Meson Capital Q1 2017 Letter
Machine learning and automation technologies are transforming the world more rapidly than anything history has seen. The opportunities and challenges for the investment landscape are multitude which is why we have made a concentrated effort to develop our own machine learning driven system for investing. What is possible with the combination of depth of research and action from an team of entrepreneurial activist investors, expert short sellers + software engineers? Introducing Meson Gravity: read on to learn more.
1
May
8
, 201
7
201
7 Q1
Partnership Letter
Dear Partner,
For the
quarter
our
performance was 13.6
% vs. indices of
3.8
% HFRI Hedge Fund Equity Index,
2.5
%
Russell 2000 and
6.1
%
S&P 500. Longs contributed
14.5
% while shorts detracted
0.9
% net of borrow
fees
and ended the quarter
29
% net long exposure.
It is no secret that it has been extremely difficult for most investors to find appealing value investments
in the current environment. The ‘Buffett
-
metric’ US market
cap to GD
P stands at 131
%, eclipsed only in
history for 6 months at the 2000 bubble peak
where it reached 145% briefly. In order to stay fully
invested, many investors have to reach outside of their value discipline and some, like ValueAct Capital
have been returning capital to investors.
In contrast
, we have been busier than ever
and have
more
opportunities than capital and bandwidth to tackle
, particularly in the field of electrification where we
are working on a major new investment
.
We refuse to stretch outside
our discipline and instead
have
deepened
our capabilities to adapt to the cu
rrent more challenging environment.
Since the industrial revolution, t
echnology
has been a wedge between those that
can adaptively use
tools
to amplify their capabilities and those that are displaced by it. A map of the 2016 US election
outcome
approximates the boundary of the amplify vs displace effect. Today, automation and AI are
accelerating this trend to unprecedented impact.
In 1990 the ‘Big 3’ in Detroit (GM, Ford, Chrysler) had
a market capitalization (inflation adjusted) of $65 billion
with 1.2 million employees. Today
–
the ‘Big 3’
in Silicon Valley (Apple, Google, Facebook) have a market cap of
over
$1.5 Trillion with 190,000
people
:
a
14
,
5
00%
increase in value per employee.
If you thought you could avoid this by excluding tech from
your investment universe, good luck: in 2016 for the
first time
, there were more $1B+ acquisitions of
tech startups by NON
-
tech companies.
After a century of dominan
ce,
Buffett favorite Gillette lost 16%
of its US market share in
just
4 years to startup Dollar Shave Club thanks to YouTube and inexpensive
overseas manufacturing.
This letter expands on
the machine learning toolset that I mentioned
previously
. For the i
nvestors who
have been with us with some time, you will know that we have always worked hard to embrace
innovation while staying deeply rooted in a foundation of long term value investing.
Our research
Meson Net
HFRI Equity
Russell 2000
S&P 500
Q1 2017
13.6%
3.8%
2.5%
6.1%
2016
0.3%
5.4%
21.3%
12.0%
2015
4.0%
-
0.8%
-
4.4
%
1.4
%
2014
4.7%
1.8%
4.9
%
13.7
%
Since Jan 1, 2014
24.1%
10.4%
24.6%
36.9%
2
toolset started in 2009 with screening the universe o
ne metric at a time and
using
E
xcel. By 2014, we
had built a database driven system to take in hundreds of screens and integrate them together. In 2017
our new system takes all those
perspectives
and
captures history going
back decades so we can
rigorous
ly
and scientifically
test all of our assumptions with hard data.
Our goal is two pronged: A) To
generate multiples of our capital in long positions in companies that we can influence and improve their
strategy and operations via entrepreneurship and targ
eted activism and B) To hedge external / macro
factors
and
generate absolute return by shorting low quality companies
. The goal is to make consistent
profits while being positioned strategically to benefit disproportionately when there is a market
disloca
tion.
As Buffett says in his recent Berkshire annual letter:
Charlie and I have no magic plan to add earnings except to dream big and to be prepared
mentally and financially to act fast when opportunities present themselves. Every decade or so,
dark
clouds will fill the economic skies, and they will briefly rain gold. When downpours of that
sort occur, it’s imperative that we rush outdoors carrying washtubs, not teaspoons. And that we
will do.
