In this presentation I discuss ways to visualize financial stress in the context of

1. VISUALIZING FINANCIAL STRESS
European Central Bank
Frankfurt, Germany
20 April 2015
Kimmo Soramäki
Founder and CEO
FNA Ltd.
FNA

2. First Financial Networks
Fedwire Interbank Payment Network (Fall
2001) was one of the first network views into
any financial system.
Of a total of around 8000 banks, the 66 banks
shown comprise 75% of total value. Of these,
25 banks completely connected
The chart was subsequently used e.g. in
congressional hearings to showcase the type
of information that should be collected by
financial institutions after the financial crisis.
The research is cited in ~300 academic
publications.
Research
paper:
Soramaki,
K.
M
Bech,
J.
Arnold,
R.J.
Glass
and
W.E.
Beyeler,
The
Topology
of
Interbank
Payment
Flows,
Physica
A,
Vol.
379,
pp
317-­‐333,
2007.

3. The Science is being Developed
New Journal ‘Network Theory in Finance’ is
launched in March 31, 2015
Editor in Chief: Kimmo Soramäki
Editorial Board included e.g.
Andrew Haldane, Bank of England
Franklin Allen, Imperial College/Wharton
Ignazio Angeloni, ECB
“Journal of Network Theory in Finance is an
interdisciplinary journal publishing rigorous
and practitioner-focused research on the
application of network theory in finance. The
journal connects academia, regulators and
practitioners in solving important issues
around financial risk”
2nd Annual Conference on
9 September 2015 in Cambridge, UK.

4. The FNA Software consists of FNA Platform
and FNA Apps.
FNA Platform is the server side workhorse
for analysis, simulation and visualization of
financial networks used by all FNA Apps.
FNA Software
FNA Apps master particular uses
cases with an interactive user
experience.
FNA Maps
FNA Payments
FNA HeavyTails

5. Network Maps
Bank Equity Cross Holdings International RemittancesKorean Interbank Payments
CLS Settlement SWIFT Message Flows US Inter-sectoral Trade flows
Other past projects: Mapping interbank exposures (Bank of England, HKMA, ESRB)
Ongoing projects: Hedge Fund - Asset Holdings, Interbank exposures (from public sources)

6. FNA HeavyTails - Visualizing Stress

7. Time Series and Correlations
…" Example: Daily returns of asset prices
(ETFs)
Difficult to understand large-scale
correlation or other dependence structures
of financial assets.
Objective is to:
Efficiently represent a complex system
moving in time
Visualize and predicts stress events in their
context
Overlay multiple dimensions of the data to
allow for visual inference

8. Correlation Networks
Not all correlations are
statistically significant.
A sparse matrix is often well
represented as a network.
We encode correlations as links
between the correlated nodes/
assets.
Red link = negative correlation
Black link = positive correlation
Absence of link marks that asset
is not significantly correlated.
A B C
A 1 0.5 0.8
B 0.5 1 0.2
C 0.8 0.2 1

9. Dimensionality Reduction
Next, we identify the Minimum
Spanning Tree (MST) and filter
out other correlations.
Rosario Mantegna (1999)
‘Hierarchical Structure in
Financial Markets’
This shows us the backbone
correlation structure where
each asset is connected with
the asset with which its
correlation is strongest.

10. Visualization
We use a radial tree layout
algorithm by
Bachmaier & Brandes (2005)
that places the assets so that:
• Shorter links in the tree indicate
higher correlations
• Longer links indicate lower
correlations
As a result, we also see how the
assets cluster (analogous to single
linkage clustering).

11. Encoding Non-spatial Data
Node color indicates last daily
return
Green = positive
Red = negative
Node size indicates magnitude
of absolute return
Large node = high return
Small node = small return

12. ‘Here be Dragons’
Didier Sornette (2009)
Dragon King: “Extreme events can be
predicted”
Benoit B. Mandelbrot (1963)
Volatility Clustering: “Large changes
tend to be followed by large changes”
-> Identify Value at Risk (VaR)
exceptions (return outside 95% VaR
bounds)
-> Map them as bright green or red
nodes

13. Summing up
Many risk models can be improved by taking into account
links in the data: interconnections, covariances,
dependencies, flows, exposures, co-occurances, etc …
Major challenges that we face are related to filtering signal
from noise in large networks and presenting the information
efficiently.
Much of the work can be summed up as:
Creating a Map - Placing you on Map - Providing Directions

