princeton quant trading conference 2016 model risk arbitrage and george soros' reflexivity...

[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, 2016

www.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 [email protected] Global Risk Management Network, LLC

757 Warren Road, Cornell Business & Technology Park, Ithaca, NY 14852-4892

http://www.modelriskarbitrage.com/

http://www.futureoffinance.org/

[2]







How to Navigate ‘Uncertainty’... When ‘Models’ Are ‘Wrong’...

October 25, 2015

... and ‘Knowledge’... ‘Imperfect’!



http://www.yogeshmalhotra.com/ModelRiskManagement.html

http://www.latimes.com/nation/la-na-celestial-navigation-20151025-story.html




http://www.capitalgazette.com/news/ph-ac-cn-celestial-navigation-1014-20151009-story.html

http://www.militarytimes.com/story/military/tech/2015/11/01/naval-academy-reinstates-celestial-navigation/74998554/




http://finrm.org/

https://www.us-cert.gov/ncas/current-activity/2015/10/21/Vulnerabilities-Identified-Network-Time-Protocol-Daemon-ntpd

[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



http://finrm.org/

[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.



[6]







Perhaps the Smartest FinTech Innovation... The Black Hat Way

Lights Out:

A Cyberattack,

A Nation

Unprepared,

Surviving the

Aftermath



http://www.scientificamerican.com/article/the-most-vulnerable-ransomware-targets-are-the-institutions-we-rely-on-most/

https://www.google.com/search?q=ransomware

[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



[8]







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 [email protected].

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

P

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MODEL RISK ARBITRAGE AND “THE BIG SHORT” FOR THE OPEN SYSTEMS FINANCE:

REFLEXIVITY THEORY, HEGELIAN DIALECTIC, AND, BLACK HAT CYBER FINANCE



http://ssrn.com/abstract=2590258

[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/publications.html



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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






















http://yogeshmalhotra.com/Future_of_Bitcoin.html


http://yogeshmalhotra.com/BitcoinProtocol.html







http://www.brint.org/expertsystems.pdf





[13]







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



[14]







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



https://en.wikipedia.org/wiki/C._West_Churchman

https://en.wikipedia.org/wiki/Frank_Knight

https://en.wikipedia.org/wiki/Thomas_Kuhn

https://en.wikipedia.org/wiki/Claude_Shannon

https://en.wikipedia.org/wiki/Norbert_Wiener

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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










[18]







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



https://en.wikipedia.org/wiki/C._West_Churchman

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NEW: RISK

NEW: FINANCE

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http://www.simoncamilleri.com/the_truth_of_the_elephant/

[21]







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







[22]







“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



http://www.ederman.com/new/index.html

http://www.ederman.com/new/index.html

http://www.wilmott.com/about.cfm

http://www.businessweek.com/magazine/content/09_02/b4115059823953.htm

http://www.businessweek.com/magazine/content/09_02/b4115059823953.htm


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“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


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.




http://www.finrm.org/


[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!





http://www.brint.com/papers/science.htm



[26]







“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…



[27]







“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!





http://www.brint.org/inquiring.pdf


[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/GriffissCyberspace.html



[30]







March 07, 2016 http://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’



[31]







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



https://med.stanford.edu/news/all-news/2016/03/misleading-p-values-showing-up-more-often-in-journals.html

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

[32]







March 07, 2016 http://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."



http://www.amstat.org/newsroom/pressreleases/P-ValueStatement.pdf



http://www.nature.com/news/scientific-method-statistical-errors-1.14700










http://www.nature.com/news/statistics-p-values-are-just-the-tip-of-the-iceberg-1.17412





















http://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503

















http://www.nature.com/news/how-scientists-fool-themselves-and-how-they-can-stop-1.18517



















http://www.nature.com/news/psychology-journal-bans-p-values-1.17001











http://www.nature.com/nmeth/journal/v12/n3/full/nmeth.3288.html

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[35]







“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”*





[36]







“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




http://www.emanuelderman.com/writing/entry/interview-with-new-scientist












http://wilmott.com/

http://www.nuclearphynance.com/

http://www.emanuelderman.com/writing/entry/model-risk

https://en.wikipedia.org/wiki/Benoit_Mandelbrot

[37]







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

“Humans are not Machines...” Round-trip delay: 0.000007 s



http://www.brint.com/papers/ciover.htm

http://www.brint.com/papers/compint.htm

[38]









[39]







Sep 28, 2015



[40]







“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


Uncertainty & Knowledge: Model Risk of ‘Physics Envy’ and ‘Physics Phobia’

