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Frequent
Once-in-a-Lifetime
Crises
© 2016 Lone Star Analysis
www.lone-star.com | © Lone Star Analysis
Agenda
2
• Introduction
• Modeling Rare Events
• Measurement of Risk
• Exposing B...
www.lone-star.com | © Lone Star Analysis
Introduction
3
• Black Swan (Nassim Taleb)
• Improbable event with colossal conse...
www.lone-star.com | © Lone Star Analysis
Modeling Rare Events
4
• Leveraging the financial markets for analogy…
• …normal ...
www.lone-star.com | © Lone Star Analysis
Measurement of Portfolio Risk
5
• Leveraging the financial markets for analogy…
•...
www.lone-star.com | © Lone Star Analysis
Exposing Black Swans
6
• What if the model architecture contains thousands of inf...
www.lone-star.com | © Lone Star Analysis
Exposing Black Swans
7
• Tornado Charts…
• …fall short from inspecting the tails ...
www.lone-star.com | © Lone Star Analysis
Exposing Black Swans
8
• Percent point functions…
• ...facilitate inspection of t...
www.lone-star.com | © Lone Star Analysis
Exposing Black Swans
9
• Stochastic optimization…
• Random variable inputs
• Rand...
www.lone-star.com | © Lone Star Analysis
Exposing Black Swans
10
• Tornado charts
• Show how input variation impacts the o...
www.lone-star.com | © Lone Star Analysis
Training and Simulation Applications
11
• Acquisition
• Random inputs modeled as ...
www.lone-star.com | © Lone Star Analysis
Summary
12
• It’s possible to identify potential black swans and in doing so, ren...
www.lone-star.com | © Lone Star Analysis
Acknowledgements
13
• I’d like to thank my co-author John Volpi (CTO of Lone Star...
www.lone-star.com | © Lone Star Analysis 14
Locations &
Contact
Information
Southern Maryland Address
44425 Pecan Court, S...
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Black Swans Dr. Randal Allen, Lone Star Analysis

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Black Swans in your data? Black swans lurk in the “tails,” i.e. less than the 10th percentile and/or greater than the 90th percentiles.

Tools exposing Balck Swans:
Tornado charts-Show how input variation impacts the output (between 10th and 90th percentile).

Percent Point Function (PPF)-Show how much impact rare events could have (<10th>90th percentile tail).

Stochastic optimization-Shows the values each input must be to achieve output extrema (min or max).

Together, these tools and their insights reduce the surprise of a black swan, rendering it gray.

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Black Swans Dr. Randal Allen, Lone Star Analysis

