WealthMark LLC. Research

1. WealthMark
Research
By:
Ben
Esget
The
Hybrid
Solution
to
Portfolio
Management
Introduction:
Most
active
fund
managers
fail
to
beat
their
desired
index.
John
Bogle,
founder
of
the
Vanguard
Group,
testified
before
the
Senate
Subcommittee
on
Financial
Management,
the
Budget,
and
International
Security
on
November
2,
2003
stating
from
1984-­‐2002
the
S&P
500
index
annualized
a
return
of
12.2%
while
the
average
mutual
fund
annualized
9.3%.
Over
multiple
decades
only
about
10%
of
mutual
funds
managers
have
been
able
to
beat
the
S&P
500
index1.
Investors
compound
manager’s
underperformance
by
chasing
high
performing
managers,
investing
only
after
the
manager
has
racked
up
impressive
gains,
then
investors
watch
their
account
underperform
as
the
manager
regresses
to
the
mean
of
all
managers.
Morningstar
Inc.
frequently
cites
examples
of
mutual
fund
investors
doing
much
worse
than
the
fund
itself
by
trying
to
time
well
performing
funds
and
futher
highlights
this
problem
noting
Star
managers
that
have
fallen
from
grace,
citing
examples
such
as
the
Legg
Mason
Value
Trust,
the
CGM
Focus
Fund,
and
the
Fairholm
Fund.
But
even
Morningstars’
own
fund
selection
process
has
failed
to
add
value;
in
fact,
their
fund
selection
process
has
done
worse
than
the
average
actively
managed
mutual
fund
(see
Exhibit
1).
It
is
the
blind,
leading
the
blind,
leading
the
blind.
Exhibit
1:
1
Burton
G.
Malkiel,
“Reflections
on
the
Efficient
Market
Hypothesis:
30
Years
Later,”
The
Financial
Review
40,
http://www.e-­‐m-­‐h.org/Malkiel2005.pdf
(2005):
1-­‐9.
1

2. Morningstar
4
and
5
star
funds
versus
the
Wilshire
5000
index
*based
on
equal
investment
in
55
funds,
after
expenses,
loads
and
redemption
fees.
Source:
Hulbert
Financial
Digest
Exhibit
2
shows
historically
hedge
funds
have
achieved
higher
returns
than
traditional
indexes,
especially
when
adjusted
for
risk.
From
1994-­‐2011
hedge
funds
returned
9.07%
annualized
while
stocks
and
bonds
returned
7.18%
and
6.25%
annualized,
respectively.
Exhibit
2:
Source:
Center
for
Hedge
Fund
Research
Hedge
funds
come
with
unique
problems
that
do
not
make
the
extra
alpha
achieved
risk
free.
One
of
the
major
problems
with
hedge
funds
is
the
extreme
fee
structure.
Traditionally
hedge
funds
have
charged
a
2%
management
fee
and
a
20%
incentive
fee.
Fees
have
been
coming
down
over
the
last
18
years,
but
during
those
18
years
hedge
funds
pocketed
28.1%
of
profits
generated
in
the
portfolios2.
Hedge
funds
generally
use
leverage
to
enhance
returns
both
directly
and
at
the
security
level.
They
deploy
leverage
directly
by
accessing
lines
of
credit
and
indirectly
through
use
of
options
and
futures.
In
addition,
hedge
funds
are
usually
not
transparent
and
quite
often
illiquid.
This
creates
a
very
unique
situation
where
there
is
a
disjoint
in
distribution
of
information
between
the
potential
investor
and
the
fund
manager
as
to
how
much
risk
is
involved
with
the
fund,
and
how
robust
the
funds
strategy
is
going
forward.
Warren
Buffett
is
famous
for
saying
“When
the
tide
goes
out
you
see
who
is
swimming
naked.”
During
the
economic
collapse
of
2008
many
hedge
fund
investors
were
dismayed
to
discover
2
Laurence
Fletcher,
Managers
Pocket
28%
of
Hedge
Fund
Profits
–
Study,
http://www.reuters.com/article/2012/04/24/us-­‐hedgefunds-­‐study-­‐idUSBRE83N0JR20120424
(April
2012).
2

