The document discusses the increased unpredictability of financial markets due to greater complexity from a growing number of interacting variables and market participants. It draws an analogy between this and the inherent unpredictability of sandpile avalanches, where a single additional grain can trigger a large or small response. The internal dynamics of markets can no longer be modeled in a simple, mechanistic way. Investors must acknowledge their inability to predict outcomes and focus on constructing resilient portfolios that can withstand a wide range of possible future scenarios, augmented by ongoing risk management activities as conditions evolve.

1. C O N T R I B U T O R S
Ted Lucas
Managing Partner
tlucas@latticestrategies.com
Joel Wagonfeld
Senior Portfolio Strategist
jwagonfeld@latticestrategies.com
Mauricio Cevallos, CFA
Senior Partner
mfcevallos@latticestrategies.com
Lattice Strategies LLC
www.latticestrategies.com
(415) 508-3400
One Embarcadero Center, 23rd
Floor
San Francisco, CA 94111
Issue 2 | Q1 2011
REAL WORLD
RISK MANAGEMENT
Creating Investment Resilience
EmbracingtheCertaintyofUncertainty
E X E C U T I V E S U M M A R Y
The financial crisis of 2008 should be viewed as one possible outcome from among a broad spectrum of
potential trajectories that could have occurred, based on conditions at the time and a confluence of
changes in market dynamics that were already well underway. The economic and financial tumult that
ultimately brought world markets to the brink of failure heightened our awareness of the many latent,
destabilizing influences that can be triggered unexpectedly and then progress in unpredictable ways,
culminating in periods of contagion and wild swings in asset values. In addition, the events during the
recent crisis underscored the difficulty of predicting financial markets by unleashing a series of cascading
events that made visible hidden risks that were previously far from the minds of many investors.
These experiences created a higher level of awareness about the “certainty of uncertainty.” By this we
mean a permanent, step-function increase in the difficulty of predicting events whose outcomes are
determined not only by an expanding number of agents and variables, but also the dynamic relationships
among them. In our view, the crisis highlighted an inflection point that requires investors to change the
way they perceive and manage risk. This implies different priorities than those that have characterized
much of mainstream investing. Such new priorities include the consideration of a much wider range of
potential market outcomes and a risk-focused approach to portfolio construction/management in order to
maximize the likelihood and magnitude of long-term capital growth.
The myth of market predictability, which is a central concept in this discussion, has been exactly that for
nearly as long as markets have existed. Nevertheless, many investors have persisted in relying on
heuristics with questionable empirical validity, while celebrating investment stars who, based on specific
performance windows, appear to have cracked the code in whatever markets they operate. This history
begs the question of whether anything has, in fact, changed.
We believe that it has. As global market activity has become driven by an increased number and broader
variety of actors and events, as well as the unpredictable relationships among them, many prior
investment classifications and approaches have less predictive value than in the past. Factors that impact
specific market behavior – such as the sources of risk and return, the cause-and-effect dynamics of specific
events and the observable relationships among seemingly unrelated variables – have become
exponentially more complex. This reality has reached the point at which it prevents our ability to
comprehend these considerations in ways that could provide predictive, or even descriptive, value on a
repeatable basis.
However, this does not suggest investors should abandon hope. To the contrary, it underscores
the importance of acknowledging uncertainty, and then re-directing attention that was previously focused
on efforts to predict future market movements toward the more achievable, yet still challenging, pursuit of
constructing resilient portfolios. By resilient, we refer to portfolios that are comprised of a widely
diversified combination of assets and strategies, which are purposefully structured to withstand a broad
range of potential risks and scenarios. Such a portfolio would include assets that respond in varying
fashions to different levels of economic growth and inflation, as well as different investor risk appetite
regimes. Within this construct, a variety of active risk management disciplines must be applied in a
consistent, deliberate manner as market dynamics shift and evolve.
The notion of uncertainty is not new,
but there has been an irreversible
increase in unpredictability. This
implies different priorities going
forward than those that have
characterized traditional investing.

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Examples of such risk management strategies include:
 ongoing tactical allocation of risk among assets, based on changes in their relative risk/reward
characteristics as implied by their valuations, fundamentals and price dynamics
 inclusion of defensive portfolio components, such as liquid hedged and absolute return
strategies, to serve as “beta buffers” during adverse periods for risk asset exposure
 creation of a sustainable source of incremental returns based on a structured daily tax-loss
harvesting discipline within asset classes that are highly efficient and have sufficiently large
numbers of investment vehicles and trading liquidity
 aggressive management of all implementation expenses, particularly opaque costs of execution
 vigilance in monitoring and maintaining intended risk thresholds and exposures
Inevitably, the application of a comprehensive set of risk management tools and techniques in a proactive
and dynamic manner requires portfolio construction based on instruments with daily liquidity,
transparency and pricing.