Today is not a storm to wait out but the best opportunity
set
with
the right skillset
Times like 2009
are not just to be weathered through, they are to be taken advantage of aggressively.
Anyone can pay for insurance, but what if you could create insurance that
pays you
a premium and then
offers purchasing power exa
ctly when the opportunity set is
most interesting? We believe in the
importance of preparing for the once
-
a
-
decade ‘do
wnpour’ so strongly that we have invested
considerable effort and expense
in our
‘ark
buil
ding’
. The upshot is that in preparing for the worst, we
end up with a powerful toolkit that can provide a significant competiti
ve advantage across all periods.
The machine learning capabilities we have developed are not ‘black boxes’ but rather systems
to
enhance
our capabilities as long term fundamental investors
. With these tools we can
apply our
principles more consistently
for our more diversified passive positions while continuing to use our
entrepreneurial efforts to grow select businesses.
The nu
mber of investors who outperform with the ‘traditional’ value
-
focused stock
-
picking
methodology has dwindled as the world has changed in deeply structural ways.
The fundamental
change driver is technological and
to a lesser degree, demographic
–
the monet
ary policy changes are
mostly
symptomatic, rather than causa
l as is commonly misunderstood.
It was
impressed upon me by a mentor,
Richard B. Fullerton,
who is one of the most successful investors
of recent times, to always be on the ‘right side’ of larger
trends. In my view the clearest and most
p
redictable trend in capitalism are t
he exponential price/performance curves in technology
: Moore’s
Law for integrated circuits has now spilled over into the price/performance of solar panels, batteries,
sensors,
drones, robotics, etc
.
Consumers of these types of products are delighted by the massive
3
increase in capability at a given price point. Producers, on the other hand must keep pace with an
exponentially deflating revenue per unit of capability!
A
‘
tradi
tional
’
stock picking approach is firmly on the wrong side of this trend which is why we have
sought to do more and physically transform companies we invest in
as entrepreneurial activists.
We
concentrate our long investments on businesses positioned stra
tegically to benefit from declining
input
costs
and capability advancements. With respect to stock picking
-
h
umans are simply bandwidth
constrained,
emotionally
biased, and can rarely appreciate the compounding effects of
change.
To be perfectly clear: w
e 100% subscribe to the idea that we seek to buy companies for less than their
intrinsic value and short companies at prices significantly above their intrinsic value. My argument is
that 1) the market has become more competitive as more parties analyze a
nd make investment
decisions this way and 2) the business environment itself changes faster and in a more exponential way
making ‘intrinsic value’ much harder to
asses
s
. To understand the ‘dynamic intrinsic value’ of a
Company requires real business depth
of
understanding how things evolve in a market and also
within a
Company
. Some types of businesses can scale and quickly saturate their market
while others may have
positive feedback loops where they get even stronger as they grow larger
. A copper miner expanding its
production will cause prices to decrease and
mean revert
its revenue growth, Amazon builds more
datacenters and
expands
its lead as it furthers its per
-
unit cost advantage over rivals.
How Quant Funds Work in 1 Paragraph
Th
e first investors to
take advantage of computers to process data and make investments were ‘quant
funds’ that continue today to be successful
at scale
. The tools at their disposal were statistics and linear
models
–
limited by the computational capacity a
nd the availability of structured data to apply statistics
to. Today
–
the successful quants have become enormous, managing $30+ billion and the smaller
players moved towards high frequency trading. At their core, traditional ‘quant’ strategies are based
on
statistical correlations
: i.e.
linear models
. At a small scale
-
stand on the surface of the earth, the
horizon looks flat
–
step back to big scale in space and you see the curvature. Same idea with statistical
correlations with time scale
–
in the n
ext day or week the linear correlation can be a fairly good
representation of reality but over the next year you’re in the flat earth society. The consequence of this
is that t
he strategies tend
to be high
-
turnover:
a 1 week holding period means
trading y
our portfolio 52x
per year
. Strategies with lots of trading tend to require scale to have low enough trading costs
(including the infrastructure to execute) to be practical. But of course scale (>$1B+ AUM) means that
you can’t take meaningful positions i
n small companies to move the needle. This size barrier to entry
has meant there have been few new entrants to the quant fund landscape for some time and why,
despite
having
an engineering background, we did not focus on this direction at first.