3. their
funds
using
copious
leverage
and
many
funds
“locked”
assets
allowing
liquidity
at
the
managers’
discretion.
Hedge
funds
also
display
massive
variance
of
the
individual
fund’s
performance.
Unlike
mutual
funds
where
most
simply
underperform
an
index,
hedge
funds
often
completely
collapse
or
experience
massive
losses.
Finally,
hedge
funds
are
required
by
law
to
set
net
worth
minimums,
limit
their
number
of
investors
and
are
restricted
in
how
they
market
in
an
effort
to
protect
unsophisticated
investors
from
risk.
To
offset
these
government
interventions
hedge
funds
usually
set
high
minimum
investment
amounts
to
maximize
their
assets
per
fund.
Overall,
the
portfolio
management
industry
lacks
a
clear
value
proposition
for
the
investor.
Where
outperformance
is
seen
it
is
often
overpriced,
difficult
to
predict
going
forward,
and
comes
with
unique
characteristics
which
generally
involve
excess
risk.
A
new
style
of
portfolio
management
is
needed
that
eliminates
these
issues
and
restores
the
investor
value
proposition.
Alpha
demystified:
There
are
three
key
ways
portfolio
managers
can
add
alpha
outside
of
employing
leverage.
1. Security
selection
2. Market
timing
3. Beta
selection
Source:
Investcorp
Security
selection
can
be
any
individual
investment
form
of
holding,
including
but
not
limited
to,
an
individual
stock,
bond,
index,
piece
of
real
estate,
an
option
or
a
futures
contract.
Beta
timing
is
the
practice
of
moving
allocations
within
a
portfolio
as
a
way
of
increasing
or
decreasing
risk.
Beta
selection
is
the
fund
managers’
choice
of
how
much
risk
in
relation
to
the
market
to
take.
The
vast
majority
of
portfolio
managers
attempt
to
add
value
through
only
one
or
two
of
these
methods.
Most
managers
often
do
so
in
a
discretionary
way
and,
as
discussed
above,
either
come
up
short
or
create
a
portfolio
exposed
to
very
unique
risks
and
exorbitant
fees.
In
order
to
combat
these
issues
we
recommend
systematically
adding
value
through
all
three
drivers
of
alpha.
Eliminating
Forced
and
Unforced
Errors:
3

4. In
investing,
like
baseball,
there
are
forced
and
unforced
errors.
Forced
errors
are
defined
as:
a
miss
caused
by
an
opponent’s
good
play.
Unforced
errors
are
defined
as:
an
error
in
service
or
a
return
shot
that
cannot
be
attributed
to
any
factor
other
than
poor
judgment
and
execution
by
the
player3.
Investors
can
greatly
reduce
unforced
errors
by
taking
a
systematic
approach
to
investing
because
it
greatly
reduces
the
chance
of
making
mistakes.
Indexes
are
a
great
example
of
this
since
indexes
are
a
manmade
systematized
approach
to
investing.
Individual
investors
like
mutual
fund
managers
often
fail
to
outperform
the
S&P
500
which
is
a
manmade
index.
Some
indexes
however
have
actually
fared
better
than
the
S&P
500.
Since
its’
inception
the
NASDAQ
index
has
outperformed
the
S&P
500
albeit
with
much
higher
volatility
(See
Exhibit
3).
Since
1990
the
global
bond
index
has
outperformed
the
S&P
500,
but
did
have
very
long
periods
of
underperformance
(See
Exhibit
2).
Exhibit
3:
The
probability
of
making
a
forced
error
goes
up
as
the
volume
of
trades
and
transactions
increase
because
there
is
a
chance
that
your
positioning
will
be
incorrect.
Each
transaction
exponentially
increases
the
odds
of
mistakes
compounding
while
every
transaction
increases
tax
liability
and
transaction
costs.
For
these
reason
robust
investment
strategies
should
not
only
be
systematic
but
will
have
a
better
chance
at
future
success
if
they
are
semi-­‐passive
with
limited
trading
limiting
the
opportunities
for
forced
or
unforced
errors.
Warren
Buffet
states
that
as
he
gets
older
his
holding
times
get
longer
and
longer
because
“When
you
sell
you
have
to
be
right
twice,
once
when
you
sell
and
once
when
you
buy
again.”
3
Wikipedia,
Glossary
of
Tennis
Terms
-­‐
Unforced
Error,
http://en.wikipedia.org/wiki/Unforced_error#U.
4

