Hello
everyone,
my
name
is
N
ilofar
Varzgani.
Today,
I'm
going
to
be
presenting
my research
study
that
I
have
conducted
using
JMP
Pro
16,
and
specifically
within
JMP Pro
16,
I
used
the
Textual
Explorer
platform
as
well
as
the
Sentiment
Analysis
functionality
within
the
Text
Explorer
platform.
The
title
of
this
study is
Textual
Analysis
of
Earnings
Conference
Calls:
Differences
Between
Firms.
This
study
that
I'm
working
on
is
with
my
two
co-
authors,
Dr. U
gras
and
Dr.
Ayaydin.
They're
not
going
to
be
presenting
with
me
here
today,
but
they
will
surely
attend
the
presentation
itself.
I'll
start
off
with
a
little
bit
of
an
introduction.
Textual
analysis
of
written
documents
has
become
an
important
analytics
tool
in
accounting
and
finance
decision-
making.
Several
research
papers
have
expanded
the
textual
analysis
and
have
also
measured
the
written
tone
in
financial
documents,
converting
the
tone
into
a
quantitative
score
which
measures
optimism
or
pessimism
in
the
tone
of
the
speaker.
Some
researchers
have
also
connected
this
tone
to
explain
abnormal
returns
in
the
financial
markets.
In
my
presentation
today,
I'm
going
to
talk
about
how
we
try
to
explore
this
connection
with
the
use
of
the
Sentiment
Analysis
platform
within
JMP
Pro
16.
Let's
get
started
here.
Sorry.
All
right.
Let's
talk
a
little
bit
about
the
motivation
behind
this
study.
For
many
years,
capital
market
studies
have
researched
whether
quantitative
analytic
information
reported
by
firms
such
as
earnings
and
revenue,
or
other
accounting
measures
influence
decision
making.
Recent
studies
have
shown
that
in
addition
to
these
type
of
quantitative
information,
qualitative
analytics
from
the
firms
and
from
the
media
influences i
nvestor
behavior.
This
qualitative
information
includes
texts
in
10-
K
reports,
earnings
press
releases,
conference
call
transcripts,
comment
letters
to
the
SEC,
analysts
remarks,
articles
in
media,
and
conversations
in
social
media.
Several
studies
have
also
shown the
importance
of
earnings
conference
calls
that
immediately
follow
the
quarterly
earnings
releases
of
public
companies.
In
our
study
here,
we're
examining
whether
the
impact
of
the
earnings
conference
call
tones
varies
across
different
groups
of
companies.
Let's
do
a
little
bit
of
background
on
the
literature
that
already
covers
textual
analysis
so
far.
Textual
analysis
has
been
used
to
analyze
a
variety
of
documents
through
alternative
approaches,
and
one
could
categorize
these
approaches
into
three
broad
categories.
The
first
one
is
the
use
of
the
Fog
Index,
which
is
basically
a
function
of
two
variables:
the
average
sentence
length
in
number
of
words,
and
the
word
complexity,
which
basically
measures
the
percentage
of
words
with
more
than
two
syllables
in
it.
The
second
category
of
techniques
is
the
length
of
reports,
although
this
one
seems
like
a
rather
simplistic
approach,
but
it
has
been
useful
because
of
the
simplistic
nature.
There
have
been
a
couple
of
studies
which
have
used
the
length
of
report
to
proxy
as
the
complexity
of
the
report
itself.
The
third
approach
is
the
use
of
a
word list.
Now,
in
terms
of
the
word list,
there
are
a
number
of
word
lists
that
people
have
created
themselves,
such
as
the
Henry
word list
or
the
Loughran
and
McDonald's
word list.
But
in
our
study
here,
we
utilized
the
inbuilt
dictionary
that
JMP
Pro
comes
with,
and
in
addition
to
that
inbuilt
dictionary,
we
augmented
that
with
some
phrases,
which
we
added
as
terms,
as
well
as
a
custom
list
of
stop
words
based
on
the
sample
data
that
we
were
working
with.
Let's
talk
a
little
bit
about
the
data
itself.
Our
data
sample
size
is
approximately
25,000
observations,
which
means
that
we
had
close
to
25,000
earnings
call
transcripts
that
we
analyzed,
and
the
date
range
for
those
transcripts
is
from
2007
Quarter 1 to
2020
Quarter
4.
We
have
tried
to
incorporate
only
the
text
portion
of
the
earnings
transcript
call,
removing
any
graphics
or
any
special
characters
that
might
be
a
part
of
the
earnings
call
transcript.
All
of
these
transcripts
were
downloaded
from
the
LexisNexis
database
in
RTF
format.
Just
to
give
you
a
little
bit
of
an
intro
as
to
what
the
earnings
call
transcript
basically
looks
like.
