The Visualization of
Financial Data
A review of information visualization tools in
the financial data domain
by Brian Sylvester
LIS 544
Spring 2008
Definitions of Financial Data
 “A time series of price and volume values for a wide
array of assets” (Roberts, 2004, pp.17)
 “Financial information can be characterized with the
following attributes: large amount, multi-dimensional and
abstract nature, complex information structure, hidden
information” (Lux, 1998, pp. 58)
 Tools looked at dealt with two areas of financial
information – Personal, Corporate or Global Finance,
and the Stock Market
Clusters of Tools
23 tools were examined, either in the form of
promotional ads / demonstrations,
documentation, or the tools themselves.
Four clusters were identified, along with a
fifth group of tools which defied
classification.
Cluster #1: Charting Tools
Tools in the Charting Tools cluster:
Expensr (https://www.expensr.com/app.php)
Finesse (http://ieeexplore.ieee.org/iel3/4136/12180/00559222.pdf)
GraphWise (http://www.graphwise.com/)
Grisbi Graphs (http://grisbi.weasel.nl/)
Share Cracker (http://share-cracker.roland-waddilove.qarchive.org/)
SpotFire (http://spotfire.tibco.com/solutions/index.cfm)
VisualCalc (http://www.visualcalc.com/products_cnstl_fc.htm)
WebFOCUS
(http://www.informationbuilders.com/products/webfocus/data_visuali
zation.html)
WireVis (http://www.viscenter.uncc.edu/TechnicalReports/CVC-UNCC07-13.pdf)
Characteristics of Charting Tools
Charting software is easily the largest
cluster of tools which deal with financial
information. It refers to tools which
generate almost any sort of chart: tables,
bar graphs, vertical and horizontal line
graphs, time lines, or pie charts. Most use
multiple charts in linked displays to show
relations and trends.
Model Charting Tool: Spotfire
http://spotfire.tibco.com/demos/financial/attri
bution.cfm - Please take a moment to
watch this short video about SpotFire
IV Toolbox
for Spot Fire
On the whole, chart making
tools do not utilize preattentive features beyond
using color and size, as in
the case of bar graphs or
pie charts, to distinguish
sets of data visually.
Perceptual Coding
Interaction
Position
Direct Manipulation
X
X
Orientation
Immediate
Feedback
Texture
Linked Displays
X
Shape
Animate Shift of
Focus
Size
Color
X
X
Shading
Surface
Motion
Stereo
Similarity
Continuity
Connectedness
X
Semantic Zoom
Depth Cues
Proximity
Dynamic Sliders
X
Focus+Context
X
Details-on-Demand
X
Output  Input
X
Information Density
Maximize Data-Ink
Ratio
Closure
Maximize Data
Density
Containment
Minimize Lie factor
X
X
Cluster #2: Dashboards
Tools in the Dashboards Cluster
iDashboards
(http://www.idashboards.com/finance.shtml)
See Stok Run (http://seestokrun.com/)
Characteristics of Dashboard Tools
Dashboard tools display a hemispherical
icon which resembles the speedometer or
tachometer displays found in the
dashboards of automobiles. The ‘needles’
on these dashboard icons can display the
relative placement of a data set between
two extremes.
Model Dashboard Tool:
iDashboards
A demo of the iDashboards tool can be accessed from here:
http://www.idashboards.com/finance.shtml
IV Toolbox
for
iDashboards
Perceptual Coding
Position
Direct Manipulation
X
Immediate
Feedback
X
Texture
Linked Displays
X
Shape
Animate Shift of
Focus
Size
x
Orientation
x
Color
There is virtually no use of
the various methods of
visually comparing data
beyond the use of color and
size, both of which are
utilized primarily through the
inclusion of charts and
graphs. Additionally, these
types of displays fail to
include a semantic zoom or
animate shift of focus.
Interaction
x
Shading
Surface
Stereo
Proximity
Similarity
Continuity
Connectedness
X
Semantic Zoom
Depth Cues
Motion
Dynamic Sliders
x
Focus+Context
X
Details-on-Demand
X
Output  Input
X
Information Density
Maximize Data-Ink
Ratio
Closure
Maximize Data
Density
Containment
Minimize Lie factor
X
Cluster #3: Geographic Models
Tools in the Geographic Models Cluster
Finance Maps (http://www.financemaps.com/)
Visualization of Financial Information
(http://delivery.acm.org/10.1145/280000/275532/
p58lux.pdf?key1=275532&key2=5657957021&c
oll=GUIDE&dl=portal,ACM&CFID=23244455&C
FTOKEN=49095553)
Characteristics of Geographic
Model Tools
 Geographic tools share the common feature of
using a visual display in the form of a map or
globe. The tools utilize color and position to
display financial data for different countries or
regions by mapping data directly onto an image
of that country or those countries. Color is the
primary feature of these particular tools which,
on the whole, do not utilize many other preattentive or interactive features. Both tools are
used to discuss global finances
Model Geographic Tool: Finance Maps
Finance Maps is freely available from here:
http://www.financemaps.com/
IV Toolbox
for Finance
Maps
These tools use hardly any
of the visualization tool
features which have been
found to be useful. The
displays are not linked to
any kind of details, forcing
the user to scroll down and
find information on a chart
rather than clicking on the
map. The map is supposed
to be manipulate-able, in so
far as the user is supposed
to be able to move it
around, zoom in, and select
items, but none of these
features work with any
reliability.
Perceptual Coding
Position
Interaction
x
Size
Linked Displays
Texture
Animate Shift of
Focus
Shape
Shading
Depth Cues
Surface
Motion
X
Immediate
Feedback
Orientation
Color
Direct Manipulation
x
Dynamic Sliders
Semantic Zoom
x
Focus+Context
Details-on-Demand
Output  Input
Stereo
Proximity
Similarity
Continuity
Connectedness
Information Density
Maximize Data-Ink
Ratio
Closure
Maximize Data
Density
Containment
Minimize Lie factor
X
Cluster #4: Treemaps
Tools in the Treemaps Cluster:
HeatMaps
(http://www.ssctech.com/assetmanagement/heatmaps/)
Market Maps
(https://ecommerce.smartmoney.com/ecommerce/login?
url=%2Fmarketmap%2Findex.cfm%3Fselectpromocode
%3D1%26adv%3Dtools%26advtype%3Dmarketmap100
0&host=selectdl.smartmoney.com)
Minority Game’ Agent Based Visualizer
(http://ideas.repec.org/p/sce/scecf5/468.html)
Characteristics of Treemap Tools
Tree maps break data down into groups which are
then visually arranged as rectangles, which are
then colored and arranged onto a rectangular
display so as to allow the user to visually
recognize patterns. Tree maps traditionally use
color, shading, size, and position as preattentive features, and rely on the use of
semantic zoom, focus in context and details-ondemand. They are used almost exclusively to
view information about the Stock Market.
Model Treemap Tool: Market Maps
1000
https://ecommerce.smartmoney.com/ecommerce/login?cm
d=displayLanding&promo_page=map
IV Toolbox
for Market
Maps 1000
Market Maps and the other
tree-map tools are among
the most interactive tools
examined. The market
maps tool uses color,
shading, position, proximity
and similarity of the colored
rectangular blocks to
display patterns and
similarities between market
segments at a quick glance.
By ‘driving down’ into the
market map the user can
find detailed information
and bring out specifics
quickly.
Perceptual Coding
Interaction
Position
x
Direct Manipulation
X
Size
X
Immediate
Feedback
X
Linked Displays
X
Animate Shift of
Focus
X
Dynamic Sliders
X
Semantic Zoom
x
Focus+Context
X
Details-on-Demand
X
Output  Input
X
Orientation
Texture
Shape
Color
x
Shading
X
Depth Cues
Surface
Motion
Stereo
Proximity
X
Similarity
X
Information Density
Maximize Data-Ink
Ratio
X
X
Closure
Maximize Data
Density
Containment
Minimize Lie factor
X
Continuity
Connectedness
Cluster #5: Other Tools
Tools in the ‘other’ category:
Financial Reminiscences
(http://ieeexplore.ieee.org/iel5/7989/22105/0102
8864.pdf?arnumber=1028864)
Spie Graphs (http://216.147.93.3/contact.htm)
Ticks (http://lineplot.com/expertise/thesis.html)
Visual Trader (http://www.visualtrader.com/)
Characteristics of Other Tools
The fifth cluster identified for financial tools
is also the second largest cluster. Most of
the tools come close to fitting in to one or
more of the other sections, but because of
important differences were considered
separately. Each will be discussed in
greater detail below, and a perceptual
coding toolbox will be filled out for each
tool.
Financial Reminiscences
http://ieeexplore.ieee.org/iel5/7989/22105/01028864.pdf?arnumber=1028864
Financial
Reminiscences is a
piece of ‘artwork’
generated using a
method of rendering
financial data. Read
the short paper at
the above website
for a detailed
explanation of how
the image was
created.
IV Toolbox
for Financial
Reminiscences
Financial Reminiscences
hardly qualifies as a
visualization tool, based on
the listing of features from the
toolbox. There is no
interactivity available with the
tool, which really only is a
method for creating a static
display. It does utilize preattentive visual features, but
by randomly and densely
mixing the data together
much of the comparison
value is lost, as is the ability
of the viewer to get detailed
information
Perceptual Coding
Interaction
Position
Direct Manipulation
Size
Immediate
Feedback
Orientation
Texture
x
Animate Shift of
Focus
Shape
Color
Shading
Depth Cues
Surface
Motion
Linked Displays
x
Dynamic Sliders
Semantic Zoom
Focus+Context
Details-on-Demand
Output  Input
Stereo
Proximity
Similarity
Continuity
Connectedness
Information Density
Maximize Data-Ink
Ratio
Closure
Maximize Data
Density
Containment
Minimize Lie factor
X
Spie Graphs
http://216.147.93.3/contact.htm
- click to the left for a video demonstration
IV Toolbox for
Spie Graphs
Spie Graphs is unique among
insofar as it is the first to use
more perceptual coding
features than interactive
features. On the whole,
through the use of depth
cues, color, and shape, Spie
Graphs is able to pack in a
great deal of data into a
single chart. These charts
can then be compared, but
not manipulated directly. Spie
Graphs could plausibly have
been included within the
cluster ‘chart making tools’
but was not because it is the
only tool examined which
utilizes depth cues.
Perceptual Coding
Interaction
Position
Size
Direct Manipulation
x
Orientation
x
Linked Displays
Texture
Shape
x
Color
x
Shading
Depth Cues
Immediate
Feedback
x
Surface
Motion
Animate Shift of
Focus
Dynamic Sliders
Semantic Zoom
x
Focus+Context
x
Details-on-Demand
x
Output  Input
Stereo
Information Density
Proximity
Similarity
x
Maximize Data-Ink
Ratio
X
x
Closure
Maximize Data
Density
Containment
Minimize Lie factor
x
Continuity
Connectedness
Ticks
http://lineplot.com/gallery/tick-gallery.html click to the right for a video demonstration
IV Toolbox
for Ticks
Ticks, used solely as a tool
for mapping the motion of the
stock market, uses an even
mix of visual encoding and
interactivity, although on the
whole it suffers from a lack of
specificity. It does not show
actual numbers, from what
can be ascertained by
reading the documentation,
and does not allow for
semantic zooming. The tool
is also the only tool to utilize
animation features in order to
keep up to date in real time, a
feature which is to be
particularly valued in the
volatile realm of financial
data.
Perceptual Coding
Interaction
Position
x
Direct Manipulation
Size
x
x
Orientation
x
Immediate
Feedback
Linked Displays
x
Animate Shift of
Focus
x
Texture
Shape
x
Color
x
Shading
x
Depth Cues
Semantic Zoom
Focus+Context
Surface
Motion
Dynamic Sliders
Details-on-Demand
x
Output  Input
x
Stereo
Information Density
Proximity
Similarity
Maximize Data-Ink
Ratio
X
x
Closure
Maximize Data
Density
Containment
Minimize Lie factor
x
Continuity
Connectedness
x
Visual Trader
http://www.visualtrader.com/ - click the link in the center of the
page to watch a demonstration of the tool
IV Toolbox
for Visual
Trader
Visual Trader uses the most
visualization features from
any of the three examined
categories and combines
them into a highly interactive,
visually appealing, and useful
tool. While it is only used for
the stock market, a platform
similar to this one could
plausibly be developed for
personal or corporate
finances. The display
features show data in
multiple ways, allowing users
to find many different
patterns and trends.
Perceptual Coding
Interaction
Position
x
Direct Manipulation
x
Size
x
Immediate
Feedback
x
Linked Displays
x
Animate Shift of
Focus
x
Dynamic Sliders
x
Semantic Zoom
x
Focus+Context
x
Details-on-Demand
x
Output  Input
x
Orientation
Texture
Shape
x
Color
x
Shading
Depth Cues
x
Surface
Motion
x
Stereo
Proximity
x
Similarity
x
Information Density
Maximize Data-Ink
Ratio
X
x
Closure
Maximize Data
Density
Containment
Minimize Lie factor
x
Continuity
Connectedness
Clusters and Data Sets
Tool Cluster
Financial data best
represented thereby
Chart Making Tools
Personal Finance
Geographic Tools
Global Finance
Dashboard Tools
Personal/Corporate
Finance
Tree Maps
Stock Market
Best Tools
In terms of Visualization Features, the best
tool for each data set seems to be:
 Personal/Corporate Finance: Spie Graphs
 Stock Market: Visual Trader
Conclusions
We may conclude from these reviews that most
tools do not make substantial use of preattentive features and rely instead on interaction
and linked displays.
The best tools for the respective domains of
personal/corporate finance and the stock market
are tools from the Other category which utilize
both interaction and pre-attentive features.
Conclusions, Cont.
Perhaps because of the need for detailed information
updated in real time, financial data is more often
described using conventional chart and graphing
software and, where visualization methods are utilized,
they predominantly do not take advantage of the preattentive features of the human visual system. This
represents a great opening in terms of future research
and development of visualization tools, for those tools
which do take advantage of these visual features are in a
position to more quickly, efficiently and effectively
monitor, track and display financial information.
Bibliography

All websites and electronic documents were retrieved on April 25th, 2008.

Animated Equity, LLC. (2008). See Stok Run 3.0: Animated stock market. Retrieved from http://seestokrun.com/

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
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Retrieved from http://www.viscenter.uncc.edu/TechnicalReports/CVC-UNCC-07-13.pdf

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
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
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
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Bibliography, cont.

Saito, Y. (2002). Financial Reminiscences: An example of art based on information visualization in finance. In
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
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
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