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Toronto Fintech Firm Leverages the Power of Financial Uncertainty and Stretches the Limits of Human Perception and Machine Intelligence

Start Leveraging the Power of Financial Uncertainty and Stretching Your Limits of Human Perception and Machine Intelligence —

This is where Running Alpha comes in!

Efrem Hoffman, CEO & Founder of Running Alpha Investments Inc. passionately believes that to move the needle and produce portfolio alpha, you need forward decision-making that sees in all directions.

“STOP A STOCK trade and avoid a catastrophic global financial crash. Seal a microscopic crack and prevent a rocket explosion. Push a button to avert a citywide blackout.” [ Oct 29th, 2013 edition of Wired magazine ]

Though such situations are mostly fantasies, a new analysis framework suggests that certain types of extreme events occurring in complex systems can be predicted and averted.

“A chaotic system may be in flux, and appear like random behavior,” said physicist Daniel Gauthier of Duke University in the Oct. 30 edition of Physical Review Letters, and as appearing in the Oct 29th, 2013 edition of Wired magazine — “USING CHAOS THEORY TO PREDICT AND PREVENT CATASTROPHIC ‘DRAGON KING’ EVENTS .”

Hoffman’s independent research on a more fundamental mathematical physics basis agrees with the article views, which mentions that: “there’s subtle tells of internal structure that leads to destabilizing events.”

Hoffman adds that systematic measurement and perception biases embedded in the market mechanism of publicly-traded auction markets make them not only highly prone to such symmetry-breaking events, but also even more favorably amenable to prediction than using Dragon-King Analysis for studying and predicting super-exponential power law behavior in natural and physical systems.

By looking at “experimental chaotic systems” like the financial markets and the weather, Running Alpha’s founder has been telegraphing when crisis events will be approaching; exploiting market aberrations of these apparent surprise events for competitive advantage.

Through deliberately constructing portfolios, he is now focusing on using these tools for helping activist investors pre-empt or at least tame otherwise extreme and unstable behaviors from adversely impacting investor portfolios.

To address this need, Efrem has launched the Alpha Idea Grid™; today’s only Financial Market Idea Map that Intuitively Self-Orders Investor Perceptions and Emerging Mega Trends on a Visual Grid, based on how Strongly Tomorrow’s Versions of Investor and Machine Decision-Makers, will be Biased to: Perceiving Forward Price Momentum and Volatility Trends; and Shaping Future Sentiment and Social Activity.


– through Augmenting Human Portfolio Construction and Intelligently Predicting the Sentiment Jet-Stream of Decision-maker Perceptions and Investment Capital in World Financial Markets.

Powered by Crowd Physics 2.0 (TM), the Alpha Idea Grid™ dynamically and anonymously tracks the future sentiment footprint caused by the movement of people, capital, and human-machine perception biases that drive them in global financial markets.

Rather than see price moving through time, we render visibility of why things occur and how asset prices come into being.

By exploiting how nature solves problems and finds smart-cuts though quantum computing, we have found ways to unlock latent value in traditional and alternative data sets with limited history. This is a big deal, because most of the newer data sources and alpha factors emerging today have a very short history and/or shelf-life.

By looking at the non-linear relationships among how financial particles ( decision-making perceptions and market order-flow ) reveal themselves to others ( the implied sentiment factor ) and the order book ( the explicit market factor ), we have discovered new ways for quantifying the half-lives of news events, investor sentiment ( convictions and social-chatter ), and their asymmetric emotional effects on the direction and persistence of emergent price, volatility, and variance of volatility trends.

This matters, because it plays a key role in not only:

(i)  knowing which assets to own — those offering the best edge in amplifying positive news and attenuating adverse outcomes, but in

(ii ) shrinking the gap between when decisions are made and executed, resulting in a significant improvement in navigating investments around volatility and illiquid market environments, with reduced market friction, efficient execution, and favorable equity to draw-down performance.

In other words, whereas the traditional game of play has involved tracking the flow of money to extrapolate the the future path of price, to see what others cannot see, we look at the factors that power the migration of capital — namely: the perceptions of influential decision-makers; who are they are ( investment capacity for each time-frame of investor ); and where the decision-making audiences’ perception spaces are correlated across price levels and time.

This is important, because if you know where the people will be showing up, and which sandboxes they are going to be playing in, you can better identify and anticipate fund flows, instead of simply recording them in hindsight.

By shining light on previously dark regions of data ( Dark Data — the perception biases of the trader ecology ), Relational Perception Calculus (TM) is recasting the content of our existing data-store;

permitting us to resurrect data elements from the dust-bins of history, and see how they are behaving more like radioactive material, with a half-life that persists on into the future;

allowing “ancient” data to come alive and continue interacting with the changing market sentiment in new ways, much like relativity theory introduces a reflexivity between gravity fields and mass.

So, it is not the prices that have memory, but the decision-makers ( people and robots ) that both re-enact and evolve their perceptions of what is happening and has happened in the past.