5. Lunch:
Not
Free,
but
More
of
It:
Academic
and
Industry
research
reveal
four
aspects
of
stocks
that
display
a
persistent
and
robust
ability
to
generate
alpha
systematically:
1. Value
2. Momentum
3. Dividends
4. Volatility
The
vast
majority
of
academic
and
industry
research
is
in
the
area
of
valuation.
Over
time
value
stocks
have
outperformed
other
stocks
whether
you
define
value
as
EV/EBITDA
(Enterprise
Value/
Earnings
Before
Interest,
Taxes,
Depreciation
and
Amortization,
EV/GP
(Enterprise
Value/Gross
Profit),
P/E
(Price
to
Earnings),
P/B
(Price
to
Book),
or
EV/FCF
(Enterprise
Value/Free
Cash
Flow).
Exhibit
4:
Source:
Gray
and
Carlisle,
“Quantitative
Value”,
Due
2013
Many
mutual
fund
managers
and
some
hedge
fund
managers
classify
themselves
as
value
investors.
Yet,
even
as
value
stocks
outperform
the
index
most
of
these
managers
fall
short.
Interestingly,
there
is
a
large
body
of
research
indicating
that
the
belief
humans
add
value
above
and
beyond
a
quantitative
model
is
incorrect.
The
data
shows
that
quant
models
are
the
ceiling
of
performance
and
human
5

6. decisions
not
only
don’t
enhance
performance
but
actually
diminish
it.4
Perhaps
it
is
cognitive
dissidence,
perhaps
it
is
the
high
level
of
testosterone
on
Wall
Street5,
or
perhaps
it
is
boyhood
fantasies
of
becoming
Warren
Buffet,
but
whatever
the
reason,
money
managers
seem
incapable
of
accepting
a
passive
approach
to
value.
Momentum
investment
strategies,
also
known
as
trend
following,
have
been
well
documented
as
a
way
to
add
alpha.
For
years
trend
followers
established
themselves
as
managed
futures
hedge
funds
but
more
recently
this
strategy
has
been
established
as
a
value
added
way
to
buy
stocks.
In
their
research
paper
“Does
Trend
Following
Work
on
Stocks?”
Willcox
and
Critenden
show
that
using
momentum
as
criteria
to
buying
and
selling
stocks
added
substantial
value
over
the
basic
index
(See
exhibit
5).
Exhibit
5:
Source:
Blackstar
Equity.
Equity
research
from
Goldman
Sachs
and
Dorsey
Wright
show
similar
results
both
on
a
hypothetical
back
tested
basis
and
on
a
real
time
performance
basis.
4
EYQUM
Investment
Management,
The
Case
for
Quantitative
Value
Investment,
http://www.scribd.com/doc/97001708/Case-­‐for-­‐Quantitative-­‐Value-­‐Eyquem-­‐Global-­‐Strategy-­‐20120613
(June
2012).
5
Sylvia
Ann
Hewlett,
Harvard
Business
Review:
Too
Much
Testosterone
on
Wall
Street?,
http://blogs.hbr.org/hbr/hewlett/2009/01/too_much_testosterone_on_wall.html
(January
2009).
6

7. Picking
stocks
based
on
dividend
yield
is
another
way
to
systematically
enhance
returns.
Similar
to
value
investing,
dividends
appear
to
add
incrementally
more
value
(See
Exhibit
6).
In
the
“Little
Book
of
Big
Dividends”,
Carlson
shows
that
not
only
does
buying
stocks
in
accordance
with
the
dividend
yield
add
value
but
more
value
can
be
added
when
run
through
an
algorithm
for
safety
as
well.
Professor
Jeremy
Siegel’s
research
shows
that
dividend
stocks
have
added
additional
alpha
going
back
as
far
as
data
exists
(1800’s).
Exhibit
6:
+
Source:
Credit
Suisse,
2011
In
recent
years
the
concept
of
a
low
volatility,
or
low
beta
index
(also
called
low-­‐variance,)
has
gained
popularity
as
a
wave
of
academic
research
shows
this
strategy
to
add
alpha.
One
of
the
more
interesting
aspects
of
low
volatility
stocks
is
that
they
appear
to
have
a
bimodal
distribution
of
beta
depending
on
market
conditions.
During
bull
markets
low
volatility
stocks
have
historically
had
a
beta
close
to
1
and
often
outperform
the
market
as
a
whole
during
this
period6.
During
bear
markets
low
volatility
stocks
have
historically
displayed
low
beta
and
have
proven
a
natural
hedge
against
6
Pim
van
Vliet,
“Ten
Things
You
Should
Know
About
Low-­‐Volatility
Investing,”
The
Journal
of
Investing
(Winter
2011):
141-­‐143.
7

