the effect of adopting xbrl on credit ratings
TRANSCRIPT
The effect of adopting XBRL on credit ratings MSc in Accounting & Financial Management
Academic Year 2014-‐2015
Master Thesis Student Name: A.J.D. Vis
Student Number: 303151 Coach: Dr. S. Kramer, Department of Accounting & Control
Coreader: Dr. N. Dalla Via, Department of Accounting & Control
Date: 14/06/2015
2
Preface The copyright of the Master thesis rests with the author. The author is responsible for its
contents. RSM is only responsible for the educational coaching and cannot be held liable
for the content.
3
Abstract This study examines whether the use of eXtensible Business Reporting Language (XBRL)
influences credit ratings. XBRL use allows stakeholders to digitally import business
information into computer systems instead of digitalising paper-‐filed financial
statements. XBRL use, in theory, improves information efficiency: The costs of
processing information are reduced. Results of several studies analysing the benefits of
XBRL for a company and its stakeholders differed. Some reported a reduction in the
information gap when using XBRL; others reported none. Although the role of credit
rating agencies (CRAs) is to reduce the information gap between a company and its
external parties by providing credit ratings, previous research showed that CRAs are
reluctant to process huge amounts of data because of cost. Using XBRL provides CRAs
with cheaper data processing methods, resulting in more accurate credit ratings and
thus reduced split ratings, i.e., the difference in long-‐term issuer credit ratings provided
by the largest three CRAs. The Securities Exchange Commission (SEC) made XBRL use
mandatory for large accelerated filers in June 2009. Split ratings were analysed before
and after June 2009 using a regression model that included the moderator variables Size
and Leverage. Results showed XBRL use had no statistically significant influence on split
ratings, the moderator variables did not result in a significant influence of XBRL on split
ratings, and there was no statistical difference in split ratings before and after XBRL’s
introduction. This study contributes to the debate regarding mandatory XBRL use by
testing proponents’ arguments on the benefits of XBRL.
4
Table of Contents
1 INTRODUCTION ....................................................................................................................................... 5 1.1 INTRODUCTION TO THE RESEARCH QUESTION ................................................................................................... 5 1.2 PROBLEM STATEMENT AND THESIS DEVELOPMENT ......................................................................................... 5 1.3 EXPECTED CONTRIBUTION .................................................................................................................................... 7 1.4 RESEARCH METHODOLOGY .................................................................................................................................... 7 1.5 CHAPTER SUMMARY ............................................................................................................................................... 7
2 LITERATURE REVIEW ............................................................................................................................ 8 2.1 INTRODUCTION ........................................................................................................................................................ 8 2.2 INTRODUCTION TO XBRL ...................................................................................................................................... 8 2.3 INFORMATION EFFICIENCY ................................................................................................................................. 10 2.3.1 Previous research on improving information efficiency ............................................................. 11
2.4 CREDIT RATINGS ................................................................................................................................................... 12 2.5 MODERATORS ....................................................................................................................................................... 13 2.5.1 Company size .................................................................................................................................................. 14 2.5.2 Leverage ........................................................................................................................................................... 14
2.6 CHAPTER SUMMARY ............................................................................................................................................ 15 3 RESEARCH DESIGN AND DATA ......................................................................................................... 17 3.1 INTRODUCTION ..................................................................................................................................................... 17 3.2 METHODOLOGY .................................................................................................................................................... 17 3.3 MEASUREMENT OF VARIABLES .......................................................................................................................... 17 3.3.1 Moderator variables ................................................................................................................................... 18 3.3.2 Control variables .......................................................................................................................................... 18
3.4 SAMPLE SELECTION ............................................................................................................................................. 20 4 RESULTS ................................................................................................................................................... 22 4.1 DESCRIPTIVE STATISTICS .................................................................................................................................... 22 4.2 PRELIMINARY TESTS ............................................................................................................................................ 23 4.2.1 Normality ......................................................................................................................................................... 24 4.2.2 Multicollinearity ........................................................................................................................................... 25 4.2.3 Outliers .............................................................................................................................................................. 26 4.2.4 Homoscedasticity ......................................................................................................................................... 26
4.3 RESULTS OF THE STATISTICAL TESTS ............................................................................................................... 27 4.3.1 Results of the multivariate regression model .................................................................................. 27 4.3.2 Robustness check .......................................................................................................................................... 28 4.3.3 Testing H2 and H3 ....................................................................................................................................... 29
4.4 CHAPTER SUMMARY ............................................................................................................................................ 30 5 CONCLUSION ........................................................................................................................................... 31 5.1 CONCLUSION AND DISCUSSION ........................................................................................................................... 31 5.2 LIMITATIONS ......................................................................................................................................................... 33 5.3 RECOMMENDATIONS FOR FUTURE RESEARCH ................................................................................................ 34
6 REFERENCES ........................................................................................................................................... 35 APPENDIX: FIGURES .................................................................................................................................... 38
5
1 Introduction
1.1 Introduction to the research question Before the rise of the Internet, business reports were printed on paper and distributed
by mail. Historically, investors had greater difficulty obtaining publicly available
information than in modern times. Today, one can easily go to a company’s website and
download the annual report on his or her own computer, print it out, and make his or
her own analysis. Using a different way of communicating makes it easier to distribute
information to investors.
The same kind of revolution is currently happening. Companies are providing their
company reports by using a digital business language, named XBRL (eXtensible Business
Reporting Language). Pepsi’s CEO, Nooyi (2006), stated that XBRL “make(s) looking at
financial information easy in every sense: easy to access, easy to use, easy to compare
with other companies” (para. 6).
XBRL enables computers to process business reports without human interaction. It is no
longer necessary to manually input the data of published business reports (Richards,
Smith, & Saeedi, 2006). Credit rating agencies (CRAs), which compose business reports
in order to determine credit ratings, can benefit from XBRL. In developed countries,
CRAs rely more on publicly available information since there are regulations that
prohibit the use of insider information (D’Amato, 2014). The use of XBRL will save CRAs
considerable time-‐consuming work1 and make it cheaper for them to prepare credit
ratings. This research will investigate the relationship between a company’s usage of
XBRL and assigned credit ratings.
1.2 Problem statement and thesis development This research is based on the notion that XBRL leads to a more efficient market by
reducing the cost associated with processing financial statements (Cong, Hao, & Zou,
2014). The usage of XBRL does not lead to a greater quantity of information; instead, it
leads to information of higher quality by adding tags to information. This addition
makes it cheaper to perform analyses/obtain financial information, which leads to the
1 Non-‐XBRL data needs to be manually re-‐entered before it can be viewed in computer
systems.
6
increased interest of analysts and investors (Chiang & Venkatesh, 1988). All investors
should benefit from this enriched information, especially those investors who utilize
ratings from credit rating agencies (Hodge, Kennedy, & Maines, 2004).
Credit rating agencies have several methods to analyse financial statements. The usage
of XBRL will enable them to better categorize and process the same information, for less
cost, which will allow credit agencies to perform more in-‐depth analyses on companies.
A more thorough analysis of a company might result in a different credit rating since
disclosed information can more efficiently be analysed. Different CRAs can provide
different ratings for companies; this difference is called a split rating. Using XBRL will
increase the quality of these ratings and result in reduced split ratings. This concept will
be explained more in detail in the literature review.
The research question is in what way credit ratings will be affected by using XBRL. Two
moderators of this effect (firm size and leverage) will be researched as well. Larger
firms are more difficult to analyse, and the change in credit rating when using XBRL will
be stronger for large firms (Weber, 2003)2.
Furthermore, highly leveraged firms are more likely to voluntarily disclose more
information in order to reduce the costs of debt (Dumontier & Raffournier, 1998). Higher
leveraged firms are, therefore, assumed to have a smaller change in credit ratings when
adopting XBRL3. In order to research this theory, the following hypotheses have been
formulated with respect to the U.S. capital market:
Hypothesis 1 (H1): The adoption of XBRL has a reducing effect on split ratings.
Hypothesis 2 (H2): The effect of XBRL adoption on split ratings is stronger in larger
firms.
Hypothesis 3 (H3): The effect of XBRL adoption on split ratings is weaker for firms
that are more leveraged.
In this research, a split rating is the difference in credit ratings of the three major CRAs
(S&P, Fitch, and Moody’s).
2 This will be discussed in detail in Section 2.5.1, Company size. 3 This will be discussed in detail in Section 2.5.2, Leverage.
7
1.3 Expected contribution One of the claimed benefits of XBRL is more easily obtained and less costly available
financial information (Pinsker & Li, 2008). Furthermore, XBRL makes it easier to
compare different financial reporting methods (Weber, 2003). There is still considerable
research being conducted on the effects of XBRL. This research study will investigate
whether there is a correlation between the adoption of XBRL and credit ratings.
