Risk reporting changes after ICFs: related to agency problems or
common sense?
Master Thesis for MSc Accountancy
ABSTRACT: Risk reporting between companies is still largely varied, despite SEC incentives and the possibility for companies to reduce information asymmetry through risk reporting. Companies can be inclined to increase risk reporting, when information asymmetry and agency problems increase. This paper investigates the relationship between risk reporting changes after internal control failures and agency problems and audit firm characteristics. The change in risk reporting is measured as the relative change in total words of the risk sections in subsequent 10-K or 20-F filings. First, in line with expectations, it was found that companies increase their risk reporting after internal control failures. Further tests for explaining differences in risk reporting changes were conducted with a sample of 230 companies that suffered from an internal control failure. Supporting one of the hypotheses, it was found that leverage changes are positively related to risk reporting changes after internal control failures. Concerning the other variables, no relationships were found for block holders, Big 4 auditor presence and auditor changes towards a Big 4 auditor. The findings are interpreted as evidence for companies making conscious reporting choices. It appears that this conscious choice is influenced by internal control failures, as agency problems increase after these failures. Furthermore, it appears that an increase in agency problems concerning debt holders influences the conscious risk reporting choice of companies.
Key words: Risk reporting; internal control failure; agency problems; Big 4.
Name: Colin van den Berg Student number: 2535831 Address: Groenendaalstraat 3 Postal code: 3521 AC Utrecht E-mail: c.van.den.berg.6@student.rug.nl
Supervisors: G.C. Helminck and Prof. Dr. J.A. Emanuels Assessor: Dr. V.A. Porumb
Date: 30-05-2017 Word count: 9935
Table of contents
1. Introduction... 4
2. Background ... 6
2.1. Literature review ...7
2.2. Internal control failures ...8
2.3. Theoretical framework ...8
2.3.1. Agency theory and capital providers. ...9
2.3.2. Agency theory and auditing. ... 10
2.4. Hypothesis development ... 10
2.3.1. Block holders. ... 10
2.3.2. Leverage. ... 11
2.3.3. Big 4 audit firm presence ... 12
2.3.4. Audit firm change. ... 13
2.4. Conceptual model ... 14
3. Methodology ... 15
3.1. Sample Selection ... 15
3.2. Risk Reporting ... 16
3.3. Failure versus non-failure ... 16
3.4. Independent Variables ... 16
3.4.1. Block Holders. ... 16
3.4.2. Leverage. ... 17
3.4.3. Big 4 auditor presence... 17
3.4.4. Auditor change. ... 17
3.5. Control Variables ... 18
3.5.1. Firm size. ... 18
3.5.2. Profitability. ... 18
3.6. Internal control failure test ... 18
3.7. Empirical Models ... 19
4. Results ... 21
4.1. Non-failure versus failure companies ... 21
4.2. Descriptive statistics ... 21
4.3. Correlations ... 22
5. Discussion and Conclusion ... 24
5.1. Findings ... 24
5.1.1. Change after ICF... 24
5.1.2. Block holders. ... 24
5.1.3. Leverage. ... 25
5.1.4. Big 4 audit firms. ... 25
5.1.5. Change towards a Big 4 auditor. ... 26
5.1.6. Research question. ... 26
5.2. Theoretical implications ... 27
5.3. Practical implications ... 27
5.4. Limitations and future research ... 28
5.4.1. Limitations... 28
1. Introduction
After numerous accounting scandals and internal control failures (ICFs), the Sarbanes Oxley act (SOx) was introduced. This act should be enforced by the Securities Exchange Commission (SEC), aiming to reduce ICFs. Despite the introduction of SOx and the attempt of the SEC, numerous firms still experience the negative effects of ICFs. These include resignations and manager charges at UBS, significant fines at HSBC, loss understatements at Merrill Lynch and profit reducing penalties at JDA (Cassin, 2014; Internal control failure leads to $500M loss understatement by Merrill Lynch, 2009; Thomas and Scott, 2012; Treanor, 2011). It seems that despite increased regulation, ICFs still exist (Mokhtar and Mellett, 2013; Rice, Weber and Wu, 2015).
As a result of the ICFs, uncertainty has increased among stakeholders leading to increased information asymmetry (Cheng, Dhaliwal and Zhang, 2013; ICAEW Financial Reporting Faculty, 2011). Consequently, stakeholders react negatively to ICFs (Järvinen and Myllymäki, 2016), which results in a lower valuation for companies with ICFs (Li, Yu, Zhang and Zhen, 2016). In this research, the goal is to analyze company responses after ICFs and the negative stakeholder reactions. This response is measured in terms of changes in risk reporting. Increasing risk reporting is a form of additional disclosure, therefore it enables companies to reduce information asymmetry (Jensen and Meckling, 1976).
Despite the possibility to reduce information asymmetry, risk reporting is still largely varied (Campbell, Chen, Dhaliwal, Lu and Steele, 2014; Dobler, 2008). The SEC is criticized for this fact, as their incentives and regulatory enforcement seem to have failed to provide strong incentives to obey rules (Rice and Weber, 2012). Because of this apparent ineffectiveness of regulation (Marshall and Weetman, 2007), other explanations need to be found for the variation in reporting. For companies, increasing risk reporting can have positive effects in terms of reduced cost of capital (Abraham and Cox, 2007; Leuz and Verrecchia, 2000). This is caused by the fact that increased risk reporting enables stakeholders to better estimate total risks, firm value and future earnings (Abraham and Cox, 2007; Arping and Sautner, 2013; Deumes and Knechel, 2008; Dobler, 2008). On the other hand, possible negative effects in terms of legal consequences and the threat of disclosing information that benefits competitors exist (Deumes and Knechel, 2008). Apparently, a tradeoff arises for companies when considering advantages and disadvantages of increasing risk reporting.
First of all, it will be investigated whether risk reporting in general will increase after an ICF. The most important argument for this is that companies will be more inclined to increase risk reporting when agency costs are higher (Deumes and Knechel, 2008). After this, factors that influence agency costs and information asymmetry will be considered, arguing that they influence the tradeoff choice of companies regarding risk reporting. The goal is to answer the question concerned with why companies will change their risk reporting more or less than others and by this add to existing literature of ICFs and risk reporting. The explaining variables for this study will consist of several stakeholder characteristics, being capital provider characteristics and audit firm characteristics. Hypotheses will be formulated in line with the agency theory (Jensen and Meckling, 1976), arguing that an increase in risk reporting can decrease the information asymmetry. I expect companies to be more inclined to change their risk reporting after an ICF when agency problems and costs are higher or increasing, because this might influence their tradeoff choice (Deumes and Knechel, 2008). This leads to the following research question: “What is the effect of capital provider changes and audit firms characteristics on the change in risk reporting after negative consequences of ICFs?”
Previous literature already tried to contribute to explanations for risk reporting differences. One strand of literature has focused on corporate governance characteristics and firm characteristics (Abraham and Cox, 2007; Barakat and Hussainey, 2013; Mokhtar and Mellett, 2013). A second strand has focused on the quality of the risk information content and consequences of disclosure (Abraham and Shrives, 2014; Linsley and Lawrence, 2007). Lastly, a strand of previous literature has focused on country and cultural differences (Abraham and Cox, 2007; Elshandidy and Neri, 2015; Elshandidy and Shrives, 2016). Although evidence has been found in previous research, to my knowledge none of these has focused on a change in risk reporting and factors or events stimulating this change. This research will add to existing literature by investigating a possible change in risk reporting, caused by ICFs.