L
ong
-
term success
is driven by competitive advantage
and
we had
little chance
of
creating against
large
incumbents
playing
the
ir
game.
Instead, our approach has been to ‘depth first search’ as investors by being entrep
reneurs and activists
and invest in smaller companies where we could be the largest and most sophisticated
–
and energetic!
4
–
stakeholder. This gave us a competitive advantage and a number of demonstrated successes. Along
the way we have thought hard abo
ut how to generalize what we have learned
about what drives the
change in intrinsic value over time
and codify it so that we could continue to screen and search for
similar situations later in a systematic way.
Lots of investors know how to look for
clues
to a stock being
mispriced relative to its apparent intrinsic value NOW but very few have been in the trenches seeing
how intrinsic value can increase or decrease from management decisions in the boardroom.
Machine Learning
Changes the Game
Starting
seve
ral years ago the landscape
around
the traditional quant funds shifted. New technology
has allowed for 1) the ability to work with unstructured data (i.e. natural language) that can be gathered
less expensively and 2) nonlinear predictive models. These t
ools were extremely expensive until the last
year or two and impractical to use for the investment process. Now it’s possible to build a machine
learning investing system with a small group of talented engineers using open source software and low
cost clo
ud computing.
Add
to the formula
a
n activist investor
,
who also happens to
be an engineer
,
to
help
direct what data factors are
important to predict how a company will perform in the future and
that is exactly what we have now built.
I introduce:
Me
son Gravity
, our machine learning system
to
predict the
long term
performance of
c
ompanies
using data
.
O
ur approach
,
al
though utilizing computational tools,
is
fundamentally the same
business
-
focused
approach
we have been
deploying
for years
employing
a long term
perspective
.
The term
“
quantamental
”
(quantitative
+ fundamental) has been
recently popularized to describe this
class
of
strategy
.
We aren’t competing with other quants
on trading
-
like timescales
–
we continue to focus on
small companies where the markets are less efficient and we can compete against other predomina
ntly
human, emotional, biased investors. Now we have a machine. Most of our competitors don’t, because
it is
very
hard to build. What usually happens when machines compete against human
s at
the same
game
? Year when machines defeated the human world cha
mpion: Checkers (1990), chess (1997),
Jeopardy! (2011), go (2016), p
oker
(2017).
Going back to principles in my very first letter to investors from 2009:
There are two potential sources of return when investing: 1) Mispricing
and 2) Intrinsic value
change
.
Phrased another way, these two sources are: 1) The Market and 2) The Business. Ben
Graham famously says, "In the short run, the market is a voting machine, but in the long run it is
a weighing machine." The market source of return is from a change in "vot
es" while the business
source is a change in "weight."
Mr. Market’s “votes” reflect the current state of the world and clarity of a company’s prospects ahead.
The data accounting for this has continued to become more available
–
from credit card transacti
on data
to Walmart parking lot satellite imagery. I can’t imagine making a strong argument for our ability to
compete from a position of strength in this dimension. We have become experts in the “weighing
machine” and understanding the effects of “diet”
on future weight of a business
,
to extend the analogy
5
to include time. This has been learned from the inside out after experience on half a dozen corporate
boards and numerous other entrepreneurial experiences. I believe understanding what drives a
busin
ess to change over time in a fundamental way is likely to be o
ut of reach from the pure quants
for
some time.
Meson Gravity Approach
: A Dynamic View of Intrinsic Value
Our investment strategy has always been to buy companies at a big discount to their int
rinsic value and
either a) be patient or b) act as a catalyst to close the gap or increase the intrinsic value over time.
Sounds simple
–
but how do you determine intrinsic value? Even if you could determine intrinsic value
today precisely, what if the
c
ompany
changes? The static view of intrinsic value is so incomplete that it
can be a dangerous concept. The world is dynamic and we can only see a little bit up the road
–
even
companies themselves can’t predict their own revenues a year out with the ben
efit of total inside
information. We can estimate a range for intrinsic values but that too
is
limited
when
framed in linear
thinking tied to today.
A 30
-
year lease on a commercial building has a pretty clear intrinsic value but the range for operating
businesses is as wide as ever. The core value of businesses is increasingly the
intangible
(i.e.
informational) component and
decreasingly
the stack of bricks or factory floor with easy to observe
GAAP
accounting
metrics. The
first
derivative of value is the quality of the people
and the thousands of
decisions that get made each day to cumulatively determine future value.