Several governments demand the use of XBRL, and those who support it argue that its
use should be mandatory (O'Kelly, 2007). The U.S. Securities and Exchange Commission
(SEC) made XBRL use compulsory for U.S. listed companies in 2009 (SEC, 2008).
Therefore, researching the effects of XBRL is relevant to this debate. Enough evidence
supporting H1 will be likely to encourage analysts and investors to argue for the
mandatory use of XBRL, thus improving the quality of credit ratings and decreasing
investors’ perceived investment risk.
This research will also increase the understanding of the practical use of XBRL from a
corporate point of view. Companies’ management might consider why they should
implement XBRL technology in their current data systems. This research will provide
them insight into the perception of external stakeholders of a company using XBRL over
a company that does not use XBRL.
1.4 Research methodology A multiple linear regression will be conducted by using a sample of U.S. listed companies
and their differences in ratings as they are provided by the largest three CRAs. The SEC
made XBRL use compulsory in 2009. This use was required for all publicly listed
companies with a minimum public float of $5 billion (SEC, 2008). The split ratings of
these companies will be compared before and after the mandatory use of XBRL. The data
will be collected by using CRSP, Bloomberg, and the Compustat database.
1.5 Chapter summary This chapter showed the background of this study regarding the effect of adopting XBRL
on credit ratings. Both the problem statement and thesis development were explained.
Furthermore, the scientific relevance of this research and the methodology used were
described.
8
2 Literature review
2.1 Introduction Since this study is designed to provide more insight into the relationship between the
usage of XBRL by companies and their assigned credit ratings, this chapter will discuss
the relevant literature in order to provide the reader with a clear understanding of the
concept of XBRL. Three hypotheses will be developed based on the literature review.
2.2 Introduction to XBRL XBRL, eXtensible Business Reporting Language, is an open standard for digital business
reporting. It is under license of the non-‐profit organization XBRL International. This
digital language adds tags to financial information. These tags enable computers to read
the accounting numbers and process them into reports. The benefit of using XBRL is that
every end user can compile his or her own reports based on his or her own needs. XBRL
does not add information to the reports; it only describes the presented information by
using tags and, therefore, adds value to the information presented (Efendi, Park & Smith,
2014; Hodge, Kennedy, & Maines, 2004).
This research is based on the theory that XBRL use allows users of financial information
to use that information more cost-‐efficiently. This theory has been called information
efficiency and will be explained in Section 2.3, Information efficiency (Elliott & Jacobson,
1994). In theory, the use of XBRL will lead to a better analysis of companies since
information is more easily available. This might reduce the information gap between
companies and their external stakeholders by improving information efficiency for
external stakeholders (Verrecchia, 1980), which is one of the objectives of XBRL
according of the SEC: “[XBRL]…has the potential to increase the speed, accuracy and
usability of financial disclosure and eventually reduce costs for investors” (SEC, 2008,
para. 1).
Figure 1 is an example of the process of converting a line of an annual report into XBRL.
The XML information in the image is called the XBRL information. XBRL can be seen as a
specific type of XML computer language.
9
Figure 1. How XBRL Works (Kapoor, 2012)
Figure 1 shows the annual report states that Share Capital equals 3,273.37. Share Capital
is part of the category Shareholders’ Funds. This information is coded into XBRL, and a
computer can easily read the XBRL-‐code. Upon request, a PDF file can be generated with
relevant financial information. This option is emphasized by the third stage of the image
that shows a line of a computer-‐generated PDF report with the numerical value of paid-‐
up share capital.
There are different benefits of using XBRL for both companies and their stakeholders.
Firms can benefit from using XBRL since both transparency and informational quality
improves after introducing XBRL. Companies’ internal costs for bookkeeping and
processing financial reports is reduced as well (Pinsker & Li, 2008). For external users of
financial statements, XBRL use will significantly reduce the errors from manually re-‐
coding information from business reports into analysts’ databases (Vasarhelyi, Yang, &
Liu, 2003). Furthermore, the SEC specifically mentioned that the adoption of XBRL
would result in cost-‐savings for external users (including the SEC itself) of a firm’s
financial statements4 (SEC, 2008). This is one of the main advantages of using XBRL, and
it will be the topic of the next sections. 4 This article http://raasconsulting.blogspot.nl/2011/01/why-‐did-‐sec-‐mandate-‐
xbrl.html comments on the theory that cost savings for the SEC itself was one of the
main drivers for demanding the use of XBRL by filing companies.
10
2.3 Information efficiency This section, and the following sub-‐section, will discuss the efficiency benefits of using
XBRL for a company’s stakeholders and introduce the concept of information efficiency.
Afterward, previous studies on improving information efficiency in relation to XBRL will
be discussed.
Historically, business reports were published on paper and, more recently, in digital
files, like PDF reports. The company decides the layout and provides the same report to
every stakeholder. Each stakeholder requires different kinds of information. For
example, an analyst has a different perspective than the local tax authority. Thus,
companies provide information in addition to their regular business reports. This kind
of information is usually converted into a format that can be used by that particular user
(SEC, 2009). The use of XBRL will make this process more convenient since companies
can generate these different reports more cheaply and quickly; this benefit has been
called information efficiency (Pinsker & Li, 2008). Information efficiency occurs for both
investors and analysts.
Secondly, stakeholders who generate their own reports can benefit from XBRL, as well,
by improving their methods of analysing information. According to Hodge, Kennedy, and
Maines (2004), investors benefit from this since they can more easily obtain and
integrate information. Analysing information is streamlined by using an XBRL-‐enabled
search program. This is a form of information efficiency. Their research was based on
investors without professional knowledge, and they found that those who use XBRL data
benefit from it. Notably, this effect is stronger for investors with lower professional
knowledge of analysing investments (Efendi, Park, & Smith, 2014).
Furthermore, definitions used for or methods of calculating financial statements are not
always similar (Richards, Smith, & Saeedi, 2006), which makes converting information
time consuming since numbers have to be analysed thoroughly before they can be
imported by analysts (Hodge, Kennedy, & Maines, 2004). Firms pay analysts who operate
on the sell side, and these analysts are more likely to perform more extensive analyses
(Groysberg, Healy, & Chapman, 2008), which differs from analysts who operate on the
buy side. Sell-‐side analysts decide the minimum information needed to perform their
11
analyses and convert only that kind of data. Analysts who operate on the buy side have
to trade off the costs and benefits of converting additional information in order to input
it into their computer systems. Since XBRL makes it cheaper to process information, it is
more likely that (both types of) analysts will import more data into their computer
systems and perform additional analyses. Thus, XBRL results in a higher level of
information efficiency (Efendi, Dong Park, & Subramaniam, 2010).
Additionally, different users might use different definitions and mistakes can be easily
made. XBRL use implies that a tag identifies every item in the financial statements. This
tag describes the meaning of the information, which makes it possible to identify items,
regardless of international interpretations or differences in definitions (Richards, Smith,
& Saeedi, 2006). It is even possible to combine both financial and non-‐financial
information (like disclosures) in an automatic analysis (Weber, 2003).
2.3.1 Previous research on improving information efficiency An information gap exists between companies and their stakeholders. Information
efficiency is the way new information is distributed to a firm’s stakeholders. A low
efficiency rate indicates a significant information gap between a company and its
stakeholders (Elliott & Jacobson, 1994).
Several researchers have studied the theory that XBRL use will improve information
efficiency. The Korean, Japanese, and American authorities forced certain groups of
companies listed in their national stock markets to use XBRL at once (Bai, Sakaue, &
Takeda, 2012). The reported results were not the same and led to different conclusions.
Empirical research in the Chinese capital market suggested the usage of XBRL leads to
reduced information efficiency (Chen & Li, 2013). Different conclusions were found by
Blankespoor, Miller, and White (2014). They studied U.S. stock market data for
companies that had switched to XBRL for reporting purposes. Their research found
evidence that the information playing field did not improve for the first year after XBRL
use was mandatory. Geiger, North, and Selby’s (2014) study supported this perspective
on the effect of using XBRL in order to improve information efficiency. They performed
research on companies in the United States that voluntarily used XBRL. They argued
that, based on their research, XBRL reduces the information gap between a company
and its stakeholders for large companies. A study of companies listed on the Korean
12
stock market showed that XBRL use reduces the information gap. This effect is stronger
for large companies than for medium or small companies (Yoon, Zo, & Ciganek, 2011).
This result was confirmed by later research (Kim, Lim, & No, 2012).