Findings of the research can be relevant for stakeholders, particularly investors, companies or managers and audit firms. A change in risk reporting is relevant for stakeholders, because it adds to their knowledge about the company and thus decreases information asymmetry (Jensen and Meckling, 1976). Investors, in particular, will be able to make better estimates of company values and future earnings (Arping and Sautner, 2013; Deumes and Knechel, 2008; Dobler, 2008). They will consequently be better able to make good investment decisions when reporting increases (Hermanson, 2000). Considering agency costs and information asymmetry, managers may be better able to estimate stakeholder reaction and recognize the extent of cost of capital reduction when choosing to report. Therefore managers
can be more aware of their tradeoff choice and its consequences in terms of advantages and costs of risk reporting. Lastly, dependent on stakeholder results and auditor change results, auditors might have a better image of stakeholder needs and auditor change consequences, which enables them to better act according to stakeholder needs and anticipate to changes. By this, audit firms might be better able to determine workload and contribute to risk reporting credibility.
This paper proceeds as follows. In section 2, risk reporting definitions, existing literature and the theoretical framework will be discussed. Lastly, hypotheses will be developed. In section 3, the methods used to test hypotheses will be discussed. In section 4 the results will be presented and in section 5 results will be discussed and conclusions will be provided.
2. Background
Before further literature discussion and execution of the research, I will discuss what risk is and how risk reporting is identified in this research. Two commonly known risk management frameworks are COSO ERM and ISO 31000. In the recently released updated framework of COSO ERM, the risk definition is: “the possibility that events will occur and affect the achievement of strategy and business objectives” (COSO, 2016, p.9). ISO 31000 defines risk as: “the effect of uncertainty on objectives. This effect may be positive, negative or a deviation from the expected. Risk is often described by an event, change in circumstances or a consequence” (Airmic, Alarm and IRM, 2010, p.4). The major difference between COSO ERM and ISO 31000 is that ISO acknowledges risks can have positive effects, where COSO ERM focusses on the downsides of risks.
Under SEC registrants, which consist of US stock trading companies or foreign companies that trade stocks on the US market, risk reporting is mandatory and can be found in 10-K or 20-F reports. Risk information in 10-K filings include item “1A: risk factors”, which includes information about the most significant risks to the company and item “7A: quantitative and qualitative disclosures about market risk”, which includes information about market risks (SEC, 2011). In 20-F reports, the same items can be found under item “3D: risk factors” and under item “11: quantitative and qualitative disclosures about market risk.” Although risk reporting is still largely varied (Campbell et al, 2014; Dobler, 2008), the reports do provide an easy way of finding risk reporting of companies.
Another relevant fact for this study is the enforcement of the SOx act by the SEC. I already acknowledged ICF still exist and effectiveness of the SEC is discussed, nevertheless companies do have to acknowledge their ICFs according to SOx and the SEC. SOx section 302 is concerned with statements about internal control effectiveness and SOx section 404 is concerned with a separate report in the effectiveness of internal control. With the help of these statements, ICFs can be identified.
2.1. Literature review
Before the development of hypotheses, existing literature regarding risk reporting will be discussed. Numerous articles conducted research focused on linking corporate governance characteristics and firm characteristics to risk reporting (Abraham and Cox, 2007; Barakat and Hussainey, 2013; Mokhtar and Mellett, 2013). First of all, firm size is positively related to risk reporting (Elshandidy, Fraser and Hussainey, 2013; Elshandidy and Shrives, 2016; Linsley and Shrives, 2006). Regarding ownership, large outside ownership appears to be positively related to the quality of operational risk reporting (Barakat and Hussainey, 2013), while executive ownership and long term institutional ownership are negatively related to risk reporting (Abraham and Cox, 2007; Barakat and Hussainey, 2013). Considering boards, total board size and the proportion of outside independent board directors appear to be positively related to risk reporting (Abraham and Cox, 2007; Barakat and Hussainey, 2013; Mokhtar and Mellett, 2013), Lastly, other positive variables related to risk reporting are activities by the audit committee and the presence of laws encouraging competition (Barakat and Hussainey, 2013).
Another part of the existing literature of risk reporting focused on the quality of the risk information content and consequences of disclosure. Considering quality, risk reporting content was difficult to read and use (Linsley and Lawrence, 2007). This is supported by findings indicating that risk reporting may be limited, because managers do not always fully disclose information in their possession (Abraham and Shrives, 2014; Dobler, 2008). Reasons might be self-interest, refusal to spread useless information, the impossibility of credibility and verification (Dobler, 2008) or the fear for proprietary costs (Abraham and Shrives, 2014). Lastly risk reporting can predict volatility, sensitivity and value declines of future stock prices (Deumes, 2008).
A next explored sub area of risk reporting research is that of country or cultural research. In the United Kingdom (UK), firms listed on a United States (US) stock exchange disclose more risk information than non-US listed UK firms (Abraham and Cox, 2007). Next, in UK based and Italian based companies the degree of risk reporting is dependent on strong governance
(Elshandidy and Neri, 2015), where in German companies risk reporting is more influenced by type of risks rather than company structure or governance characteristics (Elshandidy and Shrives, 2016). It can be concluded that companies in different countries can have different motives for risk reporting.
As mentioned, this research will investigate possible risk reporting changes following ICFs. This research thus adds to existing findings, by focusing on companies affected by internal control deficiencies and focusing on possible explanations for varying changes of risk reporting. To my knowledge none of the existing researches has focused on the change of risk reporting after an event, here being an internal control failure. In expanding the risk reporting literature, I will apply agency an audit firm variables. These will be block holder changes, leverage changes, the presence of a ‘big 4’ auditor and auditor changes.
2.2. Internal control failures
I will first discuss how agency theory is linked to ICFs, as this research will apply the agency theory. ICFs or weak internal control systems increase information asymmetry and therefore stakeholders will react negatively (Chen et al., 2013; Church and Schneider, 2016). The increased information asymmetry inevitably leads to higher agency costs (Jensen and Meckling, 1976). These agency costs are expressed in terms of lower valuation of companies, increased debt costs (Kim, Song and Zhang, 2011; Li et al., 2016). It can be concluded that information asymmetry and agency costs increase when ICFs are reported. Reducing these agency problems can be achieved by disclosing additional information (Jensen and Meckling, 1976), in this case increasing risk reporting (Elshandidy et al., 2013). As mentioned, managers are more inclined to increase risk reporting when agency costs are higher (Deumes and Knechel, 2008). Therefore, I expect risk reporting to increase after an ICF, compared to when no ICF has occurred. This expectation will be tested before testing actual hypothesis, because hypotheses are partly based on the outcome of this expectation.
2.3. Theoretical framework
Numerous research on risk reporting have applied agency theory in the development and testing of relationships (Abraham and Cox, 2007; Barakat and Hussainey, 2013; Elshandidy et al., 2013; and Mokhtar and Mellett, 2013). The agency theory, explained by Jensen and Meckling (1976), states that agency problems and information asymmetry lead to monitoring activities, bonding costs and residual loss called agency costs (Jensen and Meckling, 1976).
Agency theory will be used in this research to explore relationships between the chosen variables and the change in risk reporting after negative ICF consequences.