This framework differs markedly from traditional investment approaches, which often begin with portfolio
design aimed at optimizing returns based on a singular view of how various assets will perform,
emanating from assumptions about forward-looking market and economic conditions. While this
approach may result in a portfolio that is “optimized” for that set of assumptions, the resulting portfolio
would clearly be vulnerable to whichever assumptions deviate from future reality. This is inevitable given
the complexity of today’s market environment as measured on virtually any basis, taking into account the
interactions of geopolitical, economic and financial system dynamics in an increasingly interlinked world.
In addition, such a portfolio may simply have exposures, perhaps unintentional, that make it more fragile
than otherwise realized. Such vulnerabilities – two examples being unrecognized liquidity risk and
converging correlations between risk assets – were widely in evidence during 2008.
Critically, we note that portfolios engineered for resilience are also designed with the aim of “optimal”
positioning. However, in contrast to more conventional approaches that are structured to benefit from one
particular scenario, resilient portfolios are designed to optimally balance the ability to withstand a wide
range of outcomes with the least downside capture, against maintaining the opportunity to participate in
positive markets. Achieving this positive asymmetry of returns – an advantageous “skew” between
upside capture in rising markets versus downside capture in adverse markets – is the primary determinant
of long-term capital growth. Therefore, in order to create portfolios that express this return shape, it
becomes essential to focus research efforts not only on points of potential opportunity but also areas of
potential failure, with greater emphasis on the latter. Investors who seek resilience will have to re-focus
and re-orient their mindset and approach toward designing portfolios that provide maximum robustness
across an expansive range of future scenarios, from the apocalyptic to the exuberant.
In summary, we believe there has been a permanent increase in the unpredictability related to trying to
forecast financial market activity. The primary implication for investors is that, in order to maximize the
probability of achieving optimal long-term capital growth, they need to constantly be asking and
objectively considering the following questions:
 What range of possible market conditions/developments could occur?
 How would the portfolio respond in each such scenario?
 What can be done to structure the portfolio for maximum resilience across a full spectrum of
possibilities?
 What ongoing activities can be incorporated to dynamically manage portfolio risk as market
conditions evolve and asset valuations and risk characteristics change?
Investors must change the way they
perceive and manage risk. The focus
must shift from past performance to
assessing how a portfolio would
respond across a spectrum of future
outcomes.
Resilient portfolios are designed to
withstand a wide range of scenarios,
augmented by an integrated series of
ongoing risk management activities
as market conditions evolve.

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T H E C E R T A I N T Y O F U N C E R T A I N T Y
Investment complexity has increased exponentially as an ever-growing set of influences, factors and
participants interact, impacting financial markets in ways that cannot be predicted with any certainty. This
reflects a convergence of disparate factors that impact markets, investor behavior and the varied decision-
making processes of a growing number of participants transacting across markets globally. In theory, the
cumulative effect of widespread, near-instantaneous access by greater numbers of investors to increased
amounts of data should inevitably result in a relatively more efficient market. Ironically, we believe it has
actually resulted in greater instability, precisely because market activity now reflects the actions of so
many independent global participants, each of whom has access to similar data but unique perspectives,
timeframes, motivations, biases and objectives that affect how data is interpreted and acted upon. Yet it is
also impossible to ignore how recent episodes of herding have driven various asset classes to highly
inflated levels and then pummeled them back down to unduly depressed levels.
Key changes in market dynamics include:
 the proliferation of market actors and increased global linkages
 organic, iterative feedback loops among these agents, also having their own dynamics that can
affect viewpoints and corresponding actions
 the destabilizing impact of rapidly shifting investor psychology
 correlations among various asset classes that are dynamic and conditional versus stable
 interdependent cascading effects that are inherently unpredictable
 the rise of what has been termed “the money grid” pulsating with flows driven by leveraged
players, often trading multi-legged positions
 the sheer speed and wide dissemination of data via new communication technologies and
devices
Although the notions of “new normal” and “old normal” have become overused financial vernacular in the
wake of the events of 2008, they have nevertheless served an important function by capturing popular
attention and casting light on the question of whether anything has in fact fundamentally changed
regarding the nature of investing and what it takes to be successful at it over the long term. The most
recent asset collapse was simply one potential manifestation of an economic and market context defined
by a significant, irreversible increase in the unpredictability of not only various fundamentals that impact
market activity but also the market reaction to those changes in fundamentals as they develop. This
increasing unpredictability will likely persist due to foundational changes in the way information is
distributed, interpreted and reflected in markets – as well as changes in the number and nature of actors
who can impact markets in observable, as well as unobservable, ways.
