Predictive Intelligence for Hunting Down Financial Crisis Tipping Points

The Orbit of Panic: Telegraphing Financial Market Crises:
Critical Transition Analysis of Mass Human Behavior

YOUR FINANCIAL DATA IS TRYING TO TELL YOU SOMETHING.

IS YOUR WEALTH MANAGEMENT PROCESS BUILT TO HEAR IT?

CMLs Trading Facility and Data Analysis Control Center

CATALLACTIC – TRANSFORMING PERCEIVED RISK INTO OPPORTUNITY

Mr. Hoffman heads the Catastrophe Modeling Research Unit and Risk-Surveillance Observatory at
Catallactic Wealth Management Group of Companies (CML), an organization established in 2009 for
deploying state-of-the-art mission-resilient and time-sensitive trading technologies. Originally
developed in-house over a 16 year period; versatility, transparency, and scalability are the
hallmarks of our investment process.

Catallactic Group of Companies | L'Horizon, Gunsite Road, Brittons Hill, St. Michael, Barbados BB14027
Copyright Protected June 2011 1


A NEW PARADIGM FOR HUNTING DOWN CRISIS TIPPING POINTS

Everyone is looking for proxies of extreme event phenomena; unfortunately statistical physics and
conventional Capital Asset Pricing are littered with the usage of "weak-signals" for capturing upcoming
aberrations of nature. They offer little, if any causal analysis or understanding of the endogenous factors
influencing the outcome.

In support of this mission, Efrem Hoffman, the founder of the recently launched LinkedIn Group, Global
Macro Dragon King Outliers, encourages inter-disciplinary discussion of an emerging class of financial
anomalies -- known as “Dragon-Kings” -- which was originally coined in 2008 by Marc Groz of Topos, LLC,
and studied and popularized by esteemed Swiss research scientist Didier Sornette of ETH Zurich.

Expanding on the analytic foresight of these broad-based features of natural and economic systems, Mr.
Hoffman is ambitiously working on his tenth year at engineering frontier quantitative tools and scientific
data instrumentation / visual metrics for de-cohering the intricate entanglements – that hold clues of the
shared beliefs and co-existent financial self-interests held among market agents across a broad-band of
transaction scales and term-structures -- without making assumptions about their underlying
distributional tendencies.

He parallels this mission in developing the next incarnation of tools that offer guidance in making real-time
inferences of those high-frequency and low-frequency trader/investor groups that are driving today's most
disruptive irrational outbursts in both the equity and currency markets.

His 10 years of research, funded by a variety of Canadian and US private equity investor groups, strongly
supports the following views:

Applications of Dragon-King Analysis can help identify those special situations, when the range of human
behavior of mass groups of economic, financial, and institutional actors become so unusually unstable, that
the symmetry in the relationships among what they perceive – in magnitude and frequency (at different
scales) -- starts to breakdown.

This class of naturally-occurring phenomena is pervasive throughout financial, social, and economic
systems; yet they cannot be adequately modeled or arbitraged away by many of today’s most sophisticated
statistical or numerical innovations. Through identifying the structural markers and precursory signatures
of these symmetry-breaking events, Mr. Hoffman is developing technologies to make predictions of these
extreme phenomena possible.



He has drilled deep into the market’s micro-structure to unearth the underlying mechanisms of these
elusive flags. Today’s quantitative models in both engineering and finance are placing too much emphasis
on using probability theory for identifying the constancy of relationships among data (and the objects they
represent), when observed at different scales or rates of change.

While these methods try to express how these variables maintain symmetry of their geometries, and
stability in the relationships among their interacting parts (known in scientific parlance as Multi-Fractal
Scaling Laws, of which 99% of Econo-Physics and Behavioral Finance is inappropriately based), they fail to
realize that nature’s most compelling stories and consistently predictive markers of extreme dynamics
(such as Black Swans and Dragon-Kings) lie in the times when such self-ordered patterns of behavior
become disassembled. This occurs when these systems become so extraordinarily complex and entangled
that they reach a critical scale of growth, a sort of tipping point or crisis state in their existence.

On one side of this fault-line rests a picture of near perfect symmetry, while on the other lies an array of
imperfections; a sequence of shattered symmetries, which gives way to dissipative structures that create a
new cycle of decay.