2.3.1. Agency theory and capital providers. Separation of ownership and control between managers (agents) and owners (principals) creates agency costs and agency problems (Jensen and Meckling, 1976). The separation inherently creates information asymmetry and because of this, managers will not always act in the interests of the owners and other shareholders. This agency problem is commonly referred to as the ‘type 1 agency problem’ (Ali, Chen and Radhakrishnan, 2007; Jensen and Meckling, 1976). Shareholders react by seeking to control managerial behavior with monitoring activities (Jensen and Meckling, 1976). Shareholders often only partially succeed in their monitoring activities and will therefore take the information asymmetry and monitoring costs into accounting when determining claim prices (Deumes and Knechel, 2008; Jensen and Meckling, 1976).
Companies having debt creates another agency problem, namely the one between creditors and shareholders. One cause is that shareholders might be willing to take more risk (Deumes and Knechel, 2008). Within this agency problem, creditors have concerns regarding manager actions that increase the risk of not receiving their monetary returns (Armstrong, Guay and Weber, 2010). These manager actions might consist of engaging risky investments or transferring dividends (Jensen and Meckling, 1976). Just as the shareholders, creditors will respond to information asymmetry, agency problems and monitoring costs. They will increase conditions and price protection to hedge alongside these costs and uncertainties or might even choose to not provide a loan (Armstrong et al., 2010).
Because of price adjustments by shareholders and increased conditions and price protections by creditors, managers might be willing to engage in resourcing activities themselves in order to reduce the efficiency loss of the agency problems (Deumes and Knechel, 2008). By doing this, they will keep shareholders and creditors satisfied and convince them that they are in line with their interests (Jensen and Meckling, 1976). One way of achieving a reduction in efficiency loss of agency problems is disclosing more information than obliged (Jensen and Meckling, 1976; Watson, Shrives and Marston, 2002) and specifically risk reporting (Elshandidy et al., 2013). This results in reduced information asymmetry and reduced investor uncertainty, leading to reduced agency costs (Chow and Wong-Boren, 1987; Elshandidy et al., 2013; Mokhtar and Mellett, 2013; Watson et al., 2002). The willingness of managers to engage in monitoring activities like additional disclosure depends on a constant cost-benefit tradeoff (Deumes and Knechel, 2008). Willingness to disclose is highest when
agency problems and information asymmetry are high, because potential benefits are the highest in such a situation (Deumes and Knechel, 2008).
2.3.2. Agency theory and auditing. Audit firms can help restrain agency problems, therefore agency problems and costs are important for auditing decisions at companies (Chow, 1982; Jensen and Meckling, 1976). Auditors generally provide assurance by verifying financial information, which reduces information asymmetry (Knechel, Niemi and Sundgren, 2008). Consequently, stakeholders will be better able to assess risks and company value resulting in a reduced cost of capital (Jensen and Meckling, 1976; Knechel et al., 2008). When agency costs are higher, stakeholder demand for a higher auditing quality will also increase (DeFond, 1992; Langli and Thomas, 2012; Lennox, 2005; Lopes and Rodrigues, 2007). This demand is based on the fact that dominant audit firms are better able to reduce agency costs by providing users of information with a higher level of certainty (Mokhtar and Mellett, 2013; Oliveira, Rodrigues and Craig; 2011). This higher level of certainty is delivered through better auditing quality of dominant audit firms (Becker, DeFond, Jiambalvo and Subramanyam, 1998; DeAngelo, 1981; Lennox and Pittman, 2010). Therefore, in response to higher agency costs, companies with higher agency costs are more likely to be audited by dominant audit firms (Fan and Wong, 2005; Oliveira et al., 2011).
Agency costs play an important role in determining whether an auditor change is desired. Managers may choose to change auditors in anticipation of agency conflicts, considering the extent of agency conflicts and desired auditing quality in doing so (DeFond, 1992). Concluding, auditor change and choice of new auditors can be motivated by agency costs (Francis and Wilson, 1988).
2.4. Hypothesis development
In this section, hypotheses for each variable will be formulated based on agency theory and prior literature discussion.
2.3.1. Block holders. The first hypothesis is concerned with the presence of large outside shareholders, also known as block holders. This variable is related to the ‘type 1 agency problem’, because it involves shareholders and managers. Some previous research findings are opposing when considering block holder influences. For example, Barakat and Hussainey (2013) found a positive relation between block holders and risk disclosure quality, while Ertimur, Sletten and Sunder (2014) conclude managers could delay additional disclosures under pressure of block holders because of selling incentives of the shareholders.
Jensen and Meckling (1976) state that when outside ownership among shareholders is more divided, thus when block holders are absent, information asymmetry and agency problems will increase. Reasoning for this is that shareholders owning small amounts of stock have greater difficulty monitoring companies and have to incur greater costs while doing this. In this case, managers’ motivation to reduce efficiency loss can be higher, because of greater potential benefits. Outside block holders, on the contrary, are more likely to monitor companies and its managers. One explanation might be the linkage to a significantly higher part of their income (Admati, Pfleiderer and Zechner, 1994; Maug, 1998). In addition, outside block holders appear to be better at monitoring companies as well (Admati et al., 1994; Huddart, 1993). Therefore, block owners mitigate agency problems and information asymmetry (Deumes and Knechel, 2008; Francis and Smith, 1995). Because of these lower agency problems through monitoring activities, efficiency loss and information asymmetry will be lower when block holders are present.
In conclusion, agency theory suggests agency costs decrease when outside shareholders are more concentrated. Thus when the proportion of block holders increases, agency costs will decrease. Therefore, managers can be less inclined to increase risk reporting. In line with the findings of Deumes and Knechel (2008) regarding internal control disclosure and these agency theory arguments, I expect an increase of the proportion of block holders to have a negative relationship with the change in risk reporting after negative ICF consequences. This leads to hypothesis 1 mentioned below.
H1: There is a negative relationship between changes in the proportion of outside block holders and the change in risk reporting after negative ICF consequences.
2.3.2. Leverage. The second hypothesis is concerned with the influence of indebtedness of companies, commonly referred to as financial leverage, on risk reporting. The agency problem between shareholders and creditors is relevant here, partly caused by shareholder willingness to take more risks (Jensen and Meckling, 1976). The agency conflict between creditors and shareholders increases alongside the increase of leverage, because the risk of returns for debt holders moving to shareholders will increase (Jensen and Meckling, 1976). Creditors will react to increasing agency conflicts by increasing loan conditions and price protection activities (Armstrong et al., 2010). To reduce this agency problem between shareholders and debt providers, companies should increase transparency (Chavent Ding, Fu, Stolowy and Wang, 2006). This is confirmed by previous research, stating that financial
leverage is positively related to general disclosure and internal disclosure (Chavent et al., 2006; Deumes and Knechel, 2008) and negatively to information gaps (Marshall and Weetman, 2007). This increase in disclosure will logically lower the information asymmetry between debt holders and shareholders, resulting in more satisfied creditors (Watson et al., 2002) and thus lower loan conditions and price protection activities. Therefore, it can be argued that companies with higher leverage or an increase in leverage will be more inclined to change their risk reporting activities, especially after ICFs that can raise debt holder concerns. In line with the agency theory arguments and previous literature results (Chavent et al., 2006; Deumes and Knechel, 2008; Marshall and Weetman, 2007), I expect a positive relationship between financial leverage changes and the change in risk reporting after negative internal control consequences.
H2: There is a positive relationship between leverage changes and the change in risk reporting after negative ICF consequences.
2.3.3. Big 4 audit firm presence. The third hypothesis is concerned with big 4 auditor is presence at the company and its influence on risk reporting. big 4 refers to the audit firms EY, PwC, KPMG and Deloitte, also referred to as big audit firms or dominant audit firms. These dominant audit firms provide a higher level of certainty which reduces information asymmetry (Becker et al., 1998; DeAngelo, 1981).