This inflection point is premised on the acknowledgment that market activity has become sufficiently
dependent on dynamic interactions between a growing number of disparate factors – many of which are
unobservable, and most of which are interrelated in ways that are unidentifiable or wholly unpredictable
Investment complexity has increased
exponentially as a result of changing
market dynamics and their
interaction with one another.
Unpredictabilityapplies not only to
the various fundamentals that
impact markets, but also the market
reaction to thosechanging
fundamentals.

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due to their sheer number and complexity – that reliable prediction about specific market activity within
any particular timeframe has become an illusory concept. But this changing context has not discouraged
continued attempts by various sages to forecast just about every market and economic variable under the
sun. Though unsuccessful on a sustained basis, many have persisted to convince others of their predictive
abilities. This will likely continue to be the case due to human nature and our innate desire to believe that
it is possible to predict market outcomes, when, in fact, it is beyond our ability to do so.
One powerful cause of increased unpredictability is the huge growth in agents that can impact various
outcomes and the dynamic relationships between these agents and subsequent events. A captivating real-
world example of this concept is the sandpile problem, first considered by Danish physicist Per Bak, re-
visited in the late 1980s by IBM Research Center scientist Glenn Held, and discussed by Joshua Cooper
Ramo in his provocative book, TheAgeoftheUnthinkable:
“The problem that fascinated Bak appeared, on the surface, simple enough: if you piled sand,
grain by grain, until it made a cone about the size of your fist, how would you know when that
tiny pyramid would have a little avalanche? Bak hypothesized that after an initial period, in
which the sand piled itself into a little cone, the stack would organize itself into instability, a
state in which adding just a single grain of sand could trigger a large avalanche – or nothing at
all. What was radical about his idea was that it implied that the sand cones, which looked
relatively stable, were in fact deeply unpredictable, that you had absolutely no way
of knowing what was going to happen next, that there was a mysterious
relationship between input and output. ‘Complex behavior in nature, ‘ Bak explained,
‘reflects the tendency of large systems to evolve into a poised critical state, way out of balance,
where minor disturbances may lead to events, called avalanches, of all sizes.’
It wasn’t that he didn’t see stability in the world, but that he saw stability as a passing
phase, a pause in a system of incredible – and unmappable – dynamism. Scientists
call systems like the sandpile or the universe ‘non-linear,’ precisely because their internal
dynamics routinely disrupt the idea that you can expect a given action to produce the same
reaction every time.
Once a pile reached a certain size, Held saw, it entered into that strange ‘critical’ state Bak had
anticipated. Sometimes one additional grain would cause an avalanche; other times, Held
could add thousands of grains before the sand started sliding off. But the most interesting
thing about the sandpile was its fundamental unpredictability. There was no magic
number. One additional grain of sand was as likely to start an avalanche as a dozen. What
happened within the pile, the shifting and sliding of grains, was as important as what
happened to the pile. There was no explicit link between how you hit the pile and how it
responded, no ‘proportionality’ between cause and effect. Just as Bak had theorized, the
sandpile was a system that could ‘break down not only under the force of a mighty blow, but
also at the drop of a pin.’
Every grain on the pile was, in a sense, linked by invisible webs of pressure and tension to every
other grain. So the full dynamics of sand physics grew in complexity a millionfold every second.
The nature of this expanding complexity demolished the concept of prediction ... Any complex
system likely expressed the same dynamics: the earth’s crust, ecosystems, stock markets, and
international politics. Past a certain point, the internal dynamics of these systems
were simply, bewilderingly unknowable. Held tried making piles of larger and larger
plates and found that, after a certain size, the system became so complex that no rules offered
even a general sense of how often a grain of sand would lead to catastrophe.
The internal dynamics of sandpiles
present a powerful metaphor for
conceptualizing the inherent
unpredictabilityof markets.

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The sandpile was in a continuous state of change; it never stood still long enough for any one
set of equations to describe it fully. Sand grains, stocks, pieces of the earth’s crust – these
moved not according to some simple input and output formula but rather because of a complex
logic, wheredense internal forces wereas important as any outside forces [...]