2.4 Credit ratings Credit rating agencies (CRAs), like Moody’s, Fitch, and S&P, provide third-‐party opinions
about the solvency of debt instruments to the public. Historically, investors paid for
these credit ratings, but this tradition has shifted. Companies who issue debt generally
need to pay for this kind of service, and these fees are a major part of a CRA’s revenues.
Companies need these credit ratings in order to attract investors and are forced to
cooperate with the issuer paid CRAs (Forster, 2008; Funcke, 2015).
CRAs provide ratings based on both publicly available information and information that
is only available to market insiders. D’Amato (2014) argued that CRAs mostly use
publicly available information in more developed countries and more insider
information in less developed countries. This theory is supported by the argument that
developed countries have stricter regulations that prohibit the spread of insider
information. The exact method of calculating credit ratings has not been disclosed by
CRAs, but this has changed since the Dodd–Frank Act (2010) required CRAs to provided
more information on their rating processes. This change was a result of the ongoing
debate as to the trustworthiness and impact of CRAs. For example, the day that Lehman
Brothers went bankrupt, the company was still rated as investment grade. However, the
exact details of the rating processes are still not made public (Funcke, 2015).
CRAs can be seen as information processing agencies that reduce the information gap
between investors and companies and thus improve information efficiency (Boot &
Milbourn, 2002). Their aim is to reduce the information gap between companies and
their (potential) investors by making information available in the form of trading advice
and credit ratings.
13
However, CRAs are reluctant to process huge amounts of data since this practice is
costly (Millon & Thakor, 1985)5. Using XBRL will enable CRAs to better categorize and
process the same information but at less cost, which will allow CRAs to perform more in-‐
depth analyses on companies. A more thorough analysis of a company might result in a
revised credit rating. Split ratings are the difference between the ratings as they are
provided by different CRAs. This research will investigate the relationship between
these two variables: 1) The adoption of XBRL by a company and 2) the difference in
credit ratings provided by CRAs on the same company6. The independent variable is the
usage of XBRL, and this influences the dependent variable, the split ratings, which leads
to the development of the following hypothesis:
Hypothesis 1 (H1): The adoption of XBRL has a reducing effect on split ratings.
The adoption of XBRL will reduce split ratings because, as Blankespoor (2012)
demonstrated in her dissertation, that reduction in the cost of processing information
leads to increased levels of voluntarily disclosure by firms. I anticipate that this
increased level of voluntarily disclosure will induce more accurate estimations of credit
ratings. As discussed in the literature review, the usage of XBRL will improve
information efficiency. More efficient and precise ratings provided by different CRAs
(i.e., reduced split ratings) will be the result of this process.
2.5 Moderators The previous sections have shown that XBRL use will improve the information efficiency
for information processors like CRAs. As previously explained, information-‐processing
companies have to determine what information is relevant for them to convert into their
analysing tools. They always need to find a trade-‐off between the costs and benefits of
processing additional information. Therefore, improved information efficiency will
result in more processed data and analyses performed, and in turn, more analyses
5 Although processing data has sped up since 1985, the total amount of data has
expanded as well, which makes this research still relevant (Rubini, 2000). 6 Moderators will be discussed in Section 2.5, Moderators, and control variables in
Section 3.3.2, Control variables.
14
performed can result in reduced split ratings. This research will measure to what extent
such a relationship exists.
However, there might be factors that will influence this relationship; these moderators
will be researched as well. Based on the literature, two moderators were selected:
Company size and leverage. These moderators will be explained in the following sub-‐
sections.
2.5.1 Company size The change in split ratings should depend on the company size. The absolute amount of
information not used for analysis purposes for larger firms is greater than that of
smaller firms. This amount of information not used is a result of CRAs who
predetermine (based on the trade-‐off between their costs and benefits) what
information seems to be relevant for them to convert for analyses. Thus, the possibility
that the credit rating changes depends on the number of additional analyses. Since more
additional analyses can be performed for larger companies, it is more likely that the
change in split ratings will be stronger for large firms.
Furthermore, larger companies operate in more business reporting jurisdictions, which
results in different methods of reporting (Premuroso & Bhattacharya, 2008). The
improvement of information efficiency for larger companies due to XBRL use will be
greater since the usage of XBRL will increase efficiency when comparing different
business reporting methods (Weber, 2003). These two factors will result in a potentially
significant reduction in split ratings for larger companies than for smaller firms when
using XBRL, which leads to the second hypothesis:
Hypothesis 2 (H2): The effect of XBRL adoption on split ratings is stronger in larger firms.
Implementing the variable Size in the regression model will test this hypothesis. Firm
size will be measured by using total assets.
2.5.2 Leverage A second important variable is a firm’s level of leverage. This variable is based on the
efficient market theory. The efficient market theory states that information is reflected
in stock prices (Fama, 1970). Both voluntarily disclosed information and hidden
15
information is returned in those prices. The level of reflection can be different and
depends on the degree of market efficiency.
This theory also applies to the market’s pricing of corporate bonds. Leveraged firms
need to disclose information to debt holders. Disclosing information directly influences
prices. Jensen and Mechling (1976) stated that firms that disclose more information
reduce the monitoring costs for creditors, which will be reflected in costs charged on
loans. Firms that disclose more information have, therefore, less costs of debt (Elliott &
Jacobson, 1994). These less costs of debt is one of the main benefits for firms to use the
services of CRAs (Sufi, 2009).
Less costs of debt provide firms the possibility to attract more debt. Higher leveraged
firms are expected to have voluntarily disclosed more information in order to reduce
costs of debt (Dumontier & Raffournier, 1998; Wallace & Naser, 1995). CRAs are
expected to obtain fewer new insights into these highly leveraged companies when they
start using XBRL. Leverage is, therefore, negatively correlated to a reduction in split
ratings, which leads to the third hypothesis:
Hypothesis 3 (H3): The effect of XBRL adoption on split ratings is weaker for firms that are
more leveraged.
Implementing the variable Leverage (debt as a percentage of equity) into the regression
model will test this hypothesis7.
2.6 Chapter summary This chapter provided an overview of the current literature in the XBRL field with
respect to information efficiency. The theoretical purpose of XBRL is clear: Improving
information efficiency. In practice, several studies were conducted to analyse the
benefits of XBRL on the information gap between a company and its stakeholders.
Results differed; some studies reported a reduction in the information gap when using
XBRL, while others report none.
7 This will be explained in Section 3.3.1, Moderator variables.
16
The role of credit rating agencies (CRAs) is to reduce the information gap between a
company and its external parties by providing credit ratings. Previous research showed
that CRAs are reluctant to process huge amounts of data, as this is costly. Using XBRL
will provide CRAs cheaper methods to process data, which will result in more accurate
credit ratings and thus reduced split ratings. Split ratings are the difference in ratings
provided by the largest three CRAs. This idea was formulated into the first hypothesis
(H1): The adoption of XBRL has a reducing effect on split ratings.
The expected reduction in split ratings will be larger for larger firms since the use of
XBRL will make it less costly to perform analyses. Larger firms have more potential data
to analyse and operate in more countries, which results in different methods of
reporting. Since more additional analyses can be performed for larger companies, it is
more likely that the reduction in split ratings will be stronger for larger firms. This idea
was formulated into the second hypothesis (H2): The effect of XBRL adoption on split
ratings is stronger in larger firms.
Research showed that firms that are more leveraged tend to voluntarily disclose more
information in order to reduce costs of debt. Voluntarily disclosing more information
will reduce the potential benefits of using XBRL on calculating credit ratings and the
reducing effect on split ratings will, therefore, be less for more leveraged firms. The idea
was formulated in the third hypothesis (H3): The effect of XBRL adoption on split ratings
is weaker for companies that are more leveraged.
Several statistical tests were performed to test these three formulated hypotheses and
will be explained in the next chapter.
17
3 Research design and data
3.1 Introduction This chapter will explain the statistical tests used to gain insight into the relationship
between XBRL use and the difference in assigned credit ratings, as well as how the data
was collected.
3.2 Methodology This research was performed by analysing a dataset. The American stock market was
selected because of the mandatory use of XBRL. The SEC made XBRL use compulsory in
2009, requiring its use for all publicly listed companies with a minimum public float of
$5 billion (SEC, 2008). The difference in split ratings for these companies were
compared with companies who did not have to file by using XBRL.
The appropriate statistical test for testing the hypotheses (H1, H2, and H3) is a multiple
regression analysis. This analysis made it possible to measure the difference in split
ratings for two time periods (before and after the mandatory use of XBRL). First, the
data and data sources will be discussed. Afterward, the regression model will be
presented and will be followed by an overview of the selected sample.
