# Statistics Project, Finance and Accounting event study

Statistics Project, Finance and Accounting event study
Project description
Conduct an event study or test a trading strategy of your choice. Be creative! It is perfectly fine if the data does not support your hypothesis. This paper does not need to exceed 10 pages. When the first draft of the paper is due, you are expected to have selected an idea. Your idea needs to be approved first by me before you can proceed with data collection and empirical analysis.

Some ideas for you to consider:
1. Within an industry, do returns of large-cap stocks lead those of small-cap stocks? (monthly data)
2. Do high volatility stocks have higher future returns?
3. Form portfolios based on some Compustat variable(s) and test for market efficiency.
4. Test the ‘Super Bowl Theory’ or some other such ‘event’.
5. Post-earnings announcement drift
6. Intermedium Momentum and long term reversal
7. Is there an illiquidity premium? (Do stocks with higher illiquidity earn higher returns?)

The paper should follow the outline below
A typical outline of an empirical paper:
I. Introduction – what is your paper about?
II. Data Description – generally describe your data, provide some summary statistics indicating number of stocks, time-period, distribution of key variables, etc.
III. Empirical Tests – describe the methodology and results of your empirical tests. Are they robust?
V. Conclude – pretty short summation

Event
Study
An
event
study
measures
the
impact
of
a
specific
event
on
the
value
of
a
security
Event
studies
traditionally
answer
the
question
Does
this
event
matter?
(e.g.
dividend
initiation,
index
addition,
stock
splits,
merger
announcements,
earnings
announcement)
But
are
increasingly
used
to
answer
the
question:
Is
the
relevant
information
impounded
into
prices
quickly?
(e.g.
Post
earnings
announcement
drift)
What’s
an
Event
Study?
Short

horizon
event
studies
examine
a
short
time
window,
ranging
from
hours
to
weeks,
surrounding
the
event
in
question.
Since
short
run
event
expected
returns
are
small
in
magnitude,
this
allows
us
to
focus
on
the
information
being
released
and
(largely)
abstract
from
modeling
expected
returns.
There
is
relatively
little
controversy
about
the
methodology
and
statistical
properties
of
short

horizon
event
studies.
Short

horizon
Event
Studies
Long

horizon
event
studies:
Do
IPO
stocks
under

perform
in
the
three
years
after
the
IPO?
Long

term
reversal.
Long

horizon
event
studies
are
problematic
because
the
results
are
sensitive
to
the
modeling
assumption
of
expected
returns.
Long

horizon
Event
Studies
Basic
event
study
methodology
is
essentially
unchanged
since
Ball
and
Brown
(1968)
and
Fama et
al.
(1969).
Abnormal
return
Anatomy
of
an
event
study:
1.
Identify
an
event
and
the
event
window,
security
selection
2.
Specification
and
estimation
of
the
reference
model,
characterizing
the
“normal”
returns
(expected
returns).
3.
Computation
and
aggregation
of
“abnormal”
returns.
4.
Hypothesis
testing
and
interpretation.
Basic
Set
up
Let
t
index
the
event
time.
t
=
0
is
the
time
of
the
event.
Event
window:
[T1+1,
T2],
[T0+1,T1]
estimation
window,
[T2+1,T3]
post

event
window
L1
=
T1

T0,
L2
=
T2

T1,
L3
=T3

T2
Estimating
the
reference
model
using
the
estimation
window.
Compute
normal
returns
in
the
event
window
and
post

event
window
using
the
parameters
estimated
from
the
estimation
window.
Compute
abnormal
returns
Analyze
the
abnormal
returns:
eg.
test
the
significance
Typical
Time

line
of
an
Event
Study
Common
choices
of
reference
models
to
characterize
normal
returns
Constant
expected
return
model

?
??
??
?
??
?,?
Market
Model

?
??
??
?
??
?
?
?,?
??
?,?
Linear
factors
models
(e.g.,
three
factors)

?
??
??
?
??
??
?,?
??
??
?,?
??
??
?,?
??
?,?
Short

horizon
event
study
results
tend
to
be
insensitive
to
the
choice
of
reference
models.
Reference
Models
Define
abnormal
return
for
the
event
window
and
post

event
window.
Suppose
that
we
use
the
market
model
as
the
reference
model,
?
?
are
estimated
in
the
estimation
window.
The
residual
?
t
?
t
?
?
?
t
for
t
in
the
event
window
and
post

event
window.
Under
H0:
the
event
has
no
impact
(on
mean
or
volatility),
then
?
t
~N(0,
)
Compute
the
abnormal
return
Abnormal
returns
need
to
be
aggregated,
typically
through
time
and
across
securities
(events)
Aggregate
through
time
for
an
individual
security
(event)
the
cumulative
return
from
t
1 to
t
2 is
Under
H0,
Aggregate
Abnormal
Returns
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