The mechanism that differentiates Dragon-Kings from their distant cousins, the Black Swans, is that not
only do the sub-divisions or replications of their patterned dynamics occur with ever greater frequency
before their onset, but also, at certain stages in their life-cycle, external market events or institutional
stimuli provide just enough “critical” energy to preempt and nudge the system away from otherwise
crossing the tipping point. This leads to mutations or artificial sub-divisions in the scale of human behavior
that is no longer related in constant proportion to its previous range of values.

This is exactly what occurred after the injection of artificial stimulus in 2009, and is the main reason why
our global economy is in a precarious situation of “unusual uncertainty.”

The marked increase in both the magnitude and frequency of such unnatural sub-divisions since the run-up
to the 1998 technology bubble, has led to an alarming increase in unstable market states – a sort of
cancerous growth that has remained with us up to this very day, and likely to play a key role in market
dynamics over the next several years.

When these external artificial stimuli and natural vibrations eventually stress the system to a point where
it can no longer support and endure the buildup of pressure on one side of the divide, the system shrugs (as
the markets did during the Flash Crash of May 6th, 2010), and the energy released on the other end



exaggerates the self-generating feedback dynamics; creating a sort of avalanche effect that pushes the
system into a dissipative cycle of decay -- which often accelerates at a faster rate than it did in its prior state
(counter to the direction preceding the crisis).

Mr. Hoffman is looking to exchange views and establish research alliances with industry peers to continue
his pursuit in hunting down the sub-divisions of market behavior that has driven this period of decay;
specifically those sub-divisions that do not scale well with the power law constants of the preceding cycle;
but rather are governed by a radically new regime that is comprised of a mixture of Dragon-Kings and
traditional exogenous Black Swans.

Through replicating their features across a broad spectrum of irregular scales, their combined effect lacks
the very symmetry that Black Swan crisis-hunters are preconditioned to recognize. That is because the
market actors would not only need to be aware of the behavioral differences between these phenomena,
but would also need to understand how their actions, upon crossing the tipping point, would impact the
demographics of investor types, namely the concentrations of fundamental versus technically-oriented
traders.

Consequently, the dynamics and accompanying events that follow the onset of Dragon-Kings are even more
pronounced, and represent a greater element of surprise than those power-law signatures preceding Black
Swans.

The arbitrage opportunity that is therefore created by a Dragon King Event is of greater measure than the
risk posed on the opposite side of the divide.

The ability to monetize these differences in reward to risk potential can provide a strong mathematical
edge, and will likely serve as the cornerstone of future alpha-generating strategies.

It has been shown that there is neither a characteristic scale that leads to this breakdown, nor a multi-
fractal scaling law that is pervasive across all markets. Mr. Hoffman's research process hones in on those
specific markets and asset classes that are expected to telegraph these differences in human behavior – all
within the context of the current market environment.

This circumvents the limitations of relying on historical precedent (and average deviations) to predict
future outcomes, especially when the distribution of events within the existing range of possible outcomes



are not only pushed to the frontiers of what is Normal (“Black Swans”), but also extended beyond the tails
of the power-law curve (thereby entering the new domain where "Dragon’s" reign).

The quantitative research enterprise should learn to embrace asymmetry -- as one of the only universal
elements that serve as the underlying generator of the fine details and imperfections that we as speculators
observe throughout natural and fractal systems – including financial markets and other complex networks.

What modern finance has done is strait-jacket these fractal assemblies and turned them into holograms –
rigid shadows of their distant past; infinite copies without any flaws – which means they lose their innate
character; thereby, stripping them of their capacity to store any predictive content. Just as the
idiosyncrasies of character can reveal insights into a person’s future behavior, so too can these
asymmetries of the differences in market dynamics, on either side of the divide, help us understand and
predict the behavior of financial markets on a more human-scale.

This new knowledge-domain can help render visible a new field of information that constitutes the source
of what most of our peers frequently misinterpret as outliers — “Black Swans” – those extremely rare high-
impact events, which previously thought to be confined to the outer valence of the Normal Bell Curve, are
now no longer even thought to belong to the wild domain of super-Linear Power Laws -- and their Fractal
Generators.

Analysis and Real-Time Recognition of these phenomena are crucial to our understanding and prediction of
crisis formation, but more importantly, they telegraph super-normal inefficiencies that can be exploited on
four significant fronts.

