which was stronger for
independent non-executive directors than for dependent non-executive directors. This
supports agency theory’s prediction that a more independent board leads to an increased
monitoring function of managers, thus resulting in reduced agency problems and a decrease
in information asymmetries through greater risk disclosure quantity/quality (Jensen and
Meckling, 1976; Fama and Jensen, 1983). Hence, it is hypothesised in the alternative form
that:
H1: Ceteris paribus, there is a significant positive association between a higher proportion of
INED’s with the quantity of voluntary risk disclosure within UK FTSE 100 non-financial
companies.
H2: Ceteris paribus, there is a significant positive association between a higher proportion of
INED’s with the quality of voluntary risk disclosure within UK FTSE 100 non-financial
companies.
2.8.2 Board Size and the Quantity/Quality of Risk Disclosures
There are two opposing theoretical viewpoints originating from an agency perspective as to
the association between board size and corporate risk disclosure (Mokhtar and Mellett, 2013).
Larger boards are thought of as providing a more extensive monitoring function in relation to
management, greater expertise and a reduction in agency problems as the CEO will be less
dominant over the board. In contrast, a larger board is perceived as suffering from slower
decision making, poor communication, risk aversion and director free-riding (Larcker and
Tayan, 2011). Smaller boards are thus more efficient decision making mechanisms and are
significant indicators of internal monitoring quality (Lakhal, 2005). A small board, however,
may lack expertise and perhaps exert less of a monitoring function leading to greater agency
problems. Given these trade-offs, an optimal board size it thought to exist (Raheja, 2005),
where optimal monitoring capabilities are achieved. Cheng and Courtenay (2006) found a
non-significant relationship between board size and voluntary disclosure. Few empirical
studies have investigated board size as a determinant of risk disclosure. Elzahar and
Hussainey (2012) found no association between risk disclosure quantity and board size within
interim reports. In contrast Mokhtar and Mellett (2013) found a statistically significant
27
positive association between board size and risk disclosure quantity, supporting the
theoretical proposition that larger boards provide a greater advisory and oversight duty
leading to a higher quantity of risk disclosure and a partial reduction in information
asymmetries. With consideration of the mixed theoretical rationales from an agency
viewpoint and inconclusive empirical literature, the 3rd and 4th hypotheses are stated in the
null:
H3: Ceteris paribus, there is no association between board size with the quantity of voluntary
risk disclosure within UK FTSE 100 non-financial companies.
H4: Ceteris paribus, there is no association between board size with the quality of voluntary
risk disclosure within UK FTSE 100 non-financial companies.
28
3. Sample Collection and Research Methodology
3.1 Sample Collection
The sample consisted of the Annual Reports of UK FTSE 100 listed non-financial companies.
Following (Beretta and Bozzolan, 2004; Linsley and Shrives, 2006; Elzahar and Hussainey,
2012) financial companies (e.g. banks, investment and insurance companies) were excluded
from the analysis. The assets and liabilities of banks or other financial institutions are
primarily financial, as opposed to the real assets held by non-financial companies. Financial
institutions are therefore exposed to predominately financial risks and should be studied
independently. The constituents of the FTSE 100 continually change throughout the year. The
31st December 2013 was the date chosen to represent companies composing the index in the
year 2013. The London Stock Exchange (LSE) information services department provided the
data on those companies which composed the index on the 31st December 2013. Annual
reports were collected from company homepages. Control variables (excluding industry)
were collected through Datastream. The FTSE industry classification benchmark system was
utilised to separate companies into industry group classifications. Board variable information
was gathered manually from the annual reports. The final sample illustrated in Table 1
consisted of 72 companies (also see appendix B).
Table 1: Sample Size Calculation
29
Final Sample Size Calculation 2013
Total FTSE 100 Companies 101
Less Financial Institutions (21)
Non-Financial Companies 80
No Principal Risks and Uncertainties section7 (5)
Annual Report not Available (1)
Missing Data from Datastream (1)
Royal Dutch Shell Combined (i.e. A+B) (1)
Final Sample Size 72
Proportion of FTSE 100 Companies 71.29%
UK FTSE 100 non-financial companies were selected for two primary reasons. Firstly, the
UK adopts a piecemeal regulatory approach in relation to risk disclosure and may choose to
adopt voluntary corporate governance codes. The regulatory environment to a large extent
allows for mangers discretion in the disclosure of risk information as companies are allowed
to choose their own board structure (Guest, 2008). Hence, boilerplate risk disclosure should
be expected in such an environment (see figure 1), an ideal regulatory setting for testing a
board’s monitoring effectiveness in relation to risk disclosure. Secondly, companies whose
shares are listed on the FTSE 100 are primarily owned by institutional investors who were
surveyed not to favour greater regulatory requirements influencing risk disclosure (Solomon
et al, 2000). Thus, the focus on the internal monitoring effectiveness of boards in this study as
a potential solution to boilerplate risk reporting, is in adherence to institutional investor’s
7 Surprisingly, despite the legal requirements in the Companies Act (2006).
30
preferences. This is of course providing that preferences have not changed since the survey
was issued.
The year 2013 was chosen for the following reasons. Firstly, it provides a most recent
representation of UK risk reporting practices in annual reports allowing for comparison with
previous UK studies (Abraham and Cox, 2007; Linsley and Shrives, 2006). Secondly, the
2007/2008 financial crisis raised awareness in internal control and risk management practices
(Solomon, 2013). It should therefore be expected that enhanced risk disclosure quality in
Annual Reports is likely to be top of the agenda for most corporate boards.
3.2 Research Methodology
3.2.1 Dependent Variable: Quantitative Dimension
Content analysis is “a research technique for making replicable and valid inferences from
texts (or other meaningful matter) to the contexts of their use” (Krippendorff, 2013, p.24). In
this study, this research methodology is used to quantify qualitative text within the risk factor
section of annual reports, consistent with other risk disclosure studies (Oliveria et al, 2011;
Linsley and Shrives, 2006). Other approaches to analysing narratives in annual reports are
subjective analyst ratings, disclosure indices, readability studies and linguistic analyses
(Beattie et al, 2004).
The interest of this study is on voluntary disclosure, hence the focus on the risk factor section
of the Annual Report which is frequently termed as ‘Principal Risks and Uncertainties’. Clear
identification of the set of units or population to be coded allows for interpretation and
comparability of results. The objective of this research technique is to capture an objective
underlying reality and in so doing, obtain an accurate value for the quantity of voluntary risk
information disclosed by FTSE 100 non-financial companies. Content analysis measures the
quantity of information a company discloses relative to other companies and therefore no true
zero point exists (Hassan and Marston, 2010). The measure is representative of an interval
scale rather than a ratio scale. Annual Reports have been manually analysed for risk
information as opposed to electronic searching (Beretta and Bozzolan, 2004; Linsley and
Shrives, 2006; Abraham and Cox, 2007). Electronic searching is considered to undervalue the
meaning of text (Krippendorff, 2013). In saying that, a disadvantage of manual coding is that
it is very time consuming.
31
Sentences were selected for both the unit of analysis and unit of measurement in this study.
Milne and Adler (1999) consider sentences the most reliable unit of analysis compared with
alternatives such as words or proportions of pages. Sentences provide context, inferences can
only be drawn from a word in isolation. A series of words to form a sentence, allows for
clarity of meaning and establishes the circumstances that create the setting for an idea to be
understood. Each sentence is analysed to determine whether it may be deemed a risk
disclosure or not and then allocated to the most appropriate risk sub-category (Beattie and
Thomson, 2007). A risk disclosure is further classified according to its semantic properties
discussed later on in this study. With regards to unit of measurement, each coded risk
sentence counts as one risk disclosure. The fundamental premise of content analysis is that
volume indicates the importance of the unit of analysis being disclosed (Unerman, 2000). It is
not merely a presence/absence activity which is a very partial form of analysis and only able
to determine the range or variety of disclosure (Beattie and Thomson, 2007). A problem
inherent with the use of sentences is in the situation where risk information contained within
a sentence may be coded in two or more risk sub-categories. A decision is required, the most
dominant theme must be identified and allocated accordingly. This approach is adopted in
this study. An alternative approach proposed by Beattie et al (2004) suggests splitting the
sentences into text units. This method is not adopted, as it introduces further complexity and
subjectivity to an already subjective process. A trade off exists whereby a greater
understanding and insight is gained through more complex coding decisions although
reduced reliability as a result (Beattie and Thomson, 2007).
Content analysis is a subjective process in the codification of textual information. Three types
of reliability known as stability, replicability and accuracy are commonly used as measures to
test the reliability of the data collected (Krippendorff, 2013). Stability refers to a single
coder’s consistent application of the same procedure over time. Although considered the
weakest of reliability tests (Milne & Adler, 1999), the coder must remain concentrated,
vigilant and attentive when coding across the sample. Replicability and accuracy require
additional individuals for testing. Accuracy is the extent to which coding conforms to a
predetermined standard, set by a selection of experts (Milne and Adler, 1999; Krippendorff,
2013). Replicability or inter-coder reliability measures the degree to which the codification
procedure can be repeated by different individuals producing the same result. Common
measures are Scott’s Pi and Krippendorff’s alpha. In this study, replicability and accuracy
were unable to be tested and therefore is a limitation of this investigation. Although, one way
32
to reduce subjectivity and allow readers to make their own meaningful interpretations or
judgements is to increase transparency (Beattie and Thomson, 2007). Hence, see appendix B
for examples of risk disclosure sentences. Additionally, coding was undertaken manually on
paper, then recorded on Excel. A measure of inter-coder reliability can therefore be
calculated in the future, if required, as records were kept.