Our world, whether we are looking at financial markets or nuclear proliferation, now resemble
Bak’s sandpiles in many nervous-making ways. To begin with, it is defined by the two
explosive bits of physics that interested him most: increasing numbers of players and
connections between them. If you like, you can think of these two effects as
granularity – the unstoppable tumbling of fresh grains of sand onto our pile – and
interdependence – the surprising connections that link one part of the pile with
another.” 1
Writing about a similar transformation in his field, the economist Brian Arthur explained it this way:
“ ’In the standard view of the economy, which has an intellectual lineage that dates back to the
Enlightenment, the economy is mechanistic. It is complicated but can be viewed as a series of
objects and linkages between them. Subject and objects – agents and the economy they
perform in – can be neatly separated.’ But in a complex order, Arthur explains, ‘subject and
object cannot be neatly separated. And so the economy [or market] shows behavior that
we can best describe as organic, rather than mechanistic.’ ” 2
We believe the increasing complexity of market dynamics fundamentally alters the way investors must
incorporate considerations about risk, reward, outcome possibilities and, ultimately, their corresponding
impacts on markets and portfolios. However, investors must begin by acknowledging their inability to
consistently predict future events, since this will in turn enable them to recognize the potential for a wide
range of outcomes. The implication for investment strategy is that the foundational goal should
be the construction of resilient portfolios, engineered by purposefully combining a
comprehensive set of return sources in a way that provides robustness through whatever
conditions prevail. Accomplishing this goal requires simultaneously employing a variety of disciplined
risk management activities as conditions change and develop.
F O C U S I N G O N T H E R I G H T T H I N G S
In light of the inability to accurately predict the market’s activity in response to any particular events or
conditions, many investors, both private and professional, expend much time and effort focusing on
activities that can be dilutive to investment results. For example, rather than attempting to develop a
wide range of potential scenarios and structuring portfolios for maximum robustness across this range,
many investors instead appear intent to focus their efforts on developing a single forecast for economic
conditions and asset prices. And, typically, their track record in doing so supports the thrust of this paper:
“Every January, hordes of highly paid experts attempt to predict what the economy and the
markets will do in the coming year. Later in the year, nearly all of the forecasts turn out to be
wrong. Forecasting is as old as markets themselves. Records from ancient Mesopotamia,
according to research by historian Alice Louise Slotsky, are full of references to omens that were
1
Joshua Cooper Ramo, The Age of the Unthinkable(New York: Little, Brown and Company, 2009).
2
Ibid.
Investors should acknowledge their
inability to consistently predict
markets, since this will free them
to turn their attention toward
considering a wide range of
outcomes and their corresponding
portfolio impacts.

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believed to predict commodity prices [...] Practically ever since, pundits have been pumping out
forecasts – and missing the mark.” 3
Even forecasters whose primary job is to focus on predictions about broad variables such as market indexes
like the S&P 500 have been historically unreliable in terms of their predictive value – especially when
forecasting change versus forecasting trend. For example, as shown below, forecasters are typically overly
optimistic up until a period of extreme “surprise” (usually in the negative direction), after which they tend
to overcompensate in their subsequent forecasts. This persists until the next such surprise, after which this
cycle repeats itself. This likely reflects their desire to avoid being wrong twice in a row in the same
direction.
Year-End Value of the S&P 500 Index: Forecasts versus Actual 4
Not surprisingly, one questions why “people with years of experience, massive expertise and mountains of
data at their disposal so often get the future wrong?” 5
The answer, in part, reflects cognitive and
behavioral biases to which investors can succumb, such as extrapolation and anchoring. Additionally,
there are often misaligned incentives resulting from the natural human tendency to focus on evidence
supporting our beliefs and disregard that which might contradict our opinions. Many prognosticators have
an incentive to take extreme positions, simply to be heard above the noise – particularly since seemingly
“accurate” predictions attract even more attention, while those that don’t pan out are quickly lost in a sea
of new data. This is exacerbated by the constant barrage of new information, which serves to reinforce
investors’ inherently short-term mentality.
3
“Making Sense of Market Forecasts”, The Wall Street Journal, January 8, 2011.
4
Birinyi Associates, in “Making Sense of Market Forecasts,” The Wall Street Journal, January 8, 2011; Lattice Strategies LLC.
5
“Making Sense of Market Forecasts”, The Wall Street Journal, January 8, 2011.
“Experts” tend to be overly
optimistic until periods of market
adversity, after which they typically
overcompensate in the other
direction ... only to succumb,
eventually, to the same cycle all
over again.