8. drawdowns7.
In
1990,
S&P
started
its
own
index
that
tracks
the
100
lowest
volatility
stocks
within
the
S&P
500
for
the
last
trading
year
and
the
results
are
impressive
(See
Exhibit
7).
Other
researchers
have
looked
back
further,
using
back-­‐tested
hypothetical
indexes.
This
work
showed
similar
results
to
that
of
S&P.
One
of
the
more
interesting
pieces
of
research
was
published
in
the
Journal
of
Portfolio
Management
in
1991
entitled
“Beta
and
Return”
by
Fisher
Black.
They
show
from
1926
to
1991
“low-­‐
beta
stocks
did
better
than
the
CAPM
(Capital
Asset
Pricing
Model)
predicts,
and
high
beta
stocks
did
worse.”
He
explains
how
this
anomaly
is
not
decreasing
in
significance
but
rather
is
increasing,
“If
anything,
the
pattern
looks
stronger
(from
1965-­‐1991)
than
it
did
for
1926-­‐1965
period.”
Interestingly,
in
1991
Black
conceded
he
had
no
way
of
knowing
if
low
volatility
stocks
would
continue
to
outperform
going
forward,
but
we
can
now
see
they
definitely
have.
Similar
results
have
been
shown
by
Robeco
Asset
Management,
Russell
Investments,
and
Thorley
and
Perry
of
BYU.
Exhibit
7:
Source:
S&P
Research
Investors
should
consider
several
things
when
deciding
if
and
how
to
use
any
of
the
four
strategies
listed
above.
The
most
important
considerations
are
the
likelihood
that
a
strategy
will
continue
to
work
going
forward,
the
investor’s
ability
to
stick
with
the
strategy
and
the
turnover
within
the
strategy.
It
should
come
as
no
surprise
that
each
of
the
strategies
discussed
above
is
not
a
free
lunch.
Each
strategy
has
long
periods
of
time
where
it
underperforms
other
indexes,
sometimes
significantly.
At
points
of
underperformance
the
investor
must
decide
whether
to
stick
with
a
strategy
based
on
its
long-­‐term
merits
or
throw
in
the
towel
for
a
different
strategy.
In
my
opinion,
this
is
the
number
one
reason
7
Pim
van
Vliet,
Low-­volatility
investing:
a
long-­term
perspective,
http://www.robeco.com/professionals/insights/quantitative-­‐investing/low-­‐volatility-­‐investing/low-­‐
volatility-­‐investing-­‐a-­‐long-­‐term-­‐perspective.jsp
(January
2012).
8