To ensure consistency and comparability, a disclosure checklist, decision rules and coding
instrument were constructed (see appendix A). In development of the risk disclosure checklist
and decision rules, a comprehensive review of the risk reporting literature was conducted
(Linsley and Shrives, 2006; Abraham and Cox, 2007; Rajab and Schachler, 2009; Cabedo
and Tirado, 2004; Mokhtar and Mellett, 2013; Solomon et al, 2000; Abraham and Shrives,
2014; Beretta and Bozzolan, 2004; Amran et al, 2009). Identification of any regulatory
requirements, IFRS’s and CG codes was also included to ensure that all the classified risk
disclosures were considered as voluntary (Hackston and Milne, 1996). A pilot study was
undertaken where two annual reports were analysed. The disclosure checklist was then
subsequently modified to include additional subcategories to produce the final framework
(Beattie and Thomson, 2007). Stability of the data was also tested during the pilot study
stage, yielding few differences between the original two companies and the subsequent
testing of the same two companies one week later. For a diagrammatic depiction of the
coding procedures see figure 5.
Figure 5: Coding Structure
33
3.2.2 Dependent Variable: Quality Dimension
Disclosure is a complex, multi-faceted concept (Beattie et al, 2004). Beattie and Thomson
(2007) suggest a quality-adjusted method of content analysis. Sentences are weighted as well
as counted to reflect their relative significance (Toms, 2002; Hasseldine et al, 2005). Quality
of risk information disclosed is an additional dimension which is erroneously assumed to be
accurately represented by quantity of risk disclosure (Linsley and Shrives, 2006). Prior risk
34
Risk Factor Section
Risk Diclosure
Risk Coded According to Risk
Dislcosure Checklist
Sub-Category
Type of Measure
Economic Sign
Outlook Profile
Future
Past
Non-Time
Good News
Bad News
Neutral News
Non-Monetary
Monetary
No Risk Diclosure
disclosure studies have tended to avoid analysis of risk disclosure quality (Linsley and
Shrives, 2006; Mokhtar and Mellett, 2013; Rajab and Schachler, 2009; Abraham and Cox,
2007) with limited exceptions (Beretta and Bozzolan, 2004; Ntim et al, 2013; Miihkinen,
2012). This study thus contributes to the minimal literature on quality of risk disclosure.
There is no definitive definition of quality as it is context dependent and a subjective
phenomenon. Quality has been defined as “the ease with which investors can read and
interpret the information” (Beattie et al, 2004, p.20). Taking this definition one step further,
in the risk disclosure context, informative risk disclosures are deemed to be characterized by
distinctive attribute(s) or characteristic(s) (Linsley and Shrives, 2006; Rajab and Schachler,
2009; Mohobbot 2005). The risk disclosure characteristics may be alternatively defined as
semantic properties. They are as follows:
1) Type of Measure (Monetary or Non-Monetary)
2) Economic Sign (Good News, Bad News or Neutral News)
3) Outlook Profile (Future, Past or Non-Time)
To measure risk disclosure quality, a number of indices were constructed to develop an
overall normalised composite quality index (NCQI). The NCQI measure is a modified model
of Beretta and Bozzolan’s (2004) framework, to capture the quality related dimension of risk
disclosure. Within Beretta and Bozzolan’s (2004) framework, it is assumed that relative
quantity is an essential component of disclosure quality. A suitable proxy are the standardised
residuals obtained from a regression of the number of risk sentences on size, industry or a
complexity variable, less the actual number of risk sentences (Beretta and Bozzolan, 2004;
Beattie et al 2004). A problem with this method identified by Hooks and Staden (2011) is that
high quality reports can be very condensed, focused and concise and therefore low quantity
and volume. A measure assuming higher quality is associated with higher quantity is
misleading as higher quality reports which are of lower quantity will be erroneously
misrepresented as lower quality. The Principal Risks and Uncertainties section of the Annual
Report is highly condensed, usually a few pages long, making this quantity proxy
questionable for development of a composite quality index. It has also been previously
suggested that disclosure quantity and quality are positively related, according to Botosan
(2004). It is important, however, to make the distinction between an implicit component of
the composite quality index design and the inputs used to measure incremental quality.
35
Each index individually is mono-dimensional, although in composite, provides a multi-
dimensional profile capturing the semantic properties of risk disclosures. NCQI by
construction, avoids the problem identified by Hooks and Staden (2011) by focusing
specifically on the incremental value provided by higher weighted disclosures. Lower
quantity reports may possess greater disclosure quality if fundamentally the semantic
properties that compose the overall composite index are of higher quality measured by higher
weighted disclosures. NCQI does not however overcome the implicit use of content analysis,
where the inputs necessary for the construction of the index are fundamentally derived from
the counting of risk sentences. Thus, disclosure quality is mechanically inferred through the
measure of risk sentences as inputs (Beretta and Bozzolan, 2008). NCQI is consequently not
fully unrelated or independent of the quantity dimension of disclosure. Although a
hypothetical quality measure totally independent of the quantity dimension of narrative
disclosures, would be unrestricted in terms of abstract notions existing in a subjective realm
which do not reflect the boundaries that the tangible quantity dimension provides. In other
words, the quantity dimension imposes reality on a somewhat arcane or intangible
phenomena.
The construction of a multi-dimensional index focuses on the incremental value associated
with higher quality risk disclosure sentences and therefore does offer valuable quality insights
which an unweighted content analysis approach is unable to obtain. The insights obtained
from NCQI outweigh the limitations associated with the intrinsic link between quantity and
quality dimensions.
The first semantic property to be weighted was type of measure:
TOM i=1
RDQi∑j=1
RDQi
[ (W ij × M ij)+(W ij × NM ij) ]
Where:
TOM i = type of measure index for company i;
RDQi= risk disclosure quantity for company i;
j= risk sentence disclosed;
36
W ij = attributed weight assigned to sentence j containing non-monetary information given a
value of 1 and monetary information given a value of 2 for company i;
M ij = sentence j contains monetary information on company i;
NM ij=¿ sentence j contains non-monetary information on company i.
The economic sign index is constructed as follows:
ESi=1
RDQi∑j=1
RDQi
[ (W ij ×GN ij )+ (W ij × BN ij )+(W ij × NN ij) ]
Where:
ESi = economic sign index for company i;
W ij = attributed weight assigned to sentence j containing neutral news given a value of 1,
good news given a value of 2 and bad news given a value of 3 in company i;
GN ij = sentence j contains good news information on company i;
BN ij = sentence j contains bad news information on company i;
NN ij = sentence j contains neutral news information on company i.
The third semantic property to be included in the composite index was outlook profile:
OPi=1
RDQ i∑j=1
RDQi
[ ( W ij × F ij )+(W ij × Pij )+(W ij × NT ij )]
Where:
OPi = outlook profile for company i;
W ij = attributed weight assigned to sentence j containing non-time information given a value
of 1, past information given a value of 2 and future information given a value of 3 in
company i;
F ij = sentence j contains future information on company i;
Pij = sentence j contains past information on company i;
37
NT ij = sentence j contains non-time information on company i.
These three indices are normalized to prevent a scale effect from occurring. Feature scaling
or normalization are methods used to standardise features of data and bring all the values into
the range 0≤ ¿iN≤1. Similar to Beretta and Bozzolan (2004) the min-max scaling method was
used as follows:
¿iN=
¿i−¿min i
¿maxi−¿min i
Where:
¿iN = normalized index for company i;
¿i = original un-normalized index for company i.
The final weighted normalized composite index for each firm is obtained using the simple
arithmetic mean of the three indices:
NCQI iN=1
3(TOM i
N+ESiN+OPi
N )
Where:
NCQI i = normalized composite quality index for company i;
TOM iN = normalized type of measure for company i;
ESiN = normalized economic sign for company i;
OPiN = normalized outlook profile for company i.
The different attributed weights applied in each index were based on arguments presented in
prior literature to reduce subjectivity. In relation to economic sign, bad news was ranked the
highest. In addition to agency costs, there are reputational costs and negative impacts on the
stock price with the disclosure of specifically bad news (Skinner, 1994). Skinner (1994)
found that bad news disclosures cause greater stock price reactions than good news
disclosures. Thus, the company has the incentive to voluntarily disclose bad news early to
avoid the likelihood of lawsuits arising from the accusation that inadequate notice of a
negative earnings announcement was given. Bearing these costs in mind, narrative
38
disclosures in listed companies have been found to adopt a variety of concealment strategies
(Aerts, 2005). Companies tend to emphasise good news more than bad news and exhibit self-
serving attributional bias (Aerts, 2005; Clatworthy and Jones, 2003). Good news or positive
outcomes are associated with internal operations of the company and bad news or negative
outcomes are blamed on the external environment outside the company’s control (Clatworthy
and Jones, 2003). Bad news is thus weighted comparatively higher than good news as bad
news disclosures lend credibility to Annual Reports, may result in potential costs to the
company and are integral to narrative concealment strategies when management and
shareholder interests diverge. Neutral news disclosures are ranked the lowest as they are
considered the least informative in terms of economic conditions impacting on the company
(Linsley and Shrives, 2006).
