Princeton quant trading conference 2016 model risk arbitrage and george soros' reflexivity theory

[1]
www.ModelRiskArbitrage.com www.FutureOfFinance.org
Model Risk Arbitrage for Open Systems Finance
Uncertainty Modeling & Model Risk Management for Cyber-Finance
Copyright, Yogesh Malhotra, PhD, 2016www.yogeshmalhotra.com
Princeton Quant Trading Conference 2016 @ , April 16, 2016
Conference sponsors include:
Beyond Model Risk Management to Model Risk Arbitrage for FinTech Era
How to Navigate ‘Uncertainty’... When ‘Models’ Are ‘Wrong’... and ‘Knowledge’... ‘Imperfect’!
Knight Reconsidered Again: Risk, Uncertainty, & Profit beyond ZIRP & NIRP
Yogi
Dr. Yogesh Malhotra
CISSP, CISA, CEH
PhD, MSQF, MSCS, MSNCS, MSAcc, MBAEco, BE, CEng
www.yogeshmalhotra.com (646) 770-7993 dr.yogesh.malhotra@gmail.com
Global Risk Management Network, LLC
757 Warren Road, Cornell Business & Technology Park, Ithaca, NY 14852-4892

[2]
How to Navigate ‘Uncertainty’... When ‘Models’Are ‘Wrong’...
October 25, 2015
... and ‘Knowledge’... ‘Imperfect’!

[3]
George Soros’ Reflexivity Theory:
The Most Practical Theory I have known.
Reflexivity is the missing link in Finance theory,
research, and, practice that can help understand
the effect of feedback and feedforward loops
across Time and Space in information-based
non-deterministic ‘open systems’ Finance.
Dedicated to

[4]
Beyond Popper’s doctrine of the “unity of
method” for a deterministic and certain world...
Toward a doctrine of “plurality of methods”
and “plurality of theories” for an increasingly
non-deterministic and uncertain world.
The Hegelian Dialectic

[5]
This presentation outlines the new framework of
Model Risk Arbitrage for profit-making in global
Trading markets of FinTech era. It advances upon
latest Finance and Trading practitioner leadership
insights in Uncertainty Management for Financial
Markets, wisdom of one of the greatest wizards and
legends of global Financial Markets, and,
observations of the pioneering practitioners and
philosophers of Uncertainty and Risk Management.
THE BIGGEST SHORT
Model Risk Arbitrage: Turning Model Risk Management on its Head
The Black Hat Mindset focus is not on a Model, but how to exploit all Models at all Levels.
Hopefully, knowing Model Risk Arbitrage will make you better at Model Risk Management too.

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Perhaps the Smartest FinTech Innovation... The Black Hat Way
Lights Out:
A Cyberattack,
A Nation
Unprepared,
Surviving the
Aftermath

[7]
This presentation outlines the new framework of Model Risk
Arbitrage for profit-making in global Trading markets of FinTech
era. It advances upon latest Finance and Trading practitioner
leadership insights in Uncertainty Management for Financial Markets,
wisdom of one of the greatest wizards and legends of global Financial
Markets, and, observations of the pioneering practitioners and
Model Risk Arbitrage:
Turning Model Risk Management on its Head
The Black Hat Mindset focus is not on a Model, but how
to exploit all Models at all Levels.
Hopefully, knowing Model Risk Arbitrage will make you
better at Model Risk Management too.
MODEL RISK ARBITRAGE

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 FinTech is the digital, virtual, interconnected, global-everything Finance.
 ‘Model Risk Arbitrage’ is the new framework for profit-making in global markets.
 Model Risk is greater than ever before and will get exponentially greater.
 Cyber Risk will continue to subsume most Financial & Other Risks.
 Cyber Risk integrated with Financial Risks will cause greater-more shocks.
 So, timelines of profit-making speculations will get ever smaller-faster.
 With higher intra-day volatility and circuit-breakers markets may end flat.
 Expect ‘Perfect Storms’ of catastrophic shocks with higher frequency-impact.
 Hi-dimensional analytic algorithms will play greater role in profit-making.
 Multi-asset, multi-model, non-deterministic algorithms will enable execution.
 Illustrative non-deterministic technologies for execution are identified.
 Comments welcome, write to dr.yogesh.malhotra@gmail.com.
Overview: Beyond ‘Closed Systems’ Finance to ‘Open Systems’ Finance
Observations & Speculations

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 Background: Beyond Risk to Uncertainty
 Uncertainty & Model Risk Arbitrage
 Black Swans & Perfect Storms of FinTech
 FinTech: From BizTech to Mobile Apps
 Perfect Storms of Cyber-Finance (CF)
 Non-Deterministic Technologies for CF
“Intuition takes intimate knowledge of the world that can be acquired
only by careful observation and painstaking effort.”- Emanuel Derman
NEW: RISK
NEW: FINANCE
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Background: Beyond Risk to Uncertainty
MODEL RISK ARBITRAGE AND “THE BIG SHORT” FOR THE OPEN SYSTEMS FINANCE:
REFLEXIVITY THEORY, HEGELIAN DIALECTIC, AND, BLACK HAT CYBER FINANCE

[11]
“If we are to understand the workings of the economic system we must examine
the meaning and significance of uncertainty; and to this end some inquiry
into the nature and function of knowledge itself is necessary.”
— Frank H. Knight in Risk, Uncertainty, and Profit, 1921
“As far as the propositions of mathematics refer to reality they are not
certain, and so far as they are certain, they do not refer to reality.”
— Albert Einstein (1879-1955), U. S. physicist, born in Germany
“It is this "true" uncertainty, and not risk, as has been argued, which forms
the basis of a valid theory of profit and accounts for the divergence between
actual and theoretical competition... It is a world of change in which we live,
and a world of uncertainty...”
— Frank H. Knight in Risk, Uncertainty, and Profit, 1921
http://www.yogeshmalhotra.com/ModelRiskManagement.html