9. investors
fail
to
achieve
above
average
performance.
This
dilemma
is
toughest
with
momentum
strategies
because
they
are
notoriously
volatile
(sharp
and
sudden
losses
known
as
drawdowns)
and
have
high
transaction
volumes
leading
to
a
higher
chance
of
errors.
Drawdowns
are
a
measure
of
investor
pain,
and
human
psychology
is
such
that
investors
often
evaluate
how
much
pain
is
worth
a
unit
of
gain.
Large
drawdowns
coupled
with
high
transaction
volumes
will
make
this
strategy
difficult
for
most
investors
to
stick
with.
Value,
Dividends,
and
Low
Volatility
strategies
are
all
lower
turnover
giving
the
investor
a
better
chance
of
sticking
with
the
strategy.
Only
a
Low
Volatility
portfolio
by
design
will
limit
downside
risk.
The
degree
to
which
value
stocks
and
high
dividend
portfolios
limit
downside
risk
is
much
more
uncertain.
Examining
the
market
crash
of
2008,
value
metrics
showed
bank
stocks
to
be
significantly
undervalued
and
banks
appeared
on
most
dividend
screens
as
the
highest
yielding
stocks.
Of
course,
bank
stocks
were
some
of
the
hardest
hit
during
the
2008
market
collapse
leaving
many
of
these
portfolios
significantly
underperforming
other
indexes
and
investors
facing
the
dilemma
discussed
above.
The
S&P
Low
Volatility
index
however,by
design
is
low
volatility
and
experiences
significantly
less
downside.
The
S&P
500
Low
Volatility
index
includes
a
high
degree
of
value
stocks
and
both
currently
and
historically
boosts
a
higher
dividend
yield
than
the
S&P
500
index.
Low
volatility
portfolios
have
the
advantage
of
being
value
oriented
and
high
yielding;
but
value
portfolios
and
high
yielding
portfolios
do
not
necessarily
boast
low
volatility.
Low
volatility
portfolios
have
conceptual
advantages
as
well,
primarily,
because
they
are
difficult
to
arbitrage
away.
Not
only
do
the
portfolios
tend
to
favor
some
of
the
largest
most
liquid
stocks
in
the
world
these
portfolios
are
not
designed
to
“outperform”
but
rather
reduce
volatility.
In
a
world
where
portfolio
managers
are
paid
handsomely
for
outperformance
it
is
irrational
for
portfolio
managers
to
focus
on
a
strategy
that
is
not
designed
for
explicit
outperformance
(never
mind
the
difficulty
marketing
such
a
strategy
during
a
raging
bull
market)
thus
creating
a
situation
where
a
known
driver
of
alpha
is
left
underutilized.
Picking
Your
Poison:
Choosing
how
much
risk
to
accept
during
portfolio
construction
is
like
choosing
poison;
at
some
point
any
amount
of
incremental
risk
will
sting.
The
world
of
beta
is
infinite,
ranging
from;
cash
with
a
beta
of
zero
to
penny
stocks
and
options
often
approaching
Vegas
odds.
Choosing
the
right
amount
of
incremental
risk
is
an
extremely
complex
concept
where
even
the
best
portfolio
managers
quite
often
underestimate
the
extremities
within
markets89.
One
of
the
best
ways
to
construct
a
robust
portfolio
is
to
find
a
baseline
beta
for
your
portfolio
and
incrementally
add
or
subtract
beta
accordingly.
In
a
portfolio
where
only
broad
based
liquid
indexes
are
used,
securities
and
allocations
should
be
preselected
in
a
way
to
systematically
increase
or
reduce
risk.
By
preselecting
betas
and
asset
allocation
for
robust
portfolio
performance
under
periods
of
extreme
market
duress,
the
portfolio
manager
has
a
higher
probability
of
avoiding
the
large
losses
seen
by
many
hedge
funds
as
well
as
creating
a
strategy
that
is
easier
for
investors
to
stick
with.
The
beta
of
the
S&P
Low
Volatility
index
is
.57
while
that
of
the
S&P
500
is
1
.
This
means
for
every
1%
the
S&P
500
moves,
the
Low
Volatility
index
moves
.57%,
on
a
daily
basis.
We
set
a
baseline
beta
to
be
that
of
the
S&P
Low
Volatility
index.
Thus,
any
incremental
increase
or
decrease
in
beta
from
this
8
Roger
Lowenstein,
When
Genius
Failed:
The
Rise
and
Fall
of
Long-­‐Term
Capital
Management
(Random
House
Trade
Paperbacks,
2001).
9
Nassim
Nicholas
Taleb,
The
Black
Swan:
Second
Edition:
The
Impact
of
the
Highly
Improbable:
With
a
new
section:
"On
Robustness
and
Fragility
(Random
House
Trade
Paperbacks;
2
edition,
May
2010).
9