The type of measure index weighted monetary information higher than non-monetary
disclosures. It has been suggested that improvements in risk reporting quality will result
where companies place more emphasis on monetary risk disclosure (Linsley and Shrives,
2000). There is a tendency for companies to refrain from quantifying risk as its uncertain
nature may expose the company to unnecessary legal action from investors. The quantitative
measures of risk available today, such as alpha, beta, R-squared, standard deviation and the
Sharpe ratio are restricted to portfolio theory and practice (DeFusco et al, 2007). The value at
risk (VaR) methodology is also only applicable to financial risks (Jorion, 2002). Non-
financial risks are more difficult to quantify and therefore lead managers to disclose
boilerplate qualitative statements which merely inform investors of the existence of a non-
financial risk but removes the decision to make unrealistic estimates. The ICAEW (2011)
suggest that companies should focus on disclosing quantitative information which provides
information on the firm’s activities, assets, liabilities, commitments as well as industrial and
geographical sectors. Monetary disclosures are thus deemed to be weighted higher than non-
monetary disclosures as boards feel confident enough that the estimates are accurate and
allow readers to determine the impact of a particular risk (Linsley and Shrives, 2006).
Disclosures deemed forward looking, were weighted the highest compared with non-time and
past disclosures within the outlook profile index. Forward looking information allows for
evaluation of a firm’s future financial performance as well as highlighting principal risks
which may cause actual results to vary significantly compared with estimated results (Aljifri
and Hussainey, 2007). Future orientated information is considered to be of comparatively
more help to investors in making investment decisions as opposed to backward looking
39
disclosures (Clarkson et al, 1994). There are a number of associated costs such as an inherent
degree of uncertainty in predicting the future. A greater risk of litigation action arising from
inaccurate disclosures may arise (Aljifri & Hussainey, 2007). Information which contains
projections, plans, future economic performance and mitigation actions taken in the future to
tackle exposed risks contain additional incremental value to investors which non-time or past
information disclosures are unable to provide (CFA, 2014; ICAEW, 2011).
3.2.3 Model Specification
In addition to content analysis and NCQI, this study adopted a common statistical technique
used to analyse cross-sectional data. Multiple regression models were constructed using the
Ordinary Least Square (OLS) method as opposed to alternative techniques such as Maximum
Likelihood Estimation (MLE) and Generalized Method of Moments (GMM) (Wooldridge,
2009). The multivariable model for risk disclosure quantity is as follows:
RDQ=β0+β1 INED+B2 BSLOG+ β3 FSLOG+β4 INDGroup1+β5 INDGroup 2+β6 INDGroup 4+β7 INDGroup 5+β8 LEV +β9 LI+ε
The multivariable model for risk disclosure quality is as follows:
NCQI=β0+β1 INED+ β2 BSLOG+β3 FSLOG+β4 INDGroup1+β5 INDGroup2+β6 INDGroup 4+β7 INDGroup5+β8≤+ β9 LI +ε
Where:
RDQ = Risk Disclosure Quantity;
NCQI = Normalized Composite Quality Index;
β0 = Intercept;
INED = Independent Non-Executive Directors;
BSLOG = Board Size Log;
FSLOG = Firm Size Log;
INDGroup1 = Oil and Gas/Basic Materials Industries;
INDGroup2 = Industrials Industry;
INDGroup4 = Telecommunications/Technology Industries;
40
INDGroup5 = Healthcare/Utilities Industries;
LEV = Leverage;
LI = Liquidity;
ε = Error Term.
The objective of both these models is to ascertain whether a significant association exists
between the dependent variables (e.g. RDQ and NCQI) and the corporate board variables
(e.g. BS and INED), as well as the control variables.
4. Empirical Results
4.1 Content Analysis
4.1.1 Descriptive Statistics and Total Risk Disclosure Quantity
41
Descriptive statistics for risk disclosure sentences of the sample of 72 companies analysed are
contained within Table 2. The total number of risk sentences disclosed was 8512 sentences
located within the risk factor section. This is greater than previous risk disclosure studies
which analyse the entire Annual Report (Linsley and Shrives, 2006; Abraham and Cox,
2007). Collected 13 year hence, this study operationalised the same checklist and broad
definition of risk disclosure as Linsley and Shrives (2006). This allows for comparison and
provides strong evidence for a potential increasing trend in the quantity of narrative risk
information included within Annual Reports, in line with the FRC’s argument for the need to
cut irrelevant clutter (FRC, 2011). Longitudinal data would need to be collected to confirm
this trend of increasing narrative risk information. The minimum of 27 sentences
(Persimmon) and maximum of 390 sentences (Coca-Cola HBC AG) highlights the significant
variation in risk disclosure quantity between companies in the year 2013.
Table 2: Descriptive Statistics for Risk Sentences and Semantic Properties
Descriptive Statistics8 (n=72)
Total Disclosures
Minimum Maximum Mean Standard Deviation
Total risk disclosure 8512 27 390 118.222 57.229Financial risk disclosure
1340 0 73 18.611 16.186
Non-financial risk disclosure
7172 17 317 99.611 48.541
Operational risk disclosure
3388 6 168 47.056 30.034
Empowerment risk disclosure
480 0 24 6.667 5.294
IT risk disclosure 675 0 31 9.375 8.006Integrity and governance risk disclosure
588 0 46 8.167 9.986
Strategic risk disclosure
2041 0 110 28.347 19.290
Monetary risk disclosures
199 0 28 2.764 4.404
Non-monetary risk disclosures
8313 27 362 115.458 54.532
Future risk disclosures
622 0 39 8.639 8.637
Past risk disclosures 3353 1 116 46.569 24.355Non-time risk disclosures
4537 9 251 63.014 40.015
8 Sentence as unit of measurement.
42
Bad news risk disclosures
2674 4 239 37.139 35.004
Good news risk disclosures
2709 1 129 37.625 21.116
Neutral risk disclosures
3129 7 133 43.458 25.369
4.1.2 Financial versus Non-financial Risks
Non-financial risks varied to a greater extent than financial risks across the 72 non-financial
firms. The standard deviation was 32.355 sentences greater for non-financial risk disclosure
compared with financial risks. The mean for financial risks was 81 sentences less than non-
financial risk. A few companies disclosed no financial risk information at all, hence the zero
figure for the minimum value. Financial risks accounted for 15.74% of total risk disclosure in
comparison with non-financial risk disclosure of 84.26%. Consistent with prior results
(Vandemaele et al, 2009; Mohobbot, 2005), the findings do not come as a surprise.
Companies are more likely to disclose financial risks in accordance with IFRS in the notes to
the accounts, rather than provide voluntary financial risk information elsewhere (Abraham
and Cox, 2007). This does however suggest that companies are more willing to disclose
voluntary non-financial risk information in the risk factor section of the Annual Report
compared with financial risk.
In relation to non-financial risk, operational risk was the most frequently disclosed risk
category in terms of quantity, totalling 3388 sentences which composed 47.24% of non-
financial risk and 39.8% of total risk disclosure. It was the only risk category that was
disclosed by every company in the sample, with the minimum of 6 sentences provided by
Persimmon. Strategic risk was the next most commonly disclosed category of risk
information, with 2041 sentences disclosed. This equated to 28.46% of non-financial risk and
23.98% of total risk disclosure. The quantity of strategic risk disclosed which documents
external risk factors, was found to be less than operational risks which are associated with the
internal workings of the business. This result is in contrast to Linsley and Shrives (2006) as
well as Amran et al (2009), who found strategic risks to be disclosed in greater quantity than
operational risks. Similar quantities of IT and integrity and governance risk disclosures were
found at 675 and 588 sentences respectively. Integrity and governance risk disclosures were
found to be much less than the findings of Linsley and Shrives (2006) who found 1571 risk
sentences. The Principal Risks and Uncertainties section contains few, what Linsley and
43
Shrives (2006) call, ‘policy-integrity’ risk disclosures which merely describe the risk
management systems in place to mitigate risk. The types of risk that were disclosed the least
out of the 6 main categories, were empowerment risks of 480 sentences. This represented
6.69% of non-financial risk and 5.64% of total risk disclosure. 16 out of the 72 companies
disclosed no empowerment risks at all. This is surprising, considering the volatility associated
with outsourcing and the uncertain nature of performance incentives achieving their desired
outcome of improved employee performance. A diagrammatic representation of both
financial and non-financial risks is as follows:
Figure 6: Types of Risks Disclosed
Financial Risk Disclosure 1340
Operational Risk Disclosure3388
Empowerment Risk Dis-closure
480
IT Risk Disclosure675
Integrity and Governance Risk Disclosure
588
Strategic Risk Disclosure2041
Types of Risks Disclosed
4.1.3 Semantic Properties
4.1.3.1 Type of Measure
Risk disclosures were coded according to their semantic properties. With regards to type of
measure, it was found that the number of non-monetary disclosures vastly outnumbered
monetary disclosures consistent with prior literature (Oliveria et al, 2011; Rajab & Schachler,
2009; Lajili & Zeghal, 2005). Monetary risk disclosures were 2.34% of total risk disclosures
whereas non-monetary risk disclosures accounted for 97.66% of total risk disclosures.
