Research Report Information Animation Applications in the ...jmaletic/cs63903/papers/Wright95.pdf · Research Report Information Animation Applications in the ... trading analytics,

Research Report Information Animation Applications in the Capital Markets

William Wright Visible Decisions Inc.

Toronto, Canada [email protected]

Abstract

30 computer graphics can be extremely expressive. It is possible to display an entire securities market, like the S&P 500, on a single screen. A complex inventory of 3,000 positions can also easily fit on a single screen. With the correct approach to the visual design of the layout, these massive amounts of information can be quickly and easily comprehended by a human observer. By using motion and animated interaction, it is possible to use 30 as a reliable, accurate and precise decision-support tool.

Information animation applications are particularly suited to the securities industry because that is where we find huge amounts of data, the value of which declines rapidly with time, and where critical decisions are being made on this data in very short periods of time. Information animation technology is an important new tool for the securities industry, where people need to be in the decision-making loop without sufsering from information overload.

Several examples are discussed including equity trading analytics, fixed income trading analytics and fixed-income risk viewing. Risk viewing is generalized to include instruments and markets beyond fixed-income, namely equities, derivatives, and foreign exchange. In each case, the common elements are positions, models of asset value, parameterized models of risk sensitivity, and scenario projections. These visual risk models more easily allow control and guidance of risk exposure over a wide variety of scenarios and stress tests.

1 Introduction

Three-dimensional computer graphics can be extremely expressive. With the correct approach to the visual design of the layout, massive amounts of information can be quickly and easily comprehended by a human observer.

Data visualization has reached a new level of capability which can be described as Information AnimationTM. Graphics display technology and applications have moved beyond the static or interactive 2D bar charts, line charts and pie charts, and beyond the interactive 3D scatter plots and

contour plots of statistical and scientific visualization. It is now possible, on an inexpensive workstation, to build, display and have updated in real time, visual scenes comprised of abstract 3D geometrical forms. The viewer’s point of view can move through these 3D scenes, which have been constructed of simple and/or complex objects, and the objects themselves can move within the scenes. Desktop workstations can now move hundreds of thousands of independent 3D polygons per second on a display console.

Military and industrial simulations make use of this computer graphics power in applications that portray realistic visual scenes showing tank combat in urban centers, or merchant marine vessels docking in busy harbors. However this same hardware technology can be used in management and knowledge worker tasks. “Information animation” is the application of this level of computer graphics power to data intensive, time critical, decision making tasks where the 3D landscape comprises numerical / textual data and analytical models.

To support decision making tasks, information animation uses 4D (3D plus motion) graphics. By itself, 3D is not sufficient. The 3D display of data is not a new concept and has often been used as a communication medium when more emphasis on impact rather than insight is desired. 3D does not lend itself to rigorous comparative analysis because of the distortions arising from a perspective view and occlusion. However, by using motion and animated interaction, it is possible to use 3D as a reliable, accurate and precise decision-support tool. To quote James Clark, the founder of Silicon Graphics Inc., “To make 3D work, you need to make it move.”

This new 3D and motion capability, which we call information animation, allows a higher level of expression, a significant increase in the amount of data displayed, and a broader scope of application.

This paper provides a number of examples of information animation applications in the securities industry. These examples are drawn from equity trading analytics and fixed-income risk management. Many other applications are suggested as well, including OTC trading, equity trading execution and equities risk viewing. Before the discussion of these various applications, there is a brief review of how data visualization has been used in the past, why it works so well, and the importance of graphic design.

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See Color Plates, pages 136, 137.

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2 Origins of Data Visualization

The origins of data visualization are in the statistical and scientific disciplines. The majority of early work involved 2D analysis of multidimensional and multivariate data sets via static images and graphs. These static 2D images are useful in analysis but have more merit in the presentation of final results. Prominent statistician John Tukey [91 was a pioneer of exploratory data analysis.

More recently, dynamic graphics have been used in, for instance, spinning 3D data plots. “Dynamic” in this context also means direct manipulation by the user, where the user interacts directly with the graphics by use of the mouse. Dynamic graphics much more readily supports the process of finding and understanding patterns and anomalies in the data, as shown by Cleveland [ 11.