It
starts
off
with
the
title,
which
mentions
the
name
of
the
company
for
which
the
earnings
call
announcement
is
being
made.
It
has
the
word
Fair
Disclosure
Wire
in
the
next
line,
followed
by
the
date.
Then
the
main
call
itself
is
divided
into
two
sections.
You
have
the
presentation
section,
which
are
the
prepared
remarks
of
the
managerial
team,
which
is
attending
that
call,
basically,
and
then
you
also
have
a
discussion
between
the
analysts
who
are
sitting in
on
the
call
live,
and
they
ask
questions
to
the
managers,
and
the
managers
respond
to
those
questions.
So the
prepared
remarks
and
the
QnA
portion.
Now,
for
this
study
specifically,
we've
only
looked
at
the
prepared
remarks
portion
of
the
earnings
transcripts,
but
later
on,
as
an
extension
of
this
study,
we're
planning
to
also
incorporate
the
QnA
portion
of
the
earnings
call
as
well.
In
addition
to
those
two
blocks
of
text
that
are
a
part
of
the
transcript,
most
of
these
transcripts
also
include
a
list
of
all
the
participants
who
are
on
the
call,
which
includes
all
of
the
managers
from
the
company
side,
as
well
as
all
of
the
analysts
from
different
institutional
investor
sites.
Let's
talk
about
the
methodology
a
little
bit
first.
We
extracted
the
transcript
and
the
prepared
remarks
of
the
managers
section
was
titled
as
the
DocB
ody.
The
Qn
A
was
titled
at
the
discussion,
and
we
counted
the
number
of
analysts
who
attended
each
call.
Now,
keep
in
mind
that
because
of
the
fact
that
not
all
calls
have
a
Qn
A
segment,
the
Qn
A
part
might
be
missing
for
some
of
the
rows
in
our
sample.
Which
is
why
for
this
conference
and
this
study,
we've
only
focused
on
the
DocB
ody,
which
is
the
prepared
remarks
portion
of
the
earnings
call.
Then
we
also
created
columns
which
could
be
used
as
identifiers.
A
ticker
column
for
further
analysis,
the
year
quarter,
as
well
as
a
calculated
column
which
measures
the
length
in
terms
of
the
number
of
words
in
the
prepared
remarks
section.
Now,
the
distribution
of
length
is
interesting
and
we're
going
to
show
that
output
in
a
little
bit.
Before
we
moved
on
to
the T
ext
Explorer
platform,
we
changed
the
data
type
for
the
document
body
to
unstructured
text-
sorry,
the
data
type
character
and
the
modeling
type
to
unstructured
text
so
that
the
Textual
Explorer
platform
can
work.
Before
I
show
you
the
Textual
Explorer
platform,
I
just
want
to
talk
a
little
bit
about
the
terminology
that
is
going
to
be
used
a
lot
during
the
Text
Explorer
platform
and
the
output
shown.
In
textual
analytics,
a
term
or
a
token
is
the
smallest
piece
of
text
similar
to
a
word
in
a
sentence.
You
can
define
terms
in
many
ways,
although
the
use
of
regular
expressions
or the
process
of
breaking
down
the
text
into
terms
is
called
tokenization.
Another
important
term
that
is
going
to
pop
up
a
lot
when
we
see
the
output
of
the
textual
platform
is
a
phrase
which
is
a
short
collection
of
term.
The
platform
has
options
to
manage
phrases
that
can
be
specified
as
terms
in
and
of
themselves.
For
example,
for
our
earnings
call
study,
a
phrase
which
popped
up
a
lot
was
something
like
effective
tax
rate.
Although
effective,
tax,
and
rate,
are
three
separate
terms,
but
effective
tax
rate
being
used
together
most
of
the
time
we
converted
that
phrase
into
a
term
itself
so
that
we
can
analyze
how
many
times
that
particular
phrase
as
a
whole
is
being
used
in
these
conference
calls.
Next
is
the
document.
A
document
basically
refers
to
a
collection
of
words.
In
a
JMP
data
table,
the
unstructured
text
in
each
row
of
the
text
column
corresponds
to
a
document.
Then
we
also
have
a
corpus,
which
is
basically
a
collection
of
all
of
the
documents.
Now,
another
important
term
which
we're
going
to
use
later
on
during
the
output
is
the
stop
words.
Stop
words
are
basically
common
words
which
you
would
want
to
exclude
from
analysis.
JMP
does
come
with
its
own
list
of
stop
words,
but
there
might
be
specific
stop words
in
the
data
sample
that
you
are
using
which
would
apply
to
that
data
set
only.
For
us,
we
created
a
custom
list
of
stop
words
which
you
can
easily view
in
the
Text
Explorer
platform.
You
can
have
a
list
of
stop
words
in
an
excel
format
or
in
a
txt
file
and
then
upload
that
within
the
Text Explorer
platform
and
use
those
as
stop words.