[12]
SSRN 31 Top-10 Rankings: Computational Quantitative-Risk Analytics-Algorithms (2015-2016):
1. Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds (Malhotra 2014).
2. Risk, Uncertainty, and Profit for the Cyber Era: Model Risk Management of Cyber Insurance Models
Using Quantitative Finance & Advanced Analytics (Malhotra 2015).
3. Markov Chain Monte Carlo Models, Gibbs Sampling & Metropolis Algorithm for High-Dimensionality Complex
Stochastic Problems (Malhotra 2014).
Extending Above Observations to High Frequency Trading: FIX, FAST (Beyond ‘Flash Boys’).
4. A Risk Management Framework for Penetration Testing of Global Banking & Finance Networks VoIP Protocols
(Malhotra 2014).
5. Future of Bitcoin & Statistical Probabilistic Quantitative Methods: Interview, Hong Kong Institute of CPAs
(Malhotra 2014).
6. Bitcoin Protocol: Model of ‘Cryptographic Proof’ Based Global Crypto-Currency & Electronic Payments
Systems (Malhotra 2013).
7. Cryptology Beyond Shannon's Information Theory: Number Field Sieve Cryptanalysis Algorithms for Most
Efficient Prime Factorization on Composites (Malhotra 2013).
8. Quantum Computing, Quantum Cryptography, Shannon’s Entropy and Next Generation Encryption &
Decryption (Malhotra 2013)… One more reference: AI, Expert Systems, Machine Learning, Cognitive
Computing (Malhotra 2001) – inspired by Dr. John Holland, inventor of Genetic Algorithms
2015Q1 2015 Princeton Quant Trading Conference Presentation: Future of Finance

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Invited by publisher to write Quant Finance book on Managing Uncertainty
Palgrave Macmillan (UK), Macmillan Science and Education, Sep. 2014
 Deferred the RFP: [Quant] Finance undergoing Fundamental Transformation!
 SSRN 20 Top-10 Research Rankings within the first 11 Weeks, Apr. 2015
 2015 Princeton Quant Trading Conference, Invited Presentation, Apr. 2015:
 Future of Finance Beyond Flash Boys: Risk Modeling for Managing
Uncertainty in an Increasingly Non-Deterministic Cyber World
 Palgrave Macmillan released a title on 28 Dec. 2015 (UK)
 Builds interest in the ‘dialog’ we initiated last year at Princeton
 Why Finance needs to advance beyond ‘Risk Modeling’
 Great historical review of prior theoretical Finance research
 Next is the presentation on the ‘Forward Looking View’
 How FinTech needs to advance beyond [Theoretical] ‘Risk Modeling’
 Outline of that book someone may write some day!
Background

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Foundations of Uncertainty Management & Model Risk Management
 Quant Finance, MRM, Risk Modeling, Uncertainty Management (Post-Doc)
 Managing Uncertainty: Information & Control Systems, QRM (PhD)
 Systems Theory, Information Theory, Cybernetics
 West Churchman, Claude Shannon, Norbert Wiener
 Uncertainty, Knowledge (Objective-Subjective), Sciences (Natural-Social)
Background

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http://www.yogeshmalhotra.com/blackswans.html
Background: Beyond Prediction to ‘Anticipation of Surprise’
“The new business model of the Information Age, however, is marked by fundamental,
not incremental, change. Businesses can't plan long-term; instead, they must shift to
a more flexible “anticipation-of-surprise” model.”
-- Dr. Yogesh Malhotra in CIO Magazine interview, Sep. 15, 1999.
“We won’t be surprised that we are surprised...”

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Background: Beyond ERM to Model Risk Management (MRM)

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http://www.yogeshmalhotra.com/SR11-7_OCC2011-12.html
Background: Facilitating Execution of Fed-OCC MRM: SR11-7, OCC2011-12

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Foundations of Uncertainty Management & Model Risk Management
 Quant Finance, MRM, Risk Modeling, Uncertainty Management (Post-Doc)
 Managing Uncertainty: Information & Control Systems, QRM (PhD)
 Systems Theory, Information Theory, Cybernetics
 West Churchman, Claude Shannon, Norbert Wiener
 Uncertainty, Knowledge (Objective-Subjective), Sciences (Natural-Social)
Background

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NEW: RISK
NEW: FINANCE
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Uncertainty & Model Risk Arbitrage

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 Beyond Prediction to ‘Anticipation of Surprise’
 Beyond Model Risk of Assuming Perfect Knowledge
 Beyond Model Risk of Confusing Past with Future
 Beyond Model Risk of Normal Science and its ‘Silos’
 Beyond Model Risk of Confusing Finance with Physics
 Beyond Model Risk of Confusing Model with Reality
 Beyond Model Risk of Confusing Data with Information
 Beyond Model Risk of Efficient Markets Assumption
 Beyond Model Risk of Market Equilibrium Assumption
 Beyond Model Risk of ‘Physics Envy’ and ‘Physics Phobia’
Beyond Model Risk Management (MRM) to Model Risk Arbitrage
“Obsolete what you know before others obsolete it and profit by creating
the challenges and opportunities others haven't even thought about”
- Dr. Yogesh Malhotra, in Inc. Technology Interview, 2002,
- As noted by: U.S. Office of the Under Secretary of Defense (Comptroller) & DISA

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“The complex financial models that got us into this mess too often mask human
nature behind false limitations of risk ...Financial theory has tried hard to
emulate physics and discover its own elegant, universal laws. But finance and
economics are concerned with the human world of monetary value. Markets
are made of people who are influenced by events, by their feelings about
events, and by their expectations of other people’s feelings about
events...Financial theories written in mathematical notation - aka models -
imply a false sense of precision. Good modelers know that... Financial
markets are alive. A model, however beautiful, is an artifice…”
“…To confuse the model with the world is to embrace a future disaster in
the belief that humans obey mathematical principles.”
--- Dr. Emanuel Derman, and, Dr. Paul Wilmott in Financial Models Must Be
Clean and Simple, Business Week, Bloomberg, December 31, 2008.
http://www.yogeshmalhotra.com/risk.html

[23]
“It is this ‘true’ uncertainty, and not risk, as has been argued,
which forms the basis of a valid theory of profit and accounts
for the divergence between actual and theoretical
competition... It is a world of change in which we live, and a
world of uncertainty...If we are to understand the workings of
the economic system we must examine the meaning and
significance of uncertainty; and to this end some inquiry into
the nature and function of knowledge itself is necessary.”
- Frank H. Knight in Risk, Uncertainty, and Profit
http://ssrn.com/abstract=2553547
Uncertainty & Knowledge: Model Risk of Confusing Risk with Uncertainty
“If you spend some time at [the digital risk management research lab]
founded by Dr. Malhotra you will be blessed by some of the world’s most
astute thinking on the nature of knowledge and its value.”
- U.S. Army Knowledge Symposium, Theme: “Knowledge Dominance:
Transforming the Army...from Tooth to Tail”,
Department of Defense, United States Army.