(1) Most competing models of human behavior underestimate the magnitude of shocks, created by
endogenous regime-shifts in market behavior; thereby, lacking the necessary conviction to guide fund
managers in placing significant allocations ahead of these large-scale events.

(2) Market mechanisms find it particularly difficult to digest discontinuous (non-smooth) jumps in price
and volatility, which are associated with Dragon-Kings. As a logical consequence:

(3) Knowledge of their occurrence would permit volatility-based strategies to more appropriately align
their commitments of capital in the direction of the volatility expansion; thereby extracting additional
sources of return.



(4) Employing a model framework that can more suitably account for the timing and magnitude of
"unusually uncertain" events, would permit "volatility of volatility" (VoVo) models to more appropriately
minimize volatility drag -- through fine-tuning their application of leverage during regime-shifting intervals
of volatility expansion or compression.

(5) Because most research shows that Black Swan events are not predictable, many competing strategy
frameworks fail to recognize that other classes of extreme phenomena, such as Dragon-Kings, are within
the purview of predictability.

When there is little competition, or better yet, people who are dogmatically opposed to the view of
inefficient markets, it opens up greater opportunity for those who can peer into the future and see a new
dimension of the existing data fields – revealing relevancy for making informed financial decisions.

Furthermore, conventional models treat outliers as abnormal system behavior, neglecting the fact that
there are naturally organic (endogenous) processes at work, which repeatedly create these features across
a wide spectrum of scales. Those that prefer to treat outliers as intrinsic characteristics of a system are
more closely aligned with the spirit of Dragon-King Analysis, and the benefits that can be derived from
them.

In this respect, contrarian thinking is not a bad policy, especially when trying to exploit what others either
can't or choose not to see.

More importantly, through incorporating the interaction of these defining system features, Hoffman has
discovered that the natural consequence of their critical transitions, which is central to our strategy
practice, is that the more mature a market becomes, the less energy it requires per unit of applied stress to
tip it into a crisis of similar magnitude -- that is because the difference in the growth of liquidity in the
order-book and the weight of the unwinding of crowded positions, upon crossing a critical transition,
grows disproportionally (super-linearly) as we pass through its window. This feature could be compared to
larger species that consume less energy per pound of flesh than smaller ones. “For instance, while an
elephant is 10,000 times the size of a guinea pig, it needs only 1,000 times as much energy.”

Hoffman says, it doesn’t matter what the market or asset class looks like or where it is situated or how it
evolved to get into that state space — the math almost always works.



This straightforward observation has some surprising implications; it suggests, for instance, that mature
markets are strange characters; a tale of two behaviors – on the one hand they are the real centers of
sustainability, but if pushed too close to the tipping point, and especially if artificially sustained at those
levels through government intervention, before being nudged with enough internal force to cross the
chasm, the pattern of interaction among market actors can become its own agent of systemic crisis and
ruin. Knowing how to spot these points becomes absolutely crucial; and is the starting point of our due
diligence process for assessing risks that are not singularly caused by external circumstances.

When we started trading in modern financial market systems, we did something that had never happened
before in the history of economic life,” Hoffman says. “We have essentially broken away from the equations
of biology, all of which are sub-linear.

What I am referring to is the observation that every other creature gets slower as it gets bigger. That’s why
the elephant trudge along.” But in market-based systems; like sprawling cities, they conform to the
opposite.

As markets and cities evolve and get bigger, everything starts accelerating. There has yet to be an
equivalent scientific demonstration for this behavior in nature. It would be likened to discovering an "[80
year old ] elephant that’s proportionally faster than a mouse.”

Hoffman goes further, and seeks to find a generalized aphorism or feature set that serves to describe the
family (what type) of elephants in nature that can move faster than a mouse – none, he says, if you are like
everyone else, focusing on the averages. But, if you look at critical transitions and then de-cohere the
unusual nodes of social interaction that are underlying their hidden critical stresses, they can be found
everywhere and in any evolving living or manmade system that are comprised of agents that interface not
only at different scales and rates of change, but also entangle -- sharing common points of interest through
their past connections -- regardless of how far apart they are separated in space and/or time.

That is the essence of quantum design, and the piece that was always missing -- no one would look at the
Social Grid, and acknowledge these expressions of irreducible connectivity.

Hoffman indicates that this has huge implications for activist investment groups -- like Berkshire, Carlyle,
and Black Rock -- seeking to invest for significant growth in more mature mid and large-cap companies that
have lower risk profiles and higher grade debt.