44
4.1.3.2 Outlook Profile
The outlook profile disclosure dimension was also consistent with prior literature (Lajili &
Zeghal, 2005; Beretta and Bozzolan, 2004) where future risk disclosures were disclosed in
the minority, with 622 sentences, 7.31% of total risk disclosure. This result however
contradicted with Linsley and Shrives (2006) who found that past risk disclosures were less
than future risk disclosures. In this study, backward-looking disclosures were greater than
forward-looking disclosures with 3353 sentences, 39.39% of total risk disclosure. 4537 non-
time sentences were disclosed, 53.30% of total risk disclosure, which tended to be generic or
boilerplate in terms of information disclosed.
4.1.3.3 Economic Sign
In relation to economic sign, it was found that good and bad news was disclosed fairly
equally. There were 2709 good news risk sentences disclosed and 2674 bad news risk
sentences disclosed. These findings are in conflict with prior empirical evidence where good
news seems to dominate (Rajab & Schachler, 2009; Linsley and Shrives, 2006). The results,
however, are consistent with Oliveria et al (2011). One possible reason for the almost equal
disclosures of good news and bad news may be the narrative structure of the risk factor
section of the annual report. Many companies disclosed information in the format where risks
were identified, then the negative impact on the company was elaborated and mitigation
actions designed to reduce the negative impact were disclosed. It was common for these
negative impact statements to have a corresponding mitigating counterpart, hence good and
bad news disclosures were in a 1:1 ratio for many companies analysed. Neutral news was
most commonly disclosed, with 3129 sentences.
In relation to the semantic properties, companies were more inclined to voluntarily disclose a
greater quantity of risk information that was qualitative, neutral in impact and non-time
specific, in the risk factor section of the Annual Report.
4.2 Normalized Composite Quality Index
4.2.1 Descriptive Statistics
45
Sentences coded as risk disclosures were subsequently weighted to create an index for each
semantic property. Indices with a higher value possess a higher incremental quality weighting
according to that semantic property dimension. Similarly, for the composite index, a higher
value relates to a higher quality of risk disclosure.
Table 3: Descriptive Statistics for Indices Measuring Quality of Risk Disclosure
Descriptive Statistics9
(n=72)
Minimu
m
Maximu
m
Mean Standard
Deviation
TOM N 0 0.14706 0.0207
7
0.02736
ESN 0.07426 0.43333 0.2755
2
0.08015
OPN 0.06835 0.69886 0.4574
2
0.11143
NCQI N 0.10671 0.61240 0.2568
3
0.05848
4.2.2 TOM Index
The results from the TOM index indicate that 29 companies decided not to disclose monetary
information, hence the zero value for the index. Hence, those companies that disclosed no
monetary information hold zero incremental quality value. Companies that did disclose
monetary risk information as illustrated in figure 7, exhibited substantial quality variation.
This is highlighted in the difference between the mean and maximum value of 0.12629. The
interesting question that therefore arises from these results is why do some companies choose
to disclose voluntary monetary risk information while others do not? 29 out of the 72 firms
disclosed no monetary risk information which is 40.28% of the total sample. Interpretation of
these results suggests that managers are less confident to voluntarily disclose monetary risk
information. Managers may disclose voluntary monetary risk information if they feel
confident enough that the risk information will not have a future negative impact. If
9 Weighted sentences as unit of measurement.
46
manager’s confidence or attitude towards risk is low, voluntary monetary risk information
will be less likely to be disclosed.
Figure 7: Type of Measure Index
0 2 4 6 8 10 120
2
4
6
8
10
12
TOM Index
Sample firms (n=72)
TOM
inde
x va
lue
(Wei
ghte
d Se
nten
ces)
4.2.3 ES and OP Index
The sample varied greatly in terms of weighted sentences disclosed between the ES and OP
index (see figure 8). The OP index exhibited greater variation than the ES index with a
standard deviation of 0.11143 compared with 0.08015. The difference between the minimum
and maximum value was also greater for the OP index in comparison with the ES index of
0.2714410.
10 [OPMAX−OPMIN ]−[ ESMAX−ESMIN ].
47
Figure 8: Economic Sign Index and Outlook Profile Index
0 2 4 6 8 10 120
2
4
6
8
10
12
ES index and OP index
ES Index
Sample firms (n=144)
Indi
ces
Valu
es (W
eigh
ted
Sent
ence
s)
The ES index and OP index varied within different ranges. A large number of firms for the
ES index can be found between 0.2 and 0.35 illustrated with the use of a linear trend line. In
contrast most firms for the OP index are found within a greater range between 0.4 and 0.5.
This suggests that companies disclose higher quality outlook profile information compared
with economic sign disclosures as the weighted index is consistently higher across the sample
for outlook profile. Boards realise the potential negative impact on the stock price and
potentially higher disclosure costs with bad news risk disclosures. This may be a reason for
the consistently lower quality of economic sign disclosures.
4.2.4 Incremental Quality Value
The composite quality index values for the sample are illustrated in figure 9. When the 3
semantic property indices are combined, companies tend to exhibit relatively uniform quality
disclosure practices in the principal risks and uncertainties section of the annual report. With
a potential quality value of 1.0, the majority of firms score fairly low between 0.2 and 0.3 for
disclosure quality. Interpretation of these results indicates boilerplate risk disclosures as
opposed to value-relevant incremental risk information. There are 2 significant outliers.
Ashtead Group disclosed the highest quality risk information with an index value of 0.61240
and Associated British Foods disclosed the lowest quality information with an index value of
48
0.10671. These quantitative results lend support for the ICAEW’s (2011) identified risk
reporting challenges and the need for better risk reporting.
Figure 9: Normalized Composite Quality Index
0 2 4 6 8 10 120.000
2.000
4.000
6.000
8.000
10.000
12.000
NCQI index
Sample firms (n=72)
NCQ
I ind
ex v
alue
(Wei
ghte
d Se
nten
ces)
4.2.5 Comparison of RDQ with NCQI
There seems to be broad consensus in the risk reporting literature to date that risk disclosures
in Annual Reports are boilerplate, generic, symbolic and lack transparency (Abraham and
Shrives, 2014; Linsley and Shrives, 2006; Solomon et al, 2000). Evidence suggests that the
quantity of narrative risk information is increasing, however, these increased disclosures are
insubstantial so far as quality and usefulness is concerned (Campbell and Slack, 2008).
Boards of listed companies seem to be adopting a ‘window dressing’ disclosure strategy. To
the less perceptive users of narrative risk information, increased disclosure may suggest risk
reporting is improving. Although increasing generic risk disclosures may actually be an
attempt to conceal the most significant risks facing the company as the quality of information
decreases. Quantitatively this phenomena or strategy is observed in figure 10 and lends
support for qualitative statements made in previous studies that risk information is considered
boilerplate and generic. As RDQ increases, overall NCQI decreases. It is therefore expected
that a negative association exists between RDQ and NCQI.
49
Figure 10: RDQ Comparison with NCQI
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 700
50
100
150
200
250
300
350
400
450
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
RDQ and NCQI Index
RDQ Linear (RDQ) NCQI Linear (NCQI)
Sample Firms (n=72)
Num
ber o
f risk
sent
ence
s
NCQ
I Ind
ex V
alue
(Wei
ghte
d Se
nten
ces)
4.3 Cross-sectional Regression Analysis
4.3.1 Independent Variables
Table 4: Descriptive Statistics for Continuous Independent Variables
Descriptive Statistics (n=72)
Unit of Measurement Minimum Maximum Mean Standard Deviation
Firm size Log total assets 14.051 19.361 15.962 1.296Leverage Ratio of total debt to total
assets 0 0.624 0.255 0.143
Liquidity Current Ratio (current assets/current liabilities)
0 3.72 1.284 0.633
Independent non-executive directors
Ratio of independent non-executive directors/board size
0.286 0.923 0.597 0.115
Board size Log number of board members
1.609 2.944 2.368 0.225
The FTSE industry classification benchmark system was used to classify the sample of
companies into different industry categories. Consumer goods and consumer services were
merged to aid comparison (INDGroup3). Basic materials and oil and gas were also combined
50
(INDGroup1). Industrials were not grouped with any other industry (INDGroup2). Both
technology and healthcare possessed 2 companies and 3 companies respectively, thus the
decision was made to merge technology and telecommunications (INDGroup5). Health care
and utilities were also combined to aid comparison (INDGroup4). The final combinations
consisted of 5 industry groupings to be included within the regression models. Dummy
variables were created for each group minus 1. A base or reference group is created which is
excluded from the model to avoid perfect collinearity or the dummy variable trap
(Wooldridge, 2009). Consumer goods/consumer services were chosen to represent the base
group. In addition to aiding comparison, industry groups were consolidated to ensure dummy
variables were kept to a minimum. The addition of multiple dummy variables increases
model fit but reduces degrees of freedom and results in a model that does not provide any
general conclusions (Wooldridge, 2009).