Intuition… Imagination… Instinct… Insight “Intuition takes intimate knowledge of the world that can be acquired


Morris Kline “Mathematics is manmade…”*

“reflexivity of modelers”*



“Machines are not Human...”




http://www.emanuelderman.com/media/Fischer_Black_by_Derman_Bloomberg_2009.pdf

https://en.wikipedia.org/wiki/Morris_Kline

http://www.amazon.com/Mathematics-Nonmathematician-Dover-Books/dp/0486248232/

http://www.amazon.com/Calculus-Intuitive-Physical-Approach-Mathematics/dp/0486404536/


https://en.wikipedia.org/wiki/Fischer_Black

[42]







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





[44]







• Algorithms

• Data Mining

• Machine Learning

• Bayesian Analysis



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NEW: RISK

NEW: FINANCE

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“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



http://www.futuresmag.com/2015/09/17/assume-breach-cyber-threat-traders

[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’





[48]





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).


Finance Cyber

PDC x TS (EEO) F C






[49]







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



[52]





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/



https://whispersystems.org/blog/signal/


http://www.washingtonpost.com/sf/business/2015/07/22/hacks-on-the-highway
















[57]







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









[58]







2016

‘Known’ Vulnerabilities: Block Chain & Crypto-Currencies



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







[59]













[60]









[61]









[62]















NEW: RISK

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[63]




Finance-IT-Risk Analytics beyond ‘Prediction’ to ‘Anticipation of Risk’™






[64]







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




http://www.finrm.com/Background.html


http://books.google.com/books/about/Knowledge_Management_and_Virtual_Organiz.html?id=jVRReLsAnikC

http://books.google.com/books/about/Knowledge_Management_and_Business_Model.html?id=nAccO2bjrxwC

http://kmnetwork.com/

http://kmnetwork.com/



[66]







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



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




http://www.finrm.com/Background.html


[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


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








http://www.yogeshmalhotra.com/projects.html












http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553547







[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]















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[84]




‘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]







Central Bankers Can Control Time Value of Money.





[89]








https://www.us-cert.gov/ncas/current-activity/2015/10/21/Vulnerabilities-Identified-Network-Time-Protocol-Daemon-ntpd

[90]








http://support.ntp.org/bin/view/Main/SecurityNotice#Recent_Vulnerabilities

[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]











[97]









[98]







watchguardservices.co.uk



[99]















NEW: RISK

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[100]






[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.







[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=2538401 Bayesian 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=2553537 Bayesian Inference: Markov Chain Monte Carlo




[107]







Dropping the burn-in sample of first m draws

≈

Metropolis Algorithm :

http://ssrn.com/abstract=2553537 Bayesian 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



https://www.soa.org/Files/Research/Projects/research-2013-fuzzy-logic.pdf







[119]



















[120]







Possibility Theory: Fuzzy Sets: Inference












[121]








Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries. 2013.












[122]








Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries. 2013.












[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



http://arxiv.org/abs/quant-ph/9809016







http://quantum.nasa.gov/materials/2012-01-19-B1-Shor.pdf

http://quantum.nasa.gov/materials/2012-01-19-B1-Shor.pdf

http://www-math.mit.edu/~shor/

http://phys.org/news/2015-12-quantum-cryptography-vulnerable-hacking.html



[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



https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=8&cad=rja&uact=8&ved=0ahUKEwjOn_iq2IXMAhWmwYMKHYIBD5kQFgg7MAc&url=http://www.wired.com/2012/06/ari-dyckovsky/&usg=AFQjCNFMcBaUN6YIqoWpswGZhAEjAc2yiQ&bvm=bv.119028448,d.amc

http://www.wired.com/2012/06/ari-dyckovsky/





http://venturebeat.com/2015/01/13/argo-nabs-1-5m-to-help-any-employee-not-just-data-scientists-tap-into-company-data/





http://techcrunch.com/2015/01/13/small-data/










http://journals.aps.org/pra/abstract/10.1103/PhysRevA.85.052322

[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



http://www.wired.com/2016/01/the-rise-of-the-artificially-intelligent-hedge-fund/

http://www.emanuelderman.com/media/Fischer_Black_by_Derman_Bloomberg_2009.pdf

http://finrm.org/

[130]















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[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 [email protected].

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



http://finrm.org/

[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 [email protected] Global Risk Management Network, LLC

757 Warren Road, Cornell Business & Technology Park, Ithaca, NY 14852-4892



princeton quant trading conference 2016 model risk arbitrage and george soros' reflexivity...

Economy & Finance