Exploring the Nature of “Trader Intuition”

Exploring
the
Nature
of
“Trader
Intuition”
Antoine
J
Bruguier,
Steven
R
Quartz
and
Peter
Bossaerts∗ ABSTRACT
Experimental
evidence
has
consistently
confirmed
the
ability
of
uninformed
traders,
even
novices,
to
infer
information
from
the
trading
process.
We
hypothesized
that
ToM
was
involved
after
contrasting
brain
activation
in
subjects
watching
markets
with
and
without
insiders.
ToM
refers
to
the
innate
human
capacity
to
discern
malicious
or
benevolent
intent.
We
find
that
skill
in
predicting
price
changes
in
markets
with
insiders
correlates
with
scores
on
two
ToM
tests.
We
document
GARCH‐
like
persistence
in
transaction
price
changes
that
may
help
with
reading
markets
when
there
are
insiders.
California
Institute
of
Technology,
Pasadena,
CA
91125,
USA
(Bruguier;
Quartz;
Bossaerts)
and
Ecole
Polytechnique
Fédérale
Lausanne,
Switzerland
(Bossaerts).
This
work
was
supported
by
the
NSF
through
grant
SES‐0527491
to
Caltech
and
by
the
Swiss
Finance
Institute.
Comments
from
Ralph
Adolphs,
Colin
Camerer,
Fulvia
Castelli,
John
Dickhaut,
John
Ledyard,
Charles
Plott,
Tania
Singer,
the
Editor,
an
Associate
Editor
and
four
referees,
and
seminar
participants
at
BI
(Oslo),
EPFL,
HEC
(Paris),
Northwestern
University
(Kellogg),
the
National
University
of
Singapore,
Okinawa
Institute
of
Science
and
Technology,
Rice
University,
the
University
of
Zurich,
University
of
Louvain,
University
of
Texas‐Austin
and
Dallas,
the
2007
Caltech‐Tamagawa
Neuroscience
Symposium,
the
2007
Japan
Neuroscience
Society
meetings,
the
2008
meetings
of
the
Swiss
Finance
Institute,
the
2008
Western
Finance
Association
meetings
(especially
the
discussant,
Heather
Tookes),
and
the
2008
International
Meetings
of
the
Economic
Science
Association,
are
gratefully
acknowledged.
∗
1
This
paper
reports
results
from
experiments
meant
to
explore
how
uninformed
traders
manage
to
read
information
from
transaction
prices
and
order
flow
in
financial
markets
with
insiders.
Since
the
seminal
experiments
of
Charles
Plott
and
Shyam
Sunder
in
the
early
1980s
[Plott
and
Sunder
(1988)],
it
has
been
repeatedly
confirmed
(and
we
will
do
so
here
too)
that
uniformed
traders
are
quite
capable
of
quickly
inferring
the
signals
that
informed
traders
(insiders)
have
about
future
dividends,
despite
the
anonymity
of
the
trading
process,
despite
lack
of
structural
knowledge
of
the
situation,
and
despite
the
absence
of
long
histories
of
past
occurrences
of
the
same
situation
from
which
they
could
have
learned
the
statistical
regularities.
It
is
striking
that
so
little
is
understood
about
the
ability
of
the
uninformed
to
infer
the
signals
of
others.
This
ability
constitutes
the
basis
of
the
efficient
markets
hypothesis
EMH
[Fama
(1991)],
which
states
that
prices
fully
reflect
all
available
information.
Underlying
EMH
is
the
idea
that
the
uninformed
will
trade
on
the
signals
they
manage
to
infer,
and
that,
through
the
orders
of
the
uninformed,
these
signals
are
effectively
amplified
in
the
price
formation.
In
the
extreme,
prices
will
fully
reflect
all
available
information.
Without
a
better
understanding
of
how
the
uninformed
practically
manage
to
read
information
in
prices,
EMH
remains
a
hypothesis
without
a
well‐understood
foundation.
The
feedback
from
trading
based
on
inferred
information
to
price
formation
has
been
formalized
in
the
concept
of
the
rational
expectations
equilibrium
(REE)
[Green
(1973),
Radner
(1979)].
For
economists,
REE
forms
the
theoretical
2
justification
of
EMH.
But
again,
REE
takes
the
ability
of
the
uninformed
to
correctly
read
information
from
prices
as
given,
rather
than
explaining
it.
As
such,
like
EMH,
REE
lacks
a
well‐articulated
foundation.
The
goal
of
the
experiments
that
we
report
on
here
can
be
expressed
in
a
more
mundane
way,
as
an
attempt
to
better
define
what
is
meant
by
“trader
intuition,”
and
to
understand
why
some
traders
are
better
than
others.
Books
have
been
written
to
elucidate
trading
intuition
[Fenton‐O'Creevy,
Nicholson,
Soane
and
Willman
(2005)],
and
correlations
with
specific
biological
markers
have
been
discovered
[testosterone
level:
Coates
and
Herbert
(2008);
the
relative
size
of
index
and
ring
finger,
an
indication
of
a
particular
genetic
polymorphism:
Coates,
Gurnell
and
Rustichini
(2009)].
But
attempts
at
formalizing
the
phenomenon
have
so
far
failed.
In
contrast,
our
approach
was
methodic.
After
collecting
the
necessary
trading
data
from
purposely
controlled
experimental
markets
with
and
without
insiders,
we
ran
a
brain
imaging
experiment
to
explore
human
thinking
during
exposure
to
risk
from
insiders.
The
resulting
data
made
us
formulate
a
specific
hypothesis
about
what
may
be
at
work,
namely,
Theory
of
Mind
(to
be
defined
below).
Armed
with
this
hypothesis,
we
designed
a
behavioral
experiment
to
probe
whether
performance
in
predicting
prices
in
markets
with
insiders
was
correlated
with
Theory
of
Mind
skill
(and
uncorrelated
with
other
skill,
in
particular,
mathematical
and
logical
reasoning).
3
Here,
we
report
the
results
from
the
markets
experiments
that
generated
the
data
we
used
in
the
subsequent
analysis,
and
of
the
behavioral
experiments
that
confirmed
the
role
of
Theory
of
Mind
in
markets
with
insiders.
The
brain
imaging
experiment
and
its
results
are
discussed
in
the
Internet
Appendix.
While
they
constituted
an
important
step
towards
a
methodic
analysis
of
trading
intuition,
in
that,
without
it,
our
hypothesis
would
have
amounted
to
pure
speculation,1
the
technicalities
involved
distract
from
the
main
purpose
of
this
paper,
which
is
to
show
that
trading
intuition
and
Theory
of
Mind
skill
are
strongly
related.
With
hindsight,
it
makes
intuitive
sense
that
Theory
of
Mind
is
important
in
markets
with
insiders.
Let
us
elaborate.
Theory
of
Mind
(ToM
in
short)
is
the
ability
to
read
benevolence
or
malevolence
in
patterns
in
one’s
surroundings.
ToM
thus
is
the
capacity
to
read
intention
or
goal‐directness,
through,
among
others,
mere
observation
of
eye
expression
[Baron‐Cohen,
Jolliffe,
Mortimore
and
Robertson
(1997)],
movement
of
geometric
objects
[Heider
and
Simmel
(1944)],
the
moves
of
an
opponent
in
strategic
play
[McCabe,
Houser,
Ryan,
Smith
and
Trouard
(2001),
Gallagher,
Jack,
Roepstorff
and
Frith
(2002),
Hampton,
Bossaerts
and
O'Doherty
(2008)],
or
actions
that
embarrass
others
(“faux‐pas”)
[Stone,
Baron‐Cohen
and
Knight
(1998)].
See
Gallagher
and
Frith
(2003)
for
further
discussion.
What
distinguishes
markets
with
insiders
is
the
presence
of
a
winner’s
curse:
sales
are
often
successful
only
because
prices
happen
to
be
too
low
(relative
to
the
information
of
the
insiders),
while
purchases
may
occur
only
at
inflated
prices.
4
Either
way,
the
uninformed
trader
is
hurt.
While
the
winner’s
curse
is
usually
associated
with
strategic,
single‐sided
auctions,
it
also
applies
to
competitive,
double‐sided
markets,
and
indeed,
the
winners’
curse
is
not
only
implicit
in
the
theory
of
REE
but
also
very
much
of
concern
in
real‐world
stock
markets
[Biais,
Bossaerts
and
Spatt
(2009)].
From
the
point
of
view
of
the
uninformed
trader,
the
winner’s
curse
conjures
up
an
image
of
potential
malevolence
in
the
trading
process.
Detecting
this
potential
malevolence,
then,
becomes
a
Theory
of
Mind
task.
Whence
the
link.
Humans
are
actually
uniquely
endowed
with
the
capacity
to
recognize
malevolence
as
well
as
benevolence
in
their
environment.
Theory
of
Mind
is
human
(or
shared
only
with
higher
nonhuman
primates);
it
engages
brain
structures
that
have
undergone
recent
evolutionary
expansion
and
reorganization,
such
as
the
paracingulate
cortex,
the
most
frontal
and
medial
part
of
the
cortex.
In
our
brain
imaging
experiment,
distinct
activation
in
this
and
other
ToM
related
regions
helped
us
narrow
down
hypotheses
about
trading
intuition
to
ToM.
The
fact
that
markets
with
insiders
may
be
exploiting
a
skill
that
(most)
humans
are
very
good
at
should
provide
a
biological
foundation
to
the
plausibility
of
EMH.
It
could
also
explain
why
experiments
on
information
aggregation
in
financial
markets
ever
since
Plott
and
Sunder
(1988)
have
been
relatively
successful.
The
success
of
information
aggregation
experiments
is
in
sharp
contrast
with,
e.g.,
simple
experiments
on
multi‐period
asset
pricing,
such
as
the
infamous
bubble
experiments
[first
studied
in
Smith,
Suchanek
and
Arlington
(1988)].
In
theory,
5
these
two
types
of
experiments
should
give
rise
to
the
same
type
of
equilibrium
–
the
rational
expectations
equilibrium
REE
–
yet
experimentally
equilibrium
emerges
robustly
only
in
the
context
of
information
aggregation.
ToM
is
about
pattern
recognition,
something
that
recently
has
been
confirmed
formally,
but
so
far
only
for
play
in
strategic
games
[Hampton,
Bossaerts
and
O'Doherty
(2008)].
That
is,
humans
detect
malevolence
or
benevolence
by
online
tracking
of
changes
in
their
environment
(rather
than,
say,
logical
deduction
about
the
situation
at
hand).
For
markets,
however,
it
is
not
even
known
whether
there
are
any
patterns
at
all
in
the
order
and
trade
flow
that
would
allow
one
to
merely
identify
the
presence
of
insiders
(let
alone
their
intentions).
Proof
that
such
patterns
exist
is
an
important
foundation
for
the
proposition
that
ToM
may
underlie
trading
intuition.
Therefore,
we
were
curious
whether
we
ourselves
could
find
features
that
distinguished
insider
trading
from
normal
trading
in
our
own
markets
experiment.
We
discovered
that
GARCH‐like
features
[Engle
(1982)]
emerged
when
there
are
insiders.
Specifically,
autocorrelation
coefficients
of
absolute
transaction
price
changes
in
calendar
time
were
significantly
more
sizeable
in
the
presence
of
insiders.
The
analysis
in
this
paper
is
limited
to
thinking
about
and
prediction
in
markets
when
there
are
insiders.
These
constitute
only
two
of
the
three
steps
towards
successful
investment.
We
leave
for
future
research
the
third
step,
namely,
conversion
of
analysis
and
prediction
into
successful
positions,
i.e.,
the
trading
itself.
Indeed,
superior
forecasting
performance
does
not
necessarily
translate
into
6
superior
investments.
Still,
if
the
latter
is
lacking,
the
trading
can
be
delegated
to
others
who
are
better
at
it.
The
remainder
of
this
paper
is
organized
as
follows.
Section
I
describes
the
markets
and
behavioral
experiments,
while
briefly
discussing
the
imaging
results
that
identified
ToM
as
a
viable
hypothesis.
Section
II
presents
the
results,
first
of
the
markets
experiment,
followed
by
those
of
the
behavioral
experiment.
In
Section
III,
we
turn
back
to
the
markets
experiment
and
attempt
to
identify
whether
there
are
patterns
in
the
trade
flow
that
would
allow
one
to
recognize
that
there
are
insiders,
and
hence,
on
which
ToM
thinking
could
build.
Section
IV
finishes
with
concluding
remarks.
I.
Description
Of
The
Experiments
Here,
we
provide
descriptions
of
the
experiments.
We
first
ran
a
markets
experiment,
for
the
purpose
of
generating
order
and
trade
flow
in
a
controlled
setting.
Next,
we
ran
a
brain
imaging
experiment,
to
discern
how
subjects
judge
the
data,
by
localizing
areas
of
the
brain
that
were
active
during
re‐play
of
the
markets.
This
led
us
to
identify
ToM
as
a
viable
theory
about
the
nature
of
trading
intuition.
With
the
ToM
hypothesis
in
hand,
we
subsequently
organized
a
behavioral
experiment,
where
we
tested
for
correlation
between
subjects’
ability
to
predict
transaction
prices
and
their
generic
ToM
skills
(we
also
tested
for
correlation
with
mathematics
and
logic
skills,
for
comparison).
7
We
will
not
discuss
the
imaging
experiment
in
much
detail,
as
it
originally
provided
the
foundation
for
our
hypothesis
about
ToM.
Still,
the
imaging
data
do
also
constitute
confirmatory
neurobiological
evidence
of
the
behavioral
findings,
and
without
them,
the
ToM
hypothesis
would
have
been
mere
speculation.
But
the
technicalities
involved
in
proper
description
of
the
imaging
results
would
distract
from
the
main
point
of
the
paper.
The
interested
reader
will
find
a
full
discussion
of
the
imaging
experiment
in
the
Internet
Appendix.
A.
The
Markets
Experiment
Twenty
(20)
subjects
(undergraduate
and
graduate
students
at
Caltech)
participated
in
the
markets
experiment.
The
following
situation
was
replicated
several
times,
each
replication
being
referred
to
as
a
session.
In
each
session,
the
subjects
were
initially
endowed
with
notes
(a
risk
free
asset),
cash,
and
two
risky
securities,
all
of
which
expired
at
the
end
of
the
session.
The
two
risky
securities
(“stocks”)
paid
complementary
dividends
between
0
and
50¢:
if
the
first
security,
called
stock
X,
paid
x
cents,
then
the
second
security,
called
stock
Z,
would
pay
50‐x
cents.
The
notes
always
paid
50¢.
Allocation
of
the
securities
and
cash
varied
across
subjects,
but
the
total
supplies
of
the
risky
securities
were
equal;
hence,
there
was
no
aggregate
risk,
and,
theoretically
as
well
as
based
on
observations
in
prior
experiments
with
a
similar
number
of
subjects
[Bossaerts,
Plott
and
Zame
(2007)],
prices
should
converge
to
levels
that
equal
expected
payoffs;
that
is,
risk‐neutral
pricing
should
arise.