[24]
“Knowledge resides in the user and not in the
collection [of information]. It is how the user
reacts to a collection of information that
matters.”
- Charles West Churchman
Uncertainty & Knowledge: Model Risk of Confusing Means with Ends
Don’t mistake Big Data for Big Meaning!
Don’t mistake Big Meaning for Big Outcomes!
“Humans are not Machines...”

[25]
“If two people stand at the same place and gaze in the same direction,
we must, under pain of solipsism, conclude that they receive closely
similar stimuli. But people do not see stimuli; our knowledge of them
is highly theoretical and abstract. Instead they have sensations, and
we are under no compulsion to suppose that the sensations of our two
viewers are the same... Among the few things that we know about it
with assurance are: that very different stimuli can produce the same
sensations; that the same stimulus can produce very different
sensations; and, finally, that the route from stimuli to sensation is in
part conditioned by education.” – Thomas Kuhn
Uncertainty & Knowledge: Model Risk of Assuming Perfect Knowledge*
Soros Skepticism relates to Kuhn’s & My Critical Relativism!

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“Risk and uncertainty are two concepts that emanate from
randomness. Neither concept is fully understood. Although risk is
quantifiable, uncertainty is not. Rather, uncertainty arises from
imperfect knowledge about the way the world behaves. Most
importantly, uncertainty relates to the questions of how to deal with
the unprecedented, and whether the world will behave tomorrow
the way it behaved in the past.”
– Frank A. Schmid, Senior Economist, Federal Reserve Bank of St. Louis,
in The Stock Market: Beyond Risk Lies Uncertainty
https://www.stlouisfed.org/publications/regional-economist/july-2002/the-stock-market-beyond-risk-lies-uncertainty
Uncertainty & Knowledge: Model Risk of Confusing Past with Future
Sharing here… 20-year quest of how to design
systems to deal with the unprecedented, when
the future doesn’t simply project the past…

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“From a management point of view, the current
division of human knowledge into disciplines is
managerially stupid and an often evil design of
science, which blocks off inquiry into critical
issues because the issues don't fit into the
disciplines.” - Charles West Churchman
http://www.brint.com/common/random.txt
Uncertainty & Knowledge: Model Risk of Normal Science and its ‘Silos’
Soros (2003) on Hegelian Dialectic… Me (1997) Too!
Soros (2003) on Popper & Relativism… Me (1994) Too!

[28]
Thomas Kuhn on ‘Normal Science’
“Normal science, the activity in which most scientists
inevitably spend most all their time, is predicated on the
assumption that the scientific community knows what the
world is like. Normal science often suppresses fundamental
novelties because they are necessarily subversive of its basic
commitments. As a puzzle-solving activity, normal science does
not aim at novelties of fact or theory and, when successful,
finds none.”
“Scientific revolutions are inaugurated by a growing sense that
an existing paradigm has ceased to function adequately in
the exploration of an aspect of nature.”

[29]
On p-Values and File Drawer Bias in ‘Normal Science’ - YM
March 06, 2016
http://www.yogeshmalhotra.com/GriffissCyberspace.html
http://www.yogeshmalhotra.com/cyberrisk.html
http://www.yogeshmalhotra.com/risk.html

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March 07, 2016http://www.amstat.org/newsroom/pressreleases/P-ValueStatement.pdf
“Over time it appears the p-value has become a gatekeeper for whether work is publishable, at least
in some fields,” said Jessica Utts, ASA president. “This apparent editorial bias leads to the ‘file-
drawer effect,’ in which research with statistically significant outcomes are much more likely to
get published, while other work that might well be just as important scientifically is never seen in
print. It also leads to practices called by such names as ‘p-hacking’ and ‘data dredging’ that
emphasize the search for small p-values over other statistical and scientific reasoning.”
MODEL RISKS OF ‘NORMAL SCIENCE’

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http://jama.jamanetwork.com/article.aspx?articleid=201218

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March 07, 2016http://www.amstat.org/newsroom/pressreleases/P-ValueStatement.pdf
MODEL RISKS OF ‘NORMAL SCIENCE’
Scientific method: Statistical errors : Nature News & Comment, Nature, 12 February 2014
http://www.nature.com/news/scientific-method-statistical-errors-1.14700
Statistics: P values are just the tip of the iceberg, Nature, 28 April 2015
http://www.nature.com/news/statistics-p-values-are-just-the-tip-of-the-iceberg-1.17412
Statisticians issue warning over misuse of P values, Nature, 07 March 2016
http://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503
How scientists fool themselves – and how they can stop, Nature, 07 October 2015
http://www.nature.com/news/how-scientists-fool-themselves-and-how-they-can-stop-1.18517
Psychology journal bans P values, Nature, 26 February 2015
http://www.nature.com/news/psychology-journal-bans-p-values-1.17001
The fickle P value generates irreproducible results, Nature, 26 February 2015
http://www.nature.com/nmeth/journal/v12/n3/full/nmeth.3288.html
“The p-value was never intended to be a substitute for scientific reasoning,” said Ron Wasserstein,
the ASA’s executive director. “Well-reasoned statistical arguments contain much more than the
value of a single number and whether that number exceeds an arbitrary threshold. The ASA
statement is intended to steer research into a ‘post p0.05’ era."

