Slide9 - Chapter
9
 Behavioral Finance and Technical...

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Unformatted text preview: Chapter
9
 Behavioral Finance and Technical Analysis 9.1
The
Behavioral
Cri2que
 Behavioralism
bias
 •  Mo2va2on
 – Stock
prices
in
the
1990s
did
not
appear
to
 match
“fundamentals,”

 e.g.,
high
price
earnings
ra2os
 – Evidence
of
refusal
to
sell
losers
 – Economics
discipline
is
exploring
behavioral
 aspects
of
decision
making
 Informa2on
Processing
Bias
 •  Memory
bias
and
Forecas0ng
Errors
 –  People
tend
to
put
too
much
weight
to
recent
 experience
when
making
forecast.

 –  Ex:
extreme
earnings’
forecast
may
lead
to
 unsustainably
high
P/E
ra2os.

 •  Representa0ve
bias
[law
of
small
numbers]
 –  People
tend
to
infer
a
paOern
too
quickly
based
 on
a
small
sample
and
extrapolate
apparent
 trends
too
far
into
the
future.
 Informa2on
Processing
Bias
 •  Conserva0sm
 –  Investors
are
too
slow
(too
conserva2ve)
in
 upda2ng
their
beliefs
in
response
to
recent
 evidence.

 –  Many
people
become
anchored
to
their
ideas
 and
will
not
update
their
expecta2ons
when
 new
informa2on
arrives.

 –  This
under‐reac2on
to
news
leads
to
 momentum
in
stock
returns.
 Informa2on
Processing
Bias
 •  Overconfidence
 –  Some
people
exhibit
overconfidence
in
their
ability
to
 pick
stocks
or
have
an
exaggerated
belief
that
‘risk’
 will
hurt
the
other
person
but
not
them.

As
a
result,
 they
bid
stock
prices
too
high.
 •  Disposi0on
effect
 –  People
tend
to
avoid
realizing
loss
and
seek
realizing
 gain.

 –  Ex:
someone
buys
a
stock
at
$40
which
drops
to
$30
 before
rising
to
$38,
most
people
do
not
want
to
sell
 un2l
the
stock
gets
to
above
$40.
 Behavioral
Bias
 •  Framing:
 –  Decisions
are
affected
by
how
choices
are
posed.
 –  Ex:
early
bird
discount
vs.
peak‐2me
surcharge
 –  Coin
toss
example:

 •  Game
A:
$0
for
head
and
$50
for
tail
 •  Game
B:
You
are
given
$50
as
gi`,
which
is
bundled
 with
a
bet.
The
bet
imposes
a
loss
of
$0
for
tail
and
a
 loss
of
$50
for
head.
 Behavioral
Bias
 •  Mental
accoun0ng
 –  A
specific
form
of
framing
in
which
people
segregate
 certain
decisions.

 –  When
cash
is
needed
investors
may
spend
dividends,
 but
refuse
to
sell
a
small
por2on
of
stock
to
raise
the
 money.


 –  This
may
lead
to
a
preference
for
stocks
that
pay
 larger
dividends,
even
though
tax
liability
may
be
 greater.
 –  Ex:
house
money
effect,
budget
for
food
vs.
 entertaining,

 Behavioral
Bias
 •  Regret
Avoidance
 – 
Regret
from
losses
is
greater
than
joy
from
gains.

 – 
Regret
is
reduced
with
‘shared
pain.’
 • 
In
order
to
induce
investors
to
buy
out
of
favor
stocks,
 stocks
with
poor
recent
performance
for
example
(value
 stocks),
these
stocks
have
to
pay
a
higher
expected
 return.
 Behavioralism
&
Prospect
Theory
 Standard
u2lity
(sa2sfac2on)
theory
versus
prospect
theory
 •  Standard
u2lity
theory
of
investments:
 –  Investors
desire
more
wealth
and
less
risk
 –  Wealth
provides
diminishing
marginal
u2lity,
thus
a
gain
of
 $1,000
provides
less
u2lity
than
the
u2lity
loss
from
losing
 $1,000.


 •  This
gives
rise
to
risk
aversion.
 •  Prospect
theory:

 
An
alterna2ve
behavioral
theory
sugges2ng
that
investor
 u2lity
depends
on
the
change
in
wealth
from
the
start
of
the
 investment
rather
than
on
the
star2ng
level
of
wealth.
 Prospect
Theory
Illustrated
 Panel
A:
A
conven2onal
 u2lity
func2on
is
defined
 in
terms
of
wealth
and
is
 concave,
resul2ng
in
risk
 aversion.

 Panel
B:
Under
loss
 aversion,
the
u2lity
 func2on
is
defined
in
 terms
of
changes
from
 current
wealth.
It
is
 convex
to
the
le`
of
the
 origin,
giving
rise
to
risk‐ seeking
behavior
in
 terms
of
losses.
 Why
not
arbitrage
mispriced
stocks?
 •  If
some
investors
are
legng
behavioral
biases
 affect
prices,
why
don’t
other
beOer
trained
 investors
engage
in
profitable
arbitrage?
 •  Part
of
reason
for
growth
in
hedge
funds.
 Limits
to
arbitrage
 •  Fundamental
Risk
 –  Changes
in
fundamentals
can
wipe
out
any
arbitrage
 profits,
making
the
strategy
risky.
 –  Ex:
LTCM
 –  “Markets
can
remain
irra2onal
longer
than
you
can
remain
 solvent”
by
Keynes.
 –  Short
sale
constraints
make
it
difficult
to
arbitrage
 overpriced
securi2es.



 –  How
do
you
know
when
a
security
is
truly
mispriced?