8
Subjects
could
trade
their
holdings
for
cash
in
an
anonymous,
electronic
continuous
open‐book
exchange
system
called
jMarkets
(see
http://jmarkets.ssel.caltech.edu/).
Subjects
were
not
allowed
to
trade
security
Z,
however.
Consequently,
risk
or
ambiguity
averse
subjects
who
held
more
of
X
than
of
Z
would
want
to
sell
X
to
obtain
a
diversified
(or
perhaps
a
completely
balanced)
position;
those
who
held
more
of
Z
than
of
X
would
need
to
buy
X.
Because
there
was
an
equal
number
of
shares
of
Z
and
of
X,
price
pressures
from
trading
X
because
of
risk
or
ambiguity
aversion
can
be
expected
to
cancel
out.
After
markets
closed,
liquidating
dividends
were
paid,
which,
together
with
the
remaining
cash,
were
credited
to
an
account.
This
account
cumulated
the
earnings
from
each
session,
and
at
the
end
of
the
experiments,
subjects
took
home
the
balance
on
their
accounts,
in
addition
to
a
show‐up
reward
of
$5.
All
accounting
was
done
in
U.S.
dollars
and
subjects
made
$55
on
average,
with
a
range
stretching
from
$5
(minimum)
to
over
$100
(maximum).
Our
trading
system,
jMarkets,
ensured
that
subjects
could
only
submit
orders
such
that,
if
executed,
they
would
not
default
on
their
obligations
at
the
end
of
a
session.
In
calculating
whether
a
proposed
order
would
violate
this
bankruptcy
constraint,
the
system
took
into
account
the
information
a
subject
possessed.
If,
for
instance,
a
subject
knew
for
sure
that
the
dividend
on
X
was
not
going
to
be
above
15
cents,
then
we
allowed
the
subject
to
take
an
unlimited
shortsale
position
as
long
as
the
price
was
above
15.
9
In
total,
13
sessions
were
run.
Each
new
session
started
with
a
fresh
allocation
of
securities
and
cash.
As
such,
sessions
were
independent
replications
of
the
same
situation,
with
one
exception,
namely,
the
information
provided
to
subjects
about
the
final
dividend.
Indeed,
in
sessions
to
be
referred
to
as
test
sessions,
a
number
of
subjects
(the
“insiders”)
were
given
an
estimate
of
the
dividend
in
the
form
of
a
common
signal
within
10¢
of
the
actual
dividend.
All
subjects
were
always
informed
whether
there
were
insiders;
in
some
sessions,
only
the
insiders
knew
how
many
insiders
there
were.
Notice
that
all
insiders
were
given
the
same
signal.
Since
there
were
at
least
two
insiders,
insiders
at
all
times
knew
they
were
competing.
Sessions
without
inside
information
will
be
referred
to
as
control
sessions.
Full
parametrization
of
the
markets
experiment
(initial
endowments,
signals
and
outcomes,
etc.)
can
be
found
in
the
Internet
Appendix
(Part
2).
The
reader
can
also
consult
the
web
pages
through
which
the
experiment
was
run:
http://clef.caltech.edu/exp/info/.
These
pages
include
instructions
and
a
chronology
of
the
public
messages
sent
to
the
subjects.
A
copy
of
the
instruction
pages
is
included
in
the
Internet
Appendix
(Part
3).
The
results
of
the
experiment
are
typical;
the
same
setup
has
since
been
replicated
more
than
twenty
times,
with
little
qualitative
change
in
the
order
flow
and
price
evolution.
The
reader
interested
in
these
replications
is
referred
to
Bossaerts,
Frydman
and
Ledyard
(2009).
B.
The
Brain
Imaging
Experiment
10
Eighteen
(18)
new
subjects
(undergraduate
and
graduate
students
at
Caltech)
were
shown
a
replay
of
the
13
sessions
from
the
markets
experiment
(Section
I.A),
in
random
order,
and
while
their
brains
were
being
scanned
with
functional
magnetic
resonance
imaging
(fMRI).
It
is
important
to
realize
that
these
subjects
had
not
been
in
the
markets
experiment.
Since
the
fMRI
experiment
was
held
about
one
year
after
the
markets
experiment,
we
expected
little
contamination
(in
a
small
community
like
Caltech,
potential
subjects
may
talk
to
each
other
about
experiments
that
they
participated
in).
The
new
subjects
played
the
role
of
uninformed:
while
they
were
given
the
instructions
of
the
markets
experiment,
they
were
not
given
any
signals.
At
the
beginning
of
a
session,
subjects
were
first
told
whether
there
were
insiders
(but
never
how
many).
Subjects
then
had
to
chose
whether
they
would
take
a
position
in
10
units
of
stock
X
or
of
stock
Z.
This
feature
was
designed
to
add
an
element
of
“double
blind”
control:
the
experimenter
may
have
known
that
stock
X
would
not
do
well
in
the
upcoming
session,
but
the
subject
could
choose
stock
Z
instead;
likewise,
the
subject
is
in
control
of
her
choice,
but
does
not
know
the
outcome.
Subsequently,
the
order
flow
and
transaction
history
of
stock
X
was
replayed
in
a
visually
intuitive
way.
During
replay,
subjects
were
only
asked
to
push
a
button
each
time
they
saw
a
trade.
As
such,
they
could
not
change
their
position
(say,
from
a
position
in
X
to
a
position
in
the
complementary
security,
Z).
At
the
end
of
the
session,
the
liquidating
dividend
of
the
stock
they
had
chosen
was
shown,
and
the
subject
was
paid
accordingly.
11
The
fMRI
task
was
deliberately
kept
simple.
In
particular,
we
refrained
from
allowing
the
subject
to
change
trading
position
during
replay
of
the
order
and
trade
flow.
We
were
interested
in
detecting,
through
specific
patterns
in
brain
activity,
what
subjects
were
thinking
as
the
price
of
stock
X,
and
hence,
the
value
of
their
position,
went
up
or
down.
In
sessions
without
insiders,
these
price
movements
should
have
been
considered
without
consequence,
as
the
subject
was
only
compensated
based
on
the
final
dividend
on
the
initially
chosen
position.
When
insiders
were
present,
however,
price
changes
of
stock
X
would
give
the
subject
estimates
of
changes
in
the
expected
final
value
of
the
chosen
position.
We
wanted
subjects
to
think
only
about
the
implications
of
order
flow
and
market
prices
for
the
final
value
of
their
position.
If
we
had
allowed
our
subjects
to
change
positions
during
replay,
they
would
also
naturally
have
thought
about
the
effect
of
their
transactions
on
their
cash
position,
and
how
changes
in
the
latter
would
impact
the
value
of
their
final
position.
Since
we
wanted
to
focus
our
experiment
on
inferring
value
estimates
from
order
flow
and
market
prices,
we
disallowed
position
changes
during
the
session.
Subjects
paid
a
small
penalty
every
time
they
missed
a
trade
during
replay
of
a
session.
This
way,
we
made
sure
that
our
subjects
paid
attention.
Still,
the
bulk
of
the
total
earnings
came
from
the
dividends
on
the
positions
subjects
chose
before
a
session
started.
In
total,
the
typical
fMRI
experiment
lasted
about
1
1/2
hours.
The
fMRI
experiment
revealed
increased
activation
in
specific
brain
regions
when
insiders
were
present
relative
to
when
insiders
where
absent,
and
this
12
activation
differential
increased
as
the
price
moved
away
from
the
unconditional
expected
payoff
(25
cents).
Such
price
movements
should
have
indicated
that
insiders
had
received
a
signal
that
the
value
of
Stock
X
was
substantially
different
from
25
cents.
Brain
regions
that
activated
could
immediately
be
recognized
as
those
that
are
involved
in
ToM
thinking
[Gallagher
and
Frith
(2003)
list
the
regions].
The
most
important
of
these
is
the
medial
paracingulate
cortex,
a
region
in
the
middle
of
the
forebrain,
high
above
the
eyes.
[Figure
1
about
here]
Figure
1
displays
a
medial
cross‐section
of
a
typical
human
brain,
from
front
(left
hand
side)
to
back,
onto
which
the
significant
differential
activation
in
paracingulate
cortex
is
mapped
(small
squares;
color
changes
from
red
to
yellow
correlate
with
increases
in
significance
level).
The
localization
is
based
on
a
random‐effects
analysis
of
the
fMRI
signals
of
the
18
subjects.
Details
can
be
found
in
the
Internet
Appendix.
Surprisingly
(although
this
finding
will
also
be
corroborated
in
the
behavioral
experiment),
brain
regions
known
to
be
involved
in
formal
mathematical
and
logical
thinking
were
no
more
activated
when
insiders
were
present
than
when
they
were
not.
Thinking
about
markets
when
there
are
insiders
appears
to
be
an
unequivocal
ToM
occupation.
These
brain
activation
patterns
prompted
us
to
formulate
the
hypothesis
that
ToM
is
engaged
when
insiders
are
present.
However,
brain
areas
are
generally
engaged
in
multiple
activities.
As
such,
activation
does
not
necessarily
imply
use
of
13
one
specific
ability.
We
thus
turned
to
a
behavioral
experiment
that
we
predicted
would
show
the
existence
of
significant
correlation
between,
on
the
one
hand,
performance
in
predicting
price
changes
when
insiders
are
present,
and,
on
the
other
hand,
ToM
skills
as
traditionally
measured.
We
also
wanted
to
verify
the
absence
of
significant
correlation
with
other
skills
(specifically,
mathematics
and
logical
thinking),
since
the
imaging
experiment
only
revealed
engagement
of
ToM
regions.
We
now
describe
this
experiment.
C.
The
Behavioral
Experiment
Forty‐three
(43)
new
subjects
(undergraduate
and
graduate
students
at
Caltech,
Pasadena
City
College,
and
UCLA)
were
given
a
series
of
four
tasks
that
were
administered
in
random
order.
These
subjects
had
participated
neither
in
the
markets
experiment
nor
in
the
fMRI
experiment.
The
four
tasks
were
as
follows.
The
first
was
a
Financial
Market
Prediction
task
(“FMP
Test”),
in
which
the
order
and
trade
flow
from
sessions
with
insiders
in
our
markets
experiment
was
replayed
at
original
speed
and
paused
every
5
seconds.
During
half
of
the
pauses,
we
asked
subjects
to
predict
whether
the
last
trade
in
the
next
5
seconds
was
going
to
occur
at
a
higher,
lower,
or
identical
price
as
the
last
trade
before
the
pause.
(When
no
trade
occurred
in
the
5
second
interval,
the
price
was
considered
to
have
remained
the
same.)
For
the
other
half
of
the
pauses,
we
reminded
subjects
of
their
predictions
and
informed
them
of
their
success
(whether
their
bet
had
been
right
or
not).
A
penalty
was
imposed
for
absence
of
response
within
a
short
time
interval.
14
Order
and
trade
flow
was
replayed
using
an
intuitive
graphical
display,
discussed
in
more
detail
below.
The
second
task
was
a
ToM
task
based
on
the
Heider
movie
(“Heider
Test”),
a
display
of
geometric
shapes
whose
movements
imitated
social
interaction
[Heider
and
Simmel
(1944)].
As
with
the
FMP
task,
we
paused
the
movie
every
five
seconds
and
asked
subjects
to
predict
whether
two
of
the
shapes
would
get
closer
or
not.
For
the
other
half
of
the
pauses,
we
reported
the
outcome
(whether
the
shapes
had
moved
closer)
and
subjects’
success
or
failure
in
predicting
the
outcome.
We
ran
this
ToM
test
in
the
spirit
of
our
markets
prediction
test,
namely,
as
a
forecasting
exercise
where
correct
forecasts
are
rewarded
and
wrong
forecasts
are
not.
This
is
unlike
the
way
the
test
is
usually
applied
in
psychology.
Psychologists
ask
for
a
description
of
the
situation
and
rely
on
verbal
evidence
of
anthropomorphization
to
determine
to
which
extent
a
subject
engages
in
ToM
during
replay
of
the
Heider
movies.
Our
variant
of
the
test
is
more
direct
(anthropomorphization
is
sufficient
for
ToM,
but
not
necessary),
objective
(verbal
accounts
may
be
misleading),
and
it
agrees
with
the
standards
of
experimental
economics
(we
paid
for
performance).
The
third
task
was
a
ToM
task
based
on
eye
gaze
[“Eye
Gaze
Test”;
Baron‐
Cohen,
Jolliffe,
Mortimore
and
Robertson
(1997)].
A
number
of
photographs
of
eye
gazes
were
shown
consecutively,
and
the
subject
is
asked
to
pick
among
four
possibilities
which
best
described
the
mental
state
of
the
person
whose
eyes
were
shown.
To
facilitate
this
task,
the
subject
was
first
given
a
list
of
words
that
described
mental
states
(such
as
“anxious,”
“thoughtful,”
“skeptical,”
“suspicious”),
15
along
with
a
short
explanation.
This
ToM
task
provided
a
standard
test
of
ToM
skills.
In
contrast
with
practice
in
psychology,
we
paid
for
performance,
rewarding
the
subject
for
correct
answers.
Based
on
our
hypothesis,
our
conjecture
was
that
performance
in
the
FMP
task
would
be
correlated
with
scores
on
both
the
Gaze
and
Heider
tasks.
The
fourth
task,
the
Mathematics
task,
consisted
of
a
number
of
standard
mathematics
and
logic
questions
of
the
type
frequently
used
in
Wall
Street
job
interviews
–
see
Crack
(2004).
For
instance,
one
question
was
a
variation
on
the
Monty
Hall
problem,
a
test
of
understanding
of
Bayesian
inference,
which
has
been
used
elsewhere
in
experimental
finance
[Kluger
and
Wyatt
(2004);
Asparouhova,
Bossaerts,
Eguia
and
Zame
(2009)].
A
table
with
the
seven
questions
used
in
the
Mathematics
Test
is
available
in
the
Internet
Appendix.
We
added
the
Mathematics
Test
as
control:
we
were
interested
in
determining
whether
performance
in
the
market
prediction
test
was
correlated
specifically
with
ToM
skills
and
not
with
other
skills
that
arguably
may
also
play
a
role
in
reading
prices
in
markets
with
insiders.
In
the
FMP
(Financial
Markets
Prediction)
task,
we
used
an
intuitive
graphical
replay
of
the
order
and
trade
flow.
We
put
all
the
(limit)
orders
on
the
diagonal
or
counter‐diagonal,
in
the
form
of
circles.
Blue
circles,
below
the
midpoint,
indicated
offers
to
buy
(bids),
while
red
circles,
above
the
midpoint,
indicated
offers
to
sell
(asks).
The
circles
were
ordered
by
price
level.
The
price
level
itself
was
written
inside
the
circle.
The
diameter
of
the
circles
increased
with
the
number
of
16
units
bid
or
asked
at
the
corresponding
price
level.