In the sciences, 3D visualization is typically used in analysis and presentation. Faced with understanding the large amounts of data generated in simulations and computational experiments, scientists often turn to visualization as the only practical way to digest the stacks of output created by overnight runs on supercomputers. Converting the stacks of output into a static 3D image is a useful way to sift through the information overload and pick out the patterns and anomalies of interest. Visualization broadened the scope of scientific exploration by expanding the horizon of what could be understood.

The National Center for Supercomputing Applications has created innovative 3D animations for data visualization [4]. One of the most well known animation studies was of the smog formation in the Los Angeles area. The techniques makes use of traditional frame-by-frame animation methods. Each frame is rendered and then transferred to video tape, where motion can be viewed at the standard video rate of 30 frames per second. While motion is achieved, it is not interactive. The viewer is limited to a pre- determined set of presentations and the communication of pre-conceived messages. These visualizations do not support data analysis because the relationships and features have already been identified, and the information has been extracted and prioritized for communication purposes.

In scientific visualization, the 3D image is always based on an underlying physical structure. For example, the smog study presents data overlaid on a 3D map of the Los Angles basin. Whether in physics, molecular, chemical or biological studies, the images use the physical structure of the elements themselves. Coherent data sets with an underlying xyz arrangement of positions provides a natural and easily understandable framework for a 3D image.

Information visualization using 3D animation is a current research area for Xerox PARC. This work by Card, Ma&inlay and Robertson [3, 51 provides

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some of the best examples of interactive 3D information visualization. Numerous prototypes have been constructed. These examples allow users to better understand the structure of large datasets by allowing viewing from different angles, by flying through the data, and by interactively examining and rearranging information objects. In these examples, the structure of the visual presentation is provided by the hierarchical or linear structure of the data itself. This provides a natural data-oriented framework and is a step forward in providing an application that is aligned to the decision making task.

In financial data visualization, there is no physical geography to provide an organizing structure. Dimensions corresponding to variables in mathematical functions can provide an organizing structure. One of the first examples of financial data visualization was the work done by Steven Feiner and Clifford Beshers [2] on exploring the value of a portfolio of options by interactively manipulating the option market value function of six variables in a 3D space. Higher dimensionality is achieved by embedding one 3D space into another. This provided a mathematically correct coherent framework but perceptually still proved to be not as natural and easy to understand as a geographical structure.

For abstract information visualization, we believe process and decision models provide a more natural framework. A visual layout that corresponds to the rationale underlying a decision blends human perceptual strengths with the exercise of human judgement. Information visualization expands to include decision visualization.

3 Human Visual Perceptual and Cognitive Abilities

Anne Treisman describes it well [7]. If you were to step outside in an unknown city, you would immediately recognize objects organized in a coherent meaningful framework. You would see people, cars, buildings, trees. You would not be aware of detecting colors, edges, movements and distances and of assembling them into multidimensional wholes for which you would retrieve identities and labels from memory. Meaningful wholes seem to precede parts and properties. This apparently effortless achievement is repeated continuously.

Visualization works because the visual cortex dominates perception, and because key aspects of the perception process occur rapidly without conscious thought. This human visualization power can be harnessed to allow the presentation of massive amounts of data and to highlight patterns hidden in that data. Used effectively, visualization can accelerate perception of data. By designing visualizations with human strengths and weaknesses in mind, it is possible to exploit people’s natural

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ability to recognize structure and patterns, and circumvent human limitations in memory and attention.

The human brain excels at processing images and recognizing patterns. Contrast this with how the brain handles rows and columns of numbers and letters. In a stressful, time-critical environment, such as a trading desk, it would be easy to miss a crucial number displayed among dozens in rows and columns. It takes a good deal of precious time to digest a set of interrelated numbers. Using information animation, the size, color, shape, and motion of the data can all be used to indicate the information you want and its significance.

Scientific study of perception and cognition have established some explanations for why visualization is so powerful, but much still remains to be understood

Certain aspects of visual processing seem to be accomplished simultaneously for the entire visual field at once. Some aspects of visual processing are also automatic in that it does not require attention to be focused on any one part of the visual field. Other aspects of visual processing seem to depend on focused attention and are done serially, or one at a time, as if a mental spotlight were being moved from one location to another. Visualization tasks involve a combination of preattentive and attentive human behaviors. Psychological research, see Rogowitz, et al [6], has shown that certain visual stimuli attract attention, can be searched in parallel, and are perceived effortlessly by observers. These extremely efficient preattentive visual competencies are engaged before conscious or attentive thought is required. In fact, Zeki [lo] discusses how four parallel systems within the visual cortex have been identified, each concerned with a different attribute of vision: one for motion, one for color, and two for form.