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“In physics you’re playing against God, and He doesn’t
change His laws very often. In finance you’re playing
against God’s creatures*, agents who value assets based
on their ephemeral opinions.”
“To confuse the model with the world is to embrace
a future disaster driven by the belief that humans obey
mathematical rules.” - Emanuel Derman
Uncertainty & Knowledge: Model Risk of Confusing Finance with Physics
“reflexivity of market participants”*
“thinking participants”*
“markets anticipating the future”*

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“I like to maintain a high degree of scepticism. What bothers me a lot is that
people teach finance as if it were a branch of pure mathematics, not a
practical field. Economics too has an unpleasant flavour of pure mathematics
to it. That's dangerous. You need to know the mathematics, but you need
to keep a certain scepticism and be able to walk a middle line. People who
use models to make a living don't take them as seriously as academics think
they do.” - Emanuel Derman
Uncertainty & Knowledge: Model Risk of Confusing Model with Reality
http://www.emanuelderman.com/writing/entry/interview-with-new-scientist
Derman’s Skepticism relates to Soros’ & My Critical Relativism!
“This reliance on models to handle risk carries its own risks.”
– Emanuel Derman, Goldman Sachs, 1996

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Uncertainty & Knowledge: Model Risk of Confusing Data with Information
Uncertainty & Knowledge: Model Risk of Efficient Markets Assumption*
Uncertainty & Knowledge: Model Risk of Market Equilibrium Assumption*
False Confirmation In reality, there is no confirmation because one source may have
obtained its data from the second source, or both sources may have received their data from
a third common source.
Disinformation The data generated may be flawed because of disinformation, which is
incomplete or inaccurate information designed to mislead the organization's CI efforts.
Blowback Blowback may occur when the company's disinformation or misinformation
that is directed at the competitor contaminates its own intelligence channels or information.
In all such cases, the information gathered may be inaccurate or incomplete.
http://www.brint.com/papers/ciover.htm
http://www.brint.com/papers/compint.htm
*
Hyperconnectivity
Hypervelocity
Round-trip delay: 0.000007 s

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Sep 28, 2015

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“But the VIX is really a measure of how much volatility is
expected, rather than a reflection of current volatility, which
some investors feel is deceptive. If the S&P 500 swings up 50
points, for example, then down 100 points, then
finishes flat, that was a relatively volatile trading day but that
will not be reflected in the VIX at the end of the day…”
“Investors concerned with volatility, therefore, frequently
consider measures such as trading volume, the size of
intraday swings and the standard deviation of price
performance.”

[41]
“The real problem is not whether machines think, but whether men do.”
- B.F. Skinner
“Ours is the age which is proud of machines that think, and suspicious of men
who try to.” - H. Mumford Jones
“Lo! Men have become the tools of their tools.” - Henry Thoreau
http://www.brint.com/common/random.txt
Uncertainty & Knowledge: Model Risk of ‘Physics Envy’ and ‘Physics Phobia’
Intuition… Imagination… Instinct… Insight
Morris Kline“Mathematics is manmade…”*
“reflexivity of modelers”*
http://www.brint.org/expertsystems.pdf
“Machines are not Human...”

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 Beyond Prediction to ‘Anticipation of Surprise’
 Beyond Model Risk of Assuming Perfect Knowledge
 Beyond Model Risk of Confusing Past with Future
 Beyond Model Risk of Normal Science and its ‘Silos’
 Beyond Model Risk of Confusing Finance with Physics
 Beyond Model Risk of Confusing Model with Reality
 Beyond Model Risk of Confusing Data with Information
 Beyond Model Risk of Efficient Markets Assumption
 Beyond Model Risk of Market Equilibrium Assumption
 Beyond Model Risk of ‘Physics Envy’ and ‘Physics Phobia’
Beyond Model Risk Management (MRM) to Model Risk Arbitrage
“Obsolete what you know before others obsolete it and profit by creating
the challenges and opportunities others haven't even thought about”
- Dr. Yogesh Malhotra, in Inc. Technology Interview, 2002,
- As noted by: U.S. Office of the Under Secretary of Defense (Comptroller) & DISA

[43]
http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf

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• Algorithms
• Data Mining
• Machine Learning
• Bayesian Analysis

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NEW: RISK
NEW: FINANCE
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Black Swans & Perfect Storms of FinTech
“There’s no such thing as ‘secure’ anymore.”
‘Financial markets now exist in a world
where cyber threats are the new normal.’
‘You have to assume a breach will happen.’ – FireEye, 2016

[47]
“Normality has been an accepted wisdom in economics and finance for a
century or more. Yet in real-world systems, nothing could be less normal
than normality. Tails should not be unexpected, for they are the rule…
As the world becomes increasingly integrated – financially, economically,
socially – interactions among the moving parts may make for potentially
fatter tails. Catastrophe risk may be on the rise.”
-- Andrew G Haldane, Executive Director, Financial Stability and member of the
Financial Policy Committee and Benjamin Nelson, Economist, Financial Stability, Bank
of England, in 'Tails of the unexpected' speech at “The Credit Crisis Five Years On:
Unpacking the Crisis”, 8 June 2012.
Risk, Uncertainty, and Profit for the Cyber Era: ‘Knight Reconsidered’

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Of Unknowns: Known* & Unknown*
“As we know,
There are known knowns.
There are things we know we know.
We also know
There are known unknowns*.
That is to say
We know there are some things
We do not know.
But there are also unknown unknowns*,
The ones we don’t know
We don’t know.”
-- Donald Rumsfeld, US Secretary of Defense,
Feb. 12, 2002
48
Expert Systems for Knowledge Management:
Crossing The Chasm Between Information
Processing and Sense Making. Journal of Expert
Systems with Applications (Malhotra, 2001).
http://www.brint.org/expertsystems.pdf
Finance Cyber
PDC x TS (EEO) F C

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Damaging Consequences of
Low and Negative Interest
Rates on the Financial Sector
Approximately 38% of
developed country government
bonds outside the US carried
negative yields... 2016 could
be the year we start to see
some unintended disruptions
in the financial system from
these unorthodox conditions.
What if their Black Swans are our Grey Swans or White Swans

[50]
The Global Risks Report 2016, Zurich Insurance Company Ltd.
What does risk look like in your part of the world?
Cybersecurity & Cyber-Finance
Risk Management: Strategies,
Tactics, Operations, &,
Intelligence: Enterprise Risk
Management to Model Risk
Management: Understanding
Vulnerabilities, Threats, & Risk
Mitigation
Here is a Black Swan and Grey Swan and White Swan
Let us do a
thought
experiment…

[51]
“Never write if you can speak;
never speak if you can nod;
never nod if you can wink.”
-- ‘Same Gaffes, but Now on Twitter’
New York Times, June 12, 2011
51
1984: There is always smart-something watching you and listening to you

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Digital-Virtual World of FinTech: Vulnerable Time and Space

[53]
Central Bankers Can ‘Create’ ‘Money’.
So can Black Hat Hackers!