14. Use Cases
Collapse of Lehman Brothers 15 Sept 2008 EU Debt Crisis 2009 -
Gold Crash of April 2013 Energy Meltdown 2014/2015

15. Collapse of Lehman Brothers on 15 Sept 2008
View Interactive Dashboard

16. Greek Debt Crisis 2009 -
View Interactive Dashboard

17. Gold Crash of April 2013
View Video

18. Energy Meltdown 2014/2015
View Video

19. Kimmo Soramaki
Founder and CEO
kimmo@fna.fi
FNA

20. The FNA Software consists of FNA Platform
and FNA Apps.
FNA Platform is the server side workhorse
for analysis, simulation and visualization of
financial networks used by all FNA Apps.
FNA Software
FNA Apps master particular uses
cases with an interactive user
experience.
FNA Maps
FNA Payments
FNA HeavyTails

21. FNA Platform
Over a decade in making and with a wider selection of
financial network algorithms than any other software, the
FNA Platform offers a comprehensive end-to-end enterprise
solution for advanced analysis and visualizations of financial
networks.
FNA Platform is the backbone of all FNA Apps and available
as a cloud-based solution with a RESTful API, as an enterprise
installation, as a Desktop software and as a Java library.
Cutting-edge analytics
Calculate hundreds of graph metrics, perform
cluster analysis and carry out predictive stress
tests and simulations.
Complete documentation
with over 500 pages of manuals describing the
platform’s functionality with examples, tutorials
and real-life applications.
End-to-end automation
Develop scripts for fully automated and regular
analytics or use FNA REST API from external
applications.
Easy integration
tap to data most common online data sources
and vendors directly, or from local databases.
More at www.fna.fi/platform

22. FNA HeavyTails
FNA HeavyTails helps risk managers and portfolio managers
identify and communicate emerging risks and design adaptive
stress tests.
FNA combines advanced network theory and interactive data
visualizations to detect hidden patterns in complex data.
FNA HeavyTails implements cutting edge research in Financial
CartographyTM by FNA and its collaborations with top
universities. The HeavyTails dashboard makes these analytics
readily accessible through a beautiful user interface.
Monitor systemic risk
with FNA’s unique correlation maps, Value-at-Risk
(VaR) analytics and outlier detection.
Stress test portfolios
with FNA’s interactive ‘Rapid stress testing’
functionality and integrate them with your
portfolio management and risk systems.
Identify emerging risks
with statistical and visual detection of outlier
assets, days, and periods.
Evaluate investment strategies
with correlation and clustering analysis against
benchmarks, and quickly identify hidden
concentration risk.
More at www.fna.fi/heavytails

23. FNA Maps
FNA NetworkMaps helps financial institutions explore complex
financial data for managing risks, identifying new opportunities
and making better, data driven decisions.
Combining advanced network theory with interactive
visualizations FNA NetworkMaps gives its users the analytical
power to answer the most difficult questions that they face.
Find hidden patters
with the help of hundreds of graph metrics,
clustering analysis and and predictive stress tests
and simulations.
Connect the dots
with fast interactive data exploration powered by
algorithms that filter signal from noise and FNA’s
beautiful network maps.
Monitor
the network in real-time and get alerted by
abnormal events.
Communicate
Create interactive network maps from public or
internal data sources and share them freely
online or within your organization.
More at www.fna.fi/maps

24. FNA Payments
FNA PaymentSimulator helps financial market infrastructures
and central banks model liquidity and operational risks,
evaluate alternative system designs and carry out stress tests.
FNA PaymentSimulator methodologies are based on leading
research in network theory and financial infrastructures by FNA
and its collaborations with top universities and central banks.
The interactive dashboard makes these advanced analytics
easily accessible through a well-thought user interface.
Monitor system participants
with comprehensive network maps and risk
metrics, including FNA’s SinkRank™ and metrics
proposed by BIS/BCBS.
Identify emerging risks
with statistical and visual detection of outliers
activity.
Get the big picture
and drill into details. Uncover interlinkages and
second-order effects with FNA’s unique network
maps.
Carry out predictive stress tests
and payment simulations and explore the results
visually or numerically.
More at www.fna.fi/payments