3.3 Measurement of variables Several variables were used in this research. The dependent variable, Difference, was
the difference between the credit rating provide by the largest three CRAs. These three
CRAs (S&P, Moody’s, and Fitch) provide similar long-‐term company ratings that can be
converted into numbers. Only companies that were rated by at least two of the three
CRAs were used. For companies with three ratings provided, the largest difference in the
split rating was used. The assigned credit ratings conversion table and the
corresponding points are shown in Table 1 on the next page.
18
Table 1. Credit rating conversion
The explanatory (independent) variable was XBRL and refers to the mandatory use of
XBRL. XBRL was a categorical variable with the value of 0 or 1. The value for XBRL was 1
when the companies were required to file reports using XBRL and 0 when they did not
have to file by using XBRL. Two moderator variables, Size and Leverage, were measured
in the model as well.
3.3.1 Moderator variables Size Firm size was expected to be positively correlated to the increase of information
efficiency. This expectation is based on the literature review, Section 2.5.1, Company
size. The firm size was measured as the total assets of a company in millions of euros.
This measure (Size) is based on previous research (Yoon, Zo, & Ciganek, 2011).
Leverage
The leverage of a firm was expected to be negatively correlated to the increase of
information efficiency, which was explained in Section 2.5.2, Leverage. The degree of
leverage was measured as the book value of total debt as a percentage of total equity.
3.3.2 Control variables As explained in the literature review, previous studies into the effects of XBRL on
information efficiency showed that several aspects are highly important (Yoon, Zo &
Ciganek, 2011; Bini, Giunta & Dainelli). These aspects have resulted in two control
variables: Turnover and Profitability. The variances in the performed tests will be
explained by using these control variables.
SP Mooy Fitch Points SP Moody Fitch Points AAA Aaa AAA 20 BB Ba2 BB 9 AA+ Aa1 AA+ 19 BB-‐ Ba3 BB-‐ 8 AA Aa2 AA 18 B+ B1 B+ 7 AA-‐ Aa3 AA-‐ 17 B B2 B 6 A+ A1 A+ 16 B-‐ B3 B-‐ 5 A A2 A 15 CCC+ Caa1 CCC+ 4 A-‐ A3 A-‐ 14 CCC Caa2 CCC 3
BBB+ Baa1 BBB+ 13 CC-‐ Caa3 CC-‐ 2 BBB Baa2 BBB 12 C CaC C 1 BBB-‐ Baa3 BBB-‐ 11 C C C 0 BB+ Ba1 BB+ 10
19
Turnover A high turnover rate is an indicator of information efficiency, according to Copeland and
Galai (1983). The turnover rate was calculated by dividing the average daily trading
volume by the total number of outstanding shares. The average daily trading volume
was calculated by dividing the total trade volume for a given fiscal quarter by 90 days;
the total number of outstanding shares were taken from the end of the corresponding
fiscal quarter.
Profitability
Research has shown that the more profitable a firm, the higher the number of
voluntarily disclosures (Singhvi & Desai, 1971), which makes Profitability an important
control variable for this research. Profitability is negatively correlated to a reduction in
credit rating and is measured as the ROA ratio (Net income/total assets) since this
relates profit to the size of a company.
Profitable
The variable, Profitable, is a binary representation of Profitability. This variable is 0 for
companies that took a loss and 1 for companies that made a profit. This variable was
added since the profitability of the companies in the collected sample varies greatly, so it
might add explanatory value to the regression model.
The second and third hypotheses (H2 and H3) addressed whether Size and Leverage are
moderator variables by creating new variables, XBRL*SIZE and XBRL*LEVERAGE, which
were calculated as the product of XBRL and Size and Leverage, respectively.
This together will result in the following regression model:
𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒! =
𝛽! + 𝛽!𝑋𝐵𝑅𝐿! + 𝛽!𝑆𝑖𝑧𝑒! + 𝛽!𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒! + 𝛽!𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟! + 𝛽!𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦! +
𝛽!𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑙𝑒! + 𝛽!𝑋𝐵𝑅𝐿 ∗ 𝑆𝑖𝑧𝑒!+ 𝛽!𝑋𝐵𝑅𝐿 ∗ 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝜀!
Where,
i = firm
t = period: pre-‐XBRL, post-‐XBRL period 1 or post-‐XBRL period 2
20
Three periods were used in this research. The first time period was the pre-‐XBRL
period. This period was compared to two post-‐XBRL periods.
3.4 Sample selection Since the use of XBRL was mandatory for companies with a public float of over $5
billion, companies with a public float of over $5 billion by the beginning of 2009 were
selected, resulting in a data set of approximately 500 companies (SEC, 2008). This
method is based on previous research (Yoon, Zo, & Ciganek, 2011).
Credit ratings
The publicly listed companies had to file their reports within 40 to 45 days after the end
of the corresponding fiscal quarter (SEC, 2015). The use of XBRL was mandatory in the
US from the first fiscal quarter, ending after the 15th of June 2009 (SEC, 2008), which is
by the end June, July, or August. Reports had to be filed within these 40 to 45 days, but
they might have been released earlier. Credit ratings before the 15th of June 2009 were
certainly based on non-‐XBRL filings and, as a result, credit ratings of the 15th of June
2009 were determined to be those of the pre-‐XBRL period.
Credit ratings published after the 15th of June to July 2009 could be based on pre-‐XBRL
(fiscal Q1 2009) or post-‐XBRL (fiscal Q2 2009) filings. Therefore, it was necessary to
exclude the months June and July from the time period to ensure all data was in the
post-‐XBRL period. The first XBRL filings were filed by August 15th for those fiscal
quarters ending in June and by October 15th for fiscal quarters ending in August. It is
good practice to consider credit rating changes within one month as being linked to the
same event. This consideration resulted in a post-‐XBRL period 1 sample selection of
credit ratings for the 15th of November 2009. The post-‐XBRL period 2 sample selection
was one fiscal quarter later, and thus by the 15th of February 2010.
Company fundamentals
Companies can use different fiscal book years. The pre-‐XBRL data for the variables Size,
Leverage, Turnover, and Profitability were retrieved for the last fiscal quarter ending
before the 15th of June 2009. The post-‐XBRL period 1 data for the variables Size,
Leverage, Turnover, and Profitability was retrieved for the first fiscal quarter ending
after the 15th of June 2009. The post-‐XBRL period 2 data for the variables Size, Leverage,
21
Turnover, and Profitability was retrieved for the second fiscal quarter ending after the
15th of June 2009.
A total of 433 companies were identified as required to file using XBRL in 2009. A total
of 104 companies that did not have at least two credit ratings per each credit rating
selection moment (August 15th, November 15th, February 15th) were excluded. The same
applied to 75 companies that had missing information for the variables Size, Leverage,
Turnover, or Profitability for the pre-‐XBRL or post-‐XBRL period. A total of 36
companies had missing information for both split ratings and the variables Size,
Leverage, Turnover, or Profitability.
From the remaining companies, 13 firms participated in the SEC Voluntary Filing
Program (SEC, 2011) and filed at least one quarterly report using XBRL in a 12 month
period before June 2009. These 13 firms were excluded from the dataset, which resulted
in a sample of 277 companies.
22
4 Results This chapter will describe the statistical tests performed on the selected sample. First,
descriptive statistics will be discussed. This discussion will be followed by several
preliminary tests in order to prepare for a multivariate regression.
4.1 Descriptive statistics The collected data was analysed using Stata. The data was validated and no missing
values were present in the dataset. The sample consisted of 277 companies with pre-‐
XBRL and two moments of post-‐XBRL observations. These three time periods will be
referred to as the pre-‐XBRL period, the post-‐XBRL period 1, and the post-‐XBRL period 2
groups.
The descriptive statistics for all three groups are shown in Tables 2, 3 and 4. The most
significant difference between the minimum and maximum values for Size were
inspected and were determined to be logical8. The data was corrected for unusual
values, a total of four companies with a negative leverage as a result of a reported
negative equity9. This correction reduced the sample size to 273. The maximum value
for the variable Leverage differed for the pre-‐XBRL and post-‐XBRL periods. Inspection of
the data showed that this was caused by just a few companies and was corrected for, as
seen in Section 4.2.3, Outliers.