[54]

[55]
http://www.cs.bu.edu/~goldbe/papers/NTPattack.pdf
Central Bankers Can Control Time Value of Money.

[56]
Digital-Virtual World of “Frictionless” = “Insecure” Commerce
https://whispersystems.org/blog/signal/
“Internet’s inherent vulnerabilities
and why they may never be fixed.”
washingtonpost.com
The Internet can appear to be elegantly designed, but as
The Washington Post’s Craig Timberg demonstrated in
his illuminating series “Net of Insecurity,” the network
is much more an assemblage of kludges—more
Frankenstein than Ferrari—that endure because they
work, or at least work well enough.
The defects hackers use often are well-known and
ancient in technological terms, surviving only
because of an industry-wide penchant for patching
over problems rather than replacing the rot – and
because Washington largely shrugged. At critical
moments in the development of the Internet, some of
the country’s smartest minds warned leaders at the
Pentagon and in Congress, but were largely ignored.
The consequences now play out across cyberspace
every second of every day, as hackers exploit old,
poorly protected systems to scam, steal, and spy
on a scale never before possible.
http://www.washingtonpost.com/sf/business/2015/07/22/hacks-on-the-highway/
http://www.washingtonpost.com/sf/business/2015/05/30/net-of-insecurity-part-1/

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 Cybersecurity & Cyber-Finance Risk Management: Strategies, Tactics,
Operations, &, Intelligence: Enterprise Risk Management to Model Risk
Management: Understanding Vulnerabilities, Threats, & Risk Mitigation
Invited CSO-CxO Plenary Keynote, National Cybersecurity Summit, Altria Group
Inc. Headquarters, Richmond, VA, Sep. 15, 2015.
 Extreme Risk Quantitative Finance Models: Cyber Finance-Cyber Risk
Insurance Modeling Risk Modeling for Managing Uncertainty. Invited
Presentation at State Street Bank World HQ, Boston, MA, Aug. 28, 2015.
 “As the lines between traditional finance, technology firms, and
telecom companies are blurring, many innovative solutions are emerging,
there is no clear straightforward solution to navigate this fintech world.”
- Blurred lines: How FinTech is shaping Financial Services,
Global FinTech Report March 2016, PwC.
2015Q4 - 2016
FinTech: Finance, Technology, & Telecom

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2016
‘Known’ Vulnerabilities: Block Chain & Crypto-Currencies
http://yogeshmalhotra.com/BitcoinProtocol.html
http://yogeshmalhotra.com/Future_of_Bitcoin.html
Mt. Gox Bankruptcy for 22 Billion Dollars? - Bitcoin News
Almost Half a Billion of Bitcoins Vanishes - WSJ
Mt. Gox Creditors Seek More Than $22 Billion
Bitcoin, up 152%, tops $1 billion in total value

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http://yogeshmalhotra.com/BitcoinProtocol.html
http://yogeshmalhotra.com/Future_of_Bitcoin.html

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NEW: RISK
NEW: FINANCE
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FinTech: From BizTech to Mobile Apps
Finance-IT-Risk Analytics beyond ‘Prediction’ to ‘Anticipation of Risk’™

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2016
FinTech: USA, UK, Europe, Asia
www.gov.uk EY: 2016 UK FinTech On the cutting edge
www.gov.uk EY: Swiss FinTech Report 2016
China Fintech Passed Disruption Tipping Point,
Report Says
Bloomberg, March 31, 2016
Fintech firms may have more clients than banks: Citigroup
Internet firms taken market share in e-commerce, payments

[65]
E-Biz &
KM
BizTech
KM-Tech
FinTech
Evolution of FinTech: 1993-Current
WWW-VL
KMNetwork
2000 2001
1994
1998
2011
“Yogesh Malhotra says his vision is to fill the gaps between business and technology, data
and knowledge, and, theory and practice...” -- in Fortune Interview, June 1998
DIGITIZATION-VIRTUALIZATION BUSINESS MODEL INNOVATION
130,000 Opt-in
‘Members’
Team of 200
PhDs
Millions of Patrons

[66]
Evolution of FinTech: 1993-Current http://km.brint.com/CBK/KnowledgeManagementOverview.pdf

[67]

[68]
AI, ML, Algorithms, Cog. Analytics
AI, ML, Algorithms, Cog. Analytics

[69]
P2P

[70]
P2P

[71]
P2P

[72]
P2P
Apps
Branches
ATMs

[73]
Evolution of FinTech: Beyond ‘Factory Assembly’ AI & ML…
http://www.kmnetwork.com/RealTime.pdf
- Discriminant Analysis, LOGIT, PROBIT, Decision Trees, Score Cards
- Hazards and Catastrophic Risk Models, Credit Risk Models (Creditmetrics, KMV)

[74]
Evolution of FinTech: …To ‘Cognitive Analytics’ AI & ML
http://www.kmnetwork.com/RealTime.pdf
Soft Computing Models, Artificial Neural Networks, Fuzzy Logic, Genetic Algorithms,
Bayesian Neural Networks, Support Vector Machines, Credal Nets, Quantum Computing