Whenever
a
trade
occurred,
the
best
bid
(if
a
sale)
or
best
ask
(if
a
purchase)
briefly
(0.5s)
changed
color,
to
green,
after
which
the
circle
either
shrank
(if
units
remained
available
after
the
trade)
or
disappeared
(if
all
units
were
traded).
The
circles
constantly
re‐arranged
to
ensure
that
the
best
bid
and
ask
straddled
the
midpoint
of
the
screen
in
a
symmetric
way.
Time
remaining
in
the
session
was
indicated
in
the
unused
top
quadrant.
[Figure
2
about
here]
Figure
2
provides
a
snapshot
of
the
graphical
display.
We
used
this
display
instead
of
the
original
trading
interface
through
which
subjects
traded
in
the
markets
experiment,
because
it
revealed
all
the
information
without
having
to
navigate
the
page,
though
it
missed
the
functionality
to
submit
orders.
II.
Results
We
first
describe
the
trading
data
that
emerged
from
the
markets
experiment,
and
on
which
the
behavioral
(as
well
as
imaging)
experiment
was
based.
We
subsequently
discuss
the
results
from
the
four
performance
tasks
of
the
behavioral
experiment.
A.
Trading
Data
From
The
Markets
Experiment
[Figure
3
about
here]
17
Figure
3
displays
the
evolution
of
transaction
prices
throughout
the
markets
experiment.
The
horizontal
axis
denotes
time;
the
vertical
axis
price
level
(of
stock
X).
Blue
lines
delineate
sessions.
Red
vertical
line
segments
denote
final
dividend
level
(of
stock
X)
while
green
line
segments
denote
the
signal
(if
there
were
insiders).
Number
of
insiders
(I)
is
displayed
for
each
period.
“#K”
indicates
whether
everyone
(“All”)
or
only
the
insiders
(“Ins”)
knew
how
many
insiders
there
were.
Trading
was
brisk,
independent
of
the
type
of
session;
on
average,
traders
entered
or
cancelled
an
offer
every
0.7s
and
one
transaction
took
place
every
3.2s.
In
test
sessions
(when
insiders
were
present),
prices
tended
to
move
towards
the
signal,
although
revelation
is
not
perfect.
Closer
inspection
reveals
that
there
is
a
relationship
between
price
quality
(how
far
the
final
price
is
from
the
insider
signal)
and
the
proportion
of
informed
subjects,
consistent
with
the
noisy
rational
expectations
equilibrium
REE
[Grossman
and
Stiglitz
(1976);
Admati
(1985)].
This
relationship
is
explored
further
in
Bossaerts,
Frydman
and
Ledyard
(2009).
In
control
sessions,
prices
tend
to
remain
close
to
the
competitive
equilibrium
(25
cents),
but
occasionally
deviate
substantially
(e.g.,
in
session
8).
These
results
confirm
the
findings
from
many
prior
studies
on
information
aggregation
in
financial
markets,
starting
with
Plott
and
Sunder
(1988).
The
amplification
of
information
through
the
order
and
trade
flow
is
not
perfect,
however.
But
the
amount
of
revelation
is
still
surprising,
especially
because
subjects
do
not
have
the
structural
knowledge
of
the
situation
at
hand
to
known
how
prices
18
relate
to
signals,
unlike
in
the
theory
(the
rational
expectations
equilibrium
REE).
For
instance,
they
do
not
know
that
there
is
no
aggregate
risk,
and
hence,
that
in
equilibrium
pricing
is
as
if
the
marginal
investor
is
risk
neutral.
Yet
they
need
this
information
to
correctly
infer
information
from
prices.
It
is
clear
that
our
subjects
are
rather
good
at
inferring
information
from
the
order
and
trade
flow,
despite
their
lack
of
formal
financial
training.
One
can
therefore
conjecture
that
the
situation
exploits
a
skill
that
they
are
good
at.
Theory
of
Mind
is
one
skill
that
humans
are
generally
good
at,
and
our
behavioral
experiment
was
meant
to
verify
whether
indeed
Theory
of
Mind
is
at
work
in
markets
with
insiders.
The
imaging
experiment
suggested
that
subjects
did
think
“Theory
of
Mind”
when
watching
the
replay
of
a
market
in
which
they
held
a
stake.
B.
Performance
Across
Tasks
In
The
Behavioral
Experiment
In
the
Financial
Markets
Prediction
task,
subjects
were
quite
successful
at
forecasting
the
direction
of
price
changes
in
the
presence
of
insiders.
Their
forecasts
were
correct
in
approximately
2/3
of
the
cases
on
average.
Randomly
switching
between
forecasting
an
increase
in
price,
a
price
decrease,
and
a
level
price,
would
only
have
produced
a
score
of
33%;
a
better
naive
strategy,
to
always
predict
the
previous
outcome,
would
have
generated
a
score
of
56%.
As
such,
subjects
somehow
managed
to
read
enough
information
from
the
order
flow
to
beat
naive
forecasting
rules.
19
There
was
significant
variation
in
performance
across
subjects.
The
worst
subject
forecasted
correctly
in
46%
of
the
cases
(slightly
worse
than
the
best
naive
strategy),
and
the
best
one
forecasted
correctly
in
78%
of
the
cases.
We
conjectured
that
ToM
skill
provided
the
main
explanation
for
this
discrepancy
in
performance.
ToM
skill
was
measured
in
two
ways:
through
the
score
on
the
Heider
Test
and
through
the
score
on
the
Eye
Gaze
Test.
These
scores
provide
only
a
rough
metric
of
how
good
one
is
at
ToM
tasks,
so
we
refrain
from
using
them
as
explanatory
variables
in
a
regression
of
Financial
Markets
Prediction
performance.
Instead,
we
here
report
correlations
and
their
significance,
because
correlations
allow
both
dependent
variable
(FMP
task
performance)
and
explanatory
variables
(Heider
or
Eye
Gaze
test
score)
to
be
observed
with
error,
unlike
projections.
[Figure
4
about
here]
Figure
4
displays
the
correlation
line
of
performance
in
the
Financial
Market
Prediction
task
with
scores
on
the
Heider
Test,
while
Figure
5
shows
the
same
for
the
Eye
Gaze
Test
(bottom
panel).
In
both
cases,
the
correlations
are
significant
(p=0.048
and
p=0.023,
respectively).
This
confirms
our
hypothesis
that
ability
to
predict
price
changes
in
the
presence
of
insiders
is
correlated
with
ToM
skill.
[Figure
5
about
here]
In
contrast,
as
Figure
6
shows,
there
is
no
significant
correlation
between
performance
in
the
financial
task
(forecasting
price
changes
when
there
are
20
insiders)
and
the
score
on
the
Mathematics
task
(which
tests
mathematical
and
logical
reasoning
capacity).
[Figure
6
about
here]
Interestingly,
we
did
not
find
any
significant
correlation
either
between
the
scores
on
the
two
ToM
tests
(Figure
7).
It
thus
seems
that
these
two
tests
look
at
different
aspects
of
Theory
of
Mind,
a
finding
that
should
be
of
interest
to
psychologists,
who
generally
consider
the
tests
to
be
interchangeable.
In
our
case,
the
lack
of
(significant)
correlation
between
the
scores
on
the
two
ToM
tests
actually
is
a
good
thing:
it
indirectly
confirms
that
general
intelligence
or
state
of
attentiveness
cannot
explain
the
significant
correlations
between
scores
on
the
performance
on
the
Financial
Markets
Prediction
task
and
the
ToM
tests.
[Figure
7
about
here]
Self‐reports
after
the
behavioral
experiment
did
not
show
any
evidence
of
personalization
(anthropomorphization)
in
the
Financial
Markets
Prediction
task,
but
we
found
plenty
of
it
in
the
Heider
ToM
test.
We
did
not
observe
significant
gender
differences
for
any
test
(although
our
subject
pool
was
not
gender‐balanced:
only
16
out
of
43
were
female).
The
absence
of
significant
correlation
between
the
scores
on
the
Financial
Markets
Prediction
task
and
the
Mathematics
test
further
corroborates
our
hypothesis
that
the
former
is
a
ToM
task.
Indeed,
performance
on
some
ToM
tasks
has
been
found
to
be
generally
uncorrelated
with
capacity
to
perform
formal
21
mathematical
and
logical
reasoning.
Specifically,
through
brain
imaging,
it
has
recently
been
found
that
strategic
game
play
also
constitutes
a
ToM
task
[it
engages
the
usual
ToM
brain
regions;
McCabe,
Houser,
Ryan,
Smith
and
Trouard
(2001);
Gallagher,
Jack,
Roepstorff
and
Frith
(2002)].
Coricelli
and
Nagel
(2009)
have
shown
that
skill
in
playing
the
beauty
contest
game
is
not
correlated
with
the
ability
to
do
the
very
calculations
implicit
in
skillful
play
of
that
game.
Likewise,
we
find
here
that
the
ability
to
forecast
the
direction
of
price
changes
in
the
presence
of
insiders
is
not
correlated
with
the
capacity
at
formal
mathematical
reasoning.
III.
Theory
of
Mind
in
Markets
with
Insiders:
What
Patterns
To
Attend
to?
Our
finding
that
forecasting
price
changes
in
markets
with
insiders
and
ToM
skill
are
related
may
not
come
as
a
surprise.
After
all,
both
activities
concern
reading
the
mind
of
an
intentional
source
or
an
entity
behind
which
there
are
intentional
sources.
In
one
case,
it
is
the
market’s
mind
that
is
to
be
read;
in
the
other
case,
it
is
another
person’s
(or
persons’)
mind
that
is
to
be
deciphered.
Still,
formally,
ToM
remains
a
rather
elusive
concept.
It
is
mostly
defined
only
vaguely,
and
often
in
terms
of
specific
tasks
[Gallagher
and
Frith
(2003)],
or
in
terms
of
activation
of
particular
brain
regions
[McCabe,
Houser,
Ryan,
Smith
and
Trouard
(2001),
Gallagher,
Jack,
Roepstorff
and
Frith
(2002)].
It
is
generally
accepted,
however,
that
ToM
concerns
pattern
recognition.
22
In
the
context
of
strategic
games,
recent
studies
have
successfully
identified
the
patterns
in
the
moves
of
one’s
opponent
on
which
ToM
builds
[Hampton,
Bossaerts
and
O'Doherty
(2008);
Yoshida,
Dolan
and
Friston
(2008)].
The
import
of
such
findings
is
that
ToM
can
be
concretized
in
terms
of
precise
mathematical
quantities
that
characterize
an
opponent’s
actual
play.
Specifically,
ToM
concerns
“online”
or
“on
the
fly”
learning
of
game
play.
This
is
consistent
with
the
proposition
that
ToM
involves
pattern
recognition.
Therefore,
in
the
context
of
strategic
games,
ToM
contrasts
with
Nash
reasoning,
where
players
would
simply
hypothesize
that
opponents
choose
Nash
equilibrium
strategies
and
that
they
would
stick
to
them.
Nash
reasoning
can
be
“offline:”
it
works
even
if
one
never
sees
any
move
of
one’s
opponent.
Nash
reasoning
is
also
abstract:
it
posits
only
what
the
opponent
could
rationally
do
and
how
to
optimally
respond.
Consistent
with
the
idea
that
ToM
and
Nash
thinking
have
little
in
common,
brain
regions
that
are
known
to
be
engaged
in
abstract
mathematics
do
not
display
significant
activation
during
game
play,
and
–
we
mentioned
this
before
–
skill
in
strategic
play
and
mathematical
capabilities
are
uncorrelated
[Coricelli
and
Nagel
(2009)].
In
our
financial
markets
with
insiders,
however,
it
is
as
of
yet
unclear
which
patterns
subjects
could
be
exploiting
when
attempting
to
read
the
mind
of
the
market.
In
fact,
it
has
not
even
been
established
whether
there
are
any
patterns
that
distinguish
markets
with
and
without
insiders.
But
our
finding
that
subjects
engage
in
ToM
to
comprehend
insider
trading
and
the
view
that
ToM
concerns
pattern
recognition,
predict
that
such
patterns
should
exist.
This
provided
the
impetus
to
search
for
them
in
our
own
markets
data.
23
We
looked
at
a
host
of
time
series
properties
of
the
trade
flows
in
the
markets
experiment
that
formed
the
basis
of
our
study,
such
as
duration
between
trades,
or
skewness
in
transaction
price
changes.
In
the
end,
it
was
persistence
in
the
size
of
transaction
price
changes
in
calendar
time
that
provided
the
only
statistically
significant
discrimination.
As
such,
GARCH‐like
features
appeared
to
distinguish
our
sessions
with
and
without
insiders.
Specifically,
we
computed
transaction
price
changes
over
intervals
of
2s.2
We
followed
standard
practice
and
took
the
last
traded
price
in
each
2s
interval
as
the
new
price,
and
if
there
was
no
trade
during
an
interval,
we
used
the
transaction
price
from
the
previous
interval.3
[Figure
8
about
here]
We
then
computed
the
first
five
autocorrelations
in
the
size
(absolute
value)4
of
transaction
price
changes.
Figure
8
(a)
plots
the
results
for
two
adjacent
sessions,
Sessions
7
and
8.
As
can
be
inferred
from
Figure
2,
there
were
14
insiders
(out
of
20
subjects)
in
Session
7,
while
there
were
none
in
Session
8.
The
patterns
in
the
autocorrelations
of
the
absolute
price
changes
in
the
two
sessions
are
very
different.
There
is
substantial
autocorrelation
at
all
lags
for
Session
7
(when
there
were
insiders)
while
there
is
none
for
Session
8
(when
there
were
no
insiders).
Figure
8
(b)
shows
that
this
is
a
general
phenomenon.
Plotted
is
the
sum
of
the
absolute
values
of
the
autocorrelation
coefficients
for
lags
1
to
5
against
the
number
of
insiders.
We
refer
to
the
former
as
“GARCH
intensity”
because
it
measures
the
extent
to
which
there
is
persistence
in
the
size
of
price
changes.
We
24
sum
the
absolute
values
because
closer
inspection
of
the
data
revealed
that
autocorrelation
coefficients
can
be
significantly
negative
as
well
as
positive
when
there
are
insiders.
GARCH
intensity
increases
with
the
number
of
insiders;
the
slope
is
significant
at
the
5%
level
and
the
R‐squared,
at
0.32,
is
reasonably
high
given
the
noise
in
the
data.
Consequently,
it
appears
that
GARCH‐like
features
in
transaction
price
changes
provided
one
way
to
recognize
the
presence
of
insiders,
and
hence,
a
foundation
on
which
ToM
thinking
could
build.
Future
research
should
clarify
the
link,
which
is
likely
to
be
complex.
For
instance,
we
did
not
find
a
significant
correlation
between
GARCH
intensity
for
a
session
and
subjects’
performance
on
the
financial
market
prediction
task
for
the
same
session.
Also,
future
research
should
determine
whether
GARCH‐like
features
typify
markets
with
insiders
more
generally,
rather
than
just
our
experimental
markets.
IV.