  1. 1. Frequent Once-in-a-Lifetime Crises © 2016 Lone Star Analysis
  2. 2. www.lone-star.com | © Lone Star Analysis Agenda 2 • Introduction • Modeling Rare Events • Measurement of Risk • Exposing Black Swans • Tornado charts • Percent point functions • Stochastic optimization • Applications to Training and Simulation • Summary
  3. 3. www.lone-star.com | © Lone Star Analysis Introduction 3 • Black Swan (Nassim Taleb) • Improbable event with colossal consequences • Gray Swan (Benoit Mandelbrot) • Predicting some behavior of a black swan • Modeling & Simulation Probabilistic Tools • Tornado charts (specialized bar charts) • Percent Point Functions (a variation on the cumulative distribution function) • Stochastic optimization • 2015 I/ITSEC Black Swan Kickoff Questions • How can M&S be used to analyze and prepare or create a Black Swan? • Can we develop complex adaptive models and simulation tools that will enable the analysis?
  4. 4. www.lone-star.com | © Lone Star Analysis Modeling Rare Events 4 • Leveraging the financial markets for analogy… • …normal fit suggests zero probability of losses beyond 16%. Maximum losses -26% NOV 1929 -24% APR 1932 -20% OCT 2008 Maximum gain +50% AUG 1932 The log-stable is also known as a “fat tail”
  5. 5. www.lone-star.com | © Lone Star Analysis Measurement of Portfolio Risk 5 • Leveraging the financial markets for analogy… • …the log-stable portfolio fit shows losses could be as much as an order of magnitude higher! Asset Class Allocation Small Cap Stocks 20% Mid Cap Stocks 15% Large Cap Stocks 5% Int'l Developed Stocks 5% Int'l Emerging Stocks 10% Corporate Bonds 15% Government Bonds 10% Real Estate 10% Commodities 10% Maximum loss less than 7% Maximum loss more than 70%
  6. 6. www.lone-star.com | © Lone Star Analysis Exposing Black Swans 6 • What if the model architecture contains thousands of influential nodes? • How does one expose the potential (needle in the haystack) black swan? • Tools • Tornado charts • Percent point functions • Stochastic optimization
  7. 7. www.lone-star.com | © Lone Star Analysis Exposing Black Swans 7 • Tornado Charts… • …fall short from inspecting the tails of distributions. Tornado charts only show the sensitivity between the 10th and 90th percentile of a particular input on the output Black swans lurk in the “tails,” i.e. less than the 10th percentile and/or greater than the 90th percentiles Small cap stocks (IWM) can lower the return by as much as 178% or increase the return by as much as 125%
  8. 8. www.lone-star.com | © Lone Star Analysis Exposing Black Swans 8 • Percent point functions… • ...facilitate inspection of tails to see if potential rare events might be lurking. With a normal distribution, the tail ends near -7%. With a log-stable distribution, the tail continues down to -71%. The log-stable distribution exposes larger risk.
  9. 9. www.lone-star.com | © Lone Star Analysis Exposing Black Swans 9 • Stochastic optimization… • Random variable inputs • Random objective function • Random iterates • …lists all the input values, chosen so as to achieve minimum return. ETF Allocation Minimum Maximum IWM 20% -24% 14% MDY 15% -24% 14% SPY 5% -18% 10% EFA 5% -23% 12% EEM 10% -29% 16% LQD 15% -11% 13% AGZ 10% -2% 4% IYR 10% -38% 26% DBC 10% -29% 15% Portfolio -21.9% 14.1% Stochastic optimization is overkill for this example, but makes the point. All inputs will be selected from their range in order to minimize or maximize the result. For a portfolio with thousands of random inputs and complex interconnections, it soon becomes intractable to perform these calculations with a spreadsheet, let alone by hand.
  10. 10. www.lone-star.com | © Lone Star Analysis Exposing Black Swans 10 • Tornado charts • Show how input variation impacts the output (between 10th and 90th percentile). • Percent Point Function (PPF) • Show how much impact rare events could have (<10th percentile tail, >90th percentile tail). • Stochastic optimization • Shows the values each input must be to achieve output extrema (min or max). • Together, these tools and their insights reduce the surprise of a black swan, rendering it gray.
  11. 11. www.lone-star.com | © Lone Star Analysis Training and Simulation Applications 11 • Acquisition • Random inputs modeled as log-stable (fat tail) distributions could expose exorbitant costs, extreme impacts of schedule slippage, and/or poor performance. • Training proficiency • Baseline effectiveness, time, number of iterations, media factors, instructional quality factors, and skill decay could all be modeled with fat tails to see if there are any substantial impacts to proficiency. • Strategic multi-layer assessment • Numerous inputs to organizational, social network, time influence network, information diffusion, and text analysis models should leverage fat tail distributions to expose any potential black swan events.
  12. 12. www.lone-star.com | © Lone Star Analysis Summary 12 • It’s possible to identify potential black swans and in doing so, render them gray. Thus we can prepare, organize, train and equip for black swan resiliency. • The log-stable distribution is superior to the normal distribution when it comes to modeling data that includes rare events lying far from the mean. • Three tools (Tornado charts, PPFs, and stochastic optimization) and their insights can reduce the surprise of a black swan, rendering it gray.
  13. 13. www.lone-star.com | © Lone Star Analysis Acknowledgements 13 • I’d like to thank my co-author John Volpi (CTO of Lone Star Analysis) for his assistance in preparing this paper and presentation. • We thank Dr. Paul Kaplan (Morningstar) and Dr. John Nolan (University of Virginia) for personal email correspondence. • We recognize Investools / TD Ameritrade from which we were able to obtain historical monthly data for each asset class.
  14. 14. www.lone-star.com | © Lone Star Analysis 14 Locations & Contact Information Southern Maryland Address 44425 Pecan Court, Suite 125 California, Maryland 20619 Main. (240) 925-4960 Other Field/Marketing Locations Orlando, Florida Headquarters Address 4555 Excel Parkway, Suite 500 Addison, Texas 75001 Main. (972) 690-9494 National Capital Region Address 1800 Diagonal Road, Suite 600 Alexandria, Virginia 22314 Main. (571) 366-1710

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