Chapter 09 - Behavioral Finance and Technical Analysis
CHAPTER 09
BEHAVIORAL FINANCE AND TECHNICAL ANALYSIS
1. Not
ethefol
lowingmatc
hes:
a.In
v e
stor
sa r
esl
owtoupda
tet
hei
rbe
li
ef
swhe
ngi
venne
wev
ide
nce–
Conserv
a t
is
m bi
as
b
.Inve
s t
orsa
rerel
uct
antt
obe
arl
oss
esduet
othe
irunc
onv
ent
iona
lde
cis
ions
–Regre
tavoi
dance
c
.I n
vest
orsexhi
bitl
essr
is
ktol
era
nceint
heirr
eti
reme
nta
ccount
sve
rsust
hei
r
ot
herst
ocka c
count
s–Me nt
alac
count
ing
d.I
nve
stor
sar
ere
luc
tantt
ose
lls
toc
kswi
th“
pape
r”l
oss
es–Di
spos
it
ion
e
ffe ct
e
.Inve
storsdi
sr
egar
dsamples
izewhe
nfor
min
gvi
ewsa
boutt
hef
utur
efr
om
t
hepast–Repre
sent
at
ionbi
as
2. Repr
esent
at
ive
nes
sbi
as.Th
esa
mpl
esi
zei
snotc
ons
ide
redwhe
nma
kingf
utur
e
de
c i
si
ons.
3. Fundame nt
a lr
iskme ansthatevenifas e
curi
tyismis
pr i
ced,i
tsti
llcanberiskyto
at
tempttoe xploitt
hemi spr
icingbecausethecorr
ect
iontopricecoul
dha ppen
aft
erthetrader
’si n
vesti
nghori z
on.Thislimit
stheac
tionsofarbit
rag
e ur
swh otake
posi
tionsinmi spri
cedsecurit
ies.Thus,t
hebiasmaype r
sists
incenoonet ak
e s
advantag
eofi t
.
4. Thepremiseofbe havi
oralfina nc eistha
tconventi
onalfina nc i
altheor
yignore
s
howrealpeoplema kedecis
ionsa ndthatpeoplemakeadi ffe r
e nce.Behavi
oral
fina ncemayc it
ee xample
sofma r
ketineffic ie
ncies,butthe ygivenoinsi
ghtint
o
howtoexploitsuchphenome non.Thes tre
ngthofthei
ra r
gume ntr eli
esupon
obse
rvedma r
ke ti
ne ffic ienci
e sandunexplai
nedma rke tbe
ha vior
.Thereareman y
anomali
es,yetma nycanbere ver
see ngine
eredorexpla
ine d.Also,whi l
e
anomali
esexist
,theyrare
lyme e
tthet es
tofstat
is
tic
alsi
gni fic anc e.
5. Anunfortunat
econseque nceofbehaviora
lfina nce(BF)isate
ndencyf
ori
nvest
ors
toass
umemor ethanactuall
yisclai
me db yt
hefie l
d.WhileBFishi
ghl
ycri
tic
alof
EMHa ndclai
mst ooffe ral
ternat
ivetheor
ies
,itdoesnotpr
oposet
obeapredi
ctor
offut
ureretur
ns.Inve
storsshouldbewa ryofpeoplepurpor
ti
ngtoofferexc
e s
s
ret
urnsunderthefaça
deofBH. Suchc lai
msa r
elikel
ytobefal
se.
6. a
.Los
sav
ers
ion.
Co
pyr
igh
t©2
017Mc
Gra
w-Hi
llEdu
cat
ion
.Allri
ghtsr
ese
rved
.Nor e
pro
ducti
onordi
st
ri
but
ionwi
tho
utt
hepr
ior
wri
tt
encons
entofMcGraw-Hi
llEduc
ati
on.
, Chapter 09 - Behavioral Finance and Technical Analysis
7. a
.Fe
arofr
egr
et.
8. a
.Se
ll
ingl
ose
rsq
uic
kly
.
9. Stat
ementb,
tha
tapri
ceha
smo
veda
bov
eit
s52we
ekmo
vin
gav
era
ge,i
s
consi
der
edabull
is
hsi
gn.
10.Aft
ert
hefact
,youc
ana l
waysfindpatt
ernsa
ndt r
adi
ngrulest
hatwoul
dhave
ge
nera
tedenor
mousprofits.
Thisi
scal
leddatamini
ng.Forte
chni
cala
nal
yst
s,t
his
i
saprobl
ems i
ncet
heyrare
lyca
nberepr
oducedtopre
dic
tfutur
eprofits.
11.Grinblat
tandHan(2005)showthatt
hedis
posi
ti
one ffe c
tcanle
adtomome ntum
i
ns tockpri
cesev
eniffundamenta
lval
uesfol
lowarandom wal
k.Thi
smome ntum
maynotl eadt
oabnormalprofitsbutmaycausecapit
alt
oflo wtoin
vest
me ntstha
t
appeartobenefitfrom t
hemome nt
um,incontr
asttowherei
twoul
dothe
rwi se
flo w.