Although our industry peers believe that the inevitable decline in profit per employee makes large
companies increasingly vulnerable to market volatility, Hoffman asserts that we can circumvent this
bottleneck by acquiring companies or divesting publicly owned companies that are entering a critical phase
of invisible growth -- not in the proportional increase in company profits, as seen in young firms, but in the
ability for a firm to acquire competitors or divest interests in their own company, at just the point where
they are passing through a critical transition in their open-market valuation. This would naturally lead to
runaway growth or decay, by reinforcement of constant feedback. If this feedback potential is determined
to be large enough to overcome support costs and overhead growing pain expenses for an expensive staff,
companies need not be killed by their mission to keep on getting bigger.”

In a similar vein, government policymakers and security regulators could exploit the knowledge of tipping
points for stabilizing the balance of power and taming the irrational outburst on the Market’s Social Grid --
specifically by targeting the foci points of network activity with small applications of stimuli as quickly as
possible -- and as soon as they get first notice of a potential transition to a critical state. It is hoped that the
application of this domain knowledge will forever eradicate the crude and contrived methods of
conventional intervention warfare – namely through artificially propping up markets with vast amounts of
capital to sustain a false sense of balance, well after crises have erupted – which, as Hoffman adds is like
walking up an escalator going down at an incrementally faster rate – a great expense of energy and wasted
upfront assets; with tremendous opportunity costs.

Hoffman’s goal is to ultimately get policy makers to employ these tools to prevent the very condition from
roiling the markets in the first place. This would have been especially useful on May 6th, considering that
Catallactic’s Quantum Grid Circuitry was flashing warning signs for that exact date up to 6 weeks in
advance.

In the tradition of the mathematical physics paradigm, Mr. Hoffman's ongoing explorative efforts focus on
seeing where these models have made predictions that were significantly different from those of generally
accepted theory; and then, when turning to real-life measurements, we can now observe that nature
consistently votes with the “Dragon.”

Many of the nuances and special properties that enable Dragon-King Signatures to telegraph extreme
events, has been repeatedly uncovered by our visual inspection of network connections on the Financial
Information Grid.



The Dragon-King modeling framework is also uniquely well suited for inclusion in large portfolios and
Fund of Fund (FoF) allocations that are seeking to add diversification without redundancy.

That is because Mr. Hoffman's application of the Dragon-King strategy is one of the few non-probability and
non pattern-recognition models that are have been shown in the lab, to be capable of monetizing market
inefficiencies that lie outside of historical precedent.

But, since almost all alternative investment strategies utilize some form of probability distribution
modeling to identify meaningful relationships in data, they are limited by the historical facts (outputs), no
matter how far back or nearby they look for recurrent patterns.

That is why new modeling frameworks like these are non-correlated to virtually all alternative fund
strategies and market index products; thereby, providing Diversification without Redundancy.

The approach can help circumvent capacity constraints that are associated with large portfolio allocations.
Since Dragon-King phenomena are ubiquitous across all market segments -- asset classes and open
exchange products – as well as almost always incorrectly black-boxed by the industry as unpredictable
'Black Swan' aberrations (extreme outliers), large groups of market participants often ignore their
presence; thereby acting as natural liquidity providers right around the time when the model framework
indicates that one should be looking to execute on these arbitrage themes and new sources of Alpha.

This natural liquidity condition enables Dragon-King strategies to scale-up capital allocations to very
meaningful levels, without significantly impacting market prices or transaction slippage costs.

In summary, legacy methods of data analysis have lacked the focus and sophistication to efficiently exploit
these market inefficiencies, namely, because their means of data acquisition and knowledge extraction have
scrubbed away -- from their view -- the only true source of information content, which frequently lies
dormant and undiscovered in the energetic "critical" divides; embedded within the existing knowledge set.

Mr. Hoffman's strategy application of the Dragon-King Framework is shown to be robust on three separate
domains, namely in:

(i) Generating portfolios that are highly resistant to reverse-engineering practices, namely because the
mere act of seeing where positions were placed, and then looking for recurring patterns -- in historical



context that precedes them; and archetypes of market behavior that follow -- provide no viable or
consistent means for uncovering the actual kernels of truth, which lie buried from the purview of the
existing paradigm.

