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# Financial Cartography

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Presentation held at the 5th Risk Summit organized by Center for Risk Studies at the University of Cambridge. See http://www.risk.jbs.cam.ac.uk/news/events/risksummits/risksummit2014.html

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### Financial Cartography

1. 1. FINANCIAL CARTOGRAPHY 1 23 June 2014 ! Risk Summit at Center for Risk Studies  The Pulse of Risk: From Big Data to Business Value ! ! Dr. Kimmo Soramäki  Founder and CEO, FNA Ltd.
2. 2. 2 Soramaki et al, 2007
3. 3. 3 ! ! ! What can old maps teach us about   visualizing complex ﬁnancial data?!
4. 4. 4 ! ! A Map is! ! A set of points, lines, and areas all deﬁned both by position with reference to a coordinate system
5. 5. 5 Eratosthenes' map of the known world c. 194 BC
6. 6. 6 ! ! Data is encoded as size, shape, value, texture or pattern, color and orientation of the points, lines and areas! !
7. 7. 7 The Ebstorf map (Mappa mundi), 13th c.
8. 8. 8 ! ! Maps reduce multidimensional data into ! a two dimensional space and ﬁlter out details
9. 9. 9 20 zoom levels of Google maps, 2014!
10. 10. 10 Correlation Maps Difﬁcult to understand large-scale correlation or other dependence structures.! ! Especially time series.! ! How to ﬁlter signal from noise?! ! How to put the correlations and their changes in context with changes/returns and volatility?! ! ! Objective is to efﬁciently represent a complex system! …"
11. 11. 11 Signiﬁcant Correlations Common method to visualize large correlation matrices is with heat maps.! ! ! ! ! ! If we only keep statistically signiﬁcant correlations with 95% conﬁdence level, the resulting matrix is sparse (with short time periods). All correlations (last 100 days)! Statistically signiﬁcant correlations (last 100 days)!
12. 12. 12 Sample Time-series Corporate Bonds - Gov. Bonds S&P 500 - Financials Lehman Collapse Subprime Crisis
13. 13. 13 Correlation Networks A sparse matrix is often well represented as a network. ! ! We encode correlations as links between the correlated nodes/ assets.! ! ! Red link = negative correlation  Black link = positive correlation! ! ! Absence of link marks that asset is not signiﬁcantly correlated.!
14. 14. 14 Dimensionality Reduction & Filtering Next, we identify the Minimum Spanning Tree (MST) and ﬁlter out other correlations.! ! Rosario Mantegna (1999) ‘Hierarchical Structure in Financial Markets’ ! This shows us the backbone correlation structure where each asset is connected with the asset with which its correlation is strongest.
15. 15. 15 Coordinate System We use a radial tree layout algorithm (Bachmaier & Brandes 2005) that places the assets so that:! ! • Shorter links in the tree indicate higher correlations! ! • Longer links indicate lower correlations! ! As a result, we also see how the assets cluster (analogous to single linkage clustering).!
16. 16. 16 Encoding non-spatial data Node color indicates last daily return! ! Green = positive! ! Red = negative! ! Node size indicates magnitude of return! ! !
17. 17. 17 “Here be Dragons” Sornette’s Dragon King: “Extreme events can be predicted”! ! Mandelbrot’s Volatility Clustering: “Large changes tend to be followed by large changes”! ! ! -> Identify VaR exceptions (return outside 95% VaR bounds)! ! -> Map them as bright green or red nodes! Track the number of outliers each day Highlight outliers in their context
18. 18. 18 Correlations Maps ! Dimensionality Reduction & Filtering! -> Minimum Spanning Tree! ! ! Coordinate System! -> Radial Tree layout algorithm (correlation distances)! ! ! System for visual encoding of (non-spatial) data! -> Returns and Outliers! ! !
19. 19. 19 Interactive Maps: HeavyTails www.heavytails.com
20. 20. 20 ! ! Cartographer selects only the information that is essential to fulﬁll the purpose of the map! !
21. 21. 21 Other Financial Maps Network of Economic States Interbank Payment Networks Trade Flow Networks Interbank Exposure Networks
22. 22. 22 ! ! Dr. Kimmo Soramaki! Founder and CEO! kimmo@fna.ﬁ! www.fna.ﬁ! FNA