12.Ar bit
ragea ssume sthea bi
li
tytoi nit
iatetradesbasedona r
bitr
ageinf orma t
ion.A
sev e
relimi tofthet heoryisthatsimi l
ara s
setsshouldbepr icedsimi lar
ly(lawof
onepr ice).Ane xampleofal imitinwhi chsuchat radeisnotpos s
ibl eisthec as
e
ofRo yalDut chPe t
roleuma ndShe ll.Thisisac aseof“ Sia
me setwi n”c ompa ni
es,
whe rethev al
uewa snotproportionaltothepr ofitdi s
tri
buti
on.At t
empt stoprofit
from thei ncor r
ectprici
ngwoul dr esultinsubstanti
alloss.Ane quityc arveoutis
anothere xa mple.Inthis,t
hes aleofapor t
ionoft hec ompanydoe snotne cessa
ril
y
ge ner
atethee xactpercent
ageonewo uldexpectba s
edont hepe rcentaget aken
from theor iginalcompa ny.Thel astexampl einvolv
edc losedendf undst hat
typicall
ys ellforlessthanNAV.
13.Somepe opl emays ayitisconsis
tentwithboth.Thisisconsi
ste
ntwi t
he ffic i
ent
ma r
ket
ss i
ncethepr i
cedoesa pproachintr
insi
cv al
ue.Behavior
a lwoulds a
yi tis
consi
st
e ntsincethepr i
cesl
o wlya ppr
oachesintr
insi
cv al
ueafte
rt hema rkethasa
tr
ackrec ordofti
mes howin
gnoot hershocksareimmi ne
nt.Lackinginforma t
ion
aboutanothe rs
hoc k,EMHs aysthepriceshoul
dg odirect
lyt
ot henewv al
ue.
Behavi
or alfina ncesayst
ha tma ytaketi
me .
Volume Declining/Number Declin es Equity in Account
14.Tr
in= =
Value Advancing/Number Advanc es Value of Stock
Equity in Account 1,332,273,,534
=0.
9180
Value of Stock 1,515,532,,602
Thi
str
inr
ati
o,whi
chi
sbe
low1.
0,wo
uldbet
ake
nasabul
li
shs
igna
l.
15.Br
eadt
h:
Co
pyr
igh
t©2
017Mc
Gra
w-Hi
llEdu
cat
ion
.Allri
ghtsr
ese
rved
.Nor e
pro
ducti
onordi
st
ri
but
ionwi
tho
utt
hepr
ior
wri
tt
encons
entofMcGraw-Hi
llEduc
ati
on.
CHAPTER 09
BEHAVIORAL FINANCE AND TECHNICAL ANALYSIS
1. Not
ethefol
lowingmatc
hes:
a.In
v e
stor
sa r
esl
owtoupda
tet
hei
rbe
li
ef
swhe
ngi
venne
wev
ide
nce–
Conserv
a t
is
m bi
as
b
.Inve
s t
orsa
rerel
uct
antt
obe
arl
oss
esduet
othe
irunc
onv
ent
iona
lde
cis
ions
–Regre
tavoi
dance
c
.I n
vest
orsexhi
bitl
essr
is
ktol
era
nceint
heirr
eti
reme
nta
ccount
sve
rsust
hei
r
ot
herst
ocka c
count
s–Me nt
alac
count
ing
d.I
nve
stor
sar
ere
luc
tantt
ose
lls
toc
kswi
th“
pape
r”l
oss
es–Di
spos
it
ion
e
ffe ct
e
.Inve
storsdi
sr
egar
dsamples
izewhe
nfor
min
gvi
ewsa
boutt
hef
utur
efr
om
t
hepast–Repre
sent
at
ionbi
as
2. Repr
esent
at
ive
nes
sbi
as.Th
esa
mpl
esi
zei
snotc
ons
ide
redwhe
nma
kingf
utur
e
de
c i
si
ons.
3. Fundame nt
a lr
iskme ansthatevenifas e
curi
tyismis
pr i
ced,i
tsti
llcanberiskyto
at
tempttoe xploitt
hemi spr
icingbecausethecorr
ect
iontopricecoul
dha ppen
aft
erthetrader
’si n
vesti
nghori z
on.Thislimit
stheac
tionsofarbit
rag
e ur
swh otake
posi
tionsinmi spri
cedsecurit
ies.Thus,t
hebiasmaype r
sists
incenoonet ak
e s
advantag
eofi t
.
4. Thepremiseofbe havi
oralfina nc eistha
tconventi
onalfina nc i
altheor
yignore
s
howrealpeoplema kedecis
ionsa ndthatpeoplemakeadi ffe r
e nce.Behavi
oral
fina ncemayc it
ee xample
sofma r
ketineffic ie
ncies,butthe ygivenoinsi
ghtint
o
howtoexploitsuchphenome non.Thes tre
ngthofthei
ra r
gume ntr eli
esupon
obse
rvedma r
ke ti
ne ffic ienci
e sandunexplai
nedma rke tbe
ha vior
.Thereareman y
anomali
es,yetma nycanbere ver
see ngine
eredorexpla
ine d.Also,whi l
e
anomali
esexist
,theyrare
lyme e
tthet es
tofstat
is
tic
alsi
gni fic anc e.