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A separate, structural factor that can also lead to this same temptation to go for large pay-offs – even
among well-intentioned money managers – is the relatively basic math behind the asymmetry of returns.
While losses may be linear, the subsequent gains required to recover those losses increase exponentially as
the losses deepen.
Losses Are Linear ... But the Gains Required to Recover Increase Exponentially
Sources: Bloomberg, Lattice Strategies LLC.
Market participants have long acknowledged that even if one could accurately predict how a company
would perform during a particular time period, this did not necessarily correspond to an ability to make
money by trading based on that outcome. This lack of a direct relationship between fundamentals and
asset price behavior in the short-term has been well-documented for years, and can generally be
attributed to the inability to ascertain what is already being priced in by a presumably efficient market at
any particular point in time.
Neil Johnson succinctly articulates this important distinction in his book Simply Complexity. In the excerpt
below, he describes how human expectations and feedback loops provide a distinct source of
unpredictability because the dynamics of these invisible, iterative relationships are independent from
corresponding fundamentals. This is a key insight because many traditional investment approaches
assume a logical and stable relationship between fundamentals and market activity:
“We’d all like to be able to predict financial market movements. There is of course one huge
problem: financial markets are complicated, dynamical systems, which are
continually changing in ways that defy most experts.
Assumptions about the logical
relationship between fundamentals
and market activity are becoming
increasingly uncertain.

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Prediction in financial markets is fundamentally different from predicting the weather, the
outcome from the roulette wheel, or the outcome from tossing a coin. In a market, the
individual objects (i.e., traders) are each trying to predict price movements in order to decide
whether to buy or to sell. The net demand to buy or sell then determines the subsequent price
movement. The resulting price movement then gets fed back to the traders, who may use it in
their next decision to buy or sell. The cyclical process goes on continually, with price
movements being fed back to traders who then make decisions whether to buy or sell. And like
all humans, traders can’t help but notice what has happened before in the market. They will
tend to see patterns – or believe they see patterns – and then react to what they think they
see, or what they have heard. In other words, a financial market is riddled with
feedback. This feedback leads to new decisions of whether to buy or sell, which
leads to a new price, which leads to new feedback, which leads to a new price – and
so on.
Such intrinsic feedback does not arise when gambling on a roulette wheel or with the toss of a
coin. These objects are made up of molecules – and even though they may appear to behave in
a complicated way, they are simply following Newton’s Laws of motion. There is no decision-
making going on, and hence – unlike the market – the outcome obtained is in no way linked to
the predictions of people who are actually playing and gambling. Likewise, even if everyone
had the perfect prediction model of the weather, the weather would still do what the weather
does. All that would happen is that everyone would know exactly what to wear the next day.
However, this is not true in the markets. If everyone were to be given the perfect
prediction model, it would immediately stop being the perfect prediction model
because of this strong feedback effect. Everyone would use the prediction model in
order to decide their next trade, and this would dramatically distort the market. At
this point, the prediction model would stop working.” 6
The strength, independence and unpredictability of feedback loops are best understood by way of a real-
life example. In his book The Fearful Rise of Markets, John Authers provides an insightful analysis of the
series of market events that occurred on July 14, 2008, traditionally celebrated as Bastille Day in France. He
notes that it marked an inflection point driven by conditions that were exactly opposite the type of
fundamentals typically expected to accompany the actual macroeconomic conditions that were present at
the time. The reason: the forces driving market behavior were in fact the result of a dynamic, invisible
feedback loop that assumed a life of its own and actually caused – rather than reflected – “reality” and
market activity:
“Entering Bastille Day, there had been one way to make money in 2008, which was to bet
against Uncle Sam, and particularly the Fed. The logic: The U.S. financial system was critically
wounded and the Federal Reserve had given up any attempt to stave off inflation or to avoid
moral hazard. Instead, it would go all out to rescue U.S. banks by cutting rates and doing
anything else necessary. The market response to this was so extreme that it forced central
banks to fight inflation by raising rates – and this killed off the rally.
This was a classic example of a ‘negative feedback loop,’ or what the investor George
Soros calls ‘reflexivity.’ By this, Soros means that our perceptions of the world, as expressed
by buying or selling, can change the world itself. Once markets become reflexive, they reflect
flawed perceptions rather than a prior ‘reality’ – but the market’s version of ‘reality’ is no less
real because of that. The reflexive events of the summer of 2008 are the textbook case of how
synchronized markets have evolved so that they can force policy mistakes and damage the
economy [...] This was reflexivity – by betting on a return to inflation, traders had in a
6
Neil Johnson, Simply Complexity (Oxford: Oneworld Publications, 2007).
