Interactive Visualization of the Stock Market Graph Presented by Camilo Rostoker Department of Computer Science University of British.

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Presentation transcript:

Interactive Visualization of the Stock Market Graph Presented by Camilo Rostoker Department of Computer Science University of British Columbia

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]

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

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

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

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

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

Current Prototype

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, 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 , New York, NY, USA, ACM Press. Wayne Pullan. Phased local search. Journal TBA