We keep track of these Decisions ( driven by previous perceptions ) that are yet to be implemented, based on prevailing liquidity conditions and the network feedback effects of both market-player perceptions and their reactions to changes in Perceived Value, Price and Volatility;

because without this new source of knowledge, we would be blind-sighted to abrupt regime transitions in market behavior; those “symmetry-smashing” events punctuated by asymmetric periods of: positive feedback that amplify future perception biases; an negative reflexivity conditions that yield extreme reversion events.

We make it our business to profit from both sides of this divide.

Laboratory results and real-life experiences, explains why the frequency and impact of unexpected ( surprise ) events are generally underestimated using previous model infrastructures and ontologies ( “a set of concepts and categories in a subject area or domain that shows their properties and the relations between them” ) — i.e. we are living in an age where 100 year floods are arriving every election cycle or earnings season.                       

We’re different because we don’t see information the way others do — When we look at a data set, we see a living breathing organism that expels energy in the form of information to the environment ( sentiment context of marketplace); rendering data more uncertain over time; thereby decreasing the value of insights that may have been learned and gleaned from previous trend recognition algorithms.

To get around this trap and impart meaning back into the less ordered ( entropy increasing ) data states, we add context by leveraging the increasing self-ordered structure of sentiment and perceptions biases driving them. By measuring the pulse of where decisions are biased and being made, across a wide spectrum of market player viewing perspectives, instead of naively tracking outputs from the order-book in the form of previous transactions,

we can better identify and profit from discontinuities —

— i.e. sudden jumps and wild swings in value, such as opening market price gaps, that cannot be reliably anticipated with even today’s most sophisticated negative lag or leading indicators; since they all fall victim to quantum measurement uncertainty, as will be seen in the upcoming section) —

in market behavior that are not visible in the unfolding market data or fundamental information fields, that most all our competitors rely on as the only game in town.

By doing something others do not, we trust we can extract absolute alpha ( 20%+ excess returns above the market benchmark without the use of any leverage) in all market environments ( Bull or Bear, High Volatility, Stressed, or directionless ), and economic and geo-political cycles.

Imagine a technology advancement so powerful that it can transform basic pillars of our society in the way financial markets and economies work; disrupting the way investors and entrepreneurs think about risk and uncertainty for better managing future expectations with the least amount of discomfort.

After 20 years in the making and several million dollars of R&D investment, it now exists, and its called Relational Perception Calculus ( RPC TM ).

Money is only one of the possible applications. But we started here, as it offers the longest and most reliable record of data on the digital assembly line for monetizing its insights.

To offer this new experience of wealth-building, we exploit a subtle, overlooked feature of quantum computing systems;

a secret sauce that helps us explain why records are made to be broken and diverge from past discourse, especially as they apply to profiting from and averting the episodic fits of panic, mania, greed, fear, and despondency —

— belonging to the rare 1% event class, which history repeatedly shows giving rise to the 99% of market pressures — information energy fields we, as investors, are otherwise collaterally exposed to each and every trading day.

As will be described in the proceeding section, this inconspicuous feature solves the problem of efficiently processing complex data and weighting and blending the factors ( unique technical / fundamental data fields ) of its many billions of interacting parts — human and machine based decisions.

To give you a sense of why this task has been a “wicked” challenge for the investment community, one must first understand its greater fundamental significance in the field of physics, namely: Chaos Theory, and Quantum Uncertainty — expressed by the “Heisenberg Uncertainty Relation” in scientific circles.

First off, Chaos Theory ( “Deterministic Chaos” in scientific parlance ) simply means that extraordinary precision would be needed in measurements, in order to know what is going to happen in the long run — i.e. “Butterfly Effect” — something as appearing innocuous as a flap of a butterfly’s wings in the Canadian Rockies can cause global weather patterns to yield economic peril across the Atlantic.

In other words, sensitive dependence on initial conditions translates into widening divergences as time marches on, so much so, that what first starts out as an independent path in price, time, and value, quickly starts crossing others ( alternate paths of equally independent agents of market change ) in the future;

causing interactions at the crossroads to deem the initial extrapolation of past trends inaccurate across all fractal scales of human and machine observation.

Relational Perception Calculus renders high-definition visibility of these interactions ( in perception space ) before they show up in the macroscopic behavior of price, sentiment or fundamental economic or financial indicators.

Chaos Theory is the least of what should be keeping our quant competitors up at night;

Quantum Uncertainty is the “Elephant in the Room.” It simply means that precise values for both position and momentum — velocity*mass ( transaction volume ) of a particle ( price or value ) are, in principle, unknowable.

In a sense these numbers don’t even exist until they are measured, because before they are observed they exist in more than one state at the same time;

until the final moments of decision-making, when the observer probes the region where the particles reside, and disturbs the configuration, tipping the system into a state that is radically different from what was intended to be measured.

