As the financial system becomes more complex, new methods to understand its inherent risks and dynamics are needed. Kimmo Soramäki will discuss how network analysis of large‐scale financial transaction data can be used to improve our understanding systemic risk. He will also show case studies how visual analytics and accurate data driven maps of asset correlations and tail risks can enable a stronger intuition of market dynamics.

1. Boğaziçi University
3rd February 2014
Financial Cartography
Dr. Kimmo Soramäki
Founder and CEO
Financial Network Analytics
www.fna.fi

2. Agenda
Mapping Interbank Payment Flows and Exposures
Soramäki, K. M.L. Bech, W.E. Beyeler, R.J. Glass and J. Arnold (2007). ‘The Topology of
Interbank Payments’ Physica A, Vol. 379, pp 317-333.
Soramäki, K. and S. Cook (2013). ‘Algorithm for Identifying Systemically important
Banks in Payment Systems’. Economics E-Journal, Vol. 7.
Langfield, S. and K. Soramaki (forthcoming). ‘Interbank Networks’. Journal of
Computational Economics.
Asset Correlation Networks
Soramäki, K., S. Cook and A. Laubsch (forthcoming). ‘A Network-Based Method for
Visual Identification of Systemic Risks’.
FNA Platform
Soramäki, K., S. Cook. (forthcoming) ‘Financial Network Analytics with FNA’. ISBN: 978952-67505-1-4
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3. Networks
2003
2003
2004
3

4. Fedwire – First Maps
Fedwire Interbank
Payment Network
Fall 2001
Around 8000 banks, 66
banks comprise 75% of
value,25 banks completely
connected
Soramäki, Bech, Beyeler, Glass and Arnold
(2007), Physica A, Vol. 379, pp 317-333.
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5. Network Theory
The behavior of a node
cannot be understood
on the basis its own
properties alone.
Financial
Network Analysis
Social Network
Analysis
Network Science
NETWORK
THEORY
Graph & Matrix
Theory
Computer
Science
Biological
Network Analysis
To understand the
behavior of one node,
one must understand
the behavior of nodes
that may be several
links apart in the
network.

6. Networks Brings us Beyond the Data Cube
For example:
Entities:
100 banks
Variables:
Liquidity, Opening
Balance, Collateral, …
Time:
Daily data
Links:
Bilateral payment flows
Links are the 4th dimension to data
(Tesseract)
Information on the links
allows us to develop better
models for banks' liquidity
situation in times of stress
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7. Systemic Risk
News articles mentioning “systemic risk”, Source: trends.google.com
Not
“The risk that a system composed of many interacting parts
fails (due to a shock to some of its parts)”
In Finance, the risk that a disturbance in the financial
system propagates and makes the system unable to
perform its function – i.e. allocate capital efficiently.
Or
Domino effects, cascading failures, financial
interlinkages, … -> i.e. a process in the financial network
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8. More Network Maps
Federal funds
Bech, M.L. and Atalay, E. (2008), “The Topology of
the Federal Funds Market”. ECB Working Paper No. 986.
Italian money market
Iori G, G de Masi, O Precup, G Gabbi and G
Caldarelli (2008): “A network analysis of the Italian
overnight money market”, Journal of Economic
Dynamics and Control, vol. 32(1), pages 259-278
Unsecured Sterling
money market
Wetherilt, A. P. Zimmerman, and K. Soramäki
(2008), “The sterling unsecured loan market
during 2006–2008: insights from network
topology“, in Leinonen (ed), BoF Scientific
monographs, E 42
Cross-border bank lending
Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global
banking:1978-2009. IMF Working Paper WP/11/74.
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9. Common Centrality Metrics
Centrality metrics aim to summarize some notion of importance
Degree: Number of links
Closeness: Distance from/to other
nodes via shortest paths
Betweenness: Number of shortest
paths going through the node
Eigenvector: Nodes that are linked by
other important nodes are more central, eg.
Google’s PageRank

10. How to Calculate a Metric for Payment Flows
Depends on process that takes place in the network!
Trajectory
–
–
–
–
Geodesic paths (shortest paths)
Any path (visit no node twice)
Trails (visit no link twice)
Walks (free movement)
Transmission
– Parallel duplication
– Serial duplication
– Transfer
Source: Borgatti (2004)

11. SinkRank Models Payment Flows
NASA’s model of ocean currents
around the Caribbean
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12. Network Simulation
Interactive demo at: www.fna.fi/demos/sofe/viz/simulation.html
Failure Scenario
Black node = can receive
but cannot send (click to fail
a node)
Normal Scenario
Green node = Liquidity
available. Amount shown as
node size.
Red node = No, liquidity.
Queues build up. Number
12
queued shown as node size.

13. Predictive Modeling
• Predictive modeling is the process by which a model is
created to try to best predict the probability of an outcome
• For example: Given a distribution of liquidity among the
banks at noon, how is it going to be at 5pm?
– What is the distribution if bank A has an operational disruption
at noon?
– Who is affected first?
– Who is affected most?
– How is Bank C affected in an hour?
• Valuable information for decision making
– Crisis management
– Participant behavior
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14. SinkRank in BoK-Wire+
https://www.dropbox.com/s/rckmclzzstlmiht/S
creenshot%202014-01-20%2010.32.39.png
Baek, Soramäki and Yoon (forthcoming). ‘Network Indicators for
Monitoring Intraday Liquidity in BOK-Wire+ ‘
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15. Market Signals
• Markets are a great information processing device that create
vast amounts of data useful for trading, risk management and
financial stability analysis
• Main signals: asset returns, volatilities and correlations
• There is no easy way to monitor
large numbers of assets and their
dependencies
-> Correlation Maps
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16. Data in Example
…
Pairwise correlations of daily
returns on 35 global assets
(ETFs), incl.
•
•
•
•
•
Equity indices
FX
Commodities
Debt
Derivatives
One year of daily correlations with
exponentially-weighted moving
average (EWMA) estimate of the
(daily) returns’ standard deviation.

17. Data
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18. Significant Correlations
Common method to visualize large
correlation matrices is via heat
maps
Keep statistically significant
correlations with 95% confidence
level
Carry out 'Multiple comparison' correction -> Expected error rate
<5%
All correlations
(last 100 days)
Statistically
significant
correlations
(last 100 days)

19. Color Perception
A and B are the same
shade of gray
Right?

20. Color Perception
A and B are the same
shade of gray

21. Correlation Network
Problem:
Heat maps can be
misleading due to
human color
perception

22. Correlation Network
Nodes are assets
Links are correlations:
Red = negative
Black = positive
Absence of link marks
that asset is not
significantly correlated

23. Minimum Spanning Tree
Hierarchical Structure in Financial Markets
Rosario Mantegna (1999)
‘Hierarchical Structure in
Financial Markets’
We use the Minimum
Spanning Tree (MST) of the
network to filter signal from
noise.

24. Phylogenetic Tree Layout
We lay out the assets by
their hierarchical structure
using Minimum Spanning
Tree of the asset network.
Shorter links indicate
higher correlations. Longer
links indicate lower
correlations.
Bachmaier, Brandes, and Schlieper (2005). Drawing Phylogenetic
Trees. Proceeding ISAAC'05 Proceedings of the 16th international
conference on Algorithms and Computation, pp. 1110-1121

25. Data Reduction + Adding Dimensions
Mapping Returns and Outliers
Network layout allows for
the display of multiple
dimensions of the same
data set on a single map:
Node color indicates latest
daily return
- Green = positive
- Red = negative
Node size indicates
magnitude of return
Bright green and red
indicate an outlier return

26. FNA HeavyTails
Interactive demo at: www.heavytails.com
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27. Blog, Library and Demos at www.fna.fi
Dr. Kimmo Soramäki
kimmo@soramaki.net
Twitter: soramaki