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Mapping Financial Landscapes
Kimmo SoramäkiFounder and CEOFNA, www.fna.fi
OnsdagsseminarNorges BankOslo, 10 October 2012
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“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
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We did not have maps …
4Eratosthenes' map of the known world c. 194 BC
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… 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
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I. Mapping Systemic Risk
II. Mapping Financial Markets
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I. Mapping Systemic Risk
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Systemic risk ≠ systematic risk
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.
Domino effects, cascading failures, financial interlinkages, … -> i.e. a process in the financial network
News articles mentioning “systemic risk”, Source: trends.google.com
Not:
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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 (2007), Physica A, Vol. 379, pp 317-333.See: www.fna.fi/papers/physa2007sbagb.pdf
M. Boss, H. Elsinger, M. Summer, S. Thurner, The network topology of the interbank market, Santa Fe Institute Working Paper 03-10-054, 2003.
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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 ComplexityHarvard Business Review Blogs, 9/2012
11Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global banking:1978-2009. IMF Working Paper WP/11/74.
Federal funds
Bech, M.L. and Atalay, E. (2008), “The Topology of the Federal Funds Market”. ECB Working Paper No. 986.
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
Italian 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
Unsecured Sterling money market
More Maps
Cross-border bank lending
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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.
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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
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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
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Norges Bank - Oversight Monitor
• Implementing …
(above network is fictional)
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II. Mapping Financial Markets
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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
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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
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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
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Corporate Bonds
CDS on Government Debt, 5 years
FX Rates
Government Bond Yields
Stock Exchange Indices
2004-2007
-1
0
+1
Correlation
i) Heat Maps
t-2 t-1
t+1 t+2 t+3
2004-2007
Collapse of Lehman, t=month
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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
1. Calculate (daily) asset returns2. Calculate pairwise Pearson correlations of
returns3. Convert correlations to distances4. Extract Minimum Spanning Tree (MST)
5. Visualize (as phylogenetic trees)
MST
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Correlation filtering
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, Long Range Covariance, etc.
New graph, information-theory, economics & statistics -based models are being actively developed
PMFG
Influence Network
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Demo
(see http://www.fna.fi/demos/files/assetmonitor.html)
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iii) Sammon’s Projection
Iris Setosa
Iris Versicolor
Iris Virginica
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 oflower dimensionality. Example:
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Demo
(see http://www.fna.fi/demos/files/lehmansammon.html )
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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
• Algorithms don’t fare well in periods of abrupt change, algorithms do not think outside the box
• Build intuition and mental maps
Game of Go (from China).
Computer programs only get to human amateur level due to good pattern recognition capabilities needed in the game.
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“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
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