Table 2. Pre-‐XBRL Period Variable Mean Std. Dev. Min Max
Difference 1.16 1.17 0 7 Size 90,057 282,325 2,525 2,789,352 Leverage 4.01 6.83 0.14 61.45 Turnover 0.01 0.01 0.00 0.09 Profitability 0.01 0.02 -‐0.19 0.13 Profitable 0.84 0.37 0 1
Table 3. Post-‐XBRL Period 1 Variable Mean Std. Dev. Min Max Difference 1.14 1.16 0 7 Size 89,172 266,507 2,613 2,429,488 Leverage 3.50 5.30 0.14 49.45
8 Size was measured in millions of euros. Firms in the dataset with a size greater than one trillion euros were banks. 9 For example, total equity of Ford Motor Company was negative by the end of 2009.
23
Turnover 0.01 0.01 0.00 0.09 Profitability 0.01 0.01 -‐0.03 0.07 Profitable 0.88 0.32 0 1
Table 4. Post-‐XBRL Period 2 Variable Mean Std. Dev. Min Max
Difference 1.11 1.18 0 7 Size 90,425 267,013 2,666 2,427,932 Leverage 3.33 4.93 0.14 46.98 Turnover 0.01 0.01 0.00 0.06 Profitability 0.01 0.02 -‐0.18 0.06 Profitable 0.90 0.30 0 1
The paired t-‐test results are shown in Tables 5 and 6. These results show that there was
no statistical difference for the variable differences for both periods in relation to the
pre-‐XBRL period. The same applied to Size. The p-‐value of the paired t-‐test for the
variables Leverage, Turnover, and Profitability was less than 0.05 therefore, the
difference was statistically significant.
Table 5. Paired t-‐test Post-‐XBRL Period 1
Table 6. Paired t-‐test Post-‐XBRL Period 2 Variable T-‐value p-‐value
Variable T-‐value p-‐value
Difference -‐1.51 0.13
Difference -‐1.39 0.17 Size -‐0.63 0.53
Size 0.24 0.81
Leverage -‐3.59 0.00
Leverage -‐3.79 0.00 Turnover -‐8.05 0.00
Turnover -‐11.54 0.00
Profitability 2.12 0.03
Profitability 2.36 0.02 Profitable 1.91 0.06
Profitable 2.75 0.01
4.2 Preliminary tests The first hypothesis assumes that there is a relationship between the use of XBRL and
credit ratings: The adoption of XBRL has a reducing effect on split ratings. The paired t-‐
test showed that there was no statistically significant difference between the means of
the difference in split ratings of these groups. Thus H1 is rejected and the null
hypothesis (H0), that there is no statistically significant difference for the variable
Difference in the pre-‐XBRL and post-‐XBRL periods, is accepted.
However, this research continued by performing a regression analysis. Before this test
could be conducted, the dataset was checked on normality, significant outliers,
24
multicollinearity and homoscedasticity by performing several preliminary tests. The
preliminary tests ensured that the various conditions of each statistical test held.
4.2.1 Normality A result of empirical data is that the dataset is usually not normally distributed. The
dependent variable, Difference, was visually and numerically checked for all periods on
normality. The normality of a variable is theoretically bell-‐shaped with most values in
the middle. Less frequent scores are reported on the sides. The variable, Difference, did
not seem to be normally distributed, which was confirmed when examining the
frequency histograms shown in Figures 2 through 4 below. This distribution was a
result of the coding process; Difference was described as the absolute value of the
largest difference among the credit ratings, creating the variable’s absolute results in
this positively skewed distribution.
Figures 2-‐4. Frequency of Difference of respectively pre-‐XBRL, post-‐XBRL period 1 and post-‐XBRL period 2
Normality can be checked numerically as well by assessing the skewness and kurtosis
values of variables, which was accomplished by using the SKTEST command in Stata.
This command tests the dataset for normality by testing against the null hypothesis that
there is normality. The p-‐values for the skewness and kurtosis values of Difference are
seen in Table 7. The p-‐value was below 0.05 for all groups, which rejects the null
hypothesis that there is normality.
Table 7. Skewness and Kurtosis test P-‐values Skewness Kurtosis Joint Pre-‐XBRL 0.00 0.00 0.00 Post-‐XBRL period 1 0.00 0.00 0.00 Post-‐XBRL period 2 0.00 0.00 0.00
Since the dependent variable was not normally distributed, several transformations
were used to normalize it: Square root, quartile, inverse, and logarithmic (Bowerman,
O'Connell, & Murphree, 2009). These transformations were applied, and the logarithm
020
4060
8010
0Fr
eque
ncy
0 2 4 6 8DIFFERENCE PRE
020
4060
8010
0Fr
eque
ncy
0 2 4 6 8DIFFERENCE POST
020
4060
8010
0Fr
eque
ncy
0 2 4 6 8DIFFERENCE POST2
25
transformation resulted in the most normal distribution. Thus, the variable Difference
became the logarithm transformation of Difference.
4.2.2 Multicollinearity A regression analysis was performed to determine the separate influences of the
independent variables on the difference in split ratings. The independent variables
should not be strongly correlated to each other. Multicollinearity occurs when two or
more independent variables correlate with each other. The data was checked on
multicollinearity by showing the Pearson correlation coefficients and the Variance
Inflator Factor (VIF) and tolerance (1/VIF) (O'Brien, 2007).
The Pearson correlation coefficients are shown in Tables 8 to 10. In all three groups
(pre-‐XBRL, post-‐XBRL Period 1, and post-‐XBRL period 2), no variables strongly
correlated to each other. The strongest correlations were, in all three groups, between
Leverage and Size. However, this correlation was still considered moderate. The
correlation between Profitability and Profitable is obvious since the variable Profitable
is a binary variable and is based on the variable Profitability.
Table 8. Pre-‐XBRL Group correlation coefficients Variable Difference Size Leverage Turnover Profitability
Difference Size 0.12 Leverage 0.14* 0.61** Turnover 0.11 0.18* 0.11 Profitability -‐0.08 -‐0.08 -‐0.08 -‐0.21** Profitable -‐0.06 -‐0.01 -‐0.07 -‐0.24** 0.54**
Table 9. Post-‐XBRL Period 1 correlation coefficients Variable Difference Size Leverage Turnover Profitability
Difference Size 0.11 Leverage 0.16* 0.65** Turnover 0.15* 0.14* 0.07 Profitability -‐0.15* -‐0.18* -‐0.25** -‐0.33** Profitable -‐0.06 0.05 0.05 -‐0.31** 0.55**
Table 10. Post-‐XBRL Period 2 correlation coefficients Variable Difference Size Leverage Turnover Profitability
Difference Size 0.11
26
Leverage 0.15* 0.67** Turnover 0.12 0.01 -‐0.04 Profitability -‐0.17* -‐0.15* -‐0.23** -‐0.22** Profitable -‐0.14* -‐0.09 -‐0.07 -‐0.32** 0.52** *p < 0.01 **p < 0.01
The VIF and tolerance are shown in Tables 11 and 12 on page 28. No variable had a VIF
value greater than 10 and the tolerance values (1/VIF) were below 1 as well (O'Brien,
2007). These results imply that these variables can be seen as linear combinations of
other independent variables. Therefore, there was no multicollinearity.
4.2.3 Outliers Another important preliminary test was to check if there were significant outliers. As
described in the sample selection and descriptive statistics, the sample was already
corrected for erroneous data entry. However, some highly leveraged data points, which
would influence the results, might still exist. Since they would be correct data points,
they could not simply be excluded from the dataset. Robust regression corrects for
highly leveraged data points (Rousseeuw & Leroy, 1987). This correction was
accomplished by performing the regression twice, a regular regression and a robust
regression.
4.2.4 Homoscedasticity Another preliminary test investigated whether the variables were homoscedastic.
Variables are homoscedastic if the residuals have similar variances. Homoscedasticity is
the opposite of heteroscedasticity and can be tested mathematically. A mathematical
method of testing was performed by using the Breusch-‐Pagan test. The Breusch-‐Pagan
test investigates the dependency of the residuals’ variances on the independent
variables (Breusch & Pagan, 1979). The tests were performed against H0 that there is
constant variance. The test for the post-‐XBRL period 1 group resulted in a χ2 score of
0.01 with a corresponding p-‐value of 0.92. Secondly, the result for the post-‐XBRL period
2 group was a χ2 score of 0.01 with a corresponding p-‐value of 0.92. Therefore, there
was no evidence for significant heteroscedasticity for these groups.
27
4.3 Results of the statistical tests After the preliminary tests were performed, the three different hypotheses were tested.
The first hypothesis was rejected by performing a paired t-‐test. All three hypotheses
were then tested against the multivariate regression model.