5. Anunfortunat
econseque nceofbehaviora
lfina nce(BF)isate
ndencyf
ori
nvest
ors
toass
umemor ethanactuall
yisclai
me db yt
hefie l
d.WhileBFishi
ghl
ycri
tic
alof
EMHa ndclai
mst ooffe ral
ternat
ivetheor
ies
,itdoesnotpr
oposet
obeapredi
ctor
offut
ureretur
ns.Inve
storsshouldbewa ryofpeoplepurpor
ti
ngtoofferexc
e s
s
ret
urnsunderthefaça
deofBH. Suchc lai
msa r
elikel
ytobefal
se.
6. a
.Los
sav
ers
ion.
Co
pyr
igh
t©2
017Mc
Gra
w-Hi
llEdu
cat
ion
.Allri
ghtsr
ese
rved
.Nor e
pro
ducti
onordi
st
ri
but
ionwi
tho
utt
hepr
ior
wri
tt
encons
entofMcGraw-Hi
llEduc
ati
on.
, Chapter 09 - Behavioral Finance and Technical Analysis
7. a
.Fe
arofr
egr
et.
8. a
.Se
ll
ingl
ose
rsq
uic
kly
.
9. Stat
ementb,
tha
tapri
ceha
smo
veda
bov
eit
s52we
ekmo
vin
gav
era
ge,i
s
consi
der
edabull
is
hsi
gn.
10.Aft
ert
hefact
,youc
ana l
waysfindpatt
ernsa
ndt r
adi
ngrulest
hatwoul
dhave
ge
nera
tedenor
mousprofits.
Thisi
scal
leddatamini
ng.Forte
chni
cala
nal
yst
s,t
his
i
saprobl
ems i
ncet
heyrare
lyca
nberepr
oducedtopre
dic
tfutur
eprofits.
11.Grinblat
tandHan(2005)showthatt
hedis
posi
ti
one ffe c
tcanle
adtomome ntum
i
ns tockpri
cesev
eniffundamenta
lval
uesfol
lowarandom wal
k.Thi
smome ntum
maynotl eadt
oabnormalprofitsbutmaycausecapit
alt
oflo wtoin
vest
me ntstha
t
appeartobenefitfrom t
hemome nt
um,incontr
asttowherei
twoul
dothe
rwi se
flo w.
12.Ar bit
ragea ssume sthea bi
li
tytoi nit
iatetradesbasedona r
bitr
ageinf orma t
ion.A
sev e
relimi tofthet heoryisthatsimi l
ara s
setsshouldbepr icedsimi lar
ly(lawof
onepr ice).Ane xampleofal imitinwhi chsuchat radeisnotpos s
ibl eisthec as
e
ofRo yalDut chPe t
roleuma ndShe ll.Thisisac aseof“ Sia
me setwi n”c ompa ni
es,
whe rethev al
uewa snotproportionaltothepr ofitdi s
tri
buti
on.At t
empt stoprofit
from thei ncor r
ectprici
ngwoul dr esultinsubstanti
alloss.Ane quityc arveoutis
anothere xa mple.Inthis,t
hes aleofapor t
ionoft hec ompanydoe snotne cessa
ril
y
ge ner
atethee xactpercent
ageonewo uldexpectba s
edont hepe rcentaget aken
from theor iginalcompa ny.Thel astexampl einvolv
edc losedendf undst hat
typicall
ys ellforlessthanNAV.
13.Somepe opl emays ayitisconsis
tentwithboth.Thisisconsi
ste
ntwi t
he ffic i
ent
ma r
ket
ss i
ncethepr i
cedoesa pproachintr
insi
cv al
ue.Behavior
a lwoulds a
yi tis
consi
st
e ntsincethepr i
cesl
o wlya ppr
oachesintr
insi
cv al
ueafte
rt hema rkethasa
tr
ackrec ordofti
mes howin
gnoot hershocksareimmi ne
nt.Lackinginforma t
ion
aboutanothe rs
hoc k,EMHs aysthepriceshoul
dg odirect
lyt
ot henewv al
ue.
Behavi
or alfina ncesayst
ha tma ytaketi
me .
Volume Declining/Number Declin es Equity in Account
14.Tr
in= =
Value Advancing/Number Advanc es Value of Stock
Equity in Account 1,332,273,,534
=0.
9180
Value of Stock 1,515,532,,602
Thi
str
inr
ati
o,whi
chi
sbe
low1.
0,wo
uldbet
ake
nasabul
li
shs
igna
l.
15.Br
eadt
h:
Co
pyr
igh
t©2
017Mc
Gra
w-Hi
llEdu
cat
ion
.Allri
ghtsr
ese
rved
.Nor e
pro
ducti
onordi
st
ri
but
ionwi
tho
utt
hepr
ior
wri
tt
encons
entofMcGraw-Hi
llEduc
ati
on.