Concluding
Remarks
We
reported
here
how
skill
in
forecasting
price
changes
in
markets
with
insiders
is
correlated
with
the
general
ability
to
detect
intentionality
in
one’s
environment,
namely,
Theory
of
Mind
(ToM).
This
possibility
was
first
suggested
by
a
brain
imaging
experiment,
whereby
we
contrasted
activations
during
replay
of
markets
with
and
without
insiders.
The
emergence
of
activation
in
specific
brain
regions
(and
absence
of
activation
in
others)
suggested
that
ToM
may
be
at
work.
25
Thus,
the
results
from
the
original
imaging
experiment,
and
those
from
the
behavioral
experiment,
fully
corroborate
each
other.
We
did
not
find
any
(significant)
correlation
between
performance
in
the
financial
markets
prediction
task
and
ability
to
solve
abstract
mathematical
and
logical
problems,
nor
did
the
imaging
experiment
reveal
any
activation
in
the
brain
regions
known
to
be
engaged
in
solving
such
problems.
This
finding
resonates
well
with
the
extant
ToM
literature.
Skill
in
playing
strategic
games,
for
instance,
is
uncorrelated
with
ability
to
explicitly
perform
the
calculations
that
such
skill
implies
[Coricelli
and
Nagel
(2009)].
Our
findings
are
of
interest
not
only
to
finance.
In
psychology,
the
scope
of
ToM
has
always
been
confined
to
small‐scale
social
interaction.
Here,
we
demonstrate
that
ToM
is
relevant
for
thinking
about
large‐scale,
anonymous
social
structures
as
well.
In
our
case,
it
concerns
competitive
financial
markets.
One
can
envisage
that
it
encompasses
political
systems
as
well,
like
voting
in
an
election
where
the
chance
that
one
is
pivotal
is
miniscule
[which
has
left
political
scientists
wondering
why
people
vote
at
all;
see,
e.g.,
Blais
(2000)].
Our
finding
that
scores
on
two
widely
accepted
ToM
tests
are
not
correlated
should
also
be
of
interest
to
the
psychology
community;
it
suggests
that
ToM
is
not
one‐dimensional.
And
our
results
demonstrate
that
forecasting
prices
in
markets
with
insiders
involves
multiple
aspects
of
ToM,
as
performance
correlates
with
scores
on
both
tests.
26
We
set
out
to
study
“trading
intuition,”
but
we
should
caution
the
reader.
For
reasons
spelled
out
earlier,
in
the
imaging
experiment,
we
only
looked
at
what
people
where
thinking
when
replaying
markets
(subjects
did
have
to
take
a
position,
but
only
before
the
replay,
and
could
not
change
it
during
replay).
Likewise,
the
behavioral
experiment
is
about
forecasting
price
changes
in
markets
with
insiders.
Subjects
were
paid
for
the
accuracy
of
their
(directional)
forecasts,
and
could
not
take
positions.
Of
course,
thinking
about
prices
and
forecasting
them
are
integral
to
successful
trading,
but
it
leaves
out
the
placing
of
orders
itself.
Trading
intuition
concerns
not
only
assessment
of
what’s
going
on
in
the
market
and
prediction
of
prices
in
the
future,
but
also
submission
of
the
right
orders.
Our
study
only
considers
the
first
two
facets;
future
work
should
shed
light
on
the
third.
Our
discovery
that
transaction
prices
in
our
sessions
with
insiders
exhibit
GARCH
patterns
should
instill
further
work.
One
would
like
to
know
whether
this
is
true
in
general.
Also,
we
still
miss
identification
of
the
precise
aspects
of
GARCH
patterns
that
allow
uninformed
market
participants
to
read
the
“mind
of
the
market”
(i.e.,
the
information
of
the
insiders).
We
don’t
know
whether
the
ToM
brain
activation
we
recorded
was
in
response
to
GARCH
features.
One
could
potentially
get
at
it
by
running
further
markets
experiments
like
the
one
presented
here,
and
recording
subjects’
choices,
eye
gaze,
and
brain
signals.
The
latter
would
be
facilitated
by
the
knowledge
decision
neuroscience
has
gained
in
recent
years
about
the
nature
and
location
of
brain
signals
related
to
updates
of
expected
reward
[McClure,
Berns
and
Montague
(2003);
O'Doherty,
Dayan,
Friston,
Critchley
and
Dolan
(2003)],
and
of
reward
risk
[Preuschoff,
Quartz
and
Bossaerts
(2008)].
27
Our
findings
should
inspire
research
to
improve
visual
representation
of
order
and
trade
flow.
Since
humans
often
are
best
at
recognizing
the
nature
of
intention
in
moving
(animate
or
inanimate)
objects
[Heider
and
Simmel
(1944),
Castelli,
Happe,
Frith
and
Frith
(2000)],
we
suggest
that
traders
may
be
more
likely
to
successfully
detect
insider
trading
when
order
and
trade
flows
are
presented
in
a
moving
display,
as
opposed
to
the
purely
numerical
listings
commonly
found
in
the
industry.
Our
(untrained!)
subjects
were
successful
in
forecasting
price
change
in
the
presence
of
insiders
(on
average,
they
performed
significantly
better
than
the
best
naïve
strategy).
One
may
wonder
whether
this
success
should
be
attributed
to
our
using
a
purely
graphical
interface,
where
order
and
trade
flows
are
translated
into
movement
of
circles
of
various
sizes
and
colors.
Finally,
the
finding
that
markets
with
insiders
prompt
people
to
use
a
skill
(ToM)
that
they
are
generally
good
at,
may
explain
the
popularity
of
betting
and
prediction
markets,
where
information
asymmetries
abound.
Uninformed
participants
may
feel
confident
that
they
will
detect
insider
trading
when
it
emerges.
It
may
also
explain
why
people
are
willing
to
participate
in
markets
that
require
advanced
problem
solving
skills
even
when
they
know
that
there
are
others
in
the
marketplace
that
are
better
[Meloso,
Copic
and
Bossaerts
(2009)].
ToM
by
itself
cannot
explain,
however,
why
people
want
to
participate
if
such
markets
constitute
zero‐sum
games.
Other
explanations
need
to
be
invoked,
such
as
overconfidence.
28
Figure
1.
Main
result
from
the
imaging
experiment.
Subjects’
brain
activation
was
contrasted
during
replay
of
markets
with
insiders
against
replay
of
markets
without
insiders.
Shown
is
a
cross‐section
of
the
typical
human
brain
from
front
(left
hand
side)
to
back
(“saggital
cross‐section”)
along
the
brain
midline.
Voxels
(cubic
sections
of
3mm3)
are
mapped
where
the
fMRI
signal
increased
more
intensely
as
a
function
of
the
difference
between
the
transaction
price
and
the
unconditional
expected
payoff
on
stock
X
(25
cents)
when
there
were
insiders
relative
to
when
there
were
none.
Significance
level
increases
with
color
grade,
from
red
to
yellow.
The
two
clusters
of
significant
voxels
are
in
the
paracingulate
cortex.
See
Internet
Appendix
for
precise
coordinates
of
the
voxel
with
highest
significance
level
(located
in
the
cluster
inside
the
yellow
circle).
29
Figure
2.
Snapshot
of
the
graphical
replay
of
the
order
and
trade
flow
in
the
Financial
Markets
Prediction
(FMP)
task.
Red
circles
are
asks;
blue
circles
are
bids;
size
of
the
circles
increases
with
number
of
units
available;
the
best
ask
(bid)
temporarily
turns
green
when
a
purchase
(sale)
occurs;
time
remaining
is
indicated
in
the
top
left
corner
(minutes:seconds:hundreds).
30
Figure
3.
Evolution
of
transaction
prices
(of
stock
X)
in
the
markets
experiment.
The
thirteen
sessions
are
delineated
by
the
blue
vertical
lines;
trading
in
a
session
lasted
5
minutes.
Red
line
segments
denote
final
liquidating
dividends
of
stock
X.
Green
line
segments
denote
insider
signals.
Red
asterisks
denote
trade
prices.
In
text
boxes:
I
denotes
number
of
insiders;
K#
denotes
whether
All
(subjects)
or
only
insiders
(Ins)
knew
how
many
insiders
there
were.
All
subjects
always
knew
whether
there
were
insiders,
even
if
not
all
may
have
known
how
many.
31
Figure
4.
Correlation
between
score
on
the
Heider
ToM
test
(horizontal
axis)
and
performance
on
the
Financial
Markets
Prediction
Task
(vertical
axis).
Number
of
observations:
43;
Correlation
Coefficient:
0.348
(p=0.022).
32
Figure
5.
Correlation
between
score
on
the
Eye
Gaze
ToM
test
(horizontal
axis)
and
performance
on
the
Financial
Markets
Prediction
Task
(vertical
axis).
Number
of
observations:
43;
Correlation
Coefficient:
0.303
(p=0.048).
33
Figure
6.
Correlation
between
score
on
the
Mathematics
test
(horizontal
axis)
and
performance
on
the
Financial
Markets
Prediction
Task
(vertical
axis).
Number
of
observations:
43;
Correlation
Coefficient:
0.061
(p=0.699).
34
Figure
7.
Correlation
between
scores
on
the
Heider
ToM
test
(horizontal
axis)
and
the
Eye
Gaze
ToM
test
(vertical
axis).
Number
of
observations:
43;
Correlation
Coefficient:
0.019
(p=0.904).
35
(a)
(b)
Figure
8.
(a)
Autocorrelation
coefficients
(lags
1
to
5)
of
absolute
transaction
price
changes
over
2s
intervals
in
the
markets
experiment,
Sessions
7
(insiders)
and
8
(no
insiders).
Autocorrelation
is
more
sizeable
in
Session
7.
Vertical
line
segments
indicate
90%
confidence
intervals.
(b)
Sum
of
absolute
values
of
first
five
autocorrelation
coefficients
of
absolute
price
changes
(“GARCH
intensity”)
for
all
sessions
in
the
markets
experiment,
arranged
by
number
of
insiders;
Fitted
line
is
significant
at
p=0.05.
36
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1
Our
study
was
set
up
as
an
open‐ended
reverse
correlation
exercise;
see
Hasson,
Uri,
Yuval
Nir,
Ifat
Levy,
Galit
Fuhrmann,
and
Rafael
Malach,
2004,
Intersubject
Synchronization
of
Cortical
Activity
During
Natural
Vision,
Science
303,
1634‐1640.
By
examining
brain
activation
during
particular
episodes
of
market
replay,
and
armed
with
knowledge
of
functionality
of
brain
regions
in
tasks
involving
financial
risks,
one
can
potentially
identify
what
subjects
were
thinking.
We
were
initially
looking
at
signals
in
the
striatal
regions
of
the
brain
as
well
as
the
anterior
insula,
because
of
known
correlations
with
changes
in
assessment
of
expected
reward
and
risk,
respectively.
See,
e.g.,
Kuhnen,
C
M,
and
B
Knutson,
2005,
The
neural
basis
of
financial
risk
taking,
Neuron
47,
763‐770.
Activation
in
ToM
regions
came
as
a
surprise,
as
these
rarely
activate
in
financial
tasks,
unless
they
involve
a
significant
strategic
component.
See
Hampton,
Alan
N.,
Peter
Bossaerts,
and
John
P.
O'Doherty,
2008,
Neural
correlates
of
mentalizing‐related
computations
during
strategic
interactions
in
humans,
Proceedings
of
the
National
Academy
of
Sciences
105,
6741‐
6746.
2
A
trade
occurred
every
3.7s
on
average,
so
calendar
time
tick
size
was
chosen
to
be
slightly
shorter
than
average
duration
between
trades.
3
Note
that
we
compute
autocorrelations
in
calendar
time.
Autocorrelations
are
affected
by
stale
prices,
because
when
no
trade
occurs,
the
transaction
price
is
set
equal
to
the
last
traded
price.
As
such,
autocorrelations
of
absolute
price
changes
indirectly
capture
persistence
in
duration
between
trades
as
well.
Still,
we
found
no
significant
correlation
between
autocorrelation
in
duration
between
trades,
on
the
one
hand,
and
numbers
of
insiders,
on
the
other
hand.
4
We
focused
on
autocorrelations
of
absolute
values
of
price
changes
because,
in
field
markets,
persistence
is
known
to
be
higher
for
absolute
values
instead
of
the
more
widely
investigated
squared
price
changes.
See
Zhuanxin,
Ding,
W.
J.
Granger
Clive,
and
F.
Engle
Robert,
2001,
A
long
memory
property
of
stock
market
returns
and
a
new
model,
in
Essays
in
econometrics:
collected
papers
of
Clive
W.
J.
Granger
(Harvard
University
Press).
42
Internet
Appendix
for
‘Exploring
the
Nature
of
“Trader
Intuition”’∗ The
Internet
Appendix
consists
of
six
parts.
In
the
first
part,
we
elaborate
on
the
brain
imaging
experiment.
The
second
part
discusses
the
parametrization
of
the
markets
experiment.
The
third,
fourth
and
fifth
parts
cover
the
instructions
for
the
markets,
imaging
and
behavioral
experiments,
respectively.
The
sixth
part
lists
the
questions
on
the
mathematics
section
of
the
behavioral
experiment,
along
with
the
correct
answers.
The
markets
and
brain
imaging
experiments
were
run
at
Caltech
and
were
approved
by
Caltech’s
ethics
committee.
The
behavioral
experiments
were
run
at
Caltech
and
UCLA
and
were
approved
by
the
ethics
committees
of
both
institutions.
Antoine
J.
Bruguier,
Steven
R.
Quartz
and
Peter
Bossaerts,
2009,
Internet
Appendix
to
“Exploring
the
Nature
of
“Trader
Intuition,””
Journal
of
Finance
[vol
#],
[pages],
http://www.afajof.org/IA/[year].asp.
Please
note:
Wiley‐Blackwell
is
not
responsible
for
the
content
or
functionality
of
any
supporting
information
supplied
by
the
authors.
Any
queries
(other
than
missing
material)
should
be
directed
to
the
authors
of
the
article.
∗
1
Part
1.
Brain
Imaging
Experiment
Experimental
Design
We
replayed
the
order
and
trade
flow
to
18
subjects
while
we
recorded
their
brain
activity.
The
subjects
had
not
been
in
either
the
markets
experiment
or
behavioral
experiment.
First,
we
explained
to
the
subjects
how
we
had
acquired
the
order
and
trade
flow.
We
familiarized
them
with
the
markets
experiment,
showing
the
instructions
that
the
subjects
in
that
experiment
were
given,
and
how
they
would
have
traded.
Second,
we
gave
them
instructions
for
the
imaging
experiment
and
asked
them
to
sign
a
consent
form.
(The
instructions
are
in
Part
3
of
this
Internet
Appendix.)
We
made
sure
that
they
understood
the
experiment
by
administering
a
quiz.
We
reminded
subjects
that
they
would
not
be
able
to
trade
in
the
market
after
taking
an
initial
position,
and
hence,
that
they
would
only
act
as
observer
of
the
replay
of
a
previously
recorded
market.