4.3.1 Results of the multivariate regression model A multivariate regression is described as “a technique that allows additional factors to
enter the analysis separately so that the effect of each can be estimated. It is valuable for
quantifying the impact of various simultaneous influences upon a single dependent
variable” (Sykes, 2000, p. 8). Before such a regression can be performed, the preliminary
tests should be used to verify that the data meets certain assumptions. These
preliminary tests were performed as stated in the previous sections. The regression
formula was used to test for the influence of the independent (XBRL), moderator (Size
and Leverage), and control (Turnover and Profitability) variables on the dependent
variable (Difference).
𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒! =
𝛽! + 𝛽!𝑋𝐵𝑅𝐿! + 𝛽!𝑆𝑖𝑧𝑒! + 𝛽!𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒! + 𝛽!𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟! + 𝛽!𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦! +
𝛽!𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑙𝑒! + 𝛽!𝑋𝐵𝑅𝐿 ∗ 𝑆𝑖𝑧𝑒!+ 𝛽!𝑋𝐵𝑅𝐿 ∗ 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝜀!
i = firm
t = period: pre-‐XBRL, post-‐XBRL period 1, or post-‐XBRL period 2
Regarding period 1, the regression model statistically significantly predicted
Difference (F = 3.66, p < 0.005). Notably, the overall fit of the model was extremely low
(adj. R2 = 0.0284), which implies that the regression model explained 2.84% of the
differences in the variable Difference. The variables XBRL, Size, Profitability, and
Profitable were not statistically significant to the prediction. The variables Leverage and
Turnover were statistically significant (p < 0.05) with beta coefficients of 0.005 and
2.897, respectively. Regression coefficients and standard errors can be found in Table 11
on the next page.
Similar results were found for period 2: The regression model statistically significantly
predicted Difference (F = 3.73, p < 0.005). The overall fit of the model was slightly higher
than for period 1 but still low (adj. R2 = 0.292). The variables XBRL, Size, Turnover,
28
Profitability, and Profitable were not statistically significantly to the prediction. The
variable Leverage was statistically significant (p < 0.05) with a beta coefficient of almost
zero (0.004). Regression coefficients and standard errors can be found in Table 12.
Table 11. Post-‐XBRL Period 1 VIF Tolerance Beta t-‐value p-‐value Model statistics
XBRL 1.01 0.99 0.004 0.23 0.82 Dependent variable Size 1.68 0.59 0.000 0.17 0.86 Difference Leverage 1.65 0.61 0.005 2.34 0.02 N = 546 Turnover 1.13 0.88 2.897 2.29 0.02 Adjusted R 2 =0.0284 Profitability 1.45 0.69 -‐0.650 -‐1.17 0.24 F = 3.66 (p-‐value = 0.0014) Profitable 1.46 0.68 0.003 0.08 0.94 Mean VIF 1.40
Table 12. Post-‐XBRL Period 2 VIF Tolerance Beta t-‐value p-‐value Model statistics
XBRL 1.05 0.95 0.001 0.14 0.89 Dependent variable Size 1.66 0.60 0.000 0.47 0.64 Difference Leverage 1.67 0.60 0.004 2.10 0.04 N = 546
Turnover 1.15 0.87 2.560 1.82 0.07 Adjusted R2 = 0.0292 Profitability 1.43 0.70 -‐0.683 -‐1.35 0.18 F = 3.73 (p-‐value = 0.0012) Profitable 1.46 0.69 -‐0.018 -‐0.54 0.59 Mean VIF 1.40
4.3.2 Robustness check The regression was performed again with the command robust to control for points of
high leverage (significant outliers). This model predicted Difference for period 1 as still
significant (F = 6.11, p < 0.005). The overall fit of the model was low with an R2 of
0.0391. XBRL, Size, Profitability, and Profitable were not statistically significant to the
regression model, whereas the variables Leverage and Turnover were statistically
significant. Results can be seen in Tables 17 and 18, in the appendix.
Similar findings are found for period 2. The robust model still predicted the value of
Leverage as significant (F = 6.07, p < 0.005). The overall fit of the model was still low
(R2 = 0.0398). XBRL, Size, Profitability, and Profitable were not statistically significant to
the regression model. The variables Leverage and Turnover were statistically significant
to the regression model. The difference in the non-‐robust model is that Turnover was
29
statistically significant; the p-‐value was 0.05. The overall fit of the model was higher
with the robust model (R2 was higher).
4.3.3 Testing H2 and H3 The second (H2) and third (H3) hypotheses were tested by implementing XBRL*Size
and XBRL*Leverage into the regression model. The output is shown in Tables 13 and 14
(for XBRL*Size for H2) and Tables 15 and 16 (for XBRL*Leverage for H3).
Table 13. Testing H2: Post-‐XBRL Period 1 VIF Tolerance Beta t-‐value p-‐value Model statistics
XBRL 1.12 0.89 0.004 0.19 0.85 Dependent variable Size 2.66 0.38 0.000 0.10 0.92 Difference Leverage 1.66 0.60 0.005 2.34 0.02 N= 546 Turnover 1.13 0.88 2.899 2.29 0.02 Adjusted R 2= 0.0266
Profitability 1.46 0.69 -‐0.649 -‐1.16 0.25 F = 3.13 (p-‐value = 0.003)
Profitable 1.46 0.68 0.003 0.08 0.94 XBRL*Size 2.01 0.50 0.000 0.07 0.94 Mean VIF 1.64
Table 14. Testing H2: Post-‐XBRL Period 2 VIF Tolerance Beta t-‐value p-‐value Model statistics
XBRL 1.15 0.87 0.001 0.05 0.96 Dependent variable Size 2.72 0.37 0.000 0.19 0.85 Difference Leverage 1.68 0.60 0.004 2.11 0.04 N = 546 Turnover 1.16 0.86 2.605 1.84 0.07 Adjusted R2 = 0.0275
Profitability 1.43 0.70 -‐0.680 -‐1.34 0.18 F = 3.2 (p-‐value = 0.0025)
Profitable 1.46 0.69 -‐0.018 -‐0.53 0.60 XBRL*Size 2.04 0.49 0.000 0.28 0.78 Mean VIF 1.66
The regression model with XBRL*Size was still statistically significantly predicting the
dependant variable, Difference. However, the overall fit of the model was still extremely
low with an adjusted R2 of less than 0.0275 for both period 1 and 2. The p-‐values for the
variable XBRL*Size in period 1 and 2 were, respectively, 0.94 and 0.78. Therefore,
XBRL*Size did not add a significant explanation to the regression model. Similar findings
were discovered for the variable XBRL*Leverage in both period 1 (p = 0.57) and period
2 (p = 0.62), which is shown in Tables 15 and 16.
30
Table 15. Testing H3: Post-‐XBRL Period 1 VIF Tolerance Beta t-‐value p-‐value Model statistics
XBRL 1.41 0.71 -‐0.002 -‐0.11 0.91 Dependent variable Size 1.70 0.59 0.000 0.12 0.91 Difference Leverage 2.13 0.47 0.004 1.79 0.07 N = 546 Turnover 1.13 0.88 2.913 2.31 0.02 Adjusted R2 = 0.0272
Profitability 1.47 0.68 -‐0.619 -‐1.11 0.27 F = 3.18 (p-‐value = 0.0027)
Profitable 1.47 0.68 0.001 0.03 0.98 XBRL*Leverage 2.00 0.50 0.002 0.57 0.57 Mean VIF 1.62
Table 16. Testing H3: Post-‐XBRL Period 2 VIF Tolerance Beta t-‐value p-‐value Model statistics
XBRL 1.44 1.44 0.69 -‐0.002 -‐0.20 Dependent variable Size 1.7 1.70 0.59 0.000 0.37 Difference Leverage 2.03 2.03 0.49 0.004 1.63 N = 546 Turnover 1.16 1.16 0.86 2.644 1.87 Adjusted R2 = 0.0281
Profitability 1.44 1.44 0.69 -‐0.655 -‐1.29 F = 3.25 (p-‐value = 0.0022)
Profitable 1.46 1.46 0.69 -‐0.018 -‐0.55 XBRL*Leverage 1.94 1.94 0.52 0.001 0.62 Mean VIF 1.60
4.4 Chapter summary The selected data sample was analysed, and the results were provided in this chapter.
After describing the dataset with descriptive statistics, preliminary tests regarding
normality, outliers, multicollinearity, and heteroscedasticity were performed. The
regression model was tested by using a multivariate regression analysis. The data was
corrected for significant outliers by using a robust model. The variables XBRL, Size,
Profitability, and Profitable were not statistically significant to the regression model in
period 1. The variables Turnover and Leverage were significantly to the regression with
p-‐values below 0.05. Regarding period 2, the variables XBRL, Size, Turnover,
Profitability, and Profitable were not statistically significant to the prediction. However,
the variable Leverage was statistically significant (p < 0.05). The robust regression
showed both Turnover and Leverage to be statistically significant to the regression
model for both period 1 and period 2. The variables XBRL*Size and XBRL*Leverage were
not statistically significantly to the regression model.