10. starting
point
must
be
justified
and
should
be
done
in
a
very
controlled
way.
To
reduce
beta
we
incrementally
add
portions
of
the
Barclays
bond
aggregate
and
to
increase
beta
we
incrementally
add
portions
of
the
NASDAQ
index,
up
to
60%.
The
beta
of
the
Barclays
bond
aggregate
against
the
S&P
500
is
.17
with
a
.20
correlation
and
the
beta
of
the
NASDAQ
against
the
S&P
500
is
1.18
with
a
correlation
of
.9210.
Notice,
the
highest
beta
we
are
willing
to
accept
in
any
portion
of
our
portfolio
is
only
1.18
and
this
is
blended
with
an
index
with
a
beta
of
.57
bringing
the
overall
maximum
portfolio
beta
to
.814
(much
lower
than
the
S&P
500).
For
reference
the
S&P
High
Beta
index
has
a
beta
of
1.7
and
the
Oil
Service
Sector
index
has
a
beta
of
1.4711.
The
standard
deviation
of
the
S&P
Low
Volatility
index,
the
Barclays
Bond
Aggregate
index
and
the
NASDAQ
are,
11.37%,
2.86%
and
18.43%,
respectively.
By
systematically
controlling
the
beta
in
the
portfolio,
the
portfolio
manager
increases
the
probability
of
avoiding
extreme
losses,
enhancing
investors’
ability
to
stick
with
the
portfolio
during
extreme
conditions,
the
probability
or
repeatability
of
performance,
and
alpha.
Macro
Advantage:
Beta
timing
has
a
terrible
reputation
littered
with
high
frequency
trading
styles
and
massive
losses
but
let
us
be
clear,
any
type
of
portfolio
change
is
beta
timing.
The
decision
to
rebalance
a
portfolio,
sell
one
stock
and
buy
another,
or
implement
tactical
asset
allocation
are
all
decision
that
involve
changing
the
beta
mix
of
a
portfolio
and
are
therefore
market
timing.
As
discussed
above,
such
decisions
can
be
made
in
a
systematic
way
or
in
a
haphazard
way.
Even
indexes
perform
some
market
timing
when
they
rebalance
the
indices.
In
its
purest
form,
beta
timing
is
going
from
100%
in
the
S&P
500
to
100%
cash,
moving
beta
from
1
to
0;
any
other
portfolio
reconstruction
is
simply
the
same
thing
to
a
lesser
degree.
Macro
strategies
can
increase
alpha
while
reducing
risk
(See
Exhibit
8).
Macro
strategies
add
value
via
careful
beta
timing
and
controlling
risk
via
VaR
(Value
at
Risk)
methodologies
but,
as
discussed
above,
often
deploy
leverage
and
liberal
use
of
options.
The
power
of
Macro
strategies
are
found
in
deep
seeded
human
responses
and
tendencies
and
will
not
be
easily
arbitraged
away.
In
fact,
as
the
world
becomes
more
efficient
at
a
micro
(bottom-­‐up)
level,
the
world
has
become
increasingly
inefficient
at
the
macro
level
(top
down).
To
defend
this
point
famed
investor
Peter
Thiel
points
out
“in
the
last
30
years
we
have
had
more
boom/bust
cycles
than
in
the
history
of
the
stock
market.”
Even
in
the
face
of
less
efficient
markets
at
the
macro
level
investors
often
still
cling
to
passive
indexes.
This
behavior
creates
alpha
generating
opportunities
for
those
willing
and
capable
to
exploit
systematic
macro
factors.
Human
biases,
including
confirmation,
optimism,
loss
aversion,
the
planning
fallacy,
herding,
recency
bias,
cognitive
dissidence,
and
the
story
bias12,
all
create
unique
behavioral
economic
tendencies
that
cause
investors
to
make
forced
and
unforced
errors13.
These
errors
can
be
exploited
through
systematic
macroeconomic
models
and
because
they
are
deeply
ingrained
in
humanity
they
are
not
easily
arbitraged
away,
although
some
conceptually
offer
a
higher
probability
of
success
going
forward
than
others.
But
can
a
macro
strategy
add
beta
timing
value
to
index
funds?
10
Bill
Harding
and
Marta
Norton,
In
Practice:
A
New
Guardrail
Against
Risk,
http://www.morningstar.com/advisor/t/42987553/in-­‐practice-­‐a-­‐new-­‐guardrail-­‐against-­‐risk.htm
(February
2011).
11
Yahoo
Finance,
Market
Vectors
Oil
Services
ETF
(OIH),
http://finance.yahoo.com/q?s=oih&ql=1
(August
2012).
12
Barry
Ritholtz,
Investors
10
Most
Common
Mistakes,
http://www.ritholtz.com/blog/2012/07/investors-­‐10-­‐most-­‐
common-­‐mistakes/
(July
2012).
13
Michael
m.
Pompain,
CFA,
Readings
7-­‐9
Portfolio
Management
Study
Session
Chapter
3
Level
III
CFA
Study
Material-­‐Behavioral
Finance,
Individual
Investors,
and
Institutional
Investors.
10

11. There
is
a
good
deal
of
research
that
shows
very
simplistic
macro
overlays
are
effective
in
adding
value
to
a
buy
and
hold
index
strategy.
Beta
timing
indicators
shown
to
add
value
include
the
dividend
yield
of
the
S&P
50014,
the
P/E
1015,
Price
to
Peak
Earnings16,
P/E
10
adjusted
for
periods
of
high
and
low
liquidity17,
P/B18,
change
in
liquidity19,
volatility20
21,
and
momentum22.
In
addition,
our
research
shows
changes
in
credit
spreads
and
deterioration
in
industrial
economic
data
can
add
value
when
used
as
an
indicator
to
lower
beta.
Exhibit
8:
Comparison
HFRI
MACRO
Index
vs
S&P
500
(Source:
HFR)
14
Ben
Stein
and
Phil
DeMuth,
Yes,
You
Can
Time
the
Market!
(Wiley,
April
2003).
15
Robert
Shiller,
Homepage
of
Robert
Shiller,
http://www.econ.yale.edu/~shiller/
(August
2012).
16
John
P.
Hussman,
Should
Come
as
No
Shock
to
Anyone,
http://hussmanfunds.com/wmc/wmc091116.htm
(November
2009).
17
Clarium
Capital,
A
Macro
Framework
for
Valuing
the
S&P
500
http://www.scribd.com/doc/14673020/A-­‐Macro-­‐
Framework-­‐for-­‐Equity-­‐Valuation
(2009).
18
Goldman
Sachs,
“Global
Tactical
Asset
Allocation
(GTAA),”
Asset
Management
Primer
(2003).
19
Lubos
Pastor
and
Robert
F.
Stambaugh,
“Liquidity
Risk
and
Expected
Stock
Returns,”
National
Bureau
of
Economic
Research,
http://www.nber.org/papers/w8462.pdf
(September
2001).
20
Russell
Investments,
Viewpoint:
Volatility-­‐responsive
asset
allocation
http://www.russell.com/Institutional/research_commentary/PDF/Volatility_responsive_asset_allocation_.pdf
(August
2011).
21
Goldman
Sachs,
“Global
Tactical
Asset
Allocation
(GTAA),”
Asset
Management
Primer
(2003).
22
Mebane
T.
Faber
and
Eric
W.
Richardson,
The
Ivy
Portfolio:
How
to
Invest
Like
the
Top
Endowments
and
Avoid
Bear
Markets
(Wiley,
April
2011).
11