We
also
instructed
them
that
the
term
“insider”
did
not
refer
to
illegal
“insider
trading,”
but
only
to
the
fact
that
insiders
had
superior
information.
We
replayed
the
thirteen
sessions
of
the
original
markets
experiment
in
a
different
random
order
for
each
subject.
Each
session
began
with
a
“blind
bet:”
we
asked
subjects
to
choose
between
Stock
X
or
Stock
Z,
after
we
informed
them
whether
there
were
insiders.
Subjects
automatically
took
a
position
in
10
units
of
their
chosen
stock.
Because
of
the
perfect
complementarity
between
payoffs
on
X
and
Z,
taking
a
2
position
in
10
units
of
Z
is
equivalent
to
holding
10
notes
and
a
short
position
in
10
units
of
X.
After
the
subjects
had
made
a
choice,
we
replayed
the
order
and
trade
flow
for
stock
X,
regardless
of
their
choice,
at
double
the
speed
(2
minutes
and
30
seconds
instead
of
5
minutes).
Finally,
we
displayed
the
dividend
on
the
stock
that
they
had
chosen
before
the
replay.
The
various
steps
for
a
single
session
are
shown
in
Figure
IA1.
3
Figure
IA1.
Timeline
of
fMRI
experiment
and
sample
screenshots.
(i)
At
the
start
of
each
session,
subjects
were
shown
a
screen
that
informed
them
of
whether
or
not
the
session
contained
insiders
and
instructed
them
to
make
a
choice
(a
blind
bet)
between
stock
X
or
the
(complementary)
stock
Z.
They
were
then
endowed
with
10
units
of
the
stock
they
chose.
(ii)
A
blank
screen
was
subsequently
presented
for
10
seconds,
(iii)
a
market
session
was
then
replayed
at
double
speed
(the
screenshot
shown
is
illustrative;
red
circles
indicated
asks;
blue
circles
indicated
bids;
orders
turned
green
for
0.5
seconds
to
indicate
a
trade;
bids
and
asks
were
arranged
along
the
diagonal
or
counter‐diagonal
in
increasing
order
of
price;
price
level
was
displayed
inside
the
circle
(not
shown);
size
of
circles
correlated
with
number
of
units
available
at
the
corresponding
price),
(iv)
after
the
session,
a
blank
screen
was
shown
again
for
10
seconds,
after
which
(v)
subjects
were
informed
of
the
dividend
paid
to
the
stock
they
had
chosen.
This
was
repeated
13
times
(for
the
13
sessions
in
the
markets
experiment).
ITI=”inter
session
interval.”
We
replayed
the
order
and
trade
flow
(section
(iii)
of
Figure
IA1)
with
an
intuitive
interface
(Video
IA1).
This
video‐game‐like
representation
was
necessary
for
4
fMRI
analysis.
Indeed,
the
more
complex
the
representation
is,
the
higher
the
number
of
unwanted
signal
processes
in
the
brain.
The
representation
contains
all
the
information
actual
traders
in
the
markets
experiment
had,
but
does
not
have
trading
functionality.
Available
at:
http://www.bruguier.com/pub/stockvideo.html
Video
IA1.
Display
of
the
trading
activity.
Each
circle
represents
an
offer
to
buy
(bid,
blue
circle)
or
to
sell
(ask,
red
circle)
at
a
certain
price
indicated
by
the
number
inside
the
circle.
The
diameter
of
the
circle
indicates
the
number
of
units
of
the
stock
offered.
This
number
is
the
aggregate
of
all
the
offers
at
this
price.
We
ordered
the
circles
by
increasing
value,
along
one
diagonal,
chosen
at
random.
The
circles
move,
grow,
and
shrink
with
the
incoming
orders.
Every
time
a
trade
occurs,
the
corresponding
circle
(bid
or
ask
quote
that
is
involved
in
the
transaction)
turns
green
for
500ms,
shrinks
by
the
number
of
stocks
traded,
and
then
returns
to
its
original
color
(unless
no
more
stocks
remain
to
be
traded,
in
which
case
it
disappears).
Specifically,
we
represented
the
price
levels
for
the
offers
to
buy
and
sell
(“bids”
and
“asks”)
with
a
circle.
The
number
inside
a
circle
indicated
the
price
in
cents
and
the
5
diameter
of
the
circle
represented
the
number
of
units
offered.
Blue
circles
represented
bids,
and
red
circles
represented
asks.
For
example,
if
at
a
given
time
there
was
a
single
bid
at
25¢,
three
asks
at
27¢,
and
one
ask
at
28¢,
the
subject
would
see
three
circles:
a
small
blue
circle
with
the
number
25
inside,
a
larger
red
circle
with
the
number
27
inside,
and
a
small
red
circle
with
the
number
28
inside.
A
example
is
in
figure
IA1
(iii).
When
a
trade
occurred
at
a
certain
price,
the
corresponding
circle
turned
green
for
500ms,
after
which
the
circle’s
diameter
shrank,
reflecting
the
lower
number
of
bids
or
asks
available
after
trade.
In
case
no
more
units
remained
available,
the
circle
would
disappear.
The
circles
are
aligned
roughly
along
one
diagonal
of
the
screen,
by
increasing
price.
The
middle
of
the
book
remained
at
the
center
of
the
screen.
As
the
trading
advanced
in
time,
the
circles
grew,
shrank,
appeared,
and
disappeared,
reflecting
the
changes
in
outstanding
asks
and
bids.
We
rearranged
the
circles
dynamically
to
reflect
the
changes
in
price
levels
of
the
offers
and
trades.
See
Video
IA1
for
examples.
The
locomotion
between
sessions
with
and
without
insiders
did
not
display
any
obvious
differences.
In
order
to
monitor
attention,
we
asked
subjects
to
press
a
key
every
time
a
trade
occurred.
Subjects
paid
a
small
penalty
for
missed
trades
($0.05).
Discussion
of
the
Design
Our
design
was
chosen
purposely.
First,
the
subjects
did
not
trade
during
the
replay
(they
did
have
to
take
positions
before
the
replay).
While
the
question
of
how
the
human
brain
executes
financial
decisions
is
interesting,
we
needed
first
to
understand
how
humans
perceived
or
judged
a
stock
market
with
insiders.
By
not
introducing
6
decision‐making,
we
avoided
a
confounding
factor.
Second,
the
periods
without
insiders
were
controls.
Since
the
data
acquisition
method,
the
display
screens,
and
the
number
of
traders
were
the
same,
the
two
types
of
sessions
were
identical
in
every
respect
except
for
the
presence
or
absence
of
insiders.
Third,
by
adding
a
blind
bet,
we
elicited
a
feeling
of
“randomness.”
Indeed,
if
we
had
forced
subjects
to
choose
stock
X
for
every
session,
the
payoff
would
have
been
the
same
fixed
number
for
every
subject.
Moreover,
we
could
not
have
separated
an
increase
in
stock
price
from
a
higher
expected
reward,
as
these
two
signals
would
have
been
perfectly
correlated.
Instead,
by
introducing
a
blind
bet,
we
orthogonalized
changes
in
subjects’
expected
rewards
and
stock
prices.
Brain
Imaging
Analysis
During
the
experiment,
subjects
were
scanned
using
functional
Magnetic
Resonance
Imaging
(fMRI).
This
technique
allows
one
to
locate
temporary
changes
in
distribution
of
oxygenated
blood
throughout
the
brain.
Concentration
of
oxygenated
blood
increases
where
neurons
are
active.
It
is
generally
understood
that
this
activity
reflects
signaling
from
upstream
neurons
[Goense
and
Logothetis
(2008)].
The
fMRI
signal
is
generally
referred
to
as
the
BOLD
signal,
short
for
Blood
Oxygen
Level
Dependent
signal.
To
identify
areas
of
significant
brain
activation
in
the
insider
sessions
relative
to
the
control
sessions,
we
used
a
standard
approach
as
implemented
in
the
package
BrainVoyager.
We
fit
a
time
series
General
Linear
Model
(GLM)
to
the
(filtered,
motion‐
corrected)
BOLD
signal
for
each
“voxel”
(a
cubic
volume
element
of
27mm3)
with,
aside
7
from
auxiliary
predictors
(regressors
that
capture
activation
due
to
motion
and
visual
effects),
the
following
predictors
(see
Figure
IA2
for
sample
time
series
plots
of
the
predictors/regressors
and
the
variables
used
to
construct
them):
•
The
expected
reward,
computed
as
follows:
o When
there
were
insiders,
the
expected
reward
was
proxied
by
the
transaction
price
(if
the
subject
chooses
to
bet
on
X)
or
0.50
minus
the
transaction
price
(if
subject
bet
on
Z).
o When
there
were
no
insiders,
the
expected
reward
was
set
equal
to
a
constant
(0.25).
This
construction
reflected
the
following
reasoning.
During
sessions
with
insiders,
a
higher
price
for
stock
X
indicates
that
the
dividend
was
likely
to
be
higher,
resulting
in
a
higher
expected
reward
in
the
case
of
a
blind
bet
on
stock
X
and
a
lower
expected
reward
on
stock
Z.
When
there
were
no
insiders,
the
price
does
not
carry
any
information,
so
we
kept
the
expected
reward
at
0.25
irrespective
of
the
price.
•
Two
parametric
regressors,
based
on
the
absolute
deviation
of
the
stock’s
trading
price
and
25¢.
One
tracked
this
deviation
in
sessions
with
insiders,
and
the
other
one
in
sessions
without
insiders.
When
there
are
insiders,
this
regressor
should
quantify
the
effect
of
the
insiders
on
the
stock
price.
The
separate
parametric
regressor
for
sessions
without
insiders
is
used
as
control,
against
which
brain
activation
during
the
sessions
with
insiders
could
be
compared.
8
•
Two
block
regressors
(dummy
variables
in
the
language
of
econometrics),
to
identify
sessions
with
and
without
insiders
–
to
capture
activation
not
modulated
by
the
transaction
price.
9
Figure
IA2.
Construction
of
predictors
in
the
GLM
used
in
the
analysis
of
brain
activation
data.
Shown
are
five
fictive
periods
of
different
combinations
of
presence/absence
of
insiders
and
whether
the
subject
chose
stock
X
or
Z.
On
top,
the
evolution
of
the
stock
price
of
X
is
displayed;
this
price
was
used
to
construct
parametric
predictors.
The
first
parametric
predictor
was
the
expected
reward
(ER);
it
was
computed
from
the
stock
price,
the
presence/absence
of
insiders,
and
the
blind
bet
(see
main
text
for
detail).
As
a
proxy
of
insider
activity,
we
used
the
absolute
value
of
the
difference
between
the
trading
price
(top
)
and
25¢
(|price‐25|).
From
this
proxy,
two
parametric
predictors
were
constructed.
First,
a
parametric
predictor
(ParamIns)
modeled
the
effect
of
insider
activity
during
session
with
insiders.
Second,
an
analogous
predictor
(Paramno
Ins)
was
constructed
for
sessions
without
insiders.
In
addition,
we
added
the
following
block
predictors
(dummy
variables):
a
block
predictor
capturing
mean
brain
activation
during
sessions
with
insiders
(BlockIns),
and
a
block
predictor
capturing
mean
brain
activation
during
sessions
without
insiders
(Blockno
Ins).
10
The
effect
of
neuronal
activation
on
blood
oxygen
level
is
both
delayed
and
spread
over
time.
Thus
a
sharp,
on/off
neuronal
signal
related
to,
e.g.,
a
change
in
the
expected
reward,
will
not
result
in
a
sharp,
on/off
response
in
the
BOLD
signal.
Instead,
we
expect
to
see
a
smooth
ramping
up
and
down.
The
effect
is
referred
to
as
the
“hemodynamic
response,”
and
the
function
known
to
describe
this
response
is
a
gamma
function.
The
impact
of
a
sequence
of
changes
in
a
regressor
is
additive.
Because
of
this,
all
regressors
only
needed
to
be
convolved
with
this
hemodynamic
response
function.
Effectively,
we
transformed
the
original
time
series
of
a
predictor
xi
to
a
new
time
series
y,
as
follows:
where
h
is
the
hemodynamic
response
function
and
t
denotes
time.
We
then
fit
the
transformed
regressors
to
the
BOLD
signal
using
least
squares:
We
used
Generalized
Least
Squares,
because
the
error
process
e
is
(first‐order)
autocorrelated.
The
GLS
is
repeated
for
all
other
subjects,
providing
a
cross‐section
of
estimated
slope
coefficients
bi,
referred
to
as
betas,
and
more
importantly,
their
differences
(difference
between
a
beta
for
the
insider
sessions
and
the
corresponding
beta
for
the
control
sessions).
The
t
statistic
and
associated
p
value
are
then
computed
using
11
standard
analysis
of
random
effects
models
(thus
assuming
that
subject‐specific
betas
reflect
both
a
population
effect
and
an
individual
effect).
We
repeat
this
procedure
for
every
voxel
in
the
brain.
For
each
difference
in
betas,
this
gives
us
a
geometric
map
of
t
statistics.
We
eliminate
of
voxels
where
the
t
statistic
does
not
reach
a
cut‐off
p
value
(here:
p<0.001)
or
for
which
neighboring
voxels
(here:
at
least
4)
do
not
reach
this
cut‐off
p
level.
The
result
is
a
map
like
the
one
displayed
in
Figure
1
in
the
main
text
of
the
paper.
Results
We
found
a
significant
contrast
in
the
betas
for
the
parametric
regressors
in
one
large
region
of
paracingulate
cortex
(PCC;
Figure
1
in
the
main
text
of
the
paper
and
re‐
produced
here
as
Figure
IA3
(a),
and
Table
IAI).
The
contrast
reveals
that
activation
in
PCC
is
more
sensitive
to
deviations
of
prices
from
0.25
during
sessions
with
insiders
than
during
sessions
without
insiders.
We
also
found
significant
differences
in
the
betas
across
insider
sessions
and
sessions
without
insiders
in
a
smaller
region
in
the
frontal
part
of
the
anterior
cingulate
cortex
(Figure
1
in
the
main
text
of
the
paper
and
re‐
produced
here
as
Figure
IA3
(a),
and
Table
IAI).
Finally,
we
found
strong
differential
sensitivity
to
deviations
of
prices
from
0.25
in
right
amygdala
(Figure
IA3
(b)
and
Table
IAI)
and
left
insula
(Figure
IA3
(c)
and
Table
IAI).
12
Figure
IA3.
Location
of
significant
contrasts
of
slope
coefficients
(“betas”)
of
the
parametric
regressors
between
insider
and
no‐insider
sessions
(p<0.001,
random
effects,
minimum
cluster
size=5
voxels):
(a)
Sagittal
view
of
the
activation
in
paracingulate
cortex
(Talairach
coordinates
‐9;
41;
36;
Brodmann
areas
9/32;
extends
for
22
voxels).
(b)
Amygdala
activation
in
coronal
view
(‐14;
23;
39;
extends
for
5
voxels).