[75]
Evolution of FinTech: 1993-Current
“Managers are not confronted with problems that are
independent of each other, but with dynamic
situations that consist of changing problems that
interact with each other. I call such situations messes
... managers do not solve problems:
they manage messes.” - Russell Ackoff
And the Messes are going to get Messier…

[76]
Cyber Risk Loss
Cyber Insurance
Cyber Risk Models
CYBER
FINANCE
Beyond VaR to
ES, EVT, Power Laws
Cyber Risk
Quantitative
ASSESS
Risk
Qualitative
- Pen Testing
Stochastic
Deterministic
- Scenarios
MANAGE
Risk
Non-Deterministic
Production Distribution Consumption Across Time & Space
Efficiency Effectiveness Optimization
Uncertainty Modeling & Model Risk Management
Enterprise Risk Management
Finance Cyber
Threat &
Vulnerability
Analysis
Risk Mitigation
Risk Transfer
Risk
Acceptance
From Risk Modeling to Uncertainty Management
From ‘Prediction’ to “Anticipation of Surprise”
PDC x TS (EEO) F C
FinTech: Today: More Tech than Fin… More Cyber than Tech… Part Crypto
VALUE
VALUATION

[77]
Which of these is Finance? Which of these is Tech? Which of these is FinTech?
FinTech: World of anything but “Perfect Competition” or “Market Equilibrium”*
**

[78]
Traditional Retail Banks vs. Online-Only Banks:
Traditional retail banks provide a valuable service, but
online-only banks can offer many of the same services with
higher rates and lower fees. 
Traditional Lenders vs. Peer-to-Peer Marketplaces: P2P
lending marketplaces are growing much faster than
traditional lenders—only time will tell if the banks strategy
of creating their own small loan networks will be
successful. 
Traditional Asset Managers vs. Robo-Advisors: Robo-
advisors like Betterment offer lower fees, lower minimums
and solid returns to investors, but the much larger traditional
asset managers are creating their own robo-products while
providing the kind of handholding that high net worth
clients are willing to pay handsomely for.
FinTech: Current Examples
 Retail banking
 Lending and Financing
 Payments and Transfers
 Wealth & Asset
Management
 Markets & Exchanges
 Insurance
 Blockchain Transactions
BI Intelligence: The Fintech
Ecosystem Report: Measuring the
effects of technology on the entire
financial services industry,
December 17, 2015.

[79]
The company's £1 million crowdfunding
campaign is part of a £6 million fundraising
that values the startup at £30 million.
“a bank
that's just
an app”
“one of several so-called neobanks — online-only
banks. Rivals include Atom Bank, which was valued
at £150 million in a £45 million investment by
Spanish lender BBVA last year, Starling Bank, which
raised £48 million in January”

[80]
“loans to students in Europe using proprietary algorithms to asses their credit-worthiness”
Future Finance has raised $266 million, or £185 million, since going live in May 2014.

[81]
“Deep data portfolio risk solutions for asset managers”
An innovative risk analytics company that detects disruptive events in global financial markets
and anticipates price movements hours or days in advance of the event…shows how key market
shifts can be detected using deep data analytics and no historical data.”
The deep data algorithms detect anomalies in the financial markets and anticipate price
movements hours (or days) in advance of the event. Unlike competitive solutions, their real
time analysis does not rely on historical data or previous disruptive events.
Early detection of
cluster formations
(‘anomaly’)
providing advance
warning of a
disruptive event.

[82]
“Using real-time exchange data, the system searches for clusters of
behavior. The technology is called ‘entropy feature clustering,’ which
is a very nice way of saying we look at the order book, we look at the
order depth, we look at the order activities and we look for trades that
have the same unique signature… In an electronic world, many high
frequency trading algorithms, likewise, are known to identify block
orders – or repeating order patterns that come from larger institutions
selling through an algorithm that breaks up orders into smaller
patterns…”
“As people trade, as they go throughout the day, there is some
information that they leave behind…We’re tracking that unique
fingerprint, and that allows us to put the trade in a cluster.”
Example of ML Paper on related methodology:
http://machinelearning.org/proceedings/icml2004/papers/253.pdf

[83]
NEW: RISK
NEW: FINANCE
P
A
A
A
O

[84]
Perfect Storms of Cyber-Finance (CF)
‘Trust Online is at the Breaking Point’ - Ponemon Institute

[85]
“As apparent from the above analysis of nascent cyber risk assessment and cyber
insurance modeling, these applications and practices are predominantly reliant upon the
VaR model. Based upon recognized limitation of VaR in terms of model risks, tail
risks, and systemic risks, one comes away with the unsettling conclusion that given
very high interdependence and correlations characterizing cyber risks, application of
VaR for cyber risk assessment and cyber insurance is fraught with peril. Based upon the
details available in publicly available sources on the applications of VaR in cyber risk
assessment and cyber insurance modeling, it appears that VaR is being adopted as a
‘black box’ in this domain. Our analysis of those applications presented earlier, that
most of those commercial practice applications of VaR in cyber risk assessment and
cyber insurance modeling did not consider model risks, tail risks, or systemic risks at all.
Furthermore, what is even more alarming is the fact that such ‘blackbox’ reliance
upon VaR (or any other model for that matter) is the strongest indicator of model
risk calling to attention the most critical need for model risk management.”
-- Yogesh Malhotra in the Post-Doctoral Thesis pioneering Cyber-Finance, 2015
Risk, Uncertainty, and Profit for the Cyber Era: ‘Knight Reconsidered’

[86]

[87]

[88]
http://www.cs.bu.edu/~goldbe/papers/NTPattack.pdf
Central Bankers Can Control Time Value of Money.