Powerful visualizations are designed to enlist both preattentive and attentive processes. A preattentively encoded attribute may be used to identify a region in the visualization which demands further attentive scrutiny.

There is virtually unlimited freedom in how we represent data. The difficult question is how best to represent it. The study of graphical perception needs to be expanded to examine the effectiveness of new representational techniques such as new forms of 3D geometry, animation, transparency, depth cues and connections. In the absence of scientifically derived rules, it is necessary to depend on the graphics design profession.

4 Graphics Design

While science can not always explain the functions and provide explicit rationales, the graphics design profession has developed highly effective

guidelines and heuristics. There is a rigor and discipline in the graphics design process whose intent is to reveal and not obscure. Graphics design methods have largely evolved from dealing with 2D graphics used in the print medium. However, their spirit and mandates are directly applicable to a 4D medium.

In 4D information animation applications, the success of the graphics visual design (i.e. the shapes, layout, colors) is critical to the success of the application. Graphical elements need to be carefully selected and arranged to reveal data and relationships. Poor graphics design will obscure the data and its meanings. The visual design simply needs to be perfect. Users must see the message and not the medium.

Edward Tufte [8] articulates this discipline best. According to Tufte, excellence in graphics consists of complex ideas communicated with clarity, precision and efficiency. Graphical displays should induce the viewer to think about the substance, present many numbers in a small space, make large data sets coherent, encourage the eye to compare different pieces of data, reveal the data at several levels of detail, from a broad overview to the fine structure. Graphics reveal data, and can be more precise and revealing than conventional numerical computations and displays.

5 Information Animation Examples in the Securities Industry

Information animation applications are particularly suited to the securities industry because that is where we find huge amounts of data, the value of which declines rapidly with time, and where critical decisions are being made on this data in very short periods of time. Information animation technology is an important new tool for the securities industry, where people need to be in the decision-making loop without suffering from information overload.

As outlined earlier, 3D seems to hold some promise as a general decision support tool - but we need to determine exactly what it means to put abstract information in 3D, and what benefits it provides. These are key questions, and to help answer them, several prototypes, or “dynamic illustrations,” were built to provoke business thinking regarding benefits, and to help further develop the basic technology.

This paper will discuss examples of work from 1992 created and operated on a Silicon Graphics workstation based on a MIPS R4000 CPU with a graphics hardware accelerator. The underlying software provides a 3D animation engine allowing data-driven and user-driven animation. Each 3D scene is completely open to interaction at any point in time. None of the interactions or animations are

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predefined or precalculated. Animation frame rates of four to six frames per second are achieved.

6 Equity Trading Analytics

Figure 1 shows a two dimensional image of the equities in the Toronto Stock Exchange index of 35. The Toronto Stock Exchange makes available each equity’s order book (i.e. all the current bids and offers and their price and size). For each equity, we are showing the depth of the book. The baseline is the price of the last trade. The offers are above, and the bids are below the baseline. The height above or below is proportional to the price of the bid or offer. The length of a bid/offer’s bar is proportional to the size of the bid/offer. This display immediately shows where bids and offers are unbalanced (e.g. more bids than offers) and where there is liquidity.

Figure 2 is a 3D extension of Figure 1. The area of each bid and offer is proportional to the size of the bid and offer. We can use Figures 1 and 2 to do a side-by-side comparison to see what added value a 3D layout provides. The 3D layout in Figure 2 appears to provide faster comprehension, and an accelerated perception. A 3D layout also provides more display area - more elbow room, so to speak. However, 3D is not sufficient. It is not possible to precisely compare two equities, and it is not possible to exactly see the book for an equity located towards the rear of the display. To make 3D work, it must be able to move. To be precise with comparisons, the view of the landscape and elements within the landscape need to move so that the user can see exactly what the values are. The prototypes allow a user to move the landscape and to zoom in to any area of interest.

In the next prototype, we explore how much information can be usefully displayed in a single screen. The landscape in Color Plate 1 shows the TSE 300 with the TSE 35 along the right forward edge. The landscape is arranged in an neutral fashion - by industry subgroