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FINANCIAL CARTOGRAPHY
1
23 June 2014
!
Risk Summit at Center for Risk Studies

The Pulse of Risk: From Big Data to Business Value
!
!
Dr. Kimmo Soramäki

Founder and CEO, FNA Ltd.
2
Soramaki et al, 2007
3
!
!
!
What can old maps teach us about 

visualizing complex financial data?!
4
!
!
A Map is!
!
A set of points, lines, and areas all defined both by
position with reference to a coordinate system
5
Eratosthenes' map of the known world c. 194 BC
6
!
!
Data is encoded as size, shape, value, texture or
pattern, color and orientation of the points, lines and
areas!
!
7
The Ebstorf map (Mappa mundi), 13th c.
8
!
!
Maps reduce multidimensional data into !
a two dimensional space and filter out details
9
20 zoom levels of Google maps, 2014!
10
Correlation Maps
Difficult to understand large-scale
correlation or other dependence
structures.!
!
Especially time series.!
!
How to filter signal from noise?!
!
How to put the correlations and
their changes in context with
changes/returns and volatility?!
!
!
Objective is to efficiently
represent a complex system!
…"
11
Significant Correlations
Common method to visualize
large correlation matrices is
with heat maps.!
!
!
!
!
!
If we only keep statistically
significant correlations with
95% confidence level, the
resulting matrix is sparse (with
short time periods).
All correlations
(last 100 days)!
Statistically
significant
correlations
(last 100 days)!
12
Sample Time-series
Corporate Bonds - Gov. Bonds
S&P 500 - Financials
Lehman Collapse
Subprime Crisis
13
Correlation Networks
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.!
14
Dimensionality Reduction & Filtering
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.
15
Coordinate System
We use a radial tree layout
algorithm (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).!
16
Encoding non-spatial data
Node color indicates last daily
return!
!
Green = positive!
!
Red = negative!
!
Node size indicates magnitude
of return!
!
!
17
“Here be Dragons”
Sornette’s Dragon King:
“Extreme events can be
predicted”!
!
Mandelbrot’s Volatility
Clustering: “Large changes tend
to be followed by large changes”!
!
!
-> Identify VaR exceptions
(return outside 95% VaR
bounds)!
!
-> Map them as bright green or
red nodes!
Track the number of outliers
each day
Highlight outliers in their context
18
Correlations Maps
!
Dimensionality Reduction & Filtering!
-> Minimum Spanning Tree!
!
!
Coordinate System!
-> Radial Tree layout algorithm (correlation
distances)!
!
!
System for visual encoding of (non-spatial) data!
-> Returns and Outliers!
!
!
19
Interactive Maps: HeavyTails
www.heavytails.com
20
!
!
Cartographer selects only the information that is
essential to fulfill the purpose of the map!
!
21
Other Financial Maps
Network of Economic States Interbank Payment Networks
Trade Flow Networks Interbank Exposure Networks
22
!
!
Dr. Kimmo Soramaki!
Founder and CEO!
kimmo@fna.fi!
www.fna.fi!
FNA

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Financial Cartography

  • 1. FINANCIAL CARTOGRAPHY 1 23 June 2014 ! Risk Summit at Center for Risk Studies
 The Pulse of Risk: From Big Data to Business Value ! ! Dr. Kimmo Soramäki
 Founder and CEO, FNA Ltd.
  • 3. 3 ! ! ! What can old maps teach us about 
 visualizing complex financial data?!
  • 4. 4 ! ! A Map is! ! A set of points, lines, and areas all defined both by position with reference to a coordinate system
  • 5. 5 Eratosthenes' map of the known world c. 194 BC
  • 6. 6 ! ! Data is encoded as size, shape, value, texture or pattern, color and orientation of the points, lines and areas! !
  • 7. 7 The Ebstorf map (Mappa mundi), 13th c.
  • 8. 8 ! ! Maps reduce multidimensional data into ! a two dimensional space and filter out details
  • 9. 9 20 zoom levels of Google maps, 2014!
  • 10. 10 Correlation Maps Difficult to understand large-scale correlation or other dependence structures.! ! Especially time series.! ! How to filter signal from noise?! ! How to put the correlations and their changes in context with changes/returns and volatility?! ! ! Objective is to efficiently represent a complex system! …"
  • 11. 11 Significant Correlations Common method to visualize large correlation matrices is with heat maps.! ! ! ! ! ! If we only keep statistically significant correlations with 95% confidence level, the resulting matrix is sparse (with short time periods). All correlations (last 100 days)! Statistically significant correlations (last 100 days)!
  • 12. 12 Sample Time-series Corporate Bonds - Gov. Bonds S&P 500 - Financials Lehman Collapse Subprime Crisis
  • 13. 13 Correlation Networks 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.!
  • 14. 14 Dimensionality Reduction & Filtering 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.
  • 15. 15 Coordinate System We use a radial tree layout algorithm (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).!
  • 16. 16 Encoding non-spatial data Node color indicates last daily return! ! Green = positive! ! Red = negative! ! Node size indicates magnitude of return! ! !
  • 17. 17 “Here be Dragons” Sornette’s Dragon King: “Extreme events can be predicted”! ! Mandelbrot’s Volatility Clustering: “Large changes tend to be followed by large changes”! ! ! -> Identify VaR exceptions (return outside 95% VaR bounds)! ! -> Map them as bright green or red nodes! Track the number of outliers each day Highlight outliers in their context
  • 18. 18 Correlations Maps ! Dimensionality Reduction & Filtering! -> Minimum Spanning Tree! ! ! Coordinate System! -> Radial Tree layout algorithm (correlation distances)! ! ! System for visual encoding of (non-spatial) data! -> Returns and Outliers! ! !
  • 20. 20 ! ! Cartographer selects only the information that is essential to fulfill the purpose of the map! !
  • 21. 21 Other Financial Maps Network of Economic States Interbank Payment Networks Trade Flow Networks Interbank Exposure Networks
  • 22. 22 ! ! Dr. Kimmo Soramaki! Founder and CEO! kimmo@fna.fi! www.fna.fi! FNA