[89]

[90]

[91]

[92]
cyveillance.com
20-30 Billion IoT Threat Vector Sources
Malware 2015 = 5 X (2010-2014)
NFC Payment Vulnerabilities

[93]
2016 BYOD & MOBILE SECURITY REPORT

[94]

[95]

[96]
http://www.yogeshmalhotra.com/cyberrisk.html

[97]

[98]
watchguardservices.co.uk

[99]
NEW: RISK
NEW: FINANCE
P
A
A
A
O

[100]
Non-Deterministic Technologies for CF

[101]
Convergent deterministic Lockean & Leibnitzian systems are for stable and
predictable organizational environments. Emerging environments defeat such
systems for predicting and reacting based on deterministic logic. Such
deterministic systems works against generation of multiple and contradictory
viewpoints necessary for meeting the challenge posed by uncertain
environments. Instead, they require anticipatory responses for which
divergent Kantian & Hegelian inquiry systems are particularly suited given
their multiplicity of world views. Facilitating multiple and contradictory
interpretations of information, Hegelian systems ensure continual non-
deterministic re-examination and modification of information to keep it aligned
with changing reality of nature.
(Scenario-Planning Logic based upon Kantian and Hegelian systems!)
Quick Background… Where this Research Started… First Conference
Malhotra, Y., Knowledge Management in Inquiring Organizations. Proceedings of the
3rd Americas Conference on Information Systems, Indianapolis, IN, August, 1997.
http://www.brint.org/inquiring.pdf

[102]
How to Manage Risk (After Risk Management Has Failed) Fall 2010 Vol. 52
Bayesian modeling instead of VaR would minimize risk management failures
- Given key role of ‘subjective judgment’ in the Bayesian methodology
Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management:
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds (Malhotra 2014)
- Subjective judgment … Bayesian priors… key limitation of Bayesian methodology
- Since before the Crisis, non-Bayesian and Bayesian VaR models in Finance practice
- Bayesian vs. VaR dilemma needs to be resolved
- To minimize model specification and estimation errors.
- Model Risk Management is crucial for VaR, Bayesian, and Bayesian VaR
Beyond Model Risks of VaR [& NHST]: MRM, ES, EVT, Bayesian,...

[103]
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
Bayesian Inference http://ssrn.com/abstract=2538401

[104]
How to Manage Risk (After Risk Management Has Failed) for Hedge Funds
http://ssrn.com/abstract=2538401Bayesian Inference

[105]
Markov Chain Monte Carlo Models, Gibbs Sampling & Metropolis Algorithm
for High-Dimensionality Complex Stochastic Problems. (Malhotra 2014)
Bayesian inference for doing high dimension parameter space analyses
- Feasible with Markov Chain Monte Carlo statistical computing algorithms
- Metropolis Hastings algorithm and Gibbs Sampling algorithm
MCMC: A common general quantitative method to find approximate
solutions to computationally complex problems in polynomial time…
Polynomial Time O(nk) s.t. k > 1
Exponential Time O(kn) s.t. k > 1
n = length of input
Source:
stackoverflow.com
Bayesian Inference

[106]
Markov Chain Monte Carlo Models, Gibbs Sampling & Metropolis Algorithm
for High-Dimensionality Complex Stochastic Problems.
Gibbs Sampling: Generating random variables from a marginal distribution
indirectly without the need for calculating the distribution density.
-E.g. Solve complex multivariate stochastic model with N parameters (i.e. N-
Dim.) using N univariate (i.e., one-dimensional) conditional distributions.
http://ssrn.com/abstract=2553537Bayesian Inference: Markov Chain Monte Carlo

[107]
Dropping the burn-in sample of first m draws
≈
Metropolis Algorithm :
http://ssrn.com/abstract=2553537Bayesian Inference: Markov Chain Monte Carlo

[108]
Metropolis-Hastings Algorithm :
Metropolis-Hastings Algorithm = Metropolis Algorithm when
Risk, Uncertainty, and Profit for the Cyber Era: Model Risk
Management of Cyber Insurance Models Using Quantitative Finance
and Advanced Analytics. (Malhotra 2015)
Bayesian Inference: Markov Chain Monte Carlo

[109]
Bayesian Inference: Bayesian Nets & Credal Nets
http://ijcai13.org/files/tutorial_slides/te1-1.pdf
Source: Bayesian Networks with Imprecise Probabilities: Theory and Applications to Knowledge-
based Systems and Classification: A Tutorial by Alessandro Antonucci, Giorgio Corani and Denis
Maua´, IJCAI-13 Beijing, August 5th, 2013.

[110]

[111]

[112]

[113]
Source: Firoozye, Nick & Ariff, Faziah. Uncertainty and Imprecise Probability in Post-Crisis Risk
Management, Nomura Internal Presentation on Managing Uncertainty, Mitigating Risk. 2016.
Bayesian Inference: Bayesian Nets: Credal Nets: Example

[114]

[115]

[116]

[117]
20-Year
Focus of
R&D
FinRM.org

[118]
Possibility Theory: Fuzzy Sets
The fundamental difference between traditional set theory and fuzzy set
theory is the nature of inclusion of the elements in the set. In traditional
sets, an element is either included in the set or is not. In a fuzzy set, an
element is included with a degree of truth normally ranging from 0 to 1.
Fuzzy logic models allow an object to be categorized in more than one
exclusive set with different levels of truth or confidence. Fuzzy logic
recognizes the lack of knowledge or absence of precise data, and it
explicitly considers the cause-and-effect chain among variables.
Source: Shang, Kailan & Hossen, Zakir. Applying Fuzzy Logic to Risk Assessment and Decision-Making,
Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries. 2013.
https://www.soa.org/Files/Research/Projects/research-2013-fuzzy-logic.pdf

[119]

[120]
Possibility Theory: Fuzzy Sets: Inference

[121]

[122]

[123]
Malhotra, Y. Quantum Computing, Quantum Cryptography, Shannon’s Entropy and
Next Generation Encryption & Decryption, November 2013. (Invited Presentation)
Information entropy of 27-char. language ~ 4.8 bits per char.
Information entropy of 5,000-char. language ~ 12.3 bits per char.
Entropy increases with a larger repertoire of symbols.
Entropy increases when meanings detached from symbols.
Quantum computer: qubits… can be 0, 1, or any superposition of
both. n-qubit system: superposition of up to 2n states
simultaneously. 2k dimensional vector (a, b, c, d, e, f, g, h)…
complex values: |a|2 + |b|2 + …+ |h|2 = 1,
|x|2 is probability amplitude of respective state. Phase between any
two states (complex-valued coefficients )… meaningful.
Quantum Computing

