1.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
020406080100
Frequency
0 2 4 6 8
DIFFERENCE PRE
020406080100
Frequency
0 2 4 6 8
DIFFERENCE POST
020406080100
Frequency
0 2 4 6 8
DIFFERENCE POST2

25.
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.
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.
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.
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.
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.