(ii) Generating portfolios that are resilient to anti-bluffing strategies. Because Dragon-Kings are a function
of long-term processes that have built up over many generations and reinforced across multiple investment
scales, it is immensely more difficult for algorithmic trading practices -- which probe and/or manipulate
the short-term flow of bids and offers in the order book -- to redirect the course of human behavior, be it
long or short-range, especially when crossing the market's critical transition.

When passing through a critical point, the sheer size of commitments that are actually required (not mere
threats of commitments based on order book adjustments), to reverse or veer the market from the path of
least action is formidable, and thus the most logical way to influence market behavior is confined to
executed transactions, which unwind such positions in a direction that is congruent with the expected
trajectory observed through the spectacle of the "Dragon."

(iii) in having a shelf-life exploitability well beyond that of alternative strategies -- that either employ data
mining, patterns of human behavior, or historical precedent matching -- namely because the new approach
is not sensitive to “data torturing” -- inadvertently reconstructing data in ways that facilitate one's
perception of “data mirages” -- relationships and/or patterns that simply do not exist.

One of the elements that makes it difficult for our competitors to see past these illusions is partly the result
of a process, whereby the mere act of market observation, across multiple scales of investor perception,
leads to adjustments or lack of flux in the order book; which further amplifies the unexpected release of
market energy (supply and demand pressures that promote the unwinding of existing financial
commitments or positions).

These pressures tends to tear down and disassociate the links within the Grid, and create an enigma that is
analogous to when a fire (the underlying process) leaves a trail of smoke (the effect) in its wake -- long
after the inferno ceases existence. Because information is not transmitted across all resolutions at the
same, this results in different lags between the processes (cause) and the by-products (effects). At any rate,
reliance on either current or previous patterns of visible behavior (the smoke), interferes with both our
perception of current conditions, and the Financial Grid's present state.



This enigma also finds its parallels in the Copenhagen Interpretation of Quantum Mechanical Systems
(those comprised of physical interactions among their numerous small-scale constituent parts), which
states that the measurement of an effect (the outcome) plays an inextricable role in shaping what we see;
and is therefore not independent of what is visible at all points forward and back. In this sense, what we see
has no arrow in time ( let alone of time ), since the measurement of progress can distort and bend our
perceptions of both our past and future experiences.

A notable consequence of this, derived by Heisenberg, is that the greater our sense of position (of a
particle) -- be it a physical object or price of an asset, which result when constituent financial agents'
perceptions' of price change collides with what they observe in the order book -- the less clear our view of
its momentum. It is important to note that "Heisenberg Uncertainty" does not restrict or preclude the
accurate, simultaneous assessment of both price and momentum in a non-classical world.

So, if we were to take our classical measurement device (empirical and pragmatic observation paradigm)
and shrink it down to a size, small enough, to fit (or be compatible) in a quantum world, then there is
nothing to stop one from reverse-engineering a calibration of price with fields of forward momentum.

So instead of measuring momentum and price at the same time, we need only measure price; and as a
natural consequence of the momentum field's calibration, we can instantly know, with zero latency, how
momentum would be affected on different time horizons for various sensitivities of price and changes in
valuation.

This is the essence of Mr. Hoffman's data capture and modeling process; and is what allows one to preserve
the quantum information that is inevitably lost in empirical models -- which are constrained by the
Copenhagen Interpretation.

Appreciation and exploitation of this radically new source of inefficiency delivers an alternative form of
diversification that can strategically enhance the Alpha-generating capabilities of any portfolio and Fund-
of-Fund strategy mix.

An equally notable deficiency with the machinery of classical finance is that it leads us in search of less
efficient solutions with lower economic utility. Their reliance on evolution -- hard-wired in humans and
conventional computer algorithms, through several millennia of natural selection or simulated iterations,
respectively -- for adapting to incoming data (new information or lack of change) is premised on two
flawed assumptions.



First, it undermines the learning process by inappropriately extrapolating experiences that have been most
useful, and/or of recent recall, and even worse, lack regard for the context changes that may be brought
about by regime-shifts that have long-range consequences.

In this respect, we should all make it our business to analyze the internal market structures that telegraph
these changes in stylized facts and distributional data tendencies. It’s not only that evolution is too slow in
the financial markets, but also incapable of making the necessary adjustments to see past the data mirages
that history presents at all scales of human perception --no matter how far back or nearby one looks.