Previous literature argues reporting increases along with increased agency costs and that dominant audit firms are more likely to be present at companies with higher agency costs (Fan and Wong, 2005; Oliveira et al., 2011). This might lead to causality concerns. Despite some research suggesting a negative relationship between dominant audit firms and extensive reporting (Mokhtar and Mellett, 2013; Rice and Weber, 2012), most research suggests a positive relationship between dominant audit firms and disclosure levels (Chalmers and Godfrey, 2004; Lopes and Rodrigues, 2007; Singhvi and Desai, 1971). Further evidence of dominant audit firms limiting opportunistic accruals (Francis, Maydew and Sparks, 1999) motivating adoption of IAS (Dumontier and Raffournier, 1998) and dominant audit firms stimulating engagement in mandatory risk reporting (Mokhtar and Mellett, 2013) are important to disprove concerns of causality. Main arguments from the dominant auditor viewpoint for these stimulations is that they have greater professional knowledge and have more reputational status to maintain or preferably increase reporting (Chalmers and Godfrey, 2004). Considering these findings, it can be stated that dominant audit firms are more active in the encouragement of additional
disclosure (McNally, Eng and Hasseldine, 1982). Most importantly, big 4 audit firms stimulate risk reporting (Oliveira et al., 2011). Big audit firms thus appear to be better capable and more motivated to reduce agency problems and information asymmetry at companies they audit. Thus it is likely that they are more motivated to pressure companies in changing their risk reporting after ICFs, to satisfy stakeholders. Therefore, I expect a positive relationship between big 4 auditor presence and the change of risk reporting after negative ICF consequences.
H3: There is a positive relationship between the presence of a big 4 audit firm and the change in risk reporting after negative ICF consequences.
2.3.4. Audit firm change. The fourth and fifth hypothesis are concerned with auditor change. An auditor change can be divided in changing from one big 4 auditor to another or the change from a non-big 4 to a big 4 auditor or vice versa.
One possible auditor change that can be considered is changing from one big 4 auditor to another. Previous literature has shown differing results. Changing from one big 4 auditor to another may not be desired because of reduced technical competence and independence (Arrunada and PazAres, 1997), this is further supported by the positive effect of tenure (Ghosh and Moon, 2005). Positive findings suggest auditor change from one big 4 to another improves quality of auditing and monitoring, which increases trust and decreases agency costs (Williams, 1988). Furthermore, auditor change from one big 4 to another would increase independence and incentives to report on risks and vulnerabilities (Weber, 2012). A possible explanation for these inconclusive findings is that the nature of reactions to switching from one big 4 audit firm to another can be dependent on whether the outgoing or incoming big 4 auditor is a specialist or not (Knechel, Naiker and Pacheco, 2007). Because of this conclusiveness, it is hard to predict a relationship regarding risk reporting, therefore I will not include this type of auditor change in this variable.
Changing from a small auditor to a big 4 auditor might influence reporting activities. Companies with higher agency costs are likely to switch to big 4 auditors (Francis and Wilson, 1988). This is in line with the previously mentioned statement that Big 4 audit firms are more likely to be present at companies with high agency costs (Fan and Wong, 2005; Oliveira et al., 2011). As previously argued, it is expected that big 4 audit firms are better capable and more motivated to reduce agency problems and information asymmetry. This is supported by research indicating higher quality audits after changing from smaller to big 4 auditors and research indicating lower quality audits after changing from big 4 to smaller auditors (Lin, Liu
and Wang, 2009; Mitra, Jaggi and Al-Hayale, 2016; Teoh and Wong, 1993). Additionally, because of higher agency costs and higher certainty provided by big 4 auditors, possible benefits of additional disclosure will be higher for managers and companies. Therefore, it can be argued that risk reporting activities will increase when companies change to a big 4 auditor. Furthermore, it can be argued that a bigger change in risk reporting can be measured at companies that recently switched to big 4 audit firms compared to companies that were already audited by big 4 audit firms before the ICF. This leads to the following two hypotheses, where the effect of audit firm change variable will also be measured separately.
H4: There is a positive relationship between the change to a big 4 auditor and the change in risk reporting after negative ICF consequences.
H5: Changing to a big 4 audit firms strengthens the positive relationship between the presence of a big 4 audit firm and the change in risk reporting after negative ICF consequences.
3. Methodology
3.1. Sample Selection
In this research, questions concerning to what extent the chosen independent variables influence the change in risk reporting can be answered by using a quantitative approach (Smith, 2015). In determining the sample, appropriate companies need to be identified. The crucial condition for companies to be part of the sample is that they need to have suffered from ICFs. Secondly, all companies in our sample are SEC registered, as it is necessary to analyze 10-K and 20-F filings among others. Audit analytics is used to identify appropriate companies, as Audit Analytics supply data regarding ICFs. Documentation regarding SOx 302 and SOx 404 is included in Audit Analytics and the conclusion regarding effectiveness of internal control is provided as well. Where internal controls are stated as not effective, firms can be included in the sample.
After filtering for companies with ICFs, only companies that suffered from an ICF in the period of 2004 until 2012 can be included in the sample. ICFs before 2004 are excluded, because SOx is enforced by the SEC since 2002. When analyzing company with a failure in 2003, both annual reports of 2003 and 2002 are needed. When including these, there will be a chance that the SOx implementation will influence results. Therefore these are excluded. The most recent year for ICFs is 2012, because more recent data are not provided. After excluding filings of 2001, 2002, 2003 and companies where most recent filings are not known, 7187 companies remain suitable for our sample. The sample size for this research is set to 235 ICF cases. This sample size is determined after G*Power program calculations and application of the formula mentioned by Mokhtar and Mellett (2013). With this sample size, it is possible to detect small to medium effects. Lastly, ICFs of companies in different countries and industries will be used, to have a rich variety of data that is not limited to US based companies. After deleting incomplete and inappropriate cases, 230 samples remain.
Data is collected as a group. To minimize possible influences of subjectivity, one precaution and one retrospective measure has been taken. As precaution, a data collection manual is written by the group to align thoughts about measuring variables and collecting data. The individually taken retrospective measure consists of randomly selecting several samples and collecting data for these selected samples again. After these measures, it can be assumed that data has been gathered in a consistent way.
3.2. Risk Reporting
The dependent variable, change in risk reporting, will be measured by determining the change of risk reporting words in subsequent annual reports. In 10-K filings, total words of section “1A: risk factors” and “7A: quantitative and qualitative disclosures about market risk” are counted with the LEN function in excel and added by the help of excel, resulting in total amount of words spent on risk reporting. For 20-F filings, the same will be done for the equally named items “3D: risk factors” and item “11: quantitative and qualitative disclosures about market risk.” This procedure will be applied to both the annual report preceding the ICF and the annual report following the ICF. With these values, the change in risk reporting can be determined by using the following formula:
( ℎ ℎ –
ℎ ℎ )
ℎ ℎ ∗ 100%
The outcome of this formula provides a percentage change in risk reporting of the companies after an ICF and these scores will be used to test our hypotheses.
3.3. Failure versus non-failure
The procedure for calculating risk reporting change will also be executed for consecutive reports of companies that have not suffered from ICFs. For these companies, a change in risk reporting will be determined as well by applying the formula mentioned above. After this, we are able to test the assumption that changes in risk reporting are indeed made after ICFs and that these changes do not happen randomly. This is expectation is formulated in the internal control failure paragraph.