(c)
Activation
of
the
left
insula
in
axial
view
(‐30;
‐7;
11;
extends
for
5
voxels).
13
X
Y
z
cluster
size
t17
Area
‐30
‐7
11
5
4.476
left
insula
‐14
23
39
5
4.688
frontal
part
of
the
anterior
cingulate
cortex
‐9
41
36
22
5.380
paracingulate
cortex
‐9
32
45
6
4.290
frontal
part
of
anterior
cingulate
cortex
17
36
43
6
6.322
frontal
part
of
the
anterior
cingulate
cortex
21
‐10
‐12
5
5.160
right
amygdale
Table
IAI.
Areas
with
significant
difference
in
slope
coefficients
(“betas”)
to
parametric
regressors
(insiders
vs.
no‐insider).
Standard
coordinates
(Talairach
x,y,z)
are
used.
We
report
regions
with
5
or
more
voxels
of
27mm3
each
activated
at
p<0.001
for
a
random
effect
GLM.
The
parametric
regressor
is
the
absolute
difference
between
the
last
traded
price
and
the
25¢.
The
cluster
size
is
specified
in
number
of
contiguous
voxels.
t17
indicates
t
statistic
for
the
difference
in
betas
(17
degrees
of
freedom).
14
Figure
IA4.
Location
of
significant
contrast
of
slope
coefficients
(“betas”)
of
the
block
regressors
between
insider
and
no‐insider
sessions.
Threshold:
p<0.001
(minimum
cluster
size=5
voxels;
random
effect).
There
are
two
clusters
of
activated
voxels,
one
in
the
lingual
gyrus
and
the
other
in
cerebellum.
Significant
contrasts
for
the
block
predictors
showed
up
in
a
large
area
of
lingual
gyrus
(Figure
IA4
and
Table
IAII),
as
well
as
a
small
area
of
cerebellum
(Figure
IA4
and
Table
IAII).
No
other
areas
with
five
or
more
voxels
exhibited
significant
contrasts
(at
p=0.001).
x
Y
z
cluster
size
t17
Area
‐13
‐58
‐30
9
4.485
Cerebellum
‐9
‐65
‐6
25
4.440
lingual
gyrus
Table
IAII.
Areas
with
significant
difference
in
slope
coefficients
(“betas”)
for
block
regressors
(insiders
vs.
no‐insider).
Standard
coordinates
(Talairach
x,y,z)
are
used.
Random
effects,
thresholded
at
p<0.001
and
with
minimum
cluster
size=5.
t17
indicates
t
statistic
for
difference
in
betas
(17
degrees
of
freedom).
These
results
suggested
that
Theory
of
Mind
(ToM)
is
involved
when
subjects
are
facing
markets
with
insiders.
Indeed,
the
activated
brain
regions
belong
to
the
brain
circuitry
that
is
known
to
be
engaged
in
traditional
ToM
tasks.
•
PCC
(paracingulate
cortex)
activation
is
standard
in
tasks
involving
ToM
[Gallagher
and
Frith
(2003)],
and
in
strategic
games
in
particular
[Gallagher,
Jack,
Roepstorff
and
Frith
(2002),
McCabe,
Houser,
Ryan,
Smith
and
Trouard
(2001),
Bhatt
and
Camerer
(2005)].
The
PCC
has
also
been
observed
in
tasks
that
involved
attribution
of
mental
states
to
dynamic
visual
images,
such
as
15
intentionally
moving
shapes
[Castelli,
Happe,
Frith
and
Frith
(2000)],
and
hence,
not
unlike
the
circles
in
our
display
that
represent
offers.
•
We
found
that
activation
in
the
right
amygdala
and
the
left
anterior
insula
increased
as
transaction
prices
deviated
from
the
uninformed
payoff.
While
a
number
of
studies
have
reported
the
involvement
of
these
structures
in
ToM
tasks
[Baron‐Cohen,
Ring,
Wheelwright
and
Bullmore
(1999),
Critchley,
Mathias
and
Dolan
(2001),
King‐Casas,
Sharp,
Lomax‐Bream,
Lohrenz,
Fonagy
and
Montague
(2008)],
they
are
more
typically
regarded
as
involved
in
affective
features
of
social
interaction.
Specifically,
the
amygdala
is
a
critical
structure
in
the
recognition
of
facial
emotional
expressions
of
others
[Phillips,
Young,
Scott,
Calder,
Andrew,
Giampietro,
Williams,
Bullmore,
Brammer
and
Gray
(1999),
Phan,
Wager,
Taylor
and
Liberzon
(2002),
Morris,
Ohman
and
Dolan
(1998)]
while
the
anterior
insula
is
thought
to
play
a
critical
role
both
in
subjective
emotional
experience
[Bechara
and
Damasio
(2005)]
and
in
the
perception/empathetic
response
to
the
emotional
state
of
others
[Singer,
Seymour,
O'Doherty,
Kaube,
Dolan
and
Frith
(2004)].
A
complementary
interpretation
of
insula
activation
is
that
it
may
reflect
subjects’
own
emotional
responses
to
the
winner’s
curse
in
markets
with
insiders,
which
would
square
our
finding
with
activation
of
insula
when
trust
is
broken
during
play
of
the
trust
game
[King‐Casas,
Sharp,
Lomax‐Bream,
Lohrenz,
Fonagy
and
Montague
(2008)].
Another
possible
interpretation
is
that
subjects
perceive
more
risk
when
there
are
insiders.
This
would
be
consistent
both
with
a
recent
report
that
insula
is
involved
in
financial
risk
learning
[Preuschoff,
Quartz
and
Bossaerts
16
(2008)]
and
with
psycho‐physiological
evidence
that
financial
market
participation
engages
somatic
marker
(emotional)
circuitry
during
heightened
market
volatility
[Lo
and
Repin
(2002)].
Future
research
should
shed
more
light
on
potential
links
between
market
participation,
emotions
and
risk
assessment.
•
We
also
found
activations
in
the
frontal
part
of
the
ACC
(anterior
cingulate
cortex).
Our
activation
is
in
a
slightly
more
posterior
and
dorsal
location
than
when
ToM
is
used
in
strategic
and
non‐anonymous,
simple
two‐person
games
[Gallagher,
Jack,
Roepstorff
and
Frith
(2002),
McCabe,
Houser,
Ryan,
Smith
and
Trouard
(2001)].
•
The
increased
activation
of
lingual
gyrus
in
the
presence
of
insiders
provides
further
support
for
the
role
of
ToM
in
market
perception.
For
example,
the
lingual
gyrus
is
involved
in
perception
of
biological
motion,
a
key
cue
for
mentalizing
[Servos,
Osu,
Santi
and
Kawato
(2002)].
However,
increased
activation
of
lingual
gyrus
may
also
be
related
to
accounts
that
this
structure
activates
in
complex
visual
tasks
where
subjects
are
asked
to
extract
global
meaning
despite
local
distractors
[Fink,
Halligan,
Marshall,
Frith,
Frackowiak
and
Dolan
(1996),
Fink,
Halligan,
Marshall,
Frith,
Frackowiak
and
Dolan
(1997)].
When
there
are
no
insiders,
subjects
can
concentrate
on
the
task
we
imposed,
namely,
to
track
trades.
In
our
display,
transactions
were
a
local
feature,
indicated
by
changes
in
color
of
circles
in
the
middle
of
the
screen.
In
contrast,
when
there
were
insiders,
the
entire
list
of
orders
may
have
reflected
information
with
which
to
re‐evaluate
the
likely
payoff
on
the
securities,
but
at
the
same
time
subjects
are
still
asked
to
report
all
transactions,
which
then
17
amounted
to
a
local
distraction.
Future
research
should
determine
to
what
extent
lingual
gyrus
activation
reflects
ToM
(through
motion
of
objects)
or
the
proverbial
conflict
between
the
“forest”
(insider
information)
and
the
“trees”
(trades).
We
did
not
observe
significant
differences
in
betas
(at
p=0.001
and
with
requiring
clusters
of
at
least
5
significant
adjacent
voxels)
between
sessions
with
and
without
insiders
in
any
other
brain
areas.
Curiously,
no
differential
activation
emerged
for
brain
regions
known
to
engage
in
formal
mathematical
reasoning.
In
particular,
there
was
no
evidence
of
estimation
of
probabilities
[Parsons
and
Osherson
(2001)]
or
arithmetic
computation
[Dehaene,
Spelke,
Pinel,
Stanescu
and
Tsivkin
(1999)].
We
also
did
not
observe
any
significant
activation
in
brain
areas
related
more
generally
to
logical
problem‐solving
or
analytical
thought
[Newman,
Carpenter,
Varma
and
Just
(2003)]
or
reasoning
[Acuna,
Eliassen,
Donoghue
and
Sanes
(2002)].
This
finding
has
also
been
highlighted
in
a
recent
study
of
ToM
in
strategic
games
[Coricelli
and
Nagel
(2009)].
18
Part
2.
Parameters
For
The
Markets
Experiment
Type
Stock X
Stock Z
Notes
Cash
1
0
7
0
$1.75
2
10
3
0
$0.75
Table
IAIII.
Subjects
in
the
Markets
Experiment
were
of
one
of
two
types,
differentiated
by
initial
allocations.
Shown
are
initial
holdings
of
X,
Z,
Notes
and
Cash.
19
How
Many
Who
Knows
How
Session
Length
Insiders?
Insiders?
Many
Insiders?
Signal
Outcome
(x)
1
5'
Yes
6
Everyone
0.25
0.21
2
5'
Yes
2
Insiders
Only
0.39
0.43
3
5'
Yes
2
Everyone
0.27
0.26
4
5'
No
0
N/A
N/A
0.10
5
5'
Yes
10
Insiders
Only
0.38
0.34
6
5'
Yes
2
Nobody
0.39
0.42
7
5'
Yes
16
Everyone
0.09
0.01
8
5'
No
0
N/A
N/A
0.34
9
5'
Yes
6
Insiders
Only
0.21
0.19
10
5'
Yes
10
Everyone
0.42
0.42
11
5'
Yes
6
Nobody
0.33
0.25
12
5'
Yes
14
Insiders
Only
0.43
0.36
13
5'
Yes
10
Nobody
0.24
0.21
Table
IAIV.
Insiders,
signals
and
outcomes
across
the
13
sessions.
20
Part
3.
Instruction
Set
For
The
Markets
Experiment
(See
also
http://clef.caltech.edu/exp/info/instructions.html)
Instructions
1.
Situation
The experiment consists of a number of replications of the same situation, referred to as periods. At the
beginning of each period, you will be given securities and cash. Markets will open and you will be free to trade
some of your securities. You buy securities with cash and you get cash if you sell securities. At the end of the
period, the securities expire, after paying dividends that will be specified below.
Your period earnings has two components: the dividends on the securities you are holding after markets close,
plus your cash balance.
Period earnings are cumulative across periods. At the end of the experiment, the cumulative earnings are yours
to keep, in addition to a standard sign-up reward.
During the experiment, accounting is done in real dollars.
2. The Securities
You will be given two types of securities, stocks and bonds. Bonds pay a fixed dividend at the end of a period,
21
namely, $0.50. Stocks pay a random dividend. There are two types of stocks, referred to as X and Z. Their
payoffs depend on the drawing of a variable x, which is a number between 0 and 0.50. The payoffs on stocks X
and Z are complementary: Stock X pays $x, and stock Z pays $0.50-x, as displayed in the following table.
Stock
X
Stock
Z
Bond
Dividend
$x
$0.50‐x
$0.50
You will be able to trade Stock X as well as the Bond, but not Stock Z.
You won't be able to buy Stock X or bonds unless you have the cash. You will be able to sell Stock X and the
Bond (and get cash) even if you do not own any. This is called short selling. If you sell, say, one Stock X, then
you get to keep the sales price, but $x will be subtracted from your period earnings after the market closes. If at
the end of a period you are holding, say, -1 Bond, $0.50 will be subtracted from your period earnings.
The trading system checks your orders against bankruptcy: you may not be able to submit orders which, if
executed, could generate negative period earnings.
3.
Inside
information
For each period, x is drawn randomly from the numbers {0.01, 0.02, 0.03, ...., 0.49, 0.50}. Outcomes in
previous periods have no effect on the drawing. The draw is not disclosed to anybody until the end of the
period.
22
Some participants, however, may get inside information about x before the start of trading. This will take the
following form. A signal S is drawn from the numbers {x-0.10, x-0.09, x-0.08, ..., x+0.09, x+0.10}. E.g., if x is
0.30, then S is drawn from {0.20, 0.21, 0.22, ..., 0.39, 0.40}. You can use S to infer what x could be: if S is 0.05,
then x could be anywhere from 0.01 to 0.15.
This signal S is then revealed to some participants. The same signal is revealed to all insiders.
The number and identities of participants who receive inside information vary across periods. Sometimes
nobody receives inside information. In certain periods when inside information is distributed, only insiders
know the number of insiders while in others nobody is told this number. This is made clear in the News page.
23
Part
4.
Instruction
Set
For
The
fMRI
Experiment
Instructions
Market
Replay
Experiment
Setup
In this experiment we replay several episodes, “periods,” of a real securities market.
You will see the history of the order flow (buys; sells) and the trades precisely as they
happened. Your actions will obviously not have any effect on the history. However, you
incur the same risk as some players did in the market – without the ability to trade.
There
were
two
types
of
securities
in
the
market,
stocks
and
bonds.
Bonds
paid
a
fixed
dividend
at
the
end
of
a
period,
namely,
$0.50.
Stocks
paid
a
random
dividend.
There
were
two
types
of
stocks,
referred
to
as
X
and
Z.
Their
payoffs
depended
on
the
drawing
of
a
variable
x,
which
is
a
number
between
0
and
0.50.
The
payoffs
on
stocks
X
and
Z
were
complementary:
Stock
X
paid
$x,
while
stock
Z
paid
$0.50‐x.
You
will
be
exposed
to
the
risk
of
either
stock
X
or
stock
Z.
Before
the
experiment
starts,
you
choose
which
stock
you
want
to
be
exposed
to.
You
only
see
a
24
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cT A
i x r cT ci
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o
n i pi x r cx
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90 hx cf h
x t r . xc r hr 1 A
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Only
offers
for
the
five
best
price
levels
are
shown.
There
might
be
more
offers
at
inferior
prices.
Orders
with
higher
price
levels
are
always
above
those
with
lower
price
levels.
The
bubbles
shrink
or
may
even
disappear
when
market
players
cancel
orders.
They
also
shrink
or
disappear
after
an
order
is
taken,
in
which
case
there
is
a
trade.
If
that
happens,
the
bubble
will
first
turn
green
for
one
second
before
shrinking
or
disappearing.
Your
Task
Besides
watching
the
orders
and
trades,
you
are
to
perform
a
simple
task.
Each
time
you
see
a
trade,
you
immediately
press
a
button.
You
will
be
penalized
if
you
fail
to
do
so
or
if
you
do
so
incorrectly.