12. Our
firm
has
developed
a
macro
model
that
measures
rates
of
change
in
credit
spreads
and
industrial
economic
data.
Back
tested
to
1976
the
model
adds
alpha
and
reduces
volatility
while
keeping
turnover
at
bay.
Exhibit
9
shows
the
back-­‐tested
results
of
implementing
this
strategy
as
a
simple
beta
timing
approach,
literally
taking
beta
from
1
to
0.
The
strategy
is
either
fully
invested
in
the
S&P
500
or
in
money
market
starting
in
1976.
Over
the
36
year
period
this
macro
beta
timing
model
annualized
a
compound
growth
rate
of
10.55%
while
the
S&P
500
achieved
7.4%.
In
addition,
the
model
effectively
reduced
risk
and
did
so
with
only
9
roundtrip
trades
during
this
time.
Exhibit
9:
Macro
Timing
model
vs
S&P
500
growth
of
$1000
Using
a
macro
overlay
to
determine
the
appropriate
amount
of
beta
risk
to
take
given
market
conditions
this
model
adds
value
over
the
S&P
500
index
through
higher
returns
and
reduced
volatility.
Conceptually
it
makes
sense
an
index
weighting
portfolio
risk
according
to
indicators
of
market
risk
would
add
value
over
and
above
a
traditional
index.
Putting
it
All
Together:
This
paper
has
discussed
various
techniques
that
can
be
used
to
systematically
add
value
through
the
three
drivers
of
alpha.
But
when
used
together
the
results
are
even
more
impressive.
Our
optimal
strategy
mix
takes
the
“best
of”
all
three
drivers
of
alpha.
First,
we
take
advantage
of
systematic
security
selection
through
alpha
drivers
by
maintaining
a
60%
allocation
in
the
S&P
500
Low
Volatility
index.
This
position
has
the
added
advantage
of
keeping
turnover
low,
the
risk
of
a
portfolio
12

13. blow
up
low,
and
establishes
a
low
beta
floor
from
which
beta
may
be
increased
or
decreased
accordingly.
Second,
we
take
advantage
of
beta
selection
by
pre-­‐establishing
allocation
percentages
and
beta
drivers.
Thirdly,
we
take
advantage
of
beta
timing
via
a
systematic
macro
approach
that
monitors
global
credit
spreads
and
changes
in
industrial
economic
data.
Exhibit
10
shows
the
results
of
combing
all
three
strategies
since
1991
(The
first
year
of
the
S&P
500
Low
Volatility
index.)
For
this
period
the
blended
strategy
delivered
a
15.22%
annualized
rate
of
return
versus
6.9%
for
the
S&P
500
with
only
three
roundtrip
trades
(above
an
annual
rebalancing
trade).
Exhibit
10:
Blended
Strategy
vs
S&P
500
BLENDED
S&P 500 STRATEGY
MAX DRAW
DOWN 56.78% 29.83%
STANDARD
DEV 17.63% 14.03%
SHARP RATIO 0.23 0.74
SORTINO
RATIO 0.28 1.34
*assumes a risk free return of 3%
13