Then
finally
is
the
process
of
stemming,
which
is
basically
combining
words
with
identical
beginnings
or
stems,
so
to
say,
to
basically
make
sure
that
the
result
compiles
those
similar
rooted
words
as
one
word.
For
example, jump, jumped, jumping,
all
would
be
treated
as
one
single
word
instead
of
three
separate
words.
Now,
for
our
study
over
here,
we
decided
to
go
with
the
no
stemming
option
because
there
were
some
issues
with
the
stemming
that
we
noticed.
For
example,
some
words
like
ration
could
be
used
as
a
stem
for
words
like
acceleration,
which
has
nothing
to
do
with
that
word
itself.
So
we
decided
to
go
with
the
no
stemming
option
in
our
case.
This
slide
over
here,
we
look
at
the
options
that
we
selected
for
the
Text
Explorer
platform.
The
main
variable
of
interest
is
the
DocB
ody.
We
use
the
ID
to
ID
each
row
of
observation,
and
then
we
change
the
default
options
for
each
of
these
features.
The
maximum
words
per
phrase,
the
maximum
number
of
phrases,
characters
per
word,
maximum
characters
per
word,
to
the
ones
that
we
thought would
be
suitable
for
our
particular
data
set.
A
s
you
can
see,
that
we
increased
the
maximum
ranges
to
a
lot
higher
than
what
the
defaults
are
just
to
be
on
the
safe
side.
that
we're
not
missing
out
any
important
terms
within
our
analysis.
Our
initial
output
that
pops
up
once
you
run
the
Text
Explorer
platform
gives
you
a
list
of
all
of
the
terms
which
were
highly
used
within
the
sample
of
data,
as
well
as
the
phrases.
We
reviewed
the
list
of
phrases
and
selected
the
phrases
that
could
be
used
as
terms.
There
were
a
total
of
30,000
phrases
out
of
which
1,068
phrases
were
added
to
the
term
list.
In
addition
to
that,
we
also
created
our
custom
list
of
stop
words.
W
e
found
it
easier
to
basically
import
all
the
terms
from
our
sample
from
JMP
into
Excel,
sort
those
words,
and
then
basically
remove
everything
or
count
all
of
those
words
as stop
words
which
have
certain
characteristics,
for
example,
they
had
symbols
or
commas
or
dollar
signs
in
them,
or
numbers
which
were
being
treated
as
text
or
common
names,
for
example,
John,
Michael,
David,
et
c.
W
e
added
all
of
those
to
our
stop
word
list
and
we
uploaded
that
into
the
Textual
Explorer
platform.
Let's
look
at
some
of
our
output.
The
first
analysis
that
we
did
was
on
the
variable
length
of
the
prepared
remarks.
T
he
assumption
over
here
is
if
the
prepared
remarks
section
of
the
management
is
longer,
it
basically
shows
that
they
have
more
to
explain
to
the
investors,
and
that
is
why
the
complexity
or
the
tone
of
those
reports
might
be
different
for
the
other
reports,
which
are
shorter
in
length
where
the
managers
don't
have
to
do
a
lot
of
explaining.
A
s
you
can
see
with
the
distribution
output
on
the
left,
the
length
of
our
sample
data
set
over
here
is
slightly
asymmetric
with
tiny
tail
towards
the
right
hand
side,
which
means
that
there
were
some
reports
which
were
longer
than
the
others.
The
mean
of
length
is
around
3,027
words
and
the
median
is
around
2,9
66
words.
You
can
see
the
mean
and
the
median
are
not
too
far
away
from
each
other,
which
probably
means
that
we
can
assume
it
to
be
symmetric
as
opposed
to
asymmetric
like
the
histogram
over
here
denotes.
The
difference
between
the
mean
and
the
median
is
not
too
much.
We
did
look
at
the
median
length
of
the
reports
versus
over
the
years,
and a
s
you
can
see
in
2007,
the
earnings
calls
were
much
longer
than
the
years
after
that,
and t
hen
we
did
see
a
slight
bump
in
2020
as
well.
If
you
look
at
quarter
wise
length
of
the
reports,
you'll
also
notice
that
Q4
generally
has
the
longest
reports
because
the
management
is
explaining
the
functions
and
the
operations
of
the
company
for
the
whole
year
and
they're
compiling
results
from
the
previous
three
quarters
as
well.
In
terms
of
the
tickers
which
had
the
longest
average
length,
we
had
Boston
Properties
as
reporting
the
longest
average
length,
at the
average
approximately
close
to
6,000
words,
which
is
double
the
average
length
of
the
whole
data
set
in
general.
Next
we
have
is
basically
the
word
cloud.