Secondly, evolution leads us to pursue paths on the way to our destination (targeted outcome) that favor
localized progress (avoiding short-term setbacks to satiate near-term needs), even though a few steps of
fast-forward thinking and rationally-driven gaming simulations would readily yield logically congruent
outcomes that are measurably more efficient.

By doing so, evolutionary models falsely imply that local retrenchments in progress (toward an interim
point of growth or decay) are not part of a larger critical process; that through self-ordered behavior,
produces such setbacks, in order to both maintain system-wide stability and immunity of the Financial Grid
to external shocks -- at all stages of the information discovery process.

In sharp contrast to standard investment practices, the Dragon-King Value Proposition, which is hoped to
power the future of strategy platforms, is shown to overcome these challenges, and deliver frequently
exploitable Alpha-generating opportunities in both ordinary times, and in "unusually uncertain”
environments; especially those inhabited by Dragons bearing crowns, in a minefield of financial fault-lines.

Take note; the model is currently flashing warning signals, specifically:

- indicating how the large cap S&P 100 ($OEF) and S&P 500 ($SPY) is rolling over, and is in a precarious
state of flux, as we proceed into late June and early July; and especially so, when we likely revisit these
capitulation lows, beginning in the period of October 2011 through May of next year. 1306, 1331 is major
resistance on upside -- if hit, massive selling pressure, followed by a plunge to new lows. 1337 to 1365 was
long term resistance before the selloff -- it was near this price zone on April 22nd, where model had first
knowledge of a critical downside transition. 505, 473, 465, and 445 are target support levels on the
downside for the OEX.



There is now a high potential for a flash crash in S&P 100 securities when OEX price trades below 570 for
more than 6 hours; trigger is Valid before July 7th. OEX is currently set at: 566.53. There is also extreme
risk of a meltdown in one of the broadest indices; namely the NYSE World Leaders Index has potential to
fall from near 5600 to 4650 -- with initial near term support levels near 5433 and 5205.

There are several fundamental shifts occurring in the global markets that support these views, namely:

1. Collapsing Money Velocity (Falling ratio of GDP to Money Supply, since money supply has been
expanding faster than growth, which is about to rollover);

2. The big kicker will be a reversal of the US Dollar Index ($UUP) as Budget Deficit spending on foreign
troops will be curtailed, along with contracting credit from the Fed);

Although credit may contract, the effect in an unstable economic environment will lead to a
disproportionately negative impact on declining GDP; thus acting as a positive feedback mechanism that
will further reduce the velocity of money. This is deflationary in a monetary sense.

Since securities are valued in US dollars, a rising dollar will also put downward pressure on the nominal
value of US listed assets (lesser impact on some commodities and managed health care services/products,
as there are real structural supply shocks and long term demographic wealth shifts in the growth of the
middle-class in emerging markets).

A Dollar Rally (when it occurs, it will be sharp and abrupt) has the potential for accelerating the market fall,
especially as early as the third quarter of 2011 or second quarter of 2012.

For details on the trigger conditions for a Dollar Fall, you can review market commentary on twitter under
Global_Macro;

3. Complacency in the value that people are pricing risk into the future with leveraged products;

4. Deteriorating Housing Market and Employment picture.

Information Inquiries Contact: efrem.hoffman@gmail.com or efrem@catallacticgroup.com



BIO:

Mr. Hoffman, Founding CEO & Financial Model Architect

Based in Toronto, Canada, Mr. Hoffman brings a broad business technology vision to the investment
management and data intelligence companies; and media enterprises he counsels — Catallactic
Management Ltd (CML); Clear View Analytics Inc. (CVA); and SkyScope Phynancial News Network (SPNN).

While consulting for and collaborating with industry experts on computational intelligence, financial
trading, order execution, electronic trading room design, real-time data management, extreme event
modeling and catastrophe prediction; he has gained over 16 years experience researching and developing
intelligent multi-dimensional visualization solutions for the next-incarnation of financial analytic platforms.

Mr. Hoffman heads the Catastrophe Modeling, Risk Intelligence, and Global-Growth Surveillance
Observatory at CVA -- Powered by Catallactic’s Quantum Grid Circuitry (QGC).

He is a recognized inventor of smart-data analysis architecture and self-thinking soft-computing machines,
with working applications, and information technology patents that have been cited internationally on over
46 instances on Patent granted to Fortune 500 Companies, Federal Research Agencies, revered Scientific
Think-Tanks, and preeminent Academic Institutions.