14.
For
allocation
constrained
portfolios
our
research
shows
that
using
the
beta
timing
model
discussed
above
to
shift
beta
within
asset
classes
also
generated
alpha
while
lowering
volatility.
The
degree
of
outperformance
and
change
in
volatility
from
the
original
benchmark
depends
on
how
aggressive
of
a
shift
is
made
which
is
an
individual
preference.
For
instance,
following
the
technique
described
in
our
beta
timing
section
above
to
increase
or
eliminate
exposure
to
high
yield
bonds
within
a
fully
invested
bond
allocation
added
alpha.
Likewise,
shifting
from
the
S&P
500
to
the
S&P
500
Low
Volatility
considerably
reduced
volatility
and
generated
alpha
while
remaining
fully
invested
(See
Exhibit
10).
Even
within
sub
asset
classes,
like
small
cap
stocks
or
emerging
markets,
throttling
between
a
passive
index
and
the
corresponding
low
volatility
index
increased
returns
and
limited
downside.
Exhibit
10:
S&P
500
to
S&P
500
Low
Volatility
Index
vs
S&P
500
9000
8000
7000
S&P 500 to
6000 S&P 500 Low
Volatility Index
5000 S&P 500
4000
3000
2000
1000
0
11/19/1990
11/19/1991
11/19/1992
11/19/1993
11/19/1995
11/19/1996
11/19/1997
11/19/1998
11/19/1999
11/19/2000
11/19/2001
11/19/2002
11/19/2003
11/19/2005
11/19/2006
11/19/2007
11/19/2008
11/19/2009
11/19/2010
11/19/2011
11/19/1994
11/19/2004
Discussion:
Discussed
above
are
a
multitude
of
investment
strategies
to
add
value
beyond
standard
indexing.
Our
firm
uses
two
basic
strategies
depending
on
an
individuals’
risk
tolerance
and
portfolio
constraints.
For
clients
that
desire
no
active
management
of
any
kind
we
deploy
a
multi-­‐asset
allocation,
following
the
endowment
strategy,
while
targeting
low
volatility
within
each
asset
class.
To
the
degree
we
are
free
to
do
so
we
adjust
allocations
in-­‐line
with
our
macro
model.
For
clients
that
seek
active
management
we
deploy
the
blended
strategy
as
discussed
above.
14

15. For
both
of
these
strategies
there
is
a
clear
value
proposition.
By
avoiding
an
incentive
fee,
charging
a
very
low
management
fee
and
utilizing
index
funds
we
are
able
to
pass
alpha
gains
along
to
the
client.
This
allows
us
to
avoid
the
use
of
leverage,
options,
or
deployment
of
high
beta
strategies
that
we
are
inherently
risky.
The
value
proposition
for
the
investor
is
further
enhanced
in
that
all
of
our
work
focuses
on
robust
(long-­‐term)
strategies
versus
the
best
performing
(short-­‐term).
For
instance,
none
of
the
macro
indicators
analyzed
were
chosen
because
of
the
numbers
rather
they
were
chosen
for
their
qualitative
attributes
and
then
tested
for
our
strategy.
Similarly,
allocation
percentages
were
not
chosen
to
optimize
returns
they
were
selected
with
the
aim
of
keeping
risk
within
reason.
Indexes
that
were
chosen
are
very
liquid,
stable,
and
general
(no
specialized
indexes).
Finally,
everything
is
broken
down
in
a
systematic
fashion.
By
not
over-­‐optimizing
everything
our
model
is
highly
prepared
for
the
uncertainty
of
the
future.
As
a
final
point,
the
strategies
discussed
in
this
paper
flip
the
investment
model
on
its
head.
Instead
of
trying
to
price
stocks
or
the
market
itself,
our
blended
strategy
prices
risk
both
at
the
security
level
and
at
the
macro
level.
If
anything,
our
research
indicates
investors
routinely
take
too
much
risk.
They
take
too
much
risk
picking
stocks
which
is
why
low
volatility
strategies
outperform
the
CAPM
predicts
and
they
take
too
much
risk
even
when
macro
risks
are
increasing.
This
is
why
our
macro
model
reduces
downside
volatility
and
generates
alpha.
Investors
also
take
too
much
risk
when
developing
their
investment
strategy
in
that
they
deploy
strategies
with
little
concern
for
beta
or
how
the
portfolio
will
be
under
extreme
circumstances.
Investors
have
a
seemingly
infinite
amount
of
choices
about
how
to
invest
their
hard
earned
dollars
yet
many
of
them
continue
to
invest
in
strategies
that
offer
a
weak
value
proposition.
This
paper
lays
out
a
strategy
where
the
value
proposition
is
re-­‐established.
By
embracing
a
hybrid
model
of
portfolio
management
investors
can
gain
the
best
of
indexing
and
macro
hedge
fund
models
while
alleviating
some
of
the
biggest
problems.
The
hybrid
portfolio
discussed
in
this
paper
offers
liquidity,
low
cost,
low
turnover,
risk
reduction,
improved
alpha
and
a
better
alignment
of
portfolio
risk
and
macro-­‐economic
risks.
15