Feedback loops can create a hall of
mirrors. Market activity can diverge
significantly from expectations
based on fundamentals, which in
turn can impact fundamentals.

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very real way helped inflation come to pass [...] A credit crisis would normally lead to
deflation. But as oil shot upward, central bankers took their eye off the credit market and felt
forced to declarewar on inflation.” 7
For a more commonly-experienced example of the decoupling between events and subsequent market
outcomes, consider a company reporting its quarterly results. Those assessing the company for investment
would analyze information on its fundamentals, such as the actual success of a product/technology or the
profitability of a particular project, to develop a view on earnings to be reported. However, it is important
to recognize the impact of other actors who are also anticipating this event, as well as the level of
aggressiveness and unpredictability in how they will respond to new information. Along with the factual
information that is flowing among this expanding web of players, a number of iterative feedback loops are
created by exchanges between specific subsets of actors, which can serve to reinforce or destabilize the
conviction or decision of any of the individual parties involved. Though perhaps more by luck than skill,
one can be “right” about fundamentals, but still lose money because the market impact of any particular
data point is dynamically connected in ever-changing ways to a multitude of other unobservable factors.
As a result, markets can be thought of as existing in distinct periods of temporary stability, which can be
disrupted in unpredictable ways by a wide range of factors or developments. One of those factors is that
various agents may react to the same information quite differently – and the velocity with which
information spreads increases the likelihood that this will influence the reactions of other actors, whose
subsequent reactions then influence the responses of others, and so on. This dynamic, iterative and self-
reinforcing cycle then assumes a life of its own, which can become decoupled from apparent reality but
nevertheless impact market activity in very real ways.
The increased complexity required to assess risk, and the types of efforts that sophisticated, multinational
companies such as Swiss Re and Gen Re are undertaking in order to address this increased unpredictability
was highlighted in an article by Carol Matlock of Bloomberg BusinessWeek. In the excerpt below, she
describes a chain of seemingly unrelated events – which began with a volcanic eruption in Iceland and
ended with a temporary shutdown of a BMW plant in Spartanburg, S.C. – as an example of the
fundamental changes in how sophisticated risk managers are beginning to incorporate a heightened
awareness of increased complexity and unpredictability:
“What was the probability of that [series of events] happening? That's the kind of question
Munich Re must grapple with. Munich Re is the world's biggest reinsurer, one of a handful of
industry giants [...] that sell policies to insurance companies to cover the risks they absorb from
policyholders. That gives the 130-year-old company an unparalleled view of just about
everything that could go wrong in the world.
And in a global economy, figuring out where and why those things might happen is getting a
lot more complicated. ‘The most significant changes in risk management have taken
place in the past 7 to 10 years,’ says Joachim Oechslin, Munich Re's chief risk officer. ‘Today
it's not only about data gathering’ – using geological records, say, to predict the
likelihood of a volcanic eruption – ‘but trying to figure out the relationship of
7
John Authers, The Fearful Rise of Markets(New Jersey: FT Press, 2010).
Markets alternate between
transient stability and periods of
heightened volatility and
dislocation.

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things,’ such as how an event like the Iceland volcano can ripple through a supply chain. While
Munich Re specialists are studying the eruption, they say they still lack the tools to accurately
predict the chain of damages unleashed by such a disaster.
Increasingly, Munich Re is focusing on what it calls emerging risks: subtle, often
seemingly innocuous trends that could carry the seeds of disaster – everything from
rising prices at art auctions (which can increase theft) to the widespread installation of rooftop
photovoltaic panels (a fire risk). Who knew that bundled subprime mortgages would lead to a
near collapse of the global financial system?” 8
The unprecedented events of the past few years – in conjunction with the changes in market dynamics
that we expect to continue – are fundamentally impacting investors’ ideas about conventional wisdom,
particularly regarding various relationships or expectations that were previously taken for granted. A
partial list of examples of such widely-held beliefs now being called into question would include
assumptions that:
 people behave as rational actors, and will make decisions that are necessarily consistent with
their stated investment philosophies, mandates or strategies
 the relationship between the level of risk taken and corresponding expected reward, i.e.,
investors can necessarily expect higher returns in exchange for assuming higher levels of risk
 measures/methods used to quantify, evaluate and describe risk are in fact accurate or “correct”
 the relationship between future and historical correlations between various asset classes, styles,
geographies, strategies and risk factors
 the shape of return distributions remains relatively constant, “normal” and symmetrical
Many investors still operate with the mindset that traditional measures of risk capture the primary factors
that can negatively impact portfolio performance. This approach entails two fundamental problems. The
first is simply that the process of identifying, defining and measuring any set of variables in a systematic,
consistent and accurate way is itself an extremely difficult, if not impossible, pursuit in all but the most
confined and constrained conditions. As a result, since “confined” and “constrained” are certainly not
adjectives one would associate with modern financial markets, this can lead to the pitfall commonly
referred to as false precision. A second, even more important problem is that many widely used measures
of risk, including historical volatility or value-at-risk based on normal distributions, don’t incorporate
important considerations such as the probability of extreme events or the permanent loss of capital.