Button
presses
obviously
have
no
impact
on
the
history
of
the
market;
nor
do
they
influence
the
risk
of
the
security
you
are
exposed
to.
How
You
Make
(Or
Lose)
Money
The
amount
of
money
you
receive
depends
on
the
payoff
on
the
security
that
you
choose
to
be
exposed
to.
Orders
and
transaction
prices
in
the
replay
of
the
market
do
not
determine
your
earnings,
although
they
may
be
a
good
indicator
of
the
likely
payoff.
26
Indeed,
sometimes
inside
information
about
the
payoff
was
available.
The
number
and
identities
of
players
who
received
inside
information
varied
across
periods.
Sometimes
nobody
received
inside
information;
in
other
periods,
inside
information
was
available,
but
only
insiders
knew
how
many
players
had
received
the
information;
while
in
the
remaining
periods,
everyone
was
told
how
many
insiders
there
were.
This
will
be
made
clear
in
the
first
screen
you
see
before
a
market
is
replayed.
The
attached
instructions
sheets
for
the
market
players
provide
details
of
the
nature
of
the
inside
information.
27
Attachment:
Instructions
For
Market
Players
(http://clef.caltech.edu/exp/info/instructions.html)
Instructions
1.
Situation
The experiment consists of a number of replications of the same situation, referred to as periods. At the
beginning of each period, you will be given securities and cash. Markets will open and you will be free to trade
some of your securities. You buy securities with cash and you get cash if you sell securities. At the end of the
period, the securities expire, after paying dividends that will be specified below.
Your period earnings has two components: the dividends on the securities you are holding after
markets close, plus your cash balance.
Period earnings are cumulative across periods. At the end of the experiment, the cumulative earnings
are yours to keep, in addition to a standard sign-up reward.
During the experiment, accounting is done in real dollars.
2. The Securities
You will be given two types of securities, stocks and bonds. Bonds pay a fixed dividend at the end of a period,
28
namely, $0.50. Stocks pay a random dividend. There are two types of stocks, referred to as X and Z. Their
payoffs depend on the drawing of a variable x, which is a number between 0 and 0.50. The payoffs on stocks X
and Z are complementary: Stock X pays $x, and stock Z pays $0.50-x, as displayed in the following table.
Stock
X
Stock
Z
Bond
Dividend
$x
$0.50‐x
$0.50
You will be able to trade Stock X as well as the Bond, but not Stock Z.
You won't be able to buy Stock X or bonds unless you have the cash. You will be able to sell Stock X
and the Bond (and get cash) even if you do not own any. This is called short selling. If you sell, say, one Stock
X, then you get to keep the sales price, but $x will be subtracted from your period earnings after the market
closes. If at the end of a period you are holding, say, -1 Bond, $0.50 will be subtracted from your period
earnings.
The trading system checks your orders against bankruptcy: you may not be able to submit orders
which, if executed, could generate negative period earnings.
3.
Inside
information
For each period, x is drawn randomly from the numbers {0.01, 0.02, 0.03, ...., 0.49, 0.50}. Outcomes in
previous periods have no effect on the drawing. The draw is not disclosed to anybody until the end of the
period.
29
Some participants, however, may get inside information about x before the start of trading. This will
take the following form. A signal S is drawn from the numbers {x-0.10, x-0.09, x-0.08, ..., x+0.09, x+0.10}.
E.g., if x is 0.30, then S is drawn from {0.20, 0.21, 0.22, ..., 0.39, 0.40}. You can use S to infer what x could be:
if S is 0.05, then x could be anywhere from 0.01 to 0.15.
This signal S is then revealed to some participants. The same signal is revealed to all insiders.
The number and identities of participants who receive inside information vary across periods.
Sometimes nobody receives inside information. In certain periods when inside information is distributed, only
insiders know the number of insiders while in others nobody is told this number. This is made clear in the News
page.
30
Part
5.
Instruction
Set
For
Behavioral
Experiment
In
this
experiment,
you
are
given
four
(4)
problem‐solving
tasks.
Your
earnings
depend
on
how
well
you
do
on
each
of
them.
The
tasks
cover
a
broad
range
of
skills,
so
if
you
feel
that
one
task
is
hard,
another
one
may
be
easy
for
you.
The
four
tasks
are:
1.
Moving
Objects
Task:
you
are
asked
to
predict
movements
of
geometric
shapes
2.
Stock
Market
Task:
you
are
asked
to
predict
stock
price
movements
3.
Faces
Task:
you
are
asked
to
describe
the
intentions,
beliefs
or
emotions
reflected
in
a
person’s
gaze
4.
Riddles
Task:
you
are
asked
to
solve
a
number
of
logic
problems
You
will
be
invited
to
perform
these
tasks
in
random
order.
31
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1
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x
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h c cT
Ai
farther
away
from
the
small
triangle
than
at
present.
You
indicate
your
choice
with
the
arrow
keys:
push
the
“up”
key
if
you
think
the
large
triangle
is
going
to
be
farther
away;
push
the
“down”
key
if
you
think
it
will
be
closer;
push
the
“right”
key
if
you
think
the
large
triangle
will
remain
at
the
same
distance.
In
the
second
movie,
with
a
circle
and
two
squares,
you
are
asked
to
predict
the
movement
of
the
large
square.
The
movie
will
be
stopped
every
10s,
at
which
point
you
will
be
given
5s
to
choose
whether,
in
10s,
the
large
square
is
going
to
be
closer
to
or
farther
away
from
the
small
square.
You
indicate
your
choice
with
the
arrow
keys,
just
like
for
the
first
movie.
After
your
choice,
we
play
the
movie
for
another
10s,
stop
the
movie
again,
and
a
message
will
be
displayed
to
indicate
whether
you
won
(if
your
prediction
was
right),
or
whether
you
pay
a
penalty
because
you
failed
to
make
a
decision
within
the
allotted
time.
We
then
re‐start
the
movie
for
10s,
after
which
you
are
again
asked
to
predict
the
movement
of
the
large
triangle
(first
movie)
or
large
square
(second
movie),
etc.
We
will
continue
these
cycles
until
the
end
of
the
movie.
33
You
win
$1
for
every
correct
prediction.
You
pay
a
penalty
of
$0.25
for
any
failure
to
decide.
34
Stock
Market
Task
In
this
test,
you
are
asked
to
predict
price
changes
in
a
market
where
students
traded
a
stock
for
real
money.
Explanation
Of
The
Stock
Market
About
one
year
ago,
we
collected
trading
data
in
a
financial
markets
experiment
that
was
set
up
as
follows.
In
a
large
computer
room,
20
students
were
given
cash,
as
well
as
a
certain
number
of
a
security
called
stock.
They
could
trade
this
stock
over
an
anonymous
electronic
market.
When
market
closed,
the
stock
expired,
after
paying
a
dividend.
This
dividend
was
anywhere
between
¢0
and
¢50.
The
traders
were
not
told
the
exact
size
of
the
dividend
before
markets
closed.
Traders’
earnings
depended
on
the
cash
they
were
holding
at
the
end
of
trading,
as
well
as
the
number
of
stock
and
the
stock’s
dividend.
For
example,
if,
after
markets
close,
trader
Alice
owned
$2
in
cash
and
10
units
of
the
stock,
and
if
the
dividend
was
¢45,
Alice
was
paid
$2
+
10
x
$0.45
=
$6.50.
Note
that
the
prices
at
which
Alice
could
have
traded
the
stock
do
not
directly
influence
Alice’s
earnings;
it
would,
of
course,
have
35
affected
her
cash
holdings
(if
she
bought
one
unit
of
the
stock
at
¢35,
then
¢35
would
have
been
subtracted
from
her
cash
holdings).
We
repeated
this
situation
several
times.
Every
repetition
is
referred
to
as
a
period.
Periods
were
independent
in
that
the
dividend
in
one
period
had
no
influence
on
the
dividend
in
another
period.
Please
note
that
we
actually
paid
them:
the
experiment
was
not
a
“pretend.”
Students
left
the
trading
room
with
actual
money
in
their
hands.
In
principle,
the
stock
is
worth
about
¢25,
since
it
paid
a
dividend
chosen
at
random
between
¢0
and
¢50.
But
we
did
something
to
make
the
market
more
interesting.
We
separated
the
traders
in
two
groups:
insiders
and
outsiders.
The
insiders
were
given
an
estimate
of
the
dividend.
This
estimate
was
within
¢10
of
the
true
dividend.
The
outsiders
did
not
get
this
estimate.
36
The
insiders
bias
the
market.
For
example,
if
trader
Bob
is
an
insider
and
has
an
estimate
of
the
dividend
of
¢40,
he
knows
that
the
true
dividend
of
the
stock
is
between
¢30
and
¢50.
If
he
sees
an
offer
to
sell
the
stock
at
¢25,
he
would
want
to
accept
the
offer.
He
would
make
a
profit
of
at
least
¢5
and
up
to
¢25
per
unit
bought.
But
because
Bob
buys
the
stock,
its
price
tends
to
increase,
which
is
what
we
mean
when
we
state
that
insiders
bias
the
market.
Both
insiders
and
outsiders
must
act
with
care.
Insiders
must
trade
discreetly
in
order
to
avoid
revealing
their
knowledge
of
the
estimate
to
outsiders.
Similarly,
outsiders
need
to
observe
the
trades
carefully
in
order
not
to
buy
at
too
high
a
price
or
sell
at
too
low
a
price.
Your
Task
We
will
replay
four
periods
exactly
as
they
happened.
Every
so
often,
you
will
be
asked
to
predict
the
price
at
which
the
stock
will
trade
10s
later.
Replay
Interface
37
We
will
use
an
intuitive
graphical
display
of
the
orders
and
trades
in
the
electronic
market.
To
understand
this
display,
you
should
know
how
trade
took
place.
At
any
time,
traders
could
submit
offers
to
sell
or
to
buy
a
certain
number
of
stock
at
a
certain
price.
For
example,
trader
Alice
may
offer
to
sell
3
units
at
¢37
and
trader
Bob
may
offer
to
buy
2
units
at
¢35.
If
Alice
decides
that
a
sales
price
of
¢35
isn’t
bad
after
all,
she
may
cancel
two
units
of
her
sell
offer
at
¢37
and
sell
these
two
units
at
¢35
by
submitting
a
sell
offer
for
two
units
at
a
price
of
¢35
or
lower.
Thus,
actual
sales
take
place
when
a
trader
submits
a
sell
offer
at
a
price
at
least
as
low
as
the
highest
buy
offer;
actual
purchases
take
place
when
a
trader
submits
a
buy
order
at
a
price
at
least
as
high
as
the
lowest
sell
offer.
Remember
that
our
market
was
anonymous.
It
means
that
even
though
everyone
could
see
all
the
offers,
nobody
knew
where
they
came
from.
Participants
did
not
know
how
many
traders
there
were
in
the
marketplace,
let
alone
what
other
traders’
holdings
of
cash
and
stock
were.
In
the
graphical
replay
of
the
market,
bubbles
correspond
to
offers
in
the
marketplace.
See
the
sample
video.
Blue
bubbles
are
offers
to
buy
stock
and
the
red
bubbles
are
offers
to
sell
stock.
The
number
inside
the
bubbles
indicates
the
price
(in
38
cents).
The
size
of
the
bubble
indicates
the
number
of
units
offered
at
the
indicated
price.
All
the
offers
are
aligned
along
one
diagonal,
decreasing
in
price.
Bubbles
move
constantly
so
that
the
best
buy
offer
and
the
best
sell
offer
at
any
moment
in
time
stay
close
to
the
middle
of
the
screen.
Trades
are
shown
in
green.
They
flash
for
half
a
second,
after
which
the
bubble
shrinks
or
disappears,
to
indicate
the
reduction
in
the
number
of
units
offered
as
a
consequence
of
the
trade.
From
one
period
to
another,
we
will
randomly
change
the
diagonal
along
which
offers
and
trades
are
displayed.
Your
Task
In
Detail
So
what
do
you
have
to
do?
We
want
you
to
predict
the
stock
price
changes.
Every
10s,
we
stop
the
replay.
At
every
stop,
you
will
be
reminded
of
the
latest
trading
price
and
we
will
ask
you
to
make
a
prediction:
will
the
transaction
price
in
10s
39
be
higher,
lower,
or
the
same
as
the
latest
transaction
price?
Use
the
keyboard
to
enter
your
prediction:
•
up
arrow:
•
down
arrow:
trade
price
will
go
down
•
right
arrow:
trade
price
will
stay
the
same
trade
price
will
go
up
Remember,
we
are
asking
about
the
trade
prices
(trades
are
indicated
by
green
flashing
of
offer
bubbles),
not
the
buy
or
sell
offer
prices.
If
no
trade
takes
place
in
the
subsequent
10s,
we
assume
that
the
trade
price
stays
the
same.
You
will
be
given
5s
to
respond.
After
that,
we
re‐start
the
replay
for
10s.
At
the
end
of
this
10s
interval,
we
briefly
stop
the
replay
once
more,
to
indicate
whether
you
won,
or
whether
you
paid
a
penalty
because
you
did
not
choose
within
the
allotted
5s.
We
then
restart
the
replay
for
another
10s,
after
which
we
stop
and
ask
again
for
a
prediction.
These
cycles
continue
until
markets
close.
You
will
make
$1
for
every
correct
prediction.
If
you
fail
to
answer
within
the
allotted
5s,
you
will
be
fined
$0.25.
40
41
Riddles
Task
In
this
task,
we
want
you
to
solve
logic
problems.
The
interface
should
look
as
above.
42
You
will
have
30s
to
read
the
question,
to
type
your
answer
in
the
field
at
the
bottom
and
to
click
OK.
There
are
7
problems,
and
you
will
be
paid
$2
for
each
correct
answer.
You
pay
$1
when
you
fail
to
provide
an
answer
within
the
allotted
30s.
43
Faces
Task
In
this
task,
we
want
you
to
interpret
a
person’s
gaze.
The
interface
should
look
as
above.
For
each
gaze,
click
on
the
term
that
best
describes
what
the
person
in
the
picture
is
thinking
or
feeling.
You
may
feel
that
more
than
one
term
is
applicable
but
44
please
choose
just
one
term.
Before
making
your
choice,
make
sure
that
you
have
read
all
four
(4)
terms!
You
will
only
have
10
seconds
to
observe
the
person’s
gaze,
to
read
the
terms
and
to
indicate
your
chocie.
There
will
be
36
trials,
and
you
will
be
paid
$0.25
for
each
correct
answer.
You
pay
$0.10
each
time
you
fail
to
make
a
choice
within
the
allotted
time.
Before
we
start
the
test,
please
take
some
time
to
read
the
definitions
of
the
terms
we’ll
be
using.
These
are
listed
on
the
next
page.
45
ACCUSING
blaming
The
waiter
was
very
apologetic
when
he
spilt
soup
all
over
the
customer.
The
police
officer
was
accusing
the
man
of
stealing
a
wallet.
