Download Update - UBC Computer Science - University of British Columbia

Interactive
Visualization of the
Stock Market Graph
Presented by Camilo Rostoker
[email protected]
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.

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)
 High threshold  few edges
>= threshold
Low threshold  more edges

The market graph has been shown to have
small world properties
What Are We Visualizing?

Find clusters/groups of stocks that exhibit certain
trading patterns

Maximum Cliques
 Highly
positively/negatively
correlated subsets of stocks

Independent Sets
 Completely

diversified stocks
Quasi-Cliques/Independent Sets
 Generalizations
matches
 allow for near
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,

Create “dummy nodes” to represent clusters
 encode



colour
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, 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.