[124]
Quantum Cryptography, Shor's algorithm, and Quantum Money
Integer Factorization of large primes and Discrete Logarithm problem.
Quantum computer efficiently find such factors using Shor's algorithm.
Decrypt many critical cryptographic systems in polynomial time:
RSA, secure Web pages, encrypted email, many other types of data.
“For a 1024-bit number, Shor's Algorithm requires on the order of 10243, about one
billion, operations. If each quantum operation took one second, our factorization would
last 34 years. If a quantum computer could run at the speed of today's electronic
computers (100 million instructions per second and up) then factorization of the 1024-
bit number would be a matter of seconds.”
IEEE
Quantum Computing

[125]
N-dimensional
Hilbert Space
Rieffel
Polak
Quantum
Computing
Kaye An
Introduction to
Quantum
Computing
Quantum Money
NASA: Peter Shor
Some quantum cryptography systems
vulnerable to hacking, study shows

[126]
Teen Solves Quantum Entanglement Problem for Fun | WIRED
www.wired.com/2012/06/ari-dyckovsky/
Argo nabs $1.5M to help any employee -- not just data VentureBeat
Stanford Dropouts’ “Argo” Raises $1.5M From Accel To Stitch Together
Companies’ Small Data http://techcrunch.com/2015/01/13/small-data/
FROM QUANTUM
ENTANGLEMENT
TO DATA
ANALYTICS
STARTUP

[127]
Goldman Sachs, RBS, Guggenheim Partners and
Commonwealth Bank of Australia
Quantum computers could, in theory, give investment firms
much better visibility over the longer-term to make more
accurate predictions and reduce this need to tinker with their
portfolios... It will be still 5 to 10 years before Quantum
Computing comes of age.
Quantum computing may offer potential benefits to the
financial services industry, but it also poses risks could
exponentially reduce the time to break encryption... which may
lead the governments to outlaw quantum computers entirely.

[128]
Seven stages in the development of quantum information processing
Source: Morris, MSG Jeffrey. Implications of Quantum Information Processing On Military
Operations, The Cyber Defense Review, May 29, 2015.
Quantum Computing

[129]
“Finance is a domain where
you benefit not just from being
smart, but from being smart in
a different way from others.”
Intuition… Imagination… Instinct… Insight
“Obsolete what you know before
others obsolete it and profit by
creating the challenges and
opportunities others haven't even
thought about” – Yogesh Malhotra

[130]
NEW: RISK
NEW: FINANCE
P
A
A
A
O

[131]
 FinTech is the digital, virtual, interconnected, global-everything Finance.
 ‘Model Risk Arbitrage’ is the new framework for profit-making in global markets.
 Model Risk is greater than ever before and will get exponentially greater.
 Cyber Risk will continue to subsume most Financial & Other Risks.
 Cyber Risk integrated with Financial Risks will cause greater-more shocks.
 So, timelines of profit-making speculations will get ever smaller-faster.
 With higher intra-day volatility and circuit-breakers markets may end flat.
 Expect ‘Perfect Storms’ of catastrophic shocks with higher frequency-impact.
 Hi-dimensional analytic algorithms will play greater role in profit-making.
 Multi-asset, multi-model, non-deterministic algorithms will enable execution.
 Illustrative non-deterministic technologies for execution are identified.
 Comments welcome, write to dr.yogesh.malhotra@gmail.com.
Overview: Beyond ‘Closed Systems’ Finance to ‘Open Systems’ Finance
Observations & Speculations

[132]
This presentation outlines the new framework of Model Risk
Arbitrage for profit-making in global Trading markets of FinTech
era. It advances upon latest Finance and Trading practitioner
leadership insights in Uncertainty Management for Financial Markets,
wisdom of one of the greatest wizards and legends of global Financial
Markets, and, observations of the pioneering practitioners and
Model Risk Arbitrage:
Turning Model Risk Management on its Head
The Black Hat Mindset focus is not on a Model, but how
to exploit all Models at all Levels.
Hopefully, knowing Model Risk Arbitrage will make you
better at Model Risk Management too.
MODEL RISK ARBITRAGE

[133]
This presentation outlines the new framework of
Model Risk Arbitrage for profit-making in global
Trading markets of FinTech era. It advances upon
latest Finance and Trading practitioner leadership
insights in Uncertainty Management for Financial
Markets, wisdom of one of the greatest wizards and
legends of global Financial Markets, and,
observations of the pioneering practitioners and
THE BIGGEST SHORT
Model Risk Arbitrage: Turning Model Risk Management on its Head
The Black Hat Mindset focus is not on a Model, but how to exploit all Models at all Levels.
Hopefully, knowing Model Risk Arbitrage will make you better at Model Risk Management too.

[134]
Beyond Popper’s doctrine of the “unity of
method” for a deterministic and certain world...
Toward a doctrine of “plurality of methods”
and “plurality of theories” for an increasingly
non-deterministic and uncertain world.
The Hegelian Dialectic

[135]
George Soros’ Reflexivity Theory:
The Most Practical Theory I have known.
Reflexivity is the missing link in Finance theory,
research, and, practice that can help understand
the effect of feedback and feedforward loops
across Time and Space in information-based
non-deterministic ‘open systems’ Finance.
Dedicated to

[136]
Beyond Model Risk Management to Model Risk Arbitrage for FinTech Era
How to Navigate ‘Uncertainty’... When ‘Models’ Are ‘Wrong’... and ‘Knowledge’... ‘Imperfect’!
Knight Reconsidered Again: Risk, Uncertainty, & Profit beyond ZIRP & NIRP
Yogi
Dr. Yogesh Malhotra
CISSP, CISA, CEH
PhD, MSQF, MSCS, MSNCS, MSAcc, MBAEco, BE, CEng
www.yogeshmalhotra.com (646) 770-7993 dr.yogesh.malhotra@gmail.com
Global Risk Management Network, LLC
757 Warren Road, Cornell Business & Technology Park, Ithaca, NY 14852-4892

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