We aim to
compete with other human investors while utilizing
our
machine learning tools to achieve
superior consistency and depth of research
for our own fundame
ntally driven investment process
. As
long as there are
emotional and
biased people in the market and
Jim Cramer, et al. have the attention of
investors wielding meaningful capital and
95% of sell
-
side broker recommendations are “BUY” etc. there
will be an
opportunity set
for a cool rational approach
.
Stocks are not bought, they are sold.
Core tenets of our
machine learning enabled
strategy include:
1)
A l
ong
term fundamental approach over a year or longer, not weeks or months
with
short term
traders. This t
imescale is uncompetitive with quant investors as few have the long term
conviction about a business to ride out a month or quarter that isn’t ‘working’ in the stock.
2)
We f
ocus on the deeper causal factors
in our data
–
people, business quality
over time, a
nd
supply/demand dynamics in an industry
. What drives the
change
in
intrinsic value over time for
a company? Valuation metrics are important but generally too obvious to gain an edge.
3)
We short for the long term
:
economic gravity always wins in the end wi
th low quality businesses
run by self
-
motivated people.
Requirements to follow these tenets and the barriers to entry are:
1)
A long term approach requires
non
-
linear
models to make investment decisions
–
i.e. machine
learning, not merely statistics. The tec
hnology to implement this has only recently become
6
feasible at a reasonable cost and the software
engineering
requirements are substantial.
As an
investment strategy
–
this type of modeling can capture nuances about a company the same
way that a human inv
estor does. This is in stark contrast to
‘
smart beta
’
or
‘
factor
’
investing
where the characteristics are easily
measured
(
such as low price/book value
)
and
arbitraged
away
.
2)
Knowing what data to look at requires real domain expertise as a long term investor
and
the
ability to translate that into the same language that a machine can understand
:
typically
orthogonal skillsets.
This is the most proprietary piece of our investment process and requires
meaningful effort to gather and structure data that is not available from commercial vendors.
We agree with Google’s Alon Halevy that the hardest problems and biggest breakthrough
s come
from
int
egrating
different datasets
into one multifaceted view of reality
.
3)
To maintain long term conviction in shorts, diversification is required so a short going against
you doesn’t
need to be reacted against
adversely
solely
due to price action.
A problem still
remains: i
f you are right on 99% of your shorts but 1% of time the time you short Amazon in
2003, you lose.
To validate our models, we had to build a proprietary simulation
architecture to
realistically play out thousands of alternate versions of history. The computing infrastructure
required for this would
have cost millions of dollars before
recent cost reductions and softwar
e
advances in cloud computing.
As a quick asid
e
–
why short at all if it’
s so hard? A) Despite the
rising index, the performance is mostly from the big winners and
most st
ocks
perform worse
than T
-
bills
. B) We are trying to predict the future using historical data
–
great businesses are
almost always doing something new and harder to predict whereas failure is much easier to
predict.
Note that this approach is very differ
ent from how typical
quant funds work: rather than running
1000 back
-
tests to search for what strategy
would have
worked, we
forward
-
test our fundamental
ideas with many variations on history to reduce tail risk
s before anything starts
.
Risk control is
pa
ra
mount and simple metrics like Va
R are inadequate. We will undoubtedly encounter new
market situations that have never happened before and will respond our best, but at least we can
start with knowing we won’t likely repeat the same mistakes
from
history
.
Portfolio Updates
Our biggest winner in the quarter was Sevcon, which increased as the Company disclosed a number of
contract wins highlighting the fact that the market for electrification is growing extremely rapidly. I have
been passionate about the advancement of electr
ic propulsion and its ability to displace polluting
internal combustion engines since a very young age and it’s incredible to see the economics
now
work.
We have a new investment we are working on that builds
on
this competence that I will be reaching out
to investors shortly. The key technology challenges are involved in the power electrics and software
subsystems which present new obstacles to the incumbents who are electro
-
mechanical and are not
oriented towards R&D or software. A new type of motor te
chnology that is fundamentally
7
programmable and can benefit from Moore’s
Law seems likely to displace the current standard DC &
induction motors in the $100B+ global market for electric motors
.