Now,
just
to
compare
stemming
option
versus
non
stemming
option,
on
the
screen,
you
see
both
the
word
cloud
with
the
stemming
option,
which
is
on
the
right
hand
side,
and
the
one
without
the
stemming
option.
We
preferred
the
no
stemming
option
because
it
lets
us
see
the
words
which
show
up
in
most
of
these
earnings
calls
more
often
than
the
other
words,
whereas
the
stemming
option
might
end
up
with
a
word
cloud
which
is
not
very
explanatory.
As
you
can
see,
growth,
new,
revenue,
increase.
These
are
the
words
which
pop
up
the
most,
which
basically
signal
that
managers
are
mostly
optimistic
and
positive
in
their
tones
in
their
prepared
remarks
section
of
their
reports.
Then
I
also
have
a
screenshot
of
the
Sentiment
Analysis
platform
which
basically
again
tells
you
the
distribution
of
the
overall
tone,
positive
or
negative.
A
s
you
can
see
from
this
histogram
over
here,
the
overall
tone
of
these
prepared
remarks
is
mostly
very
positive,
with
only
very
few
earnings
call
transcript
which
fall
in
the
negative
sentiment
portion
of
this
distribution,
which
again
signals
that
managers
tend
to
be
more
positive
and
more
optimistic
when
talking
about
the
operations
of
the
company
so
that
they
can
signal
the
fact
that
the
future
is
going
to
be
bright
and
it's
going
to
be
better,
and
that
definitely
affects
how
the
investors
react
to
this
tone.
Next,
we
also
decided
to
look
at
the
overall
sentiment
of
the
calls,
as
well
as
the
positive
mean
and
the
negative
mean
of
the
sentiments.
As
you
can
see
the
positive
sentiment,
mostly
it's
around
a
value
of
60,
whereas
the
negative
sentiment,
we
see
a
bump
around
the
negative
sentiment
of
minus 40
so
none
of
these
earnings
calls
were
too
negative,
even
if
the
company
performance
was
really
bad
for
that
particular quarter.
B
ecause
they
want
to
signal
a
brighter
future,
and
not
focus
too
much
on
the
history.
If
you
look
at
the
overall
sentiment
versus
the
years,
you'll
notice
that
the
overall
sentiment
was
much
lower
during
the
financial
crisis
of
2007-
2008,
and
then
it
bumped
hugely
in
2009-
2010
and
overall,
it
has
been
relatively
steady
except
for
2020
when
the
pandemic
hit.
If
you
break
it
down
quarter
wise,
you
can
see
that
the
bottom
center
graph
over
here
shows
that
some
quarters,
specifically
the
fourth
quarter,
might
see
a
drop
in
the
tone
of
the
overall
sentiment
in
the
whole
data.
If
you
look
at
the
length
versus
the
year,
again,
you'll
notice
that
the
length
was
much
higher
in
2007,
it
dropped
in
2008,
again
picked
up
a
peak
in
2009,
and
overall,
the
length
has
reduced
over
time
until
2020
itself.
It
might
be
a
safe
assumption
to
make that
when
times
are
tough
and
the
companies
have
more
to
explain,
the
earnings
call
tend
to
become
longer
and
the
prepared
remarks
are
longer.
However,
if
you
look
at
length
versus
the
overall
sentiment,
you'll
notice
that
there
seems
to
be
a
slight
positive
relationship
between
length
and
overall
sentiment,
but
it's
definitely
not
a
simple
relationship
like
a
linear
upward
trend.
Instead,
the
data
is
quite
heteroscedastic.
Here
I
have
a
list
of
companies
which
showed
up
with
the
highest
overall
sentiment
over
the
years
versus
the
companies
which
showed
up
with
the
lowest
overall
sentiment
over
the
years.
I
also
put
the
industries
in
which
they
belong
just
as
an
interesting
piece
of
information
that
we
noticed.
For
example,
a
lot
of
the
positive
sentiment
calls
were
the
ones
from
the
technology
services
area
or
financial
services,
whereas
the
lowest
sentiment
was
in
the
waste
management
or
medical
technology
industries.
In
terms
of
the
future
research
that
we
plan
to
do
on
this
topic,
we
want
to
examine
the
tone
of
these
earnings
call,
and
do
a
cross
analysis
with
variables
like
managerial
strategic
incentives
with
disclosures,
the
impact
the
tone
has
on
analysts
and
investors,
as
well
as
some
variables
which
are
specific
to
the
firm,
such
as
the
size,
their
complexity,
their
age,
et
c.
W
e
also
plan
to
explore
the
term
selection
for
building
data
mining
models
using
the
Text Explorer
platform
within
JMP
Pro.
Thank
you
so
much
for
attending
this
presentation
and
hopefully
we
can
answer
any
questions
that
you
may
have
about
our
presentation
today.
Thank
you
.
