mapping financial landscapes @ norges bank

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

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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?

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Page 1: Mapping Financial Landscapes @ Norges Bank

Mapping Financial Landscapes

Kimmo SoramäkiFounder and CEOFNA, www.fna.fi

OnsdagsseminarNorges BankOslo, 10 October 2012

Page 2: Mapping Financial Landscapes @ Norges Bank

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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 …

Page 4: Mapping Financial Landscapes @ Norges Bank

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

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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

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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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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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Blog, Library and Demos at www.fna.fi

Dr. Kimmo Soramäki [email protected]: soramaki