Since all measurement devices, no matter how small interact with the environment; it renders conventional observation tool-sets ineffective at quantifying instantaneous change without introducing uncertainty;

and when combined with deterministic chaos, can profoundly influence even macroscopic ( i.e. macro-economic ) behavior in unintended ways. As will be seen later, our algorithms not only corrects for these episodes of collateral damage ( or beauty ), but leverages them against less informed traders for competitive advantage.

This type of ambiguous measurement goes on all the time in the financial and economic marketplace, and is known as “probing the markets.” Market players do this to get inside the head-space of their adversaries, and acquire access to their intentions;

it involves placing phantom orders ( orders of magnitude more devious and inconspicuous than spy shoppers in the brick and mortar economy ) — orders lacking deliberate execution.

This type of “spoofing” was prominently displayed during the flash crash of 2010 by market predators, to game the system for short term reward and long-term strategic advantage.

Our system flagged this nefarious behavior to within a three hour orb ( window ) over one month before its onset, and continues to actively monitor such activities to circumvent future portfolio landmines.

To this end, today’s robo-traders, no matter how sophisticated, relying for the most part on Artificial Intelligent (AI ) machines, that learn from experience ( real-world measurements ), are doomed to fall prey to this uncertainty and ever-increasing disorder ( High Entropy market condition ).

While the laws of physics may inherently prevent us from observing the physical universe without ambiguity, we have created a RPC (TM) tool-set that allows traders for the first time to gain insight into their competitors perception space without the need for probing or measuring price and momentum in real-time.

Our proprietary quantum decision-making metrics allow us to calibrate fields of forward price ( yet to be actualized in the future ) with the specific values of zero momentum balance points, spanning across all investable time scales, telecoping upwards from the sub-millisecond interval of high frequency traders to the time-lines of multi-generational investors.

This enables our investment algorithms to not only provide a layer of transparency that our peers consistently lack, but allows us to overcome the cost of: being human; or running algos, whose most subtle measurements impart collateral uncertainty into the observed market outcomes.

What investors really need is to raise their “Emotional Alpha” IQ — the best returns they can achieve for the level of stress per unit of risk ( and unintended uncertainty ) they’re going to have to take on for the lifetime of their investment.

The Framework is engineered for providing “Clarity” during periods of “Low Visibility” ; when confusion reigns amidst the market noise and media circus, and where for every indication of one trend, there seems to be a counter-trend.

The key is to glean from the collective wisdom of reliable leading indicators, a clear signal that the economy is veering; and then profiting from the sudden change of course at the lowest cost, while lowering anxiety and maximizing comfort-adjusted returns.

Running Alpha’s Value Proposition:

The Fintech marketplace is in rapid flux; although we have a myriad of platforms for leveling the playing field between retail and institutional investors, they fall short at addressing the underlying physics of asset price movement and volatility behavior.

The existing way people think about how stock markets and asset prices migrate over time, is fundamentally flawed; so the founder, Efrem Hoffman, built a new class of technology to solve for this; centered on the mission of:

Helping Investors Generate Alpha and Protect their Assets by strategically “Investing in Times of Uncertainty” — Winning despite the unknown.

Originally patented ( Patent #: 6,278,799 and 6,035,057 ) for Tracking down 3D Cloud Droplet Rotation Patterns preceding hostile Tornado events, it is now being successfully applied by the Toronto-Based Fintech Firm, Running Alpha, for addressing the need for efficiently generating Comfort-Adjusted Returns TM ( True Alpha — see NASADAQ Feature Article ) in world capital markets

The strategy framework is formulated to go one step beyond  AQR’s sniff test for sustainable alpha creation on five axes of merit, namely, it:

(i) produces an analytic record and walk-forward risk-return structure that indicates that the empirical investment edge is “persistent, pervasive, and robust across all asset classes and market regimes;”

(ii) offers an [  “explanation as to why and how the market inefficiency ( creating the opportunity ) comes into existence in the first place, and why it has not been and

(iii) continues to not be arbitraged away with evolving market pricing” ];

and takes a giant leap forward by:

(iv) requiring that there are no logical inconsistencies in the alpha-signaling logic across different fractal viewing scales that have overlapping position holding time-lines

By solving for the Quantum Measurement Uncertainty Enigma in the Financial Marketplace, Running Alpha goes beyond exploiting a simple risk premium based on a systematic behavioral bias or intuitive economic rationale, but leverages a long-held deeply entrenched philosophical model paradigm blind-spot that exploits the inability of conventional decision-makers ( both human and machine ) from:

anticipating an Known Unknown — ambiguity over factoring in and determining the market impact of our own buying and selling decisions, especially others reactions over several iterations of asset price formation;


acknowledging an Unknown Known — market players who naively dismiss that their actions can adversely work counter to their intentions;

(v) [“generates market positioning histories at the times of strategic opportunity ( entry and exit ), that have a well defined group of financial actors on the other side ( with limited arbitrage flows ) ] of the contemplated open market transaction.

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