1. Interactive Visualization of the Stock Market Graph Presented by Camilo Rostoker rostokec@cs.ubc.ca Department of Computer Science University of British Columbia

2. Stock Market Data Stock market produces huge amounts of data on a daily basis, and its easy to acquire Stock market data consists of a variety of fields such as price, volume, change, change %, etc. Stock market data Take samples of stock data at regular intervals for a large set of stocks Convert dataset to correlation matrix  correlation(x,y) = [-1, +1]

3. The Market Graph Convert correlation matrix to a graph, where  Vertices represent stocks  edge(x,y)  correlation(x,y) >= threshold  High threshold  few edges Low threshold  more edges The market graph has been shown to have small world properties

4. What Are We Visualizing? Maximum Cliques  Highly positively/negatively correlated subsets of stocks Independent Sets  Completely diversified stocks Quasi-Cliques/Independent Sets  Generalizations  allow for near matches Find clusters/groups of stocks that exhibit certain trading patterns

5. Usage Scenarios Portfolio management (static) Real-time market analysis (dynamic) Exploratory analysis of trading data to gain new insights, spot patterns/trends, etc (static)

6. Implementation Extend H3 – Hyperbolic 3D browser Rational:  Good focus+context view supports interactive data exploration  Convenient API for interactive control and navigation of graphs  Stock market graph is large  hyperbolic space has good information density

7. Adapting & Extending H3 Colour-encode clusters Encode inter-cluster links  thickness, colour Create “dummy nodes” to represent clusters  encode aggregrate info Keyboard controls for basic interaction Dynamic graph capabilities Click interaction for information integration

8. Current Prototype

9. References Vladimir Boginski, Sergiy Butenko, and Panos M. Pardalos. Mining market data: A network approach. Tamara Munzner. H3: Laying out large directed graphs in 3d hyperbolic space. In Proceedings of the 1997 IEEE Symposium on Information Visualization, pages 2-10, 1997. James Chilson, Raymond Ng, Alan Wagner, and Ruben Zamar. Parallel computation of high dimensional robust correlation and covariance matrices. In KDD 04: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining, pages 533-538, New York, NY, USA, 2004. ACM Press. Wayne Pullan. Phased local search. Journal TBA. 2005.