InfuSystem has managed to navigate through the Medicare change that disrupted its revenue base May
2016. I
do not intend to
stand
for re
-
election at the coming annual shareholder meeting as my time and
effort can be redirected more profitably to our inves
tments that have
more
exponential
upside
. I’m
extremely proud of what we have accomplished at the Company since we acquired stock at $1.20/share
in November 2011 and saved it from
its
careen towards
bankruptcy.
I
stepped down as
Chairman over
two yea
rs go
and
today our
opportunity set leveraging technology in rapidly changing markets is too
exciting to spread efforts thin. Additionally, as we have developed more relationships with private
equity firms, our activist efforts will continue to be more fo
cused on situations where we can potentially
help small public companies avoid the burden and costs of public reporting during a transition period
–
that may require significant
growth
investment. In the case of InfuSystem, we made a
public friendly
bid
a
t $2.00/share with a
private equity
partner in July 2013 following the company’s unsuccessful sale
process. The special committee at the time decided that the offer was inadequate.
Onwards.
A
Challenging Market
Environment
for Long
-
Only Stock Pickers,
Better than Ever for Entrepreneurs:
The current environment
of high valuations and rapid technology change
should provide
tailwinds
for
both sides of our strategy. On the long side, it has never been better to be a
n
entrepreneurial business
builder. Cost
of
growth
capital is as low as any point in history and the amplification effects of
technology make human willpower and intelligence more economically potent than ever. On the short
side
–
increased competition and technological disruption is making the
lifecycle of poorly run
companies shorter than ever. The inflated valuations across the market allow for attractive entry points,
decreasing upside risk for these companies going forward.
Ever since I founded Meson in 2009, I have taken the path knowing
that I would be doing this for 50
years. I have always reinvested our management and incentive fee revenues for the long term to
improve our investment process and am tremendously excited for this new chapter. It has been a
significant up
-
front investmen
t and
will be ongoing
.
I believe we are uniquely positioned to take
advantage of the current and future market environment.
I continue to have virtually all of my
investable
net worth
committed
alongside
investors
in the
Partner
ship.
Please email me at
rmorris@mesoncapital.com
or call at
415
-
322
-
0486
if you have any
questi
ons
or are interested in investing
.
As always, thank you for reading.
Sincerely,
President
Meson Capital
Partner
s
,
LLC
8
Disclosure:
The information contained in this document is confidential and is being provided for information
purposes only to a limited number of financially sophisticated persons who have expressed an interest
in the matters described herein.
This is not an offering or the solicitation of an offer to purchase an interest in Meson Capital
,
LP (the
“Fund”) or any affiliate thereof. Any such offer or solicitation will only be made to qualified investors by
means of a confidential private placement memorandum and only in those jurisdictions where
permitted by law.
The views, opinions, and assu
mptions expressed in this presentation are as of the date printed on the
first page, are subject to change without notice, may not come to pass and do not represent a
recommendation or offer of any particular security, strategy or investment.
An investment
in the fund is speculative and involves a high degree of risk. Opportunities for
withdrawal, redemption and transferability of interests are restricted, so investors may not have access
to capital when it is needed. There is no secondary market for the in
terests and none are expected to
develop. The fees and expenses charged in connection with this investment may be higher than the fees
and expenses of other investment alternatives and may offset profits. No assurance can be given that
the investment objec
tive will be achieved or that an investor will receive a return of all or part of his or
her investment. Investment results may vary substantially over any given time period.
Results are compared to the performance of the S&P 500 Index for informational p
urposes only. The
Fund’s investment program does not mirror the S&P 500 Index and the volatility of the Fund’s
investment program may be materially different. The performance figures include the reinvestment of
any dividends and other earnings, unless othe
rwise noted. Past performance is not necessarily indicative
of future results. The holdings identified in this letter do not represent all of the securities purchased or
sold in the Fund.
Performance results for individual investors will be different from
the perfo
rmance results of Meson
Capital,
LP depending on their timing of capital contributions and withdrawals. Meson Capital Partners,
LLC or affiliated entities (“Meson”) is not responsible for any liabilities resulting from errors contained in
this com
munication. Meson will not notify you of any errors that it identifies at a later date.
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