Your
 model
may
be
giving
you
wrong
signals.



 •  Short
sale
constraints
 •  Model
risk
 Figure
9.2
Pricing
of
Royal
Dutch
Rela2ve
 to
Shell
(Devia2on
from
Parity)
 60:40
split
of
profits
from
merger
between
RD
and
Shell
 Stock
price
ra2o
RD/Shell
should
=
60/40
=
1.5
 If
RD/Shell
>
1.5
then
short
RD
and
buy
Shell
 If
you
had
done
this
in
1993,
you
would
have
LOST
money
un2l
1999.
 Evalua0ng
the
Behavioral
Cri0que
 •  It
provides
stories
that
fit
individual
situa2ons
 but
there
is
no
unified
theory
put
forth
and
 some
behaviors
contradict
others.
 •  Much
of
the
empirical
support

for
the
 behavioral
finance
comes
from
one
specific
 2me
period
in
the
late
1990s.

 •  The
behavioral
literature
is
s2ll
in
its
infancy.
It
 does
not
provide
guidance
as
to
how
to
 exploit
any
decision
irra2onality.

 9.2


Technical
Analysis
and

 Behavioral
Finance
 Technical
Trading
Rules
 –  All
technical
analysis
(TA)
assumes
that
there
are
recurring
 and
predictable
paOerns
in
stock
prices
which
can
be
 exploited
to
earn
abnormal
returns.
 –  Technical
analysts
believe:
 •  Disposition effect: –  investors exhibit loss aversion so that they are reluctant to sell on bad news. –  Market
prices
conform
to
new
data
only
slowly,
 giving
rise
to
price
trends
 •  Prices
are
affected
by
predictable
behavioral
or
 psychological
factors
 Basic
Types
of
Technical
Analysis:
Dow
Theory
 Dow
Theory
posits

3
forces
simultaneously
affec2ng
stock
prices
 
a)
Primary
trend:
long‐term
movement
of
prices
(several
months
to
 several
years.)
 
b)
Secondary
or
intermediate
trend:
short‐term
devia2ons
of
prices
 from
the
underlying
trend
line.

 
c)
Ter2ary
or
minor
trends:
daily
fluctua2ons.
 Dow
Theory:
Dow
Jones
Industrial
 Average
in
1988
 Basic
Types
of
Technical
Analysis:

 Point
&
Figure
Charts
 Point
and
figure
charts
with
price
 increments
of
$2.
 Point
and
figure
charts
with
price
 increments
of
$3.
 Point
&
Figure
Charts

 for
Atlan2c
Richfield
 Basic
Types
of
Technical
Analysis:
 Moving
Average
 Moving
average
 Note:
1)
Moving
average
is
a
smooth
curve.
2)
Week
16
is
a
bearish
point
according
to
 










moving
average
rule.
 Share
Price
and
50‐day
moving
 average
for
Intel
in
2008
 Point
A:
bullish
signal
–
signify
a
shi`
from
a
falling
trend
(with
prices
below
the
moving

 














average)
to
a
rising
trend
(with
prices
above
moving
average).
 Point
B:
bearish
signal
–
sell
signal
according
to
moving
average
rule.
 Basic
Types
of
Technical
Analysis:
 Rela0ve
Strength
 •  A
simple
rela2ve
strength
ra2o
could
be
 constructed
as
Pi/index.



 •  Increases
in
the
rela2ve
strength
ra2o
indicate
 the
stock
is
outperforming
the
index
and
could
 indicate
a
buy
or
bullish
signal.
 Basic
Types
of
Technical
Analysis:
Breadth
 5.  Breadth
 –  Breadth
is
the
extent
to
which
movements
in
a
broad
 index
are
reflected
widely
in
movements
of
individual
 stocks
 –  Measured
as
the
difference
between
the
number
of
 advancing
and
declining
stocks
 –  Also
used
in
industry
indexes
 Cumula2ve
Breadth
 • 

Cumula2ve
breadth
is
found
by
adding
the
current
 day’s
net
advances
or
declines
to
the
previous
day’s
total.


 • 

The
purpose
is
to
gauge
the
trend.
 TA
Sen2ment
Indicators:
Short
interest
 •  Short
interest:

 •  Total
number
of
shares
of
stock
currently
sold
 short
 •  High
short
interest
may
indicate
that
a
stock’s
 price
is
expected
to
fall.
 TA
Sen2ment
Indicators
 •  Trin
Sta2s2c:
In
TA,
ra2os
above
1.0
are
 considered
bearish
because
the
falling
stocks
 would
have
higher
average
volume
than
the
 advancing
stocks
–
considered
as
selling
 pressure.

 TA
Sen2ment
Indicators
 •  Confidence
index
 –  Ra2o
of
the
average
yield
on
10
top‐rated
 corporate
bonds
divided
by
the
average
yield
on
 10
intermediate‐grade
corporate
bonds
 •  Put/call
ra2o
 –  Call
op2ons
give
investors
the
right
to
buy
at
a
 fixed
exercise
price
and
a
put
is
the
right
to
sell
at
 a
fixed
exercise
price
 –  Change
in
ra2o
can
be
given
a
bullish
or
bearish
 interpreta2on
 A
Warning
About
Iden2fying
Trends
 •  Difficulty
in
iden2fying
common
price
paOerns
 One
of
these
paOerns
is
real
and
one
of
these
is
 computer
simulated
with
random
price
changes.

Can
 you
tell
which
is
which?
 Point?

Possibility
of
data
mining
 Figure
9.11
Actual
and
Simulated
Changes
 in
Weekly
Stock
Prices
for
52
Weeks
 ...
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