31
5 Conclusion This chapter will summarize and discuss the results of the data analysis, as shown in the
previous chapter. The implications for both theory and practice will be addressed.
Furthermore, the limitations of this research will be examined. To conclude,
recommendations for future research will be made.
5.1 Conclusion and discussion The aim of this research was to examine whether the difference in companies’ assigned
credit ratings by credit rating agencies (CRAs) were influenced by the introduction of
XBRL. The introduction of XBRL would enable external parties to analyse a company
more cheaply and quickly since they were no longer supposed to determine what data
was relevant in their computer systems. XBRL would allow them to import all available
data at a low cost, thus improving information efficiency (Pinsker & Li, 2008). The SEC
made the use of XBRL mandatory in June 2009 (SEC, 2008), and the split ratings before
and after this period were analysed. Split ratings are the difference in ratings provided
by the largest CRAs.
The findings in the literature resulted in three hypotheses: (H1) The adoption of XBRL
has a reducing effect on split ratings, (H2) The effect of XBRL adoption on split ratings is
stronger in larger firms, and (H3) the effect of XBRL adoption on split ratings is weaker for
firms that are more leveraged. The size and leverage of a company were expected to be
moderating variables for the difference in credit ratings as a result of using XBRL.
The findings in literature have been transformed into a regression model with the
following equation:
𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒! =
𝛽! + 𝛽!𝑋𝐵𝑅𝐿! + 𝛽!𝑆𝑖𝑧𝑒! + 𝛽!𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒! + 𝛽!𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟! + 𝛽!𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦! +
𝛽!𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑙𝑒! + 𝛽!𝑋𝐵𝑅𝐿 ∗ 𝑆𝑖𝑧𝑒!+ 𝛽!𝑋𝐵𝑅𝐿 ∗ 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝜀!
Where,
i = firm
t = period: pre-‐XBRL, post-‐XBRL period 1, or post-‐XBRL period 2
With respect to the first hypothesis, two tests were performed: A paired t-‐test and a
32
regression analysis. The paired t-‐test showed that there was not enough evidence to
state that XBRL use reduced split ratings in periods 1 or 2. The regression showed
similar results. Hypothesis 1 was thus rejected, and the null hypothesis (H0) was
accepted at a 95% confidence level. There was significant evidence that the dependent
variable (Difference) was not influenced by XBRL. These tests were conducted for two
time periods: Directly after the mandatory use of XBRL and one quarter later. The
results were similar for both time periods.
The variables Size, Turnover, Profitability, and Profitable were not as significant in this
model as presumed. They did not add explanatory value to the regression model. These
variables were based on previous research on the effect of XBRL on the relative spread
of shares (Yoon, Zo, & Ciganek, 2011). Therefore, the difference in credit ratings is not
controlled by these variables.
The coefficient of the control variables Leverage and Turnover was statistically
significant; the p-‐value was below 0.05. This result implies that these variables did add
explanatory value to the model. The variable Leverage was positively correlated to
Difference with a beta almost equal to zero (0.004), which is extremely weak. The beta
coefficient of Turnover was 2.90 for period 1 and 2.56 for period 2.
The robust regression model had a low R-‐square of 0.0391 for period 1 and 0.0398 for
period 2. These results imply that the provided model explained the changes in the
difference in split ratings for less than 4%. Therefore, the statistically significantly
explanatory variables, Leverage and Turnover, which were based on the literature
review, together minimally explain the difference in split ratings.
Size
Theoretically, the improvement of information efficiency for larger companies will be
greater from the usage of XBRL since the usage of XBRL will make it more efficient to
compare different business reporting methods (Weber, 2003). The size of a firm was,
therefore, expected to be positively correlated to reduction in split ratings.
This theory was formulated into the second hypothesis, The effect of XBRL adoption on
split ratings is stronger in larger firms, and was tested by adding the variable XBRL*Size
to the regression model for both period 1 and period 2. This variable was not
33
statistically significant to the regression model. At a 95% confidence level, there was not
enough evidence to support this hypothesis, and H2 was, therefore, rejected.
Leverage
Higher leveraged firms were expected have voluntarily disclosed more information in
order to reduce costs of debt (Dumontier & Raffournier, 1998; Wallace & Naser, 1995).
CRAs were expected to obtain fewer new insights into these highly leveraged companies
when they started using XBRL. Leverage was, therefore, expected to be negatively
correlated to a reduction in split ratings.
This idea was formulated into the third hypothesis: The effect of XBRL adoption on split
ratings is weaker for firms that are more leveraged. This hypothesis was tested by adding
the variable XBRL*Leverage to the regression model for both period 1 and period 2. This
variable was not statistically significant to the regression model. At a 95% confidence
level, there was not enough evidence to support this hypothesis, and H3 was, therefore,
rejected.
To conclude, this research investigated the U.S. stock market and enough evidence was
found to state that the difference in credit ratings is not influenced by the use of XBRL.
This study helps in the debate regarding making XBRL use mandatory. Those in favor of
using XBRL focus on the benefits of implementing XBRL for both companies and
stakeholders (Pinsker & Li, 2008), and it is important to perform research to test these
arguments.
5.2 Limitations One of the limitations for this research is the fact that CRAs do not easily change their
credit ratings. For example, Moody’s only takes a rating action “when it is unlikely to be
reversed within a relatively short period of time” (Cantor, 2001). Changes in credit
ratings involve a process in which the company itself can provide its opinion on changes
in credit ratings. CRAs might be conservative with certain insights since they “attempt
to avoid unnecessary rating volatility by ignoring changes in a client’s business or
financial risk profiles that occur as part of the regular business cycle and are likely to be
reversed shortly after” (Funcke, 2015, p. 20). The effect of adopting XBRL might,
therefore, occur on a longer timeframe.
34
Furthermore, this research was based on companies that were listed to the U.S. stock
market. Credit ratings for equity of listed companies are common in the US and are less
standard in other parts of the world. Therefore, it is not possible to generalize these
findings on a global level.
5.3 Recommendations for future research The recommendations for future research are linked with the limitations of the study
performed. The overall fit of the presented model was low; other control variables than
those used should be researched. It is recommend to include market-‐related factors in
future research investigating the role of the financial crisis. The difference in credit
ratings might have been influenced by the financial crisis. In addition to market factors,
different time periods might be addressed, as well, in order to strength this model for
other parts of the economic cycle.
Furthermore, this research was based on companies listed in the U.S. stock market. As
discussed in the previous section, the extensive use of credit rating agencies in the US
differs from other parts of the world. It is recommend to perform similar research in
other countries.
35
6 References Bai, Z., Sakaue, M., & Takeda, F. (2012, December). The Impact of XBRL Adoption on the
Information Environment in Japan. IPRC Discussion Paper Series No.05 . Tokyo, Japan. Bini, L., Giunta, F., & Dainelli, F. (2010). Signalling Theory and Voluntary Disclosure to the
Financial Market-‐Evidence from the Profitability Indicators Published in the Annual Report. Available at SSRN 1930177.
Blankespoor, E. (2012). The impact of investor information processing costs on firm disclosure choice: Evidence from the XBRL mandate. Michigan: Doctoral dissertation, The University of Michigan.
Blankespoor, E., Miller, B., & White, H. (2014). Initial evidence on the market impact of the XBRL mandate. Review of Accounting Studies , 19 (4), 1468-‐1503.
Boot, A., & Milbourn, T. (2002). Credit Ratings as Coordination Mechanisms. Amsterdam: Faculty of Economics and Econometrics University of Amsterdam.
Bowerman, B., O'Connell, R., & Murphree, E. (2009). Business Statistics in Practice. McGraw-‐Hill International Edition (5).
Breusch, T., & Pagan, A. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica , 47 (5), 1287-‐1294.
Cantor, R. (2001). Moody’s investors service response to the consultative paper issued by the Basel Committee on Banking Supervision and its implications for the rating agency industry. Journal of Banking and Finance , 175.
Chen, H., & Li, F. (2013). Analysis the Impact of XBRL in China’s Capital Market Using Methods of Empirical Research. Research Journal of Applied Sciences, Engineering and Technology , 1521-‐1527.
Chiang, R., & Venkatesh, P. (1988). Insider holdings and perceptions of information asymmetry: a note. Journal of Finance , 43 (4), 1041-‐1048.