Table 5: Sample Companies Industry Classification
Industry Company Distribution (n)
Percent Distribution (%)
Oil and Gas 6 0.08Basic Materials 7 0.10Industrials 18 0.25Consumer Goods 13 0.18Consumer Services 16 0.22Health Care 3 0.04Utilities 5 0.07Telecommunications 2 0.03Technology 2 0.03Financial 0 0.00Total 72 100.0
4.3.2 Gauss-Markov Assumptions
There are a number of assumptions of OLS estimation which must be satisfied to prove the
Gauss-Markov theorem and that OLS estimators are best linear unbiased estimators (BLUE)
(Greene, 2003).
Firstly, the error term of the distribution must be normal. The normality assumption was
tested both visually and statistically. Visually, using SPSS, the standardised residuals were
plotted using a histogram and a normal probability plot (P-P plot). Statistically, two tests
were used including Kolmogorov-Smirnov and Shapiro-Wilk. The error term was found to be
51
normally distributed (see appendix C). Kurtosis and skewness were therefore assumed to
likely possess an absolute z-value below 3.29 which is the critical value required to reject the
null hypothesis and conclude that the distribution of the sample is normal (Kim, 2013). This
z-value equates to this study’s sample size (50<n<300).
Secondly, a linear relationship should exist between each independent variable and the
dependent variable. To test this assumption, each independent variable was plotted
graphically in relation to the dependent variable using a scatter plot. As expected, some
variables showed an approximate negative linear relationship, others positive and some
showed no relationship. Variables that were either non-linear or not normally distributed,
both visually and statistically, were logarithmically transformed. Firm size and board size
variables were non-linear and therefore log transformed using the natural logarithm
consistent with prior literature (Ntim el al, 2013; Abraham & Cox, 2007).
Thirdly, to test for mulitcollinearity a correlation matrix was constructed using the variables
included in each regression model. It has been suggested that if pairwise correlation
coefficients exceed a collinearity threshold of 0.8, a mulitcollinearity problem may exist
(Mela and Kopalle, 2002). This value of 0.8 may still be too high and a more conservative
threshold has been suggested between 0.5-0.7 (Dormann et al, 2013). Bivariate relationships
were investigated using Pearson’s parametric coefficients and were calculated as follows (see
table 6).
52
Table 6: Correlation Matrix
Correlation Matrix: Bivariate Relationships
RDQ NCQI FSLOG LEV LI INDGroup1 INDGroup2 INDGroup4 INDGroup5 BSLOG INEDRDQ 1
NCQI -.068 1
FSLOG .381** .029 1
LEV .153 .091 .184 1
LI -.038 -.067 -.231 -.292* 1
INDGroup1 .214 -.004 .350** -.146 .226 1
INDGroup2 -.138 .173 -.314** .057 .024 -.271* 1
INDGroup4 -.072 .210 .035 -.046 -.046 -.114 -.140 1
INDGroup5 .125 -.013 .135 .282* .072 -.166 -.204 -.086 1
BSLOG .243* .051 .424** .074 -.194 .041 -.186 -.120 .100 1
INED .270* -.013 .454** -.015 .029 .244* -.067 .025 .060 .261* 1
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
53
The highest correlation found was between INED and FSLOG of 0.454. This value is
significantly lower than 0.8 and even less than the most conservative threshold of 0.5. Low
pairwise correlation coefficients do not necessarily confirm that there are no mulitcollinearity
issues and do not reveal higher order collinearity (Wooldridge, 2009). Variance Inflation
Factors (VIF’s) were therefore calculated to measure the linear relationship between one
independent variable and all other independent variables. A value greater than 10 signals a
major mulitcollinearity problem (Dormann et al, 2012). As shown in tables 7 and 8, the
highest VIF’s equalled 1.972 and 2.012 respectively for FSLOG in each model, which is far
below the threshold of 10. Both the correlation matrix as well as VIF’s suggest there are no
mulitcollinearity problems. Furthermore, confirming the results of the content analysis, RDQ
and NCQI are negatively related although statistically non-significant. This contrasts with
previous disclosure quality measurement frameworks whereby the quantity and quality of
disclosure are considered positively related (Botosan, 2004).
The 4th assumption is homoscedasticity, where the variance of the error term is constant
across different values of the independent variables (Wooldridge, 2009). A residual analysis
was conducted to test the homoscedastic assumption. The plot of the residuals against the
predicted values for both the RDQ and NCQI model respectively is as follows:
Figure 11: RDQ Model Residuals versus Predicted Values
54
Figure 12: NCQI Model Residuals versus Predicted Values
It is evident from figures 11 and 12 that no discernible pattern exists and residuals do not
seem to grow as a function of the predicted value. More extensive analysis was also
conducted plotting residuals versus independent variables in SPSS. Again, no systematic
increase in residuals was observed.
The 5th assumption assumes error observations are not correlated. To test if autocorrelation
exists, the Durban-Watson (DW) test was calculated. Generally, cross-sectional regressions
are less prone to autocorrelation issues compared with regression models using time-series
data (Wooldridge, 2009). For the RDQ model, DW was 1.388 and for the NCQI model, DW
was 1.945. The DW statistic for both models was near 2 suggesting that there are no-
autocorrelation problems. The DW test has no critical value for which the null hypotheses is
rejected. Instead there exists a zone of indecision defined by an upper and lower bound
(Wooldridge, 2009).
Violation of the 6th and final assumption is misspecification. This is where inappropriate
independent variables are selected, excluded or the incorrect functional form is chosen which
leads to endogeneity problems (Greene, 2003). In other words, the conditional mean of the
error term should be zero. To reduce the possibility of misspecification, extensive review of
past empirical literature on determinants was conducted. Control variables were included as
55
well as agency theoretical justifications investigated for the correlations between the
independent and dependent variables.
Overall, with the testing of the Gauss-Markov theorem, results suggest that the assumptions
have indeed not been violated and valid inferences may be drawn from the 2 regression
models. There is a variety of additional tests that may be conducted and it is important to note
that in interpretation of the regression models, correlation does not imply causation.
4.3.3 Cross-sectional Analysis
Outliers were removed from the regression to increase accuracy and reduce errors of
influence. There is no consensus on whether to keep or remove outliers (Osborne and
Overbay, 2004). Outliers were identified using casewise diagnostics on SPSS as well as
visually on a scatter plot (Y against X). In the RDQ model, 1 firm was removed from the
overall sample and in the NCQI model, 2 sample firms were removed.
Table 7: RDQ Multiple Regression Model
RDQ Regression Model
Unstandardized Coefficients
t Sig. VIF
B (Constant)
-138.348-1.679
.098*
FSLOG10.245
1.927
.059* 1.972
BSLOG 5.481 .219 .827 1.308 LEV 27.487 .707 .482 1.281 LI -.160 -.018 .986 1.362 INGroup1
23.2291.454
.151 1.589
INGroup2 7.004 .537 .593 1.337 INGroup4 -6.390 -.281 .780 1.144 INGroup5
21.6831.202
.234 1.353
INED102.271
2.060
.044** 1.335
R Square Adjusted R Square df
Durbin-Watson
No. of Observations
F Statistic Sig.
.340 .242 9 1.388 71 3.484 .002**
56
** significant at the 0.10 level (2-tailed). ** significant at the 0.05 level (2-tailed). *** significant at the 0.01 level (2-tailed).
Table 8: NCQI Multiple Regression Model
NCQI Regression Model
Unstandardized Coefficients
t Sig. VIFB(Constant)
.1101.580
.119 FSLOG
.0051.184
.241 2.012
LEV-.040
-1.203
.234 1.275
LI .001 .182 .856 1.359INED -.006 -.136 .892 1.319BSLOG
.0251.190
.239 1.314
INGroup1 .005 .337 .737 1.626
INGroup2.015
1.358
.180 1.319
INGroup4.065
3.368
.001*** 1.158
INGroup5 .011 .700 .487 1.344
R SquareAdjusted R Square df
Durbin-Watson
No. of Observations
F Statistic Sig.
.223 .106 9 1.94570
1.913.067*
* significant at the 0.10 level (2-tailed).*** significant at the 0.01 level (2-tailed).