C R E A T I N G P O R T F O L I O R E SI L I E N C E T H R O U G H
C O M P R E H E N S I V E R I S K M A N A G E M E N T
Despite our detailed discussion of why investors must acknowledge unpredictability, we are not
suggesting the impossibility of successful investing. Rather, investors should embrace uncertainty and
focus their attention on proactively identifying potential risks, envisioning a wide range of potential
market and economic scenarios (and subsequent cascading or secondary effects) and then purposefully
8
Matlock, Carol. “How Munich Re Assesses Risk.” Bloomberg BusinessWeek, December 2, 2010.
Changing market dynamics are
causing investors to question
much of conventional
investment wisdom.
The reliance on traditional risk
models has proven to be quite
dangerous when extreme
market conditions occur.

11. Q1 2011 | Page 11
Creating Investment Resilience| Embracing the Certainty of Uncertainty
REAL WORLD
RISK MANAGEMENT
Issue 2 | Q1 2011
structuring portfolios designed to be as resilient as possible to a wide range of outcomes. The primary
reason is disarmingly straightforward: portfolios must survive adverse markets – and the full cycles
typically exhibited by various asset classes and strategies – in order to benefit from periods of valuation
reversion, which are likely to follow periods during which risk aversion has driven risk premiums to
attractive levels, such as in early 2009.
The complexity and interrelatedness of factors that impact risk and performance make portfolio
construction based on a traditional approach – i.e., optimizing for a single set of forecasts regarding likely
asset return and risk characteristics – unreliable and potentially quite dangerous, as evidenced in 2008. In
contrast, a resilience-based approach maintains a steadfast focus on the fundamental
determinant of long-term performance: achieving a sustainably positive asymmetric skew of
upside capture in positive markets versus downside capture in negative markets, independent
of whatever conditions prevail, over a long-term time horizon.
This insight about the key driver of long-term investment performance is foundational in evaluating an
investment strategy’s resilience. The last issue of this quarterly publication, entitled “The Oracle of . . . Risk
Management?”, presented our analysis of Warren Buffett’s track record making investment decisions, using
Berkshire Hathaway’s stock performance as a proxy.9
The key take-away was that Buffett’s ability to
outperform the S&P 500 Index by six times over the last 22 years was driven by Berkshire’s positively
asymmetric skew of returns. During this period, Berkshire’s stock captured 95% of the market’s upside
during positive months for the S&P 500, while capturing only 35% of the market’s downside during
negative months for the index.
Given the dynamic nature of the numerous trends and relationships that impact market outcomes in
unpredictable ways, it is important to purposefully incorporate flexibility into the construction of resilient
portfolios. One step toward accomplishing this goal is taking an analytically rigorous and structured
approach to developing a combination of diverse assets which represent different sources of risk and return
and have varying degrees of correlation with one another. Further, one must approximate how each
asset’s risk and return characteristics might change across a variety of economic and market conditions.
This includes understanding how each asset’s relationship with other assets in the portfolio might behave
under different scenarios. For example, the strong inverse correlation between equities and government
bonds during 2008 would likely look very different in a scenario involving rising inflation and low growth,
where both equities and bonds might decline in value simultaneously.
In order to construct and maintain a resilient, comprehensively diversified portfolio, the intelligent
combination of assets is not alone sufficient. It is also necessary to actively monitor and methodically
adjust a portfolio’s components (and, as a result, its overall attributes) as the risk characteristics of assets,
as implied by their prevailing valuations, change. An example of this is how credit-based assets, such as
9
While the performance of Berkshire stock is certainly influenced by factors outside of Buffett’s control on any given day, the long-term growth of
Berkshire’s stock price has mirrored the growth of its underlying book value per share – which in turn is a function of the composition of underlying
holdings Buffett has assembled.