Cong, Y., Hao, J., & Zou, L. (2014). The Impact of XBRL Reporting on Market Efficiency. JOURNAL OF INFORMATION SYSTEMS , 28 (2), 181–207.
Copeland, T., & Galai, D. (1983). Information Effects on the Bid-‐Ask Spread. The Journal of Finance , 38 (5), 1457-‐1469.
D’Amato, S. (2014). An analysis of the credit rating agencies. International Journal of Critical Accounting , 6 (3), 258-‐283.
Dodd-‐Frank. (2010). Dodd-‐Frank Wall Street Reform and Consumer Protection Act. Washington, DC: Government Printing Office.
Dumontier, P., & Raffournier, B. (1998). Why Firms Comply Voluntarily with IAS: an Empirical Analysis with Swiss Data. Journal of International Financial Management and Accounting , 9 (3).
Efendi, J., Dong Park, J., & Subramaniam , C. (2010, September 5). Do XBRL Reports Have Incremental Information Content? – An Empirical Analysis. Retrieved March 24, 2015 from http://ssrn.com/abstract=1671723
Efendi, J., Park, J., & Smith, L. (2014). Do XBRL filings enhance informational efficiency? Early evidence from post-‐earnings announcement drift. Journal of Business Research , 67, 1099-‐1105.
Elliott, R., & Jacobson, P. (1994). Costs and benefits of business information disclosure. Accounting Horizons , 8 (4), 1-‐80.
Fama, E. (1970). The Journal of Finance. EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK , 25 (2), 383-‐417.
Forster, J. (2008). Essays on the Economics of Credit Rating Agencies and Banking. Ludwig-‐Maximilians-‐Universitat Munchen.
36
Funcke, N. (2015). Credit Ratings and the Auditor’s Going-‐Concern Opinion. Maastricht: Maastricht University.
Geiger, M., North, D., & Selby, D. (2014). Releasing Information in XBRL: Does It Improve Information Asymmetry for Early U.S. Adopters? Academy of Accounting and Financial Studies Journal , 18 (4), 66-‐83.
Groysberg, B., Healy, P., & Chapman, C. (2008). Buy-‐Side vs. Sell-‐Side Analysts’ Earnings Forecasts. Financial Analysts Journal , 64 (4), 25-‐39.
Hodge, F., Kennedy, J., & Maines, L. (2004). Does search-‐facilitating technology improve the transparency of financial reporting? Account Rev , 79 (3), 687-‐703.
Jensen, M., & Mechling, W. (1976). Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. Journal of Financial Economics , 3 (4), 305-‐360.
Kadakal, O. (2013). XBRL adoption and information asymmetry. Rotterdam: Rotterdam School of Management.
Kapoor, R. (2012, October 19). XBRL. Retrieved Februari 14, 2015 from http://www.slideshare.net/ManuVrs/webxbrl
Kim, J., Lim, J., & No, W. (2012). The Effect of First Wave Mandatory XBRL Reporting across the Financial Information Environment. Journal of Information Systems , 26 (1), 127-‐153.
Leuz, C., & Verrecchia, R. (2000). The Economic Consequences of Increased Disclosure. Journal of Accounting Research , 38 (Supplement 2000), 91-‐124.
Mann, H., & Whitney, D. (1947). On a test of whether one of two-‐random variables is stochastically larger than the other. The Annals of Mathematical Statistics , 18 (1), 50-‐60.
Millon, M., & Thakor, A. (1985). Moral Hazard and Information Sharing: A Model of Financial Information Gathering Agencies. The Journal of Finance , 40 (5), 1403-‐1422.
Nooyi, I. (2006, October 4). CEO Pepsi. (S. Johnson, Interviewer) http://ww2.cfo.com/risk-‐compliance/2006/10/the-‐good-‐and-‐bad-‐about-‐xbrls-‐future/.
O'Brien, R. (2007). A Caution Regarding Rules of Thumb for Variance Inflation Factors. Quality & Quantity 41.5 , 673-‐690.
O'Kelly, C. (2007). XBRL Europe driving a business reporting renaissance. Accounting Ireland: Financial Reporting , 39 (6), 16-‐18.
Pinsker, R., & Li, S. (2008). Costs and benefits of XBRL adoption: early evidence. Communications of the ACM , 51 (3), 47-‐50.
Premuroso, R., & Bhattacharya, S. (2008). Do early and voluntary filers of financial information in XBRL format signal superior corporate governance and operating performance? International Journal of Accounting Information Systems , 9 (1), 1-‐20.
Richards, J., Smith, B., & Saeedi, A. (2006). An Introduction to XBRL. Retrieved Februari 11, 2015 from http://ssrn.com/abstract=1007570
(1987). In P. Rousseeuw, & A. Leroy, Related Statistical Techniques. Hoboken, NJ, USA: John Wiley & Sons, Inc.
Rubini, H. (2000). De-‐Regulated Credit Bureaus: Could They Raise Credit Rationing? Pennsylvania State University.
SEC. (2008, December 18). SEC Approves Interactive Data for financial Reporting by Public Companies and Mutual Funds. Retrieved Januari 25, 2015 from http://www.sec.gov/news/press/2008/2008-‐300.htm
SEC. (2015). SEC Form 10-‐Q. Retrieved Februari 18, 2015 from http://www.sec.gov/answers/form10q.htm
SEC. (2011, September 19). SEC Interactive Data Filings and RSS Feeds. Retrieved June 04, 2015 from http://www.sec.gov/spotlight/xbrl/filings-‐and-‐feeds.shtml
SEC. (2009). SEC Interactive Data to Improve Financial Reporting. Retrieved 2 19, 2015 from http://www.sec.gov/rules/final/2009/33-‐9002.pdf
37
Singhvi, S., & Desai, H. (1971). An empirical analysis of the quality of corporate financialdisclosure. The Accounting Review (46), 129-‐138.
Sufi, A. (2009). The real effects of debt certification: Evidence from the introduction of bank loan ratings. Review of Financial Studies , 22 (4), 1659–1691.
Sykes, A. (2000). An Introduction to Regression Analysis, In E. Posner (Ed.), Chicago Lectures in Law and Economics.
Vasarhelyi, M., Yang, D., & Liu, C. (2003). A note on the using of accounting databases. Industrial Management and Data Systems , 103 (3), 204-‐210.
Verrecchia, R. (1980). Consensus Beliefs, Information Acquisition, and Market Information Efficiency. The American Economic Review , 70 (5), 874-‐884.
Wallace, R., & Naser, K. (1995). Firm-‐specific determinants of the comprehensiveness of man-‐datory disclosure in the corporate annual reports of firms listed on the stock exchangeof Hong Kong. Journal of Accounting and Public Policy (14), 311-‐368.
Wang, J. (1993). A Model of Intertemporal Asset Prices Under Asymmetric Information. The Review of Economic Studies , 60 (2), 249-‐282.
Weber, R. (2003). XML, XBRL, and the Future of Business and Business Reporting. In rust and data assurances in capital markets: The role of technology solutions (pp. 3-‐6). PricewaterhouseCoopers LLP.
Williams, R. (2009). Using heterogenous choice models to compare logit and probit coefficients across groups. Sociological Methods & Research , 37, 531-‐559.
Yoon, H., Zo, H., & Ciganek, A. (2011). Does XBRL adoption reduce information asymmetry? Journal of Business Research (64), 157-‐163.
38
Appendix: Figures
Table 17. Robust regression Post-‐XBRL Period 1 VIF Tolerance Beta t-‐value p-‐value Model statistics
XBRL 1.01 0.99 0.004 0.23 0.82 Dependent variable: Size 1.68 0.59 0.000 0.17 0.86 Difference Leverage 1.65 0.61 0.005 2.34 0.02 N=546 Turnover 1.13 0.88 2.897 2.29 0.02 R2=0.0391 Profitability 1.45 0.69 -‐0.650 -‐1.17 0.24 F=6.11 (p-‐value=0.0000) Profitable 1.46 0.68 0.003 0.08 0.94 Mean VIF 1.40
Table 18. Robust regression Post-‐XBRL Period 2 VIF Tolerance Beta t-‐value p-‐value Model statistics
XBRL 1.05 0.95 0.001 0.14 0.89 Dependent variable: Size 1.66 0.60 0.000 0.69 0.49 Difference Leverage 1.67 0.60 0.004 2.25 0.03 N=546 Turnover 1.15 0.87 2.560 1.98 0.05 R2=0.0398 Profitability 1.43 0.70 -‐0.683 -‐1.40 0.16 F=6.07 (p-‐value=0.0000) Profitable 1.46 0.69 -‐0.018 -‐0.53 0.60 Mean VIF 1.40