CMLs in-house financial tool-set is derived partly from two US patents; both invented and assigned to
Efrem Hoffman (in 2000 and 2001; accessible at US Patent Office website under Quick-search -- enter first
and last name; both patent titles start with the word Hierarchical).

As social proof of Mr. Hoffman's Patent Publications, they have been shortlisted for inclusion in BrainDex's
CD knowledge source of "215+ fascinating inventions."

With this diverse solution-base, Mr. Hoffman embraces the mathematics of nature and quantum mechanics
to observe Global-Macro Trends on a Human Scale.

Most notably, he brings a mathematical physics perspective to engineer comprehensive information murals
and knowledge maps for enhancing visibility of uncertain outcomes, and expanding the domain of
conventional market analysis and applications for behavioral finance.



His core objectives are to enhance:

 Visibility of Future Price and Volatility;
 Mission-Critical Risk Intelligence
 Zero-Latency Data Aggregation;
 Early Identification of Industry and Sector Rotation Trends;
 Global-Macro Forecasting of Super-Anomalies and Undervalued Liquid-Situations.

His market insights are inspired by observing how the fragmented structure and inherent uncertainty of
conventional market sources interfere with our perception of current conditions; yielding outbursts of
market contagion and extreme price movement, which are translated into actionable strategy.

Mr. Hoffman redefines what it means to manage information overload – by zeroing in on the behavioral
pathways and knowledge structures that form our collective perceptions, we can better target those
orientations that predispose markets to episodes of information contagion and trend reinforcement.

Furthermore, by developing a greater awareness of this social information context, we can identify those
critical points, where new incoming data or lack thereof, has the highest potential for influencing the
underlying market condition.

Through leveraging this collaborative information resource across all asset classes and geographies, Mr.
Hoffman is committed to discovering new vistas for not only flagging and exploiting upcoming and unusual
market events, but also delivering wide-screen transparency of the complex social networks that influence
and create financial fault-lines — the divide between periods of prosperity and unstable regimes of
extreme turbulence and decay.

Mr. Hoffman also holds a deep conviction for writing time-sensitive commentary, trend-pieces, and
investing opinion-focused posts that challenge conventional thinking and legacy market-analysis, especially
when it comes to analyzing emergent trends and super-anomalies of the marketplace.

For a sneak preview of tomorrow's high-impact outbursts of irrational market behavior; and to review
scheduled upcoming broadcasts of his high-conviction market calls, Link to his:

Online Radio Show: http://www.blogtalkradio.com/skyscope-phynancial-news-network



BlogSpot: http://dragonkinginvestor.blogspot.com;

Twitter: http://feeds.feedburner.com/Twitter/Global_macro.

Leveraging these market insights, he also serves as contributing editor to several Financial Blogs and Real-
Time Information Aggregators.

Real-Time tweets of Mr. Hoffman’s market calls are also made available on many primary financial news
agency sites that tag financial stock tickers with time-sensitive Just-In-Time Commentary.

By providing high-impact, time-critical behavioral market news-flow for Financial Media Bureaus,
Investment Bankers, and Market Professionals –

Mr. Hoffman's mission is to help these organizations and their clients manage business expectations in an
increasingly competitive G20 Economy.

The mathematical logic underpinning his content is inspired by frontier insights in Behavioral finance and
the Wisdom of Crowds. These concepts are supported by strategic decision-making under extreme
uncertainty, through applying time-tested strategies and rational-choice logic stemming from Quantum
Game Theory.



YOUR FINANCIAL DATA IS TRYING TO TELL YOU SOMETHING.

IS YOUR WEALTH MANAGEMENT PROCESS BUILT TO HEAR IT?

CMLs Trading Facility and Data Analysis Control Center

DISCLAIMER

The information contained herein is intended for informational, educational and research purposes
only. None of the content herein is intended to be, nor should it be construed or used as, financial,
legal, tax or investment advice, or a representation as to the appropriateness or suitability of an
investment in any financial vehicle. The information contained herein is as of the date(s) indicated
and is not a complete description of the investment platform. This document is being issued by
Catallactic’s Analytic Group for information purposes only, and no representation is being made by
the organization or any agent of any affiliate as to the accuracy or completeness of the information
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Predictive Intelligence for Hunting Down Financial Crisis Tipping Points

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