Resilient portfolios must survive
adverse marketsto benefit from
periods of recovery to follow.
Maintaining a positive skew of upside
capture during rising markets versus
downside capture in falling markets
drives long-term capital appreciation.
It is critical to understand not only
how an asset’s return and risk would
be influenced in different market
scenarios, but also how its
relationship with other portfolio
assets might change.

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Creating Investment Resilience| Embracing the Certainty of Uncertainty
REAL WORLD
RISK MANAGEMENT
Issue 2 | Q1 2011
high-yield bonds, were priced going into 2008 – trading at a historically tight spread of 260 basis points
over Treasuries – and then in early 2009, trading at an unprecedented 2,000 basis points spread. With
historical average spread levels of 700 basis points over the last twenty years, one could reasonably
conclude that valuations in early 2008 offered minimal upside and quite a bit of potential downside should
the credit cycle turn or investor risk preferences change. In early 2009, the conclusion would be the
inverse. Both points in time presented an opportunity to adjust tactical risk exposure to this portfolio
component, requiring a structured decision process and consistent assessment methods to take a
contrarian position directly opposed to the prevailing trend and sentiment.
The inclusion of “beta buffers” in an overall portfolio construct likewise presents another opportunity to
shape returns in a way that blunts periods of adversity for assets tied to investor risk sentiment, while
permitting upside participation during periods where risk is rewarded. The evolution of the liquid
alternatives universe, representing hedged and absolute return strategies in vehicles with daily liquidity
and pricing, allows for the development of integrated portfolios that are completely liquid and housed in a
single managed account, with consolidated custody and tax reporting. Such strategies span a variety of
arbitrage, relative-value and long/short approaches across the spectrum of asset classes. The challenge is
to sort through available strategies to separate the wheat from the chaff, perform effective diligence and
then assemble a diverse group of strategies that will generate a composite return stream with low
correlation to other assets in the portfolio, enhancing the overall portfolio’s risk/return characteristics.
In addition, we believe many traditional investment strategies completely ignore a potential source of
incremental returns that can be generated through tax-loss harvesting, based on a structured and
automated process. As a result of the continuing proliferation of exchange-traded funds, there is now an
increasingly broad range of vehicles that provide investors with low-cost exposure to an ever-expanding
array of global asset classes. Beyond the inherent tax efficiency of ETFs as an investment vehicle, they can
also be utilized to harvest tax losses without sacrificing intended market exposure by swapping
instruments with similar asset exposure. The opportunity to conduct harvesting activities was certainly
elevated by the heightened volatility in 2008, but nevertheless remained material during the recovery –
with recent examples being the decline in long-term Treasury prices, the underperformance in emerging
versus developed markets and the recent crisis in Japan, when Japanese equities declined substantially.
The tax benefits generated through harvesting can be carried forward and utilized by investors to offset
realized gains, either in the portfolio from which they were harvested or in other investments an investor
may hold.
There is also an opportunity to preserve return by aggressively minimizing all costs related to portfolio
implementation. This applies to all overt costs, as well as those that are opaque as in the case of trading,
where sensitivity to spreads and market impact in execution are critical to avoid performance dilution.
Finally, investors must ensure constant vigilance in monitoring and maintaining pre-defined and well-
documented risk thresholds and intended risk exposures. This approach should form a composite from a
variety of risk measurement methods to develop a more complete understanding of risk, versus reliance on
Periods of market dislocation present
the opportunity to tactically re-
allocate risk in a contrarian fashion to
improve both portfolio risk
characteristics and its return through
the market cycle.

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Creating Investment Resilience| Embracing the Certainty of Uncertainty
REAL WORLD
RISK MANAGEMENT
Issue 2 | Q1 2011
one primary indicator, such as value-at-risk. Additional methods include the modeling of estimated tail
loss exposure and both event- and factor-based stress testing. Applying this risk data to proactively adjust
portfolio exposures in an active and dynamic fashion demands the use of instruments that have daily
pricing and liquidity.
We believe that the cohesive integration of this set of risk management activities and their disciplined
application within a globally diversified multi-asset portfolio framework provides investors with the
greatest opportunity for investment success: survival in adverse markets, with the ability to profit during
periods where risk is rewarded. In an environment of certain uncertainty, a structured
investment strategy predicated on resilience, not prediction, is the most effective way to
achieve the long-term growth of capital, whatever market conditions may come.