Financial Cartography

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Presentation at University of Oxford's Keble College on 12 November 2012.

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Financial Cartography

  1. 1. Keble College at Oxford UniversityOxford, 12 November 2012FinancialCartography Dr. Kimmo Soramäki Founder and CEO FNA, www.fna.fi
  2. 2. ―When the crisis came, the serious limitations of existingeconomic and financial models immediately became apparent.[...]As a policy-maker during the crisis, I found the availablemodels of limited help. In fact, I would go further: in the face ofthe 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. 3. We did not have maps … 3
  4. 4. Eratosthenes map of the known world4c. 194 BC
  5. 5. … but what are maps―A set of points, lines, and areas all defined both by position withreference to a coordinate system and by their non-spatial attributes‖Data is encoded as size, shape, value, texture or pattern, color andorientation of the points, lines and areas – everything has a meaningCartographer selects only the information that is essential to fulfill thepurpose of the mapMaps reduce multidimensional data into a two dimensional space thatis better understood by humansMaps are intelligence amplification, they aid in decision making andbuild intuition 5
  6. 6. I. Mapping II. MappingSystemic Risk Financial Markets 6
  7. 7. Systemic risk ≠ systematic risk News articles mentioning ―systemic risk‖, Source: trends.google.comThe risk that a system composed of many interactingparts fails (due to a shock to some of its parts).In Finance, the risk that a disturbance in the financialsystem propagates and makes the system unable toperform its function – i.e. allocate capital efficiently. Not:Domino effects, cascading failures, financialinterlinkages, … -> i.e. a process in thefinancial network 7
  8. 8. 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 networksSoramäki, Bech, Beyeler, Glass and Arnold (2007), M. Boss, H. Elsinger, M. Summer, S. Thurner, ThePhysica A, Vol. 379, pp 317-333. network topology of the interbank market, SantaSee: www.fna.fi/papers/physa2007sbagb.pdf Fe Institute Working Paper 03- 8 10-054, 2003.
  9. 9. This is still shocking …―In 2006, the Federal Reserve invited a group of researchers tostudy the connections between banks by analyzing data from theFedwire system, which the banks use to back one another up.What they discovered was shocking: Just sixty-six banks — out ofthousands — accounted for 75 percent of all the transfers. Andtwenty five of these were completely interconnected to oneanother, including a firm you may have heard of called LehmanBrothers.‖ Want to Build Resilience? Kill the Complexity Harvard Business Review Blogs, 9/2012 9
  10. 10. More 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 Minoiu, Camelia and Reyes, Javier A. (2010). A network analysis of global 10 Cross-border bank lending banking:1978-2009. IMF Working Paper WP/11/74.
  11. 11. Network Theory can be to Financial Mapswhat Cartography is to Geographic MapsMain premise of network theory:Structure of links between nodesmattersTo understand the behavior of onenode, one must analyze thebehavior of nodes that may beseveral links apart in the networkTopics: Centrality, Communities,Layouts, Spreading and generationprocesses, Path finding, etc. 11
  12. 12. Centrality Measures forFinancial Systems• Traditional – 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 12
  13. 13. Where are we today?Regulatory response to recent financial crisiswas to strengthen macro-prudentialsupervision with mandates for moreregulatory data―Big data‖ and ―Complex Data‖-> Challengeto understand, utilize and operationalize thedata (network is fictional)Promise of ―Analytics based policy andregulation‖, i.e. the application of computertechnology, operations research, and Example: Oversight Monitor at Norges Bankstatistics to support human decision making The monitor will allow the identification ofGrowing body of empirical research, see systemically important banks and evaluation of the impact of bank failures on the systemwww.fna.fi/library 13
  14. 14. I. Mapping II. MappingSystemic Risk Financial Markets 14
  15. 15. OutlinePurpose of the maps – Identify price driving themes and market dynamics – Reduce complexity – Spot anomalies – Build intuitionThe maps: Heat Maps, Trees, Networksand Sammon‘s ProjectionsBased on asset correlations or taildependenceThese methods are showcased forvisualizing markets around the collapseof Lehman brothers 15
  16. 16. The CaseLehman was the fourth largest investment bank in the US (behindGoldman Sachs, Morgan Stanley, and Merrill Lynch) with 26.000employeesAt bankruptcy Lehman had $750 billion debt and $639 billion assetsCollapse was due to losses in subprime holdings and inability to findfunding due to extreme market conditionsIs seen as a divisive point in the 2007-2009 financial crisisWe create 3 visualization of a 5 month period around the failure (15September 2008) from asset price data 16
  17. 17. 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 17
  18. 18. i) Heat Maps 2004-2007CorporateBondsCDS onGovernmentDebtFX RatesGovernmentBond Yields Correlation -1StockExchange 0Indices +1 18
  19. 19. Collapse of Lehman, t=month2004-2007 t-2 t-1 t+1 t+2 t+3
  20. 20. ii) Asset TreesOriginally proposed by Rosario Mantegna in 1999Used currently by some major financial institutionsfor market analysis and portfolio optimization andvisualizationMethodology 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) 20
  21. 21. DemoClick here for interactive visualization 21
  22. 22. Correlation filtering PMFGBalance between too much and too littleinformationOne of many methods to create networksfrom correlation/distance matrices – PMFGs, Partial Correlation Networks, Influence Networks, Granger Causality, Influence Network NETS, etc.New graph, information-theory, economics& statistics -based models are beingactively developed 22
  23. 23. iii) NETS• Network Estimation for Time- Series• Forthcoming paper by Barigozzi and Brownlees• Estimates an unknown network structure from multivariate data• Captures both comtemporenous and serial dependence (partial correlations and lead/lag effects) 23
  24. 24. iv) Sammon‘s ProjectionProposed by John W. Sammon in IEEE Transactions on Computers 18: 401–409(1969)A nonlinear projection method to map ahigh dimensional space onto a space oflower dimensionality. Example: Iris Setosa Iris Versicolor Iris Virginica 24
  25. 25. DemoClick here for interactive visualization 25
  26. 26. Tail dependence• Correlation is a linear dependence. The same visual maps can be extended to non-linear dependences.• Joint work with Firamis (Jochen Papenbrock) and RC Banken (Frank Schmielewski), see www.extreme-value-theory.com• Instead of correlation, links and positions measure similarity of distances to tail losses Tail Tree Tail Sammon (Click here for interactive visualization) (click here for interactive visualization) 26
  27. 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, provide tools for trading strategies 27
  28. 28. ―In the absence of clear guidance from existing analyticalframeworks, policy-makers had to place particular reliance onour experience. Judgment and experience inevitably played akey role.‖ in a Speech by Jean-Claude Trichet, President of the European Central Bank, Frankfurt, 18 November 2010 28
  29. 29. Blog, Library and Demos at www.fna.fiDr. Kimmo Soramäkikimmo@soramaki.netTwitter: soramaki

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