Financial market turmoil has revealed the interconnected nature of modern financial systems. Industry, regulators and academics agree on the need for better analytical tools that can help monitor and safeguard against systemic risks. Kimmo Soramaki reviews new research in financial network analysis, including how network analysis of large-scale financial transaction data can be used to improve our understanding of how the financial system functions. How can visual analytics of time-series networks bring new insights? How can cross-asset networks enable stronger intuition of market dynamics?

1. Onsdagsseminar
Norges Bank
Oslo, 10 October 2012
Mapping Financial
Landscapes
Kimmo Soramäki
Founder and CEO
FNA, www.fna.fi

2. ―When the crisis came, the serious limitations of existing
economic and financial models immediately became apparent.
[...]
As a policy-maker during the crisis, I found the available
models of limited help. In fact, I would go further: in the face of
the crisis, we felt abandoned by conventional tools.‖
in a Speech by Jean-Claude Trichet, President of the
European Central Bank, Frankfurt, 18 November 2010
2

3. We did not have maps …
3

4. Eratosthenes' map of the known world4
c. 194 BC

5. … but what are maps
―A set of points, lines, and areas all defined both by position with
reference to a coordinate system and by their non-spatial attributes‖
Data is encoded as size, shape, value, texture or pattern, color and
orientation of the points, lines and areas – everything has a meaning
Cartographer selects only the information that is essential to fulfill the
purpose of the map
Maps reduce multidimensional data into a two (or three) dimensional
space that is better understood by humans
Maps are intelligence amplification, they aid in decision making and
build intuition
5

6. I. Mapping II. Mapping
Systemic Risk Financial Markets
6

7. I. Mapping Systemic Risk
7

8. Systemic risk ≠ systematic risk
News articles mentioning ―systemic risk‖, Source: trends.google.com
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.
Not:
Domino effects, cascading failures, financial
interlinkages, … -> i.e. a process in the financial
network
8

9. First Maps Fedwire Interbank Payment
Network, Fall 2001
Around 8000 banks, 66 banks
comprise 75% of value,25
banks completely connected
Similar to other socio-
technological networks
Soramäki, Bech, Beyeler, Glass and Arnold M. Boss, H. Elsinger, M. Summer, S. Thurner, The
(2007), Physica A, Vol. 379, pp 317-333. network topology of the interbank market, Santa
See: www.fna.fi/papers/physa2007sbagb.pdf Fe Institute Working Paper 03-
9
10-054, 2003.

10. This is still shocking …
―In 2006, the Federal Reserve invited a group of researchers to
study the connections between banks by analyzing data from the
Fedwire system, which the banks use to back one another up.
What they discovered was shocking: Just sixty-six banks — out of
thousands — accounted for 75 percent of all the transfers. And
twenty five of these were completely interconnected to one
another, including a firm you may have heard of called Lehman
Brothers.‖
Want to Build Resilience? Kill the Complexity
Harvard Business Review Blogs, 9/2012
10

11. More Maps
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
Federal funds 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 11
banking:1978-2009. IMF Working Paper WP/11/74.

12. Network Theory can be to Financial Maps
what Cartography is to Geographic Maps
Main premise of network theory:
Structure of links between nodes
matters
To understand the behavior of one
node, one must analyze the
behavior of nodes that may be
several links apart in the network
Topics:
Centrality, Communities, Layouts,
Spreading and generation
processes, Path finding, etc.
12

13. Centrality Measures for
Financial Systems
• Existing
– Degree, Closeness, Betweenness
centrality, PageRank, etc.
• DebtRank
– Battiston et al, Science Reports,
2012
– Feedback-centrality
– Solvency cascade
• SinkRank
– Soramäki and Cook, Kiel
Economics DP, 2012
– Transfer along walks
– Liquidity absorption
13

14. Where are we today?
Regulatory response to recent financial crisis was to strengthen macro-
prudential supervision with mandates for more regulatory data
―Big data‖ and ―Complex Data‖-> Challenge to understand, utilize and
operationalize the data
Growing body of empirical research, see www.fna.fi/library
Promise of ―Analytics based policy and regulation‖, i.e. the application
of computer technology, operations research, and statistics to support
human decision making
14

15. Norges Bank - Oversight Monitor
• Implementing …
15
(above network is fictional)

16. II. Mapping Financial Markets
16

17. Agenda
Purpose of the maps
– Identify price driving themes and
market dynamics
– Reduce complexity
– Spot anomalies
– Build intuition
The maps: Heat Maps, Asset Trees and
Sammon‘s Projections
These methods are showcased for
visualizing markets around the
collapse of Lehman brothers
17

18. The Case
Lehman was the fourth largest investment bank in the US (behind
Goldman Sachs, Morgan Stanley, and Merrill Lynch) with 26.000
employees
At bankruptcy Lehman had $750 billion debt and $639 billion assets
Collapse was due to losses in subprime holdings and inability to find
funding due to extreme market conditions
Is seen as a divisive point in the 2007-2009 financial crisis
We create 3 visualization of a 5 month period around the failure (15
September 2008) from asset price data
18

19. The Data
Pairwise correlations of
return on 141 global
assets in 5 asset classes
9870 data points per
time interval
5 intervals, 2 months
before and 3 months
after Lehman collapse
19

20. i) Heat Maps
2004-2007
Corporate
Bonds
CDS on
Government
Debt, 5 years
FX Rates
Government
Bond Yields
Correlation
-1
Stock
Exchange 0
Indices
+1
20

21. Collapse of Lehman, t=month
2004-2007 t-2 t-1
t+1 t+2 t+3

22. ii) Asset Trees
Originally proposed by Rosario Mantegna in 1999
Used currently by some major financial institutions
for market analysis and portfolio optimization and
visualization
Methodology in a nutshell MST
1. Calculate (daily) asset returns
2. Calculate pairwise Pearson correlations of returns
3. Convert correlations to distances
4. Extract Minimum Spanning Tree (MST)
5. Visualize (as phylogenetic trees)
22

23. Correlation filtering PMFG
Balance between too much and too little
information
One of many methods to create networks
from correlation/distance matrices
– PMFGs, Partial Correlation Networks,
Influence Networks, Granger Causality, Influence Network
Long Range Covariance, etc.
New graph, information-theory, economics
& statistics -based models are being
actively developed
23

24. Demo
(see http://www.fna.fi/demos/files/assetmonitor.html) 24

25. iii) Sammon‘s Projection
Proposed by John W. Sammon in IEEE Transactions on Computers 18: 401–409
(1969)
A nonlinear projection method to map a
high dimensional space onto a space of
lower dimensionality. Example:
Iris Setosa
Iris Versicolor
Iris Virginica
25

26. Demo
(see http://www.fna.fi/demos/files/lehmansammon.html ) 26

27. Intelligence Amplification
• Intelligence Amplification vs Artificial
Intelligence
William Ross Ashby (1956) in ‗Introduction to
Cybernetics‘
• Technology, products and practices
change constantly, market knowledge is
essential
Game of Go (from China).
• Algorithms don‘t fare well in periods of
Computer programs only get to
abrupt change, algorithms do not think human amateur level due to good
outside the box pattern recognition capabilities
needed in the game.
• Build intuition and mental maps
27

28. ―In the absence of clear guidance from existing analytical
frameworks, policy-makers had to place particular reliance on
our experience. Judgment and experience inevitably played a
key role.‖
in a Speech by Jean-Claude Trichet, President of the
European Central Bank, Frankfurt, 18 November 2010
28

29. Blog, Library and Demos at www.fna.fi
Dr. Kimmo Soramäki
kimmo@soramaki.net
Twitter: soramaki