ARROGANT
conceited,
self‐important,
having
a
big
opinion
of
oneself
The
arrogant
man
thought
he
knew
more
about
politics
than
everyone
else
in
the
room.
AFFECTIONATE
showing
fondness
towards
someone
Most
mothers
are
affectionate
to
their
babies
by
giving
them
lots
of
kisses
and
cuddles.
ASHAMED
overcome
with
shame
or
guilt
The
boy
felt
ashamed
when
his
mother
discovered
him
stealing
money
from
her
purse.
AGHAST
horrified,
astonished,
alarmed
Jane
was
aghast
when
she
discovered
her
house
had
been
burglarized.
ASSERTIVE
confident,
dominant,
sure
of
oneself
The
assertive
woman
demanded
that
the
shop
give
her
a
refund.
ALARMED
fearful,
worried,
filled
with
anxiety
Claire
was
alarmed
when
she
thought
she
was
being
followed
home.
BAFFLED
confused,
puzzled,
dumbfounded
The
detectives
were
completely
baffled
by
the
murder
case.
AMUSED
finding
something
funny
I
was
amused
by
a
funny
joke
someone
told
me.
BEWILDERED
utterly
confused,
puzzled,
dazed
The
child
was
bewildered
when
visiting
the
big
city
for
the
first
time.
ANNOYED
irritated,
displeased
Jack
was
annoyed
when
he
found
out
he
had
missed
the
last
bus
home.
CAUTIOUS
careful,
wary
Sarah
was
always
a
bit
cautious
when
talking
to
someone
she
did
not
know.
ANTICIPATING
expecting
At
the
start
of
the
soccer
match,
the
fans
were
anticipating
a
quick
goal.
COMFORTING
consoling,
compassionate
ANXIOUS
worried,
tense,
uneasy
The
nurse
was
comforting
the
wounded
soldier.
The
student
was
feeling
anxious
before
taking
her
final
exams.
CONCERNED
worried,
troubled
The
doctor
was
concerned
when
his
patient
took
a
turn
for
the
worse.
APOLOGETIC
feeling
sorry
46
The
animal
protester
remained
defiant
even
after
being
sent
to
prison.
CONFIDENT
self‐assured,
believing
in
oneself
The
tennis
player
was
feeling
very
confident
about
winning
his
match.
DEPRESSED
miserable
George
was
depressed
when
he
didn't
receive
any
birthday
cards.
CONFUSED
puzzled,
perplexed
Lizzie
was
so
confused
by
the
directions
given
to
her,
she
got
lost.
DESIRE
passion,
lust,
longing
for
Kate
had
a
strong
desire
for
chocolate.
CONTEMPLATIVE
reflective,
thoughtful,
considering
John
was
in
a
contemplative
mood
on
the
eve
of
his
60th
birthday.
DESPONDENT
gloomy,
despairing,
without
hope
Gary
was
despondent
when
he
did
not
get
the
job
he
wanted.
CONTENTED
satisfied
After
a
nice
walk
and
a
good
meal,
David
felt
very
contented.
DISAPPOINTED
displeased,
disgruntled
The
Red
Sox
fans
were
disappointed
not
to
win
the
World
Series.
CONVINCED
certain,
absolutely
positive
Richard
was
convinced
he
had
come
to
the
right
decision.
DISPIRITED
glum,
miserable,
low
Adam
was
dispirited
when
he
failed
his
exams.
CURIOUS
inquisitive,
inquiring,
prying
DISTRUSTFUL
suspicious,
doubtful,
wary
Louise
was
curious
about
the
strange
shaped
parcel.
The
old
woman
was
distrustful
of
the
stranger
at
her
door.
DECIDING
making
your
mind
up
The
man
was
deciding
whom
to
vote
for
in
the
election.
DOMINANT
commanding,
bossy
The
sergeant
major
looked
dominant
as
he
inspected
the
new
recruits.
DECISIVE
already
made
your
mind
up
Jane
looked
very
decisive
as
she
walked
into
the
polling
station.
DOUBTFUL
dubious,
suspicious,
not
really
believing
Mary
was
doubtful
that
her
son
was
telling
the
truth.
DEFIANT
insolent,
bold,
don’t
care
what
anyone
else
thinks
DUBIOUS
doubtful,
suspicious
47
In
the
dark
streets,
the
women
felt
fearful.
Peter
was
dubious
when
offered
a
surprisingly
cheap
television
in
a
pub.
FLIRTATIOUS
brazen,
saucy,
teasing,
playful
EAGER
keen
Connie
was
accused
of
being
flirtatious
when
she
winked
at
a
stranger
at
a
party.
On
Christmas
morning,
the
children
were
eager
to
open
their
presents.
FLUSTERED
confused,
nervous
and
upset
EARNEST
having
a
serious
intention
Sarah
felt
a
bit
flustered
when
she
realised
how
late
she
was
for
the
meeting
and
that
she
had
forgotten
an
important
document.
Harry
was
very
earnest
about
his
religious
beliefs.
EMBARRASSED
ashamed
FRIENDLY
sociable,
amiable
After
forgetting
a
colleague's
name,
Jenny
felt
very
embarrassed.
The
friendly
girl
showed
the
tourists
the
way
to
downtown.
ENCOURAGING
hopeful,
heartening,
supporting
GRATEFUL
thankful
All
the
parents
were
encouraging
their
children
in
the
school
sports
day.
Kelly
was
very
grateful
for
the
kindness
shown
by
the
stranger.
ENTERTAINED
absorbed
and
amused
or
pleased
by
something
GUILTY
feeling
sorry
for
doing
something
wrong
I
was
very
entertained
by
the
magician.
Charlie
felt
guilty
about
having
an
affair.
ENTHUSIASTIC
very
eager,
keen
HATEFUL
showing
intense
dislike
Susan
felt
very
enthusiastic
about
her
new
fitness
plan.
The
two
sisters
were
hateful
to
each
other
and
always
fighting.
FANTASIZING
daydreaming
HOPEFUL
optimistic
Emma
was
fantasizing
about
being
a
film
star.
Larry
was
hopeful
that
the
post
would
bring
good
news.
FASCINATED
captivated,
really
interested
At
the
seaside,
the
children
were
fascinated
by
the
creatures
in
the
rock
pools.
HORRIFIED
terrified,
appalled
The
man
was
horrified
to
discover
that
his
new
wife
was
already
married.
FEARFUL
terrified,
worried
48
After
seeing
Jurassic
Park,
Hugh
grew
very
interested
in
dinosaurs.
HOSTILE
unfriendly
The
two
neighbors
were
hostile
towards
each
other
because
of
an
argument
about
loud
music.
INTRIGUED
very
curious,
very
interested
A
mystery
phone
call
intrigued
Zoe.
IMPATIENT
restless,
wanting
something
to
happen
soon
IRRITATED
exasperated,
annoyed
Jane
grew
increasingly
impatient
as
she
waited
for
her
friend
who
was
already
20
minutes
late.
Frances
was
irritated
by
all
the
junk
mail
she
received.
IMPLORING
begging,
pleading
JEALOUS
envious
Nicola
looked
imploring
as
she
tried
to
persuade
her
dad
to
lend
her
the
car.
Tony
was
jealous
of
all
the
taller,
better‐looking
boys
in
his
class.
INCREDULOUS
not
believing
JOKING
being
funny,
playful
Simon
was
incredulous
when
he
heard
that
he
had
won
the
lottery.
Gary
was
always
joking
with
his
friends.
NERVOUS
apprehensive,
tense,
worried
INDECISIVE
unsure,
hesitant,
unable
to
make
your
mind
up
Just
before
her
job
interview,
Alice
felt
very
nervous.
Tammy
was
so
indecisive
that
she
couldn't
even
decide
what
to
have
for
lunch.
OFFENDED
insulted,
wounded,
having
hurt
feelings
When
someone
made
a
joke
about
her
weight,
Martha
felt
very
offended.
INDIFFERENT
disinterested,
unresponsive,
don't
care
Terry
was
completely
indifferent
as
to
whether
they
went
to
the
cinema
or
the
pub.
PANICKED
distraught,
feeling
of
terror
or
anxiety
On
waking
to
find
the
house
on
fire,
the
whole
family
was
panicked.
INSISTING
demanding,
persisting,
maintaining
After
a
work
outing,
Frank
was
insisting
he
paid
the
bill
for
everyone.
PENSIVE
thinking
about
something
slightly
worrying
Susie
looked
pensive
on
the
way
to
meeting
her
boyfriend's
parents
for
the
first
time.
INSULTING
rude,
offensive
The
baseball
crowd
was
insulting
the
umpire
after
he
gave
a
invalidated
the
home‐run.
PERPLEXED
bewildered,
puzzled,
confused
Frank
was
perplexed
by
the
disappearance
of
his
garden
gnomes.
INTERESTED
inquiring,
curious
49
The
businessman
felt
very
resentful
towards
his
younger
colleague
who
had
been
promoted
above
him.
PLAYFUL
full
of
high
spirits
and
fun
Neil
was
feeling
playful
at
his
birthday
party.
SARCASTIC
cynical,
mocking,
scornful
The
comedian
made
a
sarcastic
comment
when
someone
came
into
the
theatre
late.
PREOCCUPIED
absorbed,
engrossed
in
one's
own
thoughts
Worrying
about
her
mother's
illness
made
Debbie
preoccupied
at
work
SATISFIED
content,
fulfilled
Steve
felt
very
satisfied
after
he
had
got
his
new
flat
just
how
he
wanted
it.
PUZZLED
perplexed,
bewildered,
confused
After
doing
the
crossword
for
an
hour,
June
was
still
puzzled
by
one
clue.
SCEPTICAL
doubtful,
suspicious,
mistrusting
Patrick
looked
sceptical
as
someone
read
out
his
horoscope
to
him.
REASSURING
supporting,
encouraging,
giving
someone
confidence
SERIOUS
solemn,
grave
Andy
tried
to
look
reassuring
as
he
told
his
wife
that
her
new
dress
did
suit
her.
The
bank
manager
looked
serious
as
he
refused
Nigel
an
overdraft.
REFLECTIVE
contemplative,
thoughtful
George
was
in
a
reflective
mood
as
he
thought
about
what
he'd
done
with
his
life.
STERN
severe,
strict,
firm
The
teacher
looked
very
stern
as
he
told
the
class
off.
REGRETFUL
sorry
SUSPICIOUS
disbelieving,
suspecting,
doubting
Lee
was
always
regretful
that
he
had
never
travelled
when
he
was
younger.
After
Sam
had
lost
his
wallet
for
the
second
time
at
work,
he
grew
suspicious
of
one
of
his
colleagues.
RELAXED
taking
it
easy,
calm,
carefree
SYMPATHETIC
kind,
compassionate
On
holiday,
Pam
felt
happy
and
relaxed.
The
nurse
looked
sympathetic
as
she
told
the
patient
the
bad
news.
RELIEVED
freed
from
worry
or
anxiety
At
the
restaurant,
Ray
was
relieved
to
find
that
he
had
not
forgotten
his
wallet.
TENTATIVE
hesitant,
uncertain,
cautious
Andrew
felt
a
bit
tentative
as
he
went
into
the
room
full
of
strangers.
RESENTFUL
bitter,
hostile
50
TERRIFIED
alarmed,
fearful
The
boy
was
terrified
when
he
thought
he
saw
a
ghost.
THOUGHTFUL
thinking
about
something
Phil
looked
thoughtful
as
he
sat
waiting
for
the
girlfriend
he
was
about
to
break‐up
with.
THREATENING
menacing,
intimidating
The
large,
drunken
man
was
acting
in
a
very
threatening
way.
UNEASY
unsettled,
apprehensive,
troubled
Karen
felt
slightly
uneasy
about
accepting
a
lift
from
the
man
she
had
only
met
that
day.
UPSET
agitated,
worried,
uneasy
The
man
was
very
upset
when
his
mother
died.
WORRIED
anxious,
fretful,
troubled
When
her
cat
went
missing,
the
girl
was
very
worried
51
Part
6.
Mathematics
Section
Of
Behavioral
Experiment:
Questions
and
Answers
Question
Answer
Consider
a
game
played
with
a
deck
of
three
cards:
spades,
clubs,
and
hearts.
Your
goal
is
to
identify
the
hearts.
switch
The
cards
are
shuffled
and
displayed
in
a
row,
face
down.
You
make
your
choice.
The
dealer
then
turns
over
one
of
the
two
remaining
cards,
provided
it
is
not
hearts.
He
then
offers
you
the
possibility
to
change
your
choice
and
switch
to
the
other
card
that
is
left
face
down.
What
is
the
best
strategy?
Should
you
switch,
stay,
or
does
it
not
matter?
Answer
below
"switch",
"stay"
or
"either".
Consider
a
deck
of
four
cards:
spades,
clubs,
hearts,
and
diamonds.
The
cards
are
shuffled
and
displayed
in
a
More
row,
face
down.
You
choose
one
card
at
random
and
it
is
discarded.
Then
the
dealer
turns
over
two
cards,
chosen
at
random,
but
provided
they
are
not
hearts.
Now
there
is
only
one
card
left
unturned.
If
the
two
cards
the
dealer
turns
over
are
diamonds
and
clubs,
is
the
probability
that
the
remaining
one
is
hearts
more
than,
less
than,
or
equal
to
0.5?
Answer
below
"more",
"less"
or
"same".
There
are
8
marbles
that
weigh
the
same,
and
1
marble
that
is
heavier.
The
marbles
are
all
uniform
in
size,
3
appearance,
and
shape.
You
have
a
balance
with
2
trays.
You
are
asked
to
identify
the
heavier
marble
in
at
most
2
(two)
weightings.
How
many
marbles
do
you
initially
have
to
place
on
each
tray?
Input
a
number
below.
Divide
100
by
1/2.
Is
the
result
more,
less
than
or
equal
to
100?
More
Answer
below
"more",
"less",
or
"same".
Jenn
has
half
the
Beanie
Babies
that
Mollie
has.
Allison
has
3
times
as
many
as
Jenn.
Together
they
have
72.
More
Does
Mollie
have
more
than,
less
than,
or
equal
to,
20
Beanie
Babies?
Answer
below
"more",
"less"
or
"same".
Johnny’s
mother
had
three
children.
The
first
child
was
named
April.
The
second
child
was
named
May.
What
was
the
third
child's
name?
Type
the
name
below.
52
Johnny
The
police
rounded
up
Jim,
Bud
and
Sam
yesterday,
because
one
of
them
was
suspected
of
having
robbed
the
Jim
local
bank.
The
three
suspects
made
the
following
statements
under
intensive
questioning.
Jim:
I'm
innocent.
Bud:
I'm
innocent.
Sam:
Bud
is
the
guilty
one.
If
only
one
of
these
statements
turns
out
to
be
true,
who
robbed
the
bank?
Type
the
name
of
the
robber
below.
Table
III.
The
Mathematical
(M)
test.
We
presented
subjects
with
seven
questions
in
a
random
order.
Subjects
had
30
seconds
to
type
the
answer.
We
ignored
typing
mistakes.
53
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