3.4. Independent Variables
The independent variables and control variables of this research are presented in table 1. The table includes definitions, predicted relations with the change in risk reporting and short labels used in formulas and other tables. After table 1, block holder changes can be referred to as BLOCK, leverage changes as LEV, presence of big 4 auditors as BIG4 and auditor change as ACHANGE when using other tables or when discussing results.
3.4.1. Block Holders. In this research, block holders are defined as outside shareholders owning 5 percent or more of the shares. This is in line with SEC requirements and previous
research (Deumes and Knechel, 2008; Singh and Davidson, 2003). Data regarding this variable can be gathered by analyzing annual reports. In 10-K filings, companies have to mention outside block holders in “item 12: Security ownership of certain beneficial owners and management” and in 20-F filings, companies have to mention this in part “A. Major shareholders” of “item 7: Major shareholders and related party transactions.” In 10-K filings, companies sometimes refer to another file, which mostly is the 14DEF file in the SEC database. Using these reports, data regarding block holders will be gathered. When more block holders own a minimum 5%, percentages are added. For each sample, a percentage of total block holders will be gathered for the annual report preceding the ICF and the annual report after the ICF. Lastly, the percentage change of block holder presence will be calculated by subtracting the amount of block holders preceding the ICF by the amount of block holders after the ICF.
3.4.2. Leverage. The debt to assets ratio is used to calculate leverage of sample companies for the annual report before and after the ICF. This measure is commonly known and consistent with previous research (Deumes and Knechel, 2008; Oliveira et al., 2011). Total assets and total debt, consisting of current and long term debt, will be subtracted from both the annual report preceding the ICF as the annual report after the ICF. Change in leverage will be calculated by subtracting leverage preceding the ICF by leverage after the ICF. After this, the percentage change of leverage will be calculated by dividing the change in leverage by leverage preceding the ICF.
3.4.3. Big 4 auditor presence. A dummy variable is used to determine the presence of a big 4 auditor. Values for this variable will be manually extracted from annual reports, where the annual report following the ICF will be used and auditor company names will be noted. By using the first annual report after the ICF, the identified auditor is the one that influenced and audited the mentioned information in the report following the ICF. Consequently, this report will probably contain information about the ICF as well. By this, it has been attempted to measure auditor influence as directly as possible. When all auditors are identified, the value ‘1’ will be provided where annual reports were audited by big 4 auditors and the value ‘0’ will be provided where annual reports were audited by non-big 4 auditors (Oliveira et al., 2011; Rice and Weber, 2012). By this, non-big 4 auditor presence will be the reference group.
3.4.4. Auditor change. A dummy variable will be used to determine whether auditor change from a non-big 4 to a big 4 audit firm has occurred, which is consistent with prior research (Lin et al., 2009; Rice and Weber, 2012). Values for this variable will be manually extracted from annual reports. Audit firm names will be extracted from the annual report preceding the ICF, while audit firm names for annual reports following the ICF are already
extracted for testing big 4 auditor presence. After extracting all the audit firm names, relevant changes can be identified. If the auditor of the annual report preceding the ICF is a non-big 4 audit firm and the auditor of the annual report following the ICF is a big 4 audit firm, the value ‘1’will be provided. For all other cases, the value ‘0’ will be provided. By doing this, all cases where no change towards a big 4 auditor has taken place form the reference group.
3.5. Control Variables
3.5.1. Firm size. The first control variable of this research is the change of firm size. Firm size can influence risk reporting as costs are relatively lower for larger firms to provide risk information (Elshandidy et al., 2013). Values of total assets are already extracted for our leverage hypothesis. First, the natural logarithm of total assets will be calculated to determine firm size, consistent with previous research (Chalmers and Godfrey, 2004; Elshandidy et al., 2013). The change of firm size is calculated by subtracting the log of total assets before the ICF by the log of total assets after the ICF. After this, the percentage change of firm size is calculated by dividing the change of log of total assets by the log of total assets of the annual report preceding the ICF.
3.5.2. Profitability. The second control variable of this research is profitability. Profitable firms can be less inclined to increase risk reporting (Elshandidy and Shrives, 2016). Therefore, it can be argued that companies with an increase in profitability are less inclined to increase risk reporting. First, profitability for the annual report before and after the ICF will be measured by return on assets (ROA). Net income is extracted from annual reports, while asset values are already available. After gathering both aspects of the formula, net income can be divided by assets resulting in ROA. After this, the change in ROA is determined by subtracting the ROA preceding the ICF by the ROA after the ICF.
3.6. Internal control failure test
First, it is needed to test the assumption that, in general, a change in risk reporting will occur after an ICF. Therefore, we need to compare the change in risk reporting for companies that suffered from ICFs with the change in risk reporting of companies that did not suffer from ICFs. To do this, we use the independent samples t-test (Smith, 2015). The independent samples t-test is used here, because companies that did not suffer from ICFs are selected at random and therefore do not have to be related with companies that suffered from ICFs. 65 companies without an ICF, thus where it is stated that internal controls were effective, will be selected
from Audit Analytics for this test. A sample of 65 is used after considering G*Power calculations, because with this sample it is possible to detect small to medium differences.
3.7. Empirical Models
Before testing hypotheses, correlation coefficients will be calculated. The dependent variable consists of ratio data, therefore correlations with other ratio and interval variables can be determined using the Pearson ‘r’ correlation. These variables are block holder changes, leverage changes, firm size changes and profitability changes. The other variables, big 4 auditors and auditor change, are nominal variables and correlations among these can be measured using Phi correlation. Correlations between the ratio or interval variables and nominal variables can be calculated using Point Biserial correlation, also referred to as rpb.
For our hypotheses testing, multiple regression model-building will be used, because this fits the measurement levels of my variables (Smith, 2015). The normal multiple regression will be used for hypothesis 2, 3, 4 and 5. This results in the following formula:
RRChange = β0 + β1 BLOCK + β2 LEV + β3 BIG 4 + β4 AChange + β5 LOG_TA + β6 ROA + ε
The moderate regression will be used to include our last hypothesis, concerned with the moderating effect of auditor changes. This results in the following formula:
RRChange = β0 + β1 BLOCK + β2 LEV + β3 BIG 4 + β4 AChange + β5 (Big 4 ∗ AChange) + β6 LOG_TA + β7 ROA + ε
TABLE 1 Variable definitions
Variables Definition Expected
relationship
Label
Dependent variable
Change in risk reporting Change in risk reporting, calculated as:
(total risk reporting words after ICF – total risk reporting words before ICF) / total risk reporting words before ICF.
RRChange
Independent variables
Block holder changes Changes in outside shareholders that own 5% or more of the shares. Calculated as:
Total percentage of block holders after ICF – total percentage of block holders before ICF.
- BLOCK
Leverage changes Changes in debt divided by total assets. Calculated as:
(leverage after ICF – leverage before ICF) / leverage before ICF.
+ LEV
Big 4 auditor presence Dummy variable equals 1 if big 4 audit firm is the auditor and 0 otherwise.
+ BIG4
Auditor change Dummy variable equals 1 if change from small to big 4 auditor has occurred and 0 otherwise.
+ AChange
Control variables
Firm size changes Changes in log of total assets. Calculated as: (log of total assets after ICF – log of total assets before ICF) / log of total assets before ICF.
+ LOG_TA
Profitability changes Changes in return on assets. Calculated as: ROA after ICF – ROA before ICF.
4. Results
The results provided in this section are generated using SPSS. Before starting statistical tests, values of independent and control variables were winsorized. First, the means and standard deviations where calculated. Minimum and maximum values where determined by subtracting and adding three times the standard deviation from or to the mean. Values below and above these intervals, where set equal to these minimum and maximum values. The goal of doing this is reducing the influence of these extreme values, while at the same time keeping the sample size sufficient.
4.1. Non-failure versus failure companies
Before discussing descriptive statistics, correlations and the testing of hypotheses, it is needed to test the assumption regarding risk reporting changes after ICFs. In this research, it is assumed that companies change their risk reporting after an ICF. By testing this assumption, it can be determined whether it is relevant to execute further tests with the ICF companies only. Four extreme values were identified, with a risk reporting change value of over 10.000%. Means and standard deviations of risk reporting changes for companies with and without an ICF were calculated separately, without these extreme values. These extreme values are excluded in the calculations, because these extreme values would significantly affect the mean and standard deviation of risk reporting changes. After calculating the mean and standard deviation, risk reporting values were winsorized. All values above the allowed maximum, calculated by the mean plus three times the standard deviation, were set equal to this maximum value. All values below the allowed minimum, calculated by the mean minus three times the standard deviation, were set equal to this minimum value. This has been done separately for the non-failure and ICF group. The assumption is tested by an independent sample t-test. This study found that companies that suffered from an ICF subsequently changed their risk reporting significantly more (22,33% ± 63,63%) than companies that did not have an ICF (10,81% ± 21,50%), p = 0,021. It can be concluded that companies with an ICF change their risk reporting more than companies that did not have an ICF, therefore the assumption is supported. Because of this support, we will continue this study by using the 230 ICF companies only.
4.2. Descriptive statistics
Table 2 provides descriptive statistics derived from the data. The change in risk reporting after an ICF appears to have a minimum value of -99,69% and a maximum of
292,96%. The mean here is an increase of 22,33% and the standard deviation is 63,63%. Changes of total block holders appear to be within a range of -41,02% to 40,79%. The mean here is -0,16% with a standard deviation of 11,83%. The maximum change in leverage for our sample is 473,62% and the minimum change is -99,89%. The mean here is 44,08 % with a standard deviation of 138,00%. Our control variable change in log of total assets appears to have a maximum value of 40,29% and a minimum value of -86,42%. The other control variable, ROA change, appears to have a minimum value of -9.305,86% and a maximum value of 2.960,98%. The mean of ROA change in our tests is -206,36% and the standard deviation is 1.381,68%. Our auditing variables are not included in the descriptive statistic section, because these are dummy variables and therefore would not provide useful data. After analyzing the descriptive statistics, it can be concluded that companies on average increase their risk reporting and have a large decrease in ROA. Furthermore there is no big change in outside block holders and are increasingly leveraged after ICFs.
Table 2 Descriptive statistics
Variable N Minimum Maximum Mean Standard Deviation
RRChange 230 -99,69% 292,96% 22,33% 63,63% BLOCK 230 -41,02% 40,79% -0,16% 11,83% LEV 230 -99,89% 473,62% 44,08% 138,00% LOG_TA 230 -86,42% 40,29% 0,29% 8,54% ROA 230 -9.305,86% 2.960,98% -206,36% 1.381,68% 4.3. Correlations
Table 3 provides correlations for all variables used in this research. It appears that only the change in leverage (0,118, p<0,1) and the change in firm size (0,141, p<0,01) are significantly correlated with the change in risk reporting after an ICF.
Leverage is correlated with firm size (-0,394, p<0,01) and profitability (-0,426, p<0,01). The presence of a Big 4 auditor is correlated with the auditor change variable (0,431, p<0,01) and profitability (0,138, p<0,01). Lastly, firm size is correlated with return on assets (0,657, p<0,01).
Table 3 Correlations
RRChange BLOCK LEV BIG4 AChange LOG_TA
RRChange BLOCK 0,036 LEV 0,118* 0,010 BIG4 -0,072 -0,058 -0,090 AChange -0,054 -0,007 -0,062 0,431*** LOG_TA 0,141** -0,012 -0,394*** 0,045 0,058 ROA -0,032 -0,062 -0,426*** 0,138** 0,050 0,657***
*. Correlation is significant at 0,10 level (2-tailed). **. Correlation is significant at 0,05 level (2-tailed). ***. Correlation is significant at 0,01 level (2-tailed).
4.4. Regression results
The linear regression analysis has been conducted in SPSS to test the hypotheses. According to table 4, VIF values remain below 1,91. Together with the correlation values of Table 3, this leads to the conclusion that there are no multicollinearity concerns. Results are presented in table 4. First, control variables are tested (1). After this, all hypotheses are tested separately (table 4 (2, 3, 4, 5, 6)). Lastly all hypotheses are tested together (table 4 (7)).
First, both control variables are found to be significantly related to risk reporting changes after an ICF. The change in log of total assets is positively related to changes in risk reporting after an ICF (2,123, p<0,01) and the change in ROA is negatively related to changes in risk reporting after an ICF (-0,010, p<0,05). This test (1) has an adjusted R-square value of 0,04.
Leverage changes appear to be positively related to risk reporting changes after an ICF (0,079, p<0,05), with an R-square of 0,06. This is in line with the expectation of this research, formulated in hypothesis 2. Therefore, hypothesis 2 is supported. This positive relationship (0,078, p<0,05) remains to exist when testing all variables together (table 4 (7)).
As reported in table 4, relationships for block holder changes, big 4 presence and auditor changes are not significant. Therefore, hypothesis 1, 3, 4 and 5 are not supported.
Table 4 Regression results Variables (1) (2) (3) (4) (5) (6) (7) Intercept 19,622 19,670 16,533 22,744 21,009 22,699 19,278 LOG_TA 2,123*** 2,113*** 2,380*** 2,089*** 2,142*** 2,115*** 2,339*** ROA -0,010** -0,010** -0,008* -0,010** -0,010** -0,010** -0,007* BLOCK (H1) 0,138 0,135 LEV (H2) 0,079** 0,078** BIG 4 (H3) -7,212 -6,146 AChange (H4) -11,758 AChange moderating (H5) -8,645 -8,009 Adjusted R-squared 0,04 0,04 0,06 0,04 0,04 0,04 0,05 F-value 5,62 3,79 5,74 3,99 4,03 3,08 3,02 highest VIF 1,76 1,77 1,87 1,80 1,76 1,80 1,91
* Test is significant at 0,10 level (2-tailed). ** Test is significant at 0,05 level (2-tailed). *** Test is significant at 0,01 level (2-tailed).
5. Discussion and Conclusion
This section will first review findings of the research and conclude on these findings. Next, theoretical and practical implications will be discussed. Lastly, limitations and suggestions for future research will be explained.
5.1. Findings
5.1.1. Change in risk reporting after ICF. Before testing hypotheses of this research, it was needed to determine that companies indeed change risk reporting after an ICF. In line with expectations and agency theory (Jensen and Meckling, 1976), companies increase their risk reporting after an ICF. This increase is significantly higher than the increase in risk reporting for companies without an ICF. Chen et al. (2013) and Church and Schneider (2016) argue that an ICF will increase agency costs. Therefore, this finding is in line with agency argumentations that companies will be more inclined to increase risk reporting when agency costs are higher (Deumes and Knechel, 2008).
5.1.2. Block holders. Agency theory states that agency costs decrease when outside block holder ownership increases (Deumes and Knechel, 2008; Francis and Smith, 1995). Therefore, a negative relationship between outside block holder changes and risk reporting changes after ICFs was expected. Despite this expectation, no relationship was found for the block holder change variable. One possible explanation comes forth from the fact that companies from the banking industry were not excluded from the sample. Barakat and
Hussainey (2013) found a positive relationship between risk disclosures and block holders in the banking industry, which contradicts the prediction of this research. Another possible explanation might be found by looking at the nature of the outside block holders. Abraham and Cox (2007) document that long term block holders are positively related to risk reporting, while short term block holders are negatively related to risk reporting. As this research does not make such a distinction, both types of block holders might be included in the sample. Concluding, both the nature of our sample and the possible diversity of block holders could provide explanations for not finding a relationship.
5.1.3. Leverage. Expectations considering leverage are based on the agency theory (Jensen and Meckling, 1976), which states that agency conflicts between debt holders and shareholders increase when leverage increases. Therefore, a positive relationship between leverage changes and risk reporting changes after an ICF was expected. This expectation has been confirmed in this research. The finding is in line with previous research, stating that financial leverage is positively related to disclosure levels (Chavent et al., 2006; Deumes and Knechel, 2008). The result also supports arguments which state companies try to lower information asymmetry, and consequently agency costs, between debt holders and shareholders to keep creditors satisfied (Watson et al., 2002). An important reason for lowering this information asymmetry is that it might result in more beneficial loan conditions. Thus, risk reporting increases when agency conflicts between creditors and shareholders increase.
5.1.4. Big 4 audit firms. It was expected that Big 4 audit firms were positively related to changes in risk reporting after an ICF. This expectation was based on reported findings, stating that Big 4 audit firms are more likely to be present at companies with higher agency costs (Fan and Wong, 2005; Oliveira et al., 2011) and that Big 4 audit firms stimulate risk reporting (Oliveira et al., 2011). Despite the argumentations that Big 4 audit firms can reduce agency problems through stimulating additional disclosure, no significant relation was found in this research. One possible explanation for not finding the expected relationship, is that contrasting to other research, Mokhtar and Mellett (2013) found that Big 4 audit firms are negatively related to voluntary risk reporting. They state that this relation might be a result of possible additional audit costs if voluntary risk reporting would increase. Another possible explanation for not finding a significant relationship might emanate from the fact that this research investigated risk reporting changes. If risk reporting was relatively high already for companies audited by Big 4 firms, one could argue there might not be much need for an increase in risk reporting after an ICF. Concluding, possible audit costs and the nature of our research
can provide explanations for not finding a relationship between Big 4 audit firms and the change in risk reporting after ICFs.
5.1.5. Change towards a Big 4 auditor. In this research, it was argued that changing towards a big 4 auditor might positively influence the positive relationship between Big 4 audit firm presence and the change in risk reporting after an ICF. This expectation was partly based on Francis and Wilson (1988), stating that companies with higher agency costs are more likely to switch towards Big 4 auditors. It was argued that an even greater positive relationship with risk reporting changes after an ICF could be expected for companies that recently switched towards Big 4 auditors. Despite this expectation, no relation was found. One explanation lies in the fact that part of the argumentation and theory was based on or derived from the Big 4 presence argumentation. Therefore it might be logical to not find a relationship here, after not finding a relationship for the Big 4 presence variable. Another explanation for not finding a significant relationship for companies that changed towards a Big 4 auditor might be derived from the reason of changing towards Big 4 auditors. Companies can change towards Big 4 auditors in response to higher agency conflicts (Defond, 1992; Francis and Wilson, 1988). It can be argued that in this case, companies try to suppress increased agency problems after an ICF by changing towards Big 4 auditors. Therefore, companies might not feel the need to further increase risk reporting as well, because they have already responded to the increased agency problems. In short, preceding results of this research and the reasoning behind auditor changes could explain why no relationship was found.
5.1.6. Research question. In this research, I have tried to answer the following research question: “What is the effect of changes in capital providers and audit firms on the change in risk reporting after negative consequences of ICFs?” First, it was needed to ascertain a change in risk reporting after negative consequences of ICFs. Subsequently, variables could be tested. It appeared that, according to this research, only a change in leverage is positively related to the change in risk reporting. Therefore, it seems that the only capital provider change that causes a significant change in risk reporting after an ICF, is a change in the agency problem between debt holders and shareholders. Changes in the agency problem between large and small shareholders did not have a significant effect on changes in risk reporting after an ICF. Lastly, the audit firm characteristics of this research did not have an effect on the change in risk reporting after an ICF. According to this research, changes in capital providers and audit firms have little effect on the changes in risk reporting after an ICF. As only our leverage variable is significantly related, the question that arises is whether companies changing their risk reporting after an ICF is common sense or whether other explaining variables exist.
5.2. Theoretical implications
This research adds to existing literature by investigating changes in risk reporting. Numerous papers have already tried to explain risk reporting differences. These papers focused on corporate governance and firm characteristics (Abraham and Cox, 2007; Barakat and Hussainey, 2013), quality of risk reporting characteristics (Abraham and Shrives; 2014) and country or cultural characteristics (Elshandidy and Neri, 2015; Elshandidy and Shrives, 2016). None of these papers have investigated possible risk reporting changes and its determinants or events stimulating this change. Therefore, it can be concluded that this research adds to existing literature by showing that companies increase their risk reporting after an ICF. Secondly, this research shows that companies change their risk reporting even more after an ICF when leverage increases. Both contributions were in line with agency arguments of this research. Thus, it might be concluded that companies respond to changes of some agency problems or changes of information asymmetry through changing their risk reporting.
5.3. Practical implications
In practice, the outcomes of this research can be relevant for several parties. Firstly, investors can increase their knowledge about companies when risk reporting increases. This will result in lower information asymmetry (Jensen and Meckling, 1976). Investors are therefore better able to value companies of their interest (Arping and Sautner, 2013, Deumes and Knechel, 2008). This will eventually lead to improved investment decisions (Hermanson, 2000). Secondly, management can have increased knowledge about their agency problems when an ICF occurs. From the results of this research, management can conclude that companies increase their risk reporting after an ICF. If management chooses not to increase risk reporting after an ICF, they will not reduce agency costs. Other companies will reduce agency costs through increased risk reporting and therefore, the company will be relatively unattractive. Thus, management might be forced towards increasing their risk reporting in the trade off choice. Thirdly, as leverage changes are positively related to changes in risk reporting after an ICF, management can argue that changes in leverage influence their risk reporting tradeoff choice. Lastly, auditors have a better image of ICF consequences after this research. More specifically, auditors are now aware of increased agency costs and subsequent increases in risk reporting. Therefore, this research can raise auditor awareness of fulfilling an important role for stakeholders, as auditors can contribute to the credibility of increased risk reporting after an ICF. It can be concluded that this research can be relevant for investors, management and external auditors of companies.
5.4. Limitations and future research
In this section, limitations of this research and suggestions for future research will be discussed.
5.4.1. Limitations. Like every research, this research has several limitations as well. The largest limitations of this research are caused by lack of consideration of ICF characteristics, subjectivity and the nature of the sample.
The first limitation is lack of consideration of ICF characteristics. The failure was treated as a dummy, resulting in a straightforward yes (1) or no (0). The severity of the ICF was not considered. The ICF severity could possibly be expressed in terms of financial loss. Additionally, it was not considered whether the ICF incurred for the first time or whether it was a consecutive ICF. It could be that more severe ICFs or ICFs that incur for the first time have greater impact on agency problems and risk reporting changes.
The second limitation is caused by subjectivity. Risk reporting can be measured in several ways and can therefore be a source of subjectivity. Quantitative risk reporting can be measured by the use of sentences, words or a disclosure index (Abraham and Cox, 2007; (Deumes and Knechel, 2008; Hassan, 2009). Next to the choice of measurement, the use of risk reporting change as a dependent variable creates subjectivity. Change can be calculated in different ways, which leads to the possibility of different outcomes. Lastly, data has been collected by multiple people. Although measures have been taken, collection of data by multiple people can be influenced by subjectivity. Concluding, these three sources of subjectivity can limit credibility of the results provided by this research.
Finally, the nature of our sample is a limitation of this research. The sample was not randomly assembled, as some organizations were manually selected. In the interest of other group members, sufficient companies from different countries needed to be selected. This resulted in a more diverse sample, although results might have been different when only US companies were selected or the entire sample was randomly selected. Added to this, financial or banking companies were included in the sample in the interest of other group members as well. This research is inconsistent with prior research, as prior research suggests financial or banking companies need to be investigated separately due to restricted regulations (Barakat and Hussainey, 2013; Linsley and Shrives, 2006). As reported in section 5.1.2, not excluding financial or banking industry companies can provide an explanation for not finding a significant result for the block holder variable. Therefore, including these companies might have influenced the results of this research. In short, the nature of the sample may have affected the results of this research.
5.4.2. Future research. The limitations and results of this research provide interesting opportunities for future research.
Firstly, future research on ICF consequences can distinguish different scales of ICF severity. This severity scale can be based on financial losses or negative media attention. Furthermore, a distinction can be made between first-time ICFs and consecutive ICFs. It can be argued that companies react differently when financial loss is higher or when a first-time ICF occurs, opposed to small losses or consecutive failures.
Secondly, as this research has focused on a quantitative change in risk reporting after an ICF, future research can focus on qualitative changes of risk reporting. Hereby it can be researched whether companies actually add to the knowledge of stakeholders.
Thirdly, future research can focus on ownership structures and characteristics. This research included capital providers, one variable being block holder ownership. Although block holders have provided capital, it is part of the company ownership structure as well. Therefore, future research can deepen the understanding of ownership structure influences, by including management ownership or by distinguishing different types of outside block holders.
Finally, future research can increase knowledge of audit characteristic influences on risk reporting changes. In this research, the presence of a Big 4 auditor and the change towards are Big 4 auditor were included. Other types of auditor changes are not researched until now and can thus be relevant to include in future research. Characteristics of executed audits, like changes of work load after risk reporting changes or ICFs can be included in future research as well.
References
Abraham, S. and Cox, P. (2007). Analysing the determinants of narrative risk information in UK FTSE 100 annual reports. The British Accounting Review, 39 (3), 227-248.
Abraham, S. and Shrives, P. J. (2014). Improving the relevance of risk factor disclosure in corporate annual reports. The British Accounting Review, 46 (1), 91-107.
Admati, A., Pfleiderer, P. and Zechner, J. (1994). Large shareholder activism, risk sharing, and financial market equilibrium. Journal of Political Economy, 102 (6), 1097-1130.
Airmic, Alarm and IRM. (2010). A structured approach to enterprise risk management (ERM) and the requirements of ISO 31000. International Standards Organization.
Armstrong, C.S., Guay, W.R. and Weber, J.P. (2010). The role of information and financial reporting in corporate governance and debt contracting. Journal of Accounting and Economics, 50 (2-3), 179-234.
Arping, S., and Sautner Z. (2013). Did SOX Section 404 Make Firms Less Opaque? Evidence from Cross-Listed Firms. Contemporary Accounting Research, 30 (3), 1133-1165.
Arrunada, B. and PazAres, C. (1997). Mandatory rotation of company auditors: A critical examination. International Review of Law and Economics, 17 (1), 31-61.
Barakat, A. and Hussainey, K. (2013). Bank governance, regulation, supervision, and risk reporting: Evidence from operational risk disclosures in European banks. International Review
of Financial Analysis, 30 (1), 254-273.
Becker, C. L., DeFond, M. L., Jiambalvo, J. and Subramanyam, K. R. (1998). The Effect of Audit Quality on Earnings Management. Contemporary Accounting Research, 15 (1), 1-24.
Campbell, J. L., Chen, H., Dhaliwal, D. S., Lu, H., and Steele, L. B. (2014). The information content of mandatory risk factor disclosures in corporate filings. Review of Accounting Studies, 19 (1), 396–455.
Cassin, R.L. (2014). Software company pays SEC $750,000 for weak internal controls. http://www.fcpablog.com/blog/2014/10/7/software-company-pays-sec-750000-for-weak-internal-controls.html.
Chalmers, K. and Godfrey, J. M. (2004). Reputation costs: the impetus for voluntary derivative financial instrument reporting. Accounting, Organizations and Society, 29 (2), 95-125.
Chavent, M., Ding, Y., Fu, L., Stolowy, H. and Wang, H. (2006). Disclosure and determinants studies: An extension using divisive clustering method (DIV). European Accounting Review, 15 (2), 181-218.
Cheng, M., Dhaliwal, D. and Zhang, Y. (2013). Does investment efficiency improve after the disclosure of material weaknesses in internal control over financial reporting? Journal of
Accounting and Economics, 56 (1), 1-18.
Church, B. K. and Schneider, A. (2016). The Impact of Section 302 and 404(b) Internal Control Disclosures on Prospective Investors’ Judgments and Decisions: An Experimental Study.
International Journal of Auditing, 20 (2), 175-185.
COSO. (2016). Enterprise risk management – Integrated framework: Aligning risk with strategy and performance. Committee of Sponsoring Organizations of the Treadway Commission, New York, NY.
DeAngelo, L. (1981). Auditor size and audit quality. Journal of Accounting and Economics, 3 (3), 183-199.
DeFond, M. L. (1992). The association between changes in client firm agency costs and auditor switching. Auditing: A Journal of Practice & Theory, 11 (1), 16-31.
Deumes, R. (2008). Corporate risk reporting. Journal of Business Communication, 45 (2), 120-157.
Deumes, R. and Knechel, W.R. (2008). Economic incentives for voluntary reporting on internal risk management and control systems. Auditing: A Journal of Practice and Theory, 27 (1), 35-66.
Dobler, M. (2008). Incentives for risk reporting – A discretionary disclosure and cheap talk approach. International Journal of Accounting, 43 (2), 184-206.
Dumontier, P. and Raffournier, B. (1998). Why Firms Comply Voluntarily with IAS: An Empirical Analysis with Swiss Data. Journal of International Financial Management and
Accounting, 9 (3), 216-245.
Elshandidy, T., Fraser, I. and Hussainey, K. (2013). Aggregated, voluntary, and mandatory risk reporting incentives: Evidence from FTSE all-share companies. International Review of
Financial Analysis, 30, 320:333.
Elshandidy, T. and Neri, L. (2015). Corporate governance, risk reporting practices, and market liquidity: Comparative evidence from the UK and Italy. Corporate Governance: An
International Review, 23 (4), 331-356.
Elshandidy, T. and Shrives, P.J. (2016). Environmental Incentives for and Usefulness of Textual Risk Reporting: Evidence from Germany. International Journal of Accounting, 51 (4), 464-486.
Ertimur, Y., Sletten, E. and Sunder, J. (2014). Large shareholders and disclosure strategies: Evidence from IPO lockup expirations. Journal of Accounting and Economics, 58 (1). 79-95.
Fan, J. P. H. and Wong, T. J. (2005). Do external auditors perform a corporate governance role in emerging markets? Evidence from East Asia. Journal of Accounting Research, 43 (1), 35-72.