Tables 7 and 8 present the cross-sectional results for both the RDQ and NCQI models. The
RDQ model explained 24% of the variation in the pattern of voluntary risk disclosures across
the sample of firms. This value was similar to Oliveria et al (2011) and Mokhtar & Mellett
(2013). Other studies have found higher adjusted R2 (Amran et al, 2009; Abraham & Cox,
57
2007). A reason for the lower R2 in comparison with a few other studies may be due to
analysing only the risk factor section and additional voluntary information may be found
elsewhere in the annual report. The model was significant ( p<0.01) and therefore possesses
predictive ability. The regression model of NCQI was also significant ( p<0.10), albeit at a
lower level in comparison with the RDQ model. The NCQI model explained 11% of the
variation in the pattern of risk disclosure quality. Beretta and Bozzolan (2004) found a similar
adjusted R2 value of 14% for their quality index
With regards to the control variables, firm size was statistically significant and positive
( p<0.10) in relation to RDQ (Abraham and Cox, 2007; Konishi and Ali, 2007). This
suggests that larger firms disclose greater quantities of risk information compared with
smaller firms. A non-significant and positive association is found between firm size and
NCQI which confirms Beretta and Bozzolan’s (2004) findings that disclosure quality is not
influenced by firm size. For both regression models, leverage was non-significant although
positively related to RDQ and negatively related to NCQI. The positive relationship with
RDQ supports the findings of Oliveria et al (2011) and Deumes and Knechel (2008).
Liquidity was also non-significant and negatively related to RDQ. It was however, positively
related to NCQI. A non-significant result confirms prior empirical findings (Elzahar and
Hussainey, 2012; Mokhtar and Mellett, 2013; Hassan, 2009). In contradiction with previous
empirical studies, industry was found to be non-significant across the 4 groups in relation to
RDQ (Hassan, 2009; Handley and Schachler, 2009). Ahmed and Courtis (1999) suggest that
different industry classifications lead to differing final results. In contrast, Industry was found
to be statistically significant and positive ( p<0.01) for group 4
(Telecommunications/Technology sectors) in relation to NCQI. This suggests that
comparative to the other 3 groupings, the telecommunications and technology sectors
disclose higher quality risk information. This result conflicts with Beretta and Bozzolan’s
(2004) study who found a non-significant relationship between industry and their quality
index. NCQI therefore seems to capture cross-sectional variation, which was a concern raised
by Botosan (2004).
In relation to the board variables, a statistically significant and positive association ( p<0.05)
is found between INED and RDQ. This evidence provides statistical support for the
confirmation of hypothesis 1 (H1), that a larger proportion of independent non-executive
directors on the corporate board increases the quantity of voluntary risk disclosure. INED is
non-significant and negative in relation to NCQI. The evidence suggests that there is
58
statistical evidence to reject hypothesis 2 (H2), where a more independent board does not
increase the quality of voluntary risk disclosure. For both RDQ and NCQI, the association
with BSLOG is non-significant and positive. Thus, there is statistical evidence not to reject
the null form and confirm both hypothesis 3 (H3) and hypothesis 4 (H4). The evidence
suggests that BSLOG is not a significant determinant of the quantity/quality of risk disclosure
in UK FTSE 100 non-financial companies.
5. Discussion and Conclusions
5.1 Risk Reporting Practices
Empirical evidence was found, using content analysis and NCQI, that voluntary risk reporting
practices in UK FTSE 100 non-financial companies were boilerplate as opposed to providing
value-relevant incremental risk information. Risk disclosures are generic, lack transparency
and consistent with prior literature (Linsley and Shrives, 2006; Abraham and Cox, 2007;
Solomon et al, 2000). These findings lend support for calls for improvements in risk reporting
practices (ICAEW, 2011; Lajili and Zeghal, 2005). A large quantity of risk sentences, greater
than prior empirical studies analysing the entire Annual Report were disclosed in the
‘Principal Risks and Uncertainties’ section. This finding supports the upward trend of
narrative risk information in annual reports (FRC, 2011). A large proportion of these
sentences, however, were boilerplate in nature, as coded by the semantic properties of the risk
information. The vast majority of risk disclosures were non-monetary as opposed to
monetary. Risk information was also primarily non-time in outlook and neutral in content.
These risk disclosures provide limited useful information for professional users making
investment decisions to match their risk appetite (Abraham and Shrives, 2014).
The above analysis was restricted to quantitative dimensions. Further insight was gained with
the development of NCQI which measured the incremental quality value of higher weighted
risk disclosures. The TOM index found significant variation across companies which
disclosed monetary risk information. Many firms possessed zero incremental quality value in
relation to type of measure as no monetary risk information was disclosed. With regards to
the ES and OP index, it was found that companies tended to disclose higher quality outlook
information but lower quality economic sign information. Interpretation of these results
59
suggests that boards may value economic sign risk information to a greater extent than the
outlook profile of the risk disclosures. Boards are therefore inclined to disclose lower quality
risk information in relation to economic sign, as a preventative measure to avoid disclosure
costs. With the combination of these three indices to form NCQI, uniform risk reporting
practices of low incremental quality value was evident, consistent with boilerplate risk
disclosures. The comparison of RDQ with NCQI illustrated that as the quantity of risk
disclosure increases, the quality decreases. Interpretation of these empirical results suggests
boards are not enhancing incremental quality value and instead adopting a strategy of
‘symbolic window dressing’ (Abraham and Shrives, 2014).
5.2 Monitoring EnvironmentHypotheses Outcome Support
Significant positive association
between INED with RDQ
Significant positive association
( p<0.05)
Supported
Significant positive association
between INED with NCQI
No association Not supported
No association between BSLOG
with RDQ
No association Supported
No association between BSLOG
with NCQI
No association Supported
It was hypothesised, that if boards perform a vital monitoring function, agency problems such
as adverse selection should decrease as a greater quantity/quality of risk disclosure decreases
information asymmetries between managers and shareholders. Boards that performed their
monitoring role would thus be positively related to risk disclosure quantity/quality. The
determinants of the effectiveness of boards in providing an extensive monitoring environment
identified as board size and independence (John and Senbet, 1998) were tested in relation to
risk disclosure quantity/quality. A summary of the hypotheses testing is as follows:
Table 9: Hypotheses Testing and Key Outcomes
Interpretation of these findings suggests that board effectiveness, in terms of reducing
information asymmetries, may be considered impaired in its perceived role of disciplining
60
managers to act transparently (see figure 13). The weak monitoring environment provided by
UK boards is insufficient so far as reducing agency problems in the form of adverse selection.
Managers are prone to act opportunistically, choosing to avoid incurring bonding costs in the
form of greater quality risk information.
61
Figure 13: Summary of Empirical Results within the Agency Theoretical Framework
Corporate Board Provides a Weak Monitoring Environment
Low Quality of Risk Disclosure
High Quantity of Boilerplate Risk Disclosure
No Association with INED
No Association with BSLOG
Significant Positive Association with INED
No Association with BSLOG
Limited Decrease in Information Asymmetries between Managers and Shareholders
62
Despite the theoretical rational for the provision of an extensive monitoring environment
provided by boards in the UK, the empirical results in this study lend support for arguments
that corporate boards do not provide a comprehensive monitoring role, perhaps stemming
from voluntary or soft corporate governance codes (Guest, 2008). The solution to improved
risk reporting practices however is unlikely to stem from exogenous implementation of more
extensive regulatory requirements (Hermalin and Weisbach, 2003). It is suggested that the
inherent discretion exhibited by managers, associated with the uncertain nature of the risk
information itself, is more likely to rely on incentives provided by boards performing a
stronger monitoring function to address existing deficiencies in risk reporting practice
(Dobler, 2008). These incentives may include a lower cost of capital, increased managerial
compensation and improved reputation/credibility. This highlights the role that enforcement
mechanisms such as boards may play in establishing incentives for managers, but perhaps
most crucially, the duty to increase the awareness of managers to the benefits of greater
transparency.
If opportunism on the mangers part prevails in maintaining problems of information
asymmetries, boilerplate risk reporting practices will continue, as indicated by the empirical
results in this study. There is the requirement for a more specified formal contract where
vigilant monitoring provided by boards is continually provided. Although in establishing this
optimal contract, agency costs will inevitably accrue albeit to less of an extent if the well-
constructed contract is deemed to minimize agency costs. A trade-off may therefore exist
where managers reduce information asymmetries through the disclosure of higher
quantity/quality risk disclosures however consequently incur greater ex ante bonding costs.
The key implication of this agency theoretical insight is that poor monitoring stemming from
corporate boards, may provide a reason for why risk reporting practices are lacking
incremental quality value.
Risk reporting must therefore not be considered as a stagnant activity. It should reflect the
changing risk profile of a company’s internal and external environment as well as
professional user’s risk information requirements. My own opinion is that risk reporting
ought not to become a highly regulated activity and identifying alternative ways to increase
the board’s monitoring effectiveness will improve risk disclosure practices.
63
5.3 Information Asymmetries
The extent to which a higher quantity of risk disclosure reduces information asymmetries is
dependent upon the considered usefulness of boilerplate risk disclosures by investors. There
is no doubt that boilerplate risk disclosures decreases information asymmetries to some
degree (Abraham and Shrives, 2014), however, it is argued in this study, that the quality of
risk information is a contributing factor to overall risk disclosure informativeness. In other
words, a more independent board was found in this study to partially decrease information
asymmetries through increases in the quantity of risk disclosure. Although this risk
information was found to be largely boilerplate in nature. Hence, if the risk information was
of higher quality but the same quantity, information asymmetries would have decreased to a
larger extent possibly induced by a greater monitoring environment. There are 4 potential
scenarios which lead to either a decrease or increase in information asymmetries as follows:
Scenario 1: If an extensive monitoring environment exists (i.e. high quality + high quantity
risk disclosures):
Information asymmetries will never be reduced to zero as there will always be key risk
information not disclosed by firms to investors (ICAEW, 2011).
Scenario 2: If high quantity but quality is low (i.e. boilerplate):
Scenario 3: If low quality and quantity is low (worst scenario):
Scenario 4: If high quality but quantity is low (insufficient quantity but informative):
64
Information Asymmetries
Information Asymmetries
Information Asymmetries
Information Asymmetries
Whether higher quantity or higher quality risk disclosures decrease information asymmetries
to the same extent as in scenarios 2 and 4 or differ significantly, requires further empirical
investigation perhaps in the form of a cross-country study where a board’s monitoring
environment is influenced by different factors such as institutional ownership. The empirical
results in this study fall under scenario 2 where high quantity but low quality risk information
is disclosed, partially decreasing information asymmetries. Theoretically, the main challenge
therefore for UK corporate boards, is enhancing its monitoring capacity to prevent
opportunistic behaviour on the manager’s part. This will lead to maximal reductions in
information asymmetries as in scenario 1.
5.4 Limitations
In construction of the disclosure checklist, inherent subjectivity or bias exists, as the
researcher is imposing his perception of reality. An objective reality exists independently of
the checklist, which includes all the risk information voluntarily disclosed by a company. If
the checklist fails to include risk disclosure categories or subcategories which are deemed
necessary for a complete profile of a company’s risk factors, then the checklist has not
accurately captured the extent or consequently the quality of voluntary risk disclosure in the
principal risks and uncertainties section of the Annual Report. This study made use of a
modified version of the Linsley and Shrives (2006) checklist, constructed by a professional
accountancy body. This therefore allows for comparability of results.
Focusing only on the Principal Risks and Uncertainties section may display a fragmented
representation of voluntary risk disclosure practices. Additional voluntary risk disclosures
contained elsewhere in the Annual Report, such as in the chair’s statement, are ignored.
Future research could consider the entire Annual Report.
A measure of inter-coder reliability was not calculated to confirm that the coding of Annual
Reports was consistently achieved among greater than one coder. Furthermore, weightings
were subjectively applied to the normalised composite quality index to measure the
incremental quality value of voluntary risk disclosures. The results of this study therefore
cannot be considered objective, if there is such a thing in social science research. No claim is
made of the generalizability of the analysis of these findings.
65
The sample size of 72 companies is considered less than the ideal number of firms required to
establish an appropriately stable regression model (Wooldridge, 2009). This investigation
should be considered interpretive and offers tentative insights based on risk reporting
practices of UK FTSE 100 non-financial companies which can be further investigated in
future research.
There is no universally accepted notion of disclosure quality (Botosan, 2004). Therefore, any
inferences drawn from the findings of this study are limited to the degree of acceptance of
this measure of quality proposed as an appropriate proxy of the incremental disclosure quality
of risk disclosures.
Agency theory has been criticized as too simplistic and based on restrictive assumptions such
as rationality, utility maximisation, opportunistic behaviour and a narrow focus on the
principal-agent relationship (i.e. a single agent and a single principal) (Wright et al, 2001).
The complexity of real world phenomena requires a model with less restrictive assumptions.
5.5 Further Research
Firstly, given the theoretical argument presented in this paper, it would be interesting to test
the usefulness or value-relevance of risk disclosure quantity and quality in relation to
reducing information asymmetries (Bid-Ask Spread) in the UK. Miihkinen (2013) has
conducted such a study in Finland although excluding board variables. Do information
asymmetries decrease to a greater extent with higher quantity/higher quality risk disclosures
compared with high quantity/low quality, low quality/low quantity or high quality/low
quantity risk disclosures?
Secondly, this dissertations focus was on risk disclosure to the outside world, which is ex-
post the decision making process at board level. This required certain agency assumptions
about managerial behaviour. Endogenizing board structure will perhaps allow for exploration
of ex-ante risk information flows, exposing the interplay of decisions between managers and
board members in determining the quantity/quality of risk disclosure.
Thirdly, due to the inherent discretion associated with the disclosure of risk information,
investigation into manager’s incentives to engage in impression management strategies or
biased reporting may provide interesting insights. A strategy of ‘hiding the needle in the
haystack’, reducing textual risk information’s meaning and increasing the reading difficulty
66
may be potentially adopted by managers in corporate narratives (Linsley and Lawrence,
2007).
Fourth, expanding the sample size of this study to include the collection and testing of
longitudinal data would be useful for determining if a board’s monitoring effectiveness is a
function of time or remains relatively constant.
Fifth, Botosan (2004) suggests that the IASB framework which identifies 4 qualitative
characteristics (understandability, relevance, reliability and comparability) is a more
appropriate framework for measuring disclosure quality to enhance the usefulness of
information to economic decision makers. These qualitative characteristics are admittedly a
function of a user group’s interest. It begs the question, to whom? This implies if quality
cannot be measured directly, indirect measurement is more appropriate. Future research may
consider breaking risk information down into its constituent semantic properties, as is the
approach taken in this study, and test the extent to which the information decreases
information asymmetries proxied by the Bid-Ask spread. This method would be a
compromise between considering quality as a function of user’s interest and the semantic
properties of the risk information.
Lastly, committee structure may also impact on the monitoring effectiveness of the board.
Further research could test additional determinant variables drawn from the audit, risk,
nomination, remuneration and governance committees to develop a more complete
representation of influencing factors on risk disclosure practices emanating from the board.
67
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Appendix A: Decision Rules, Coding Template and Checklist
Decision Rules for Risk Disclosures11
1) A broad definition of risk disclosure is adopted to identify risk sentences.2) Sentences are to be coded as risk disclosures “if the reader is informed of any
opportunity, or prospect or of any hazard, danger, harm, threat or exposure, that has already impacted upon the company or may impact in the future or of the management of any such opportunity, prospect, hazard, harm, threat or exposure” (Linsley and Shrives, 2006, pp.389).
3) Risk disclosures must be specifically stated, they cannot be implied4) The risk disclosures shall be classified according to the coding instrument and by
reference to the risk disclosure checklist risk categories.5) The risk disclosure sentence shall be allocated to a general risk category and specific
risk sub-category.6) If a sentence has more than one possible classification, the information will be
classified into the category that is most emphasised within the sentence.7) Tables that provide risk information should be interpreted as one line equals one
sentence and classified accordingly.8) Any risk disclosure that is repeated shall be recorded as a risk disclosure sentence
each time it is discussed.9) If a disclosure is not emphasised enough in relation to risk, then it will not be
recorded as a risk disclosure.10) A risk disclosure is classified as:
a) “Monetary” if numerical in nature i.e. ratio, percentage, integer, decimal and the information allows the user to calculate the estimated financial impact of a risk.
b) “Non-monetary” if not numerical in nature.
11) A risk disclosure is classified as: a) “Good News” if deemed as referring to a positive event.b) “Bad News” if deemed as referring to a negative event.c) “Neutral News” if deemed to neither be good or bad news and refers to a general
policy risk disclosure statement. 12) A risk disclosure is classified as:
a) “Past” if is historical or backward looking in nature.
11Adopted from Linsley and Shrives (2006, p.402).
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b) “Future” if forward looking in nature.c) “Non-time” if deemed to neither be past or future in time orientation and refers to
a present and general risk disclosure statement. 13) Only the ‘Principal Risks and Uncertainties’ section of the annual report should be
coded.
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Table 1: Coding Template for Content Analysis12
Company X
Non-Financial Risks
Risk Disclosure Sentence Characteristics
Financial Risks 1
Operations Risks 2
Empowerment Risks 3
Information and Technology risks 4
Integrity and Governance risks 5
Strategic Risks 6 Total
Monetary/good news/future AMonetary/bad news/future BMonetary/neutral news/future CNon-monetary/good news/future DNon-monetary/bad news/future ENon-monetary/neutral news/future FMonetary/good news/past GMonetary/bad news/past HMonetary/neutral/past INon-monetary/good news/past JNon-monetary/bad news/ past KNon-monetary/neutral news/past LNon-monetary/good news/non-time MNon-monetary/bad news/non-time NNon-monetary/neutral news/non-time OMonetary/good news/ non-time PMonetary/bad news/non-time QMonetary/neutral news/non-time RTotal
12 See USB stick for data.
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Table 2: Disclosure Checklist
Risk Categories Risk Information Sub-CategoriesFinancial Risk Exchange rate
Interest rateCommodity pricesLiquidityCreditGoing concern/insolvencyFinancial derivatives/instrumentsEquity pricesFinancial marketsPensionsInsuranceTreasuryTax
Non-Financial Risk: Operational Risk Product developmentCustomer satisfactionEfficiency and performance SourcingProduct or service failureHealth and safety Brand name erosionStock obsolescence and shrinkageEnvironmental LegalOperating costsDistributionSuppliersBusiness model
Empowerment Risk OutsourcingPerformance incentivesCommunicationsLeadership and management Change readiness
Information and Technology Risk IntegrityAccessAvailabilityInfrastructure
Integrity and Governance Risk Management and employee fraud Reputation Illegal actsInternal control and risk management
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policiesCorporate governance risk disclosure
Strategic risk Competitors Pricing Valuation PlanningBusiness portfolioIndustryResearch and development Organisational structureAcquisitions, alliances, joint ventures Life cyclePoliticalEconomicInternationalResourcesCommunity relationsBusiness marketsGrowth opportunities
Appendix B: Risk Sentence Examples and Sample Companies
Table 3: Examples of Risk Disclosure Sentences
Company Risk Disclosure Example Risk Category
Semantic Properties
Experian “Separately, approximately 52% of our senior leadership roles have successors ready to cover these roles in the short and medium-term” (Experian, 2013, p.30).
Empowerment Risk
Monetary/good news/future
Smith Group “Smiths Group has approximately 5-6% of its business (measured by sales, profit or net assets) in Spain, Ireland, Portugal, Italy and Greece, which could be adversely affected by currency devaluations”. (Smith Group, 2013, p.57).13
Strategic Risk Monetary/bad news/future
Petrofac “Under our sovereign and Financial Market Risk Policy we aim to hedge on a rolling annual basis, the net profit exposure from at least 75% of our low-
Financial Risk Monetary/neutral news/future
13 This is an example of a risk sentence that has more than one possible classification. The decision was made in accordance with the decision rules to code the risk sentence as a strategic risk. Strategic risk was deemed to be emphasized to a greater extent than financial risk as the alternative classification.
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estimate of production” (Petrofac, 2013, p.29).
Unilever “In the event of an incident relating to the safety of our consumers or the quality of our products, incident management teams are activated in the affected markets under the direction of our product quality, science and communication experts, to ensure timely and effective market place action” (Unilever, 2013, p.37).
Operational Risk
Non-monetary/good news/future
Diageo “If there are significant declines in financial markets and/or deterioration in the value of fund assets or changes in discount rates or inflation rates, Diageo may need to make significant contributions to the pension funds in the future” (Diageo, 2013, p.42).
Financial Risk Non-monetary/bad news/future
SSE “Uncertainty also arises from the fact that a referendum on whether Scotland should become an independent country will take place in September 2014; a ‘Yes’ vote would extend that uncertainty until the details of Scotland’s post-independence relationship with the rest of the United Kingdom are determined.” (SSE, 2013, p.77).
Strategic Risk Non-monetary/neutral news/future
TUI Travel “Raised over £400m of long-term financing for nine aircraft scheduled for delivery during FY14” (TUI Travel, 2013, p.48).
Operational Risk
Monetary/good news/past
International Airlines Group
“Delays are generally modest but in 2013 the Venezuela Central Bank blocked the repatriation of funds leading to €184 million equivalent at the official exchange rate being held in Bolivar at the year end” (International Airlines Group, 2013, p.95).
Financial Risk Monetary/bad news/past
BG Group “These facilities which are held with a diversified group of major banks, totalled $5.2 billion and were undrawn as at 31 December 2013” (BG Group, 2013, p.39).
Financial Risk Monetary/neutral/past
Tesco “IT strategy is approved and reviewed by the Executive Committee to ensure that investments in IT systems and innovations improve business efficiency and customers shopping experience” (Tesco, 2013, p.40).
Information and Technology Risk
Non-monetary/good news/past
Intertek Group “The group operates in countries which are recognised to have higher bribery and corruption risks” (Intertek Group, 2013, p.16)
Integrity and Governance Risk
Non-monetary/bad news/ past
BP “Many of our major projects and operations are conducted through joint arrangements or associates and through contacting and sub-contracting arrangements” (BP, 2013, p.52)
Strategic Risk Non-monetary/neutral news/past
ITV “Mandatory online training modules, awareness campaigns and simplified information security policies for employees” (ITV, 2013, p.55)
Information and Technology Risk
Non-monetary/good news/non-time
Royal Dutch Shell A+B “Potential developments that could impact our business include international sanctions, conflicts including war, acts of political or economic terrorism and acts
Strategic Risk Non-monetary/bad news/non-time
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of piracy on the high seas, as well as civil unrest, including disruptions by non-governmental and political organisations, and local security concerns that threaten the safe operation of our facilities and transport of our products” (Royal Dutch Shell, 2013, p.13)
BP “The nature of the group’s operations exposes us to a wide range of significant health, safety, security and environmental risks” (BP, 2013, p.55)
Operational Risk
Non-monetary/neutral news/non-time
British Sky Broadcasting Group
“No more than 10% cash deposits are held with a single bank counterparty, with the exception of overnight deposits which are invested in a spread of AAAm rated liquidity funds” (British Sky Broadcasting, 2013, p.26).”
Financial Risk Monetary/good news/ non-time
Aggreko “If the oil price is persistently low – by which we mean under $50 per barrel – we would expect to see an adverse impact on our business in a number of oil-producing countries” (Aggreko, 2013, p.35)
Financial Risk Monetary/bad news/non-time
Morrison “We are a people business and our 129,000 colleagues make it happen for our customers” (Morrison, 2013, p.28)
Empowerment Risk
Monetary/neutral news/non-time
Table 4: Sample Companies
Companies IndustryARM Holdings Technology Hardware & Equipment Aggreko Support Services Anglo American Mining Antofagasta Mining Ashtead Group Support Services Associated British Foods Food Producers BAE Systems Aerospace & DefenceBG Group Oil and Gas ProducersBHP Billiton MiningBP Oil and Gas ProducersBT Group Fixed Line Telecommunications Babcock International Group Support Services British American Tobacco TobaccoBritish Sky Broadcasting Group MediaBunzl Support Services Burberry Group Personal GoodsCapita Support ServicesCentrica Gas, Water & MultiutilitiesCoca-Cola HBC AG BeveragesCompass Group Travel & Leisure
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Diageo BeveragesEasyjet Travel & LeisureExperian Support ServicesG4S Support ServicesGKN Automobiles & PartsGlaxoSmithKline Pharmaceuticals & BiotechnologyGlencore Xstrata MiningIMI Industrial EngineeringITV MediaImperial Tobacco Group TobaccoInterContinental Hotels Group Travel and LeisureInternational Consolidated Airlines Group Travel and LeisureIntertek Group Support ServicesJohnson Matthey ChemicalsKingfisher General RetailersMeggitt Aerospace & DefenseMondi Forestry & PaperMorrison (Wm) Supermarkets Food & Drug RetailersNational Grid Gas, Water & MultiutilitiesNext General RetailersPearson Media
PersimmonHousehold Goods & Home Construction
Petrofac Oil Equipment and ServicesRandgold Resources MiningReed Elsevier MediaRexam General IndustrialsRio Tinto Mining Rolls-Royce Holdings Aerospace & Defence Royal Dutch Shell A+B Oil & Gas ProducersRoyal Mail Industrial TransportationSABMiller BeveragesSSE ElectricitySage Group Software & Computer ServicesSainsbury (J) Food & Drug RetailersSevern Trent Gas, Water & MultiutilitiesShire Pharmaceuticals & BiotechnologySmith & Nephew Health Care Equipment & ServicesSmiths Group General IndustrialsSports Direct International General RetailersTUI Travel Travel and LeisureTate & Lyle Food Producers Tesco Food & Drug RetailersTravis Perkins Support ServicesTullow Oil Oil & Gas Producers
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Unilever Personal GoodsUnited Utilities Group Gas, Water & MultiutilitiesVodafone Group Mobile TelecommunicationsWPP MediaWeir Group Industrial EngineeringWhitbread Travel and LeisureWilliam Hill Travel and LeisureWolseley Support Services
Appendix C: Regression Models (SPSS Output)
Table 5: RDQ Regression Model
RDQ Model Summary
RR
Square
Adjusted R
SquareStd. Error of the Estimate
Durbin-Watson
.583a .340 .242 41.31300 1.388
RDQ Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.BStd.
Error Beta(Constant)
-138.348 82.391 -1.679 .098
FSLOG 10.245 5.316 .282 1.927 .059
BSLOG 5.481 25.038 .026 .219 .827
LEV 27.487 38.880 .083 .707 .482
LI -.160 9.034 -.002 -.018 .986
INGroup1 23.229 15.979 .191 1.454 .151
INGroup2 7.004 13.032 .065 .537 .593
INGroup4 -6.390 22.746 -.031 -.281 .780
INGroup5 21.683 18.039 .146 1.202 .234
INED 102.271 49.657 .248 2.060 .044
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Table 6: NCQI Regression Model
NCQI Model Summary
RR
Square
Adjusted R
SquareStd. Error of the Estimate
Durbin-Watson
.472a .223 .106 .03471 1.945
NCQI Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.BStd.
Error Beta(Constant)
.110 .070 1.580 .119
FSLOG .005 .005 .191 1.184 .241
LEV -.040 .033 -.155 -1.203 .234
LI .001 .008 .024 .182 .856
INED -.006 .042 -.018 -.136 .892
BSLOG .025 .021 .155 1.190 .239
INGROUP1 .005 .014 .049 .337 .737
INGROUP2 .015 .011 .177 1.358 .180
INGROUP4 .065 .019 .412 3.368 .001
INGROUP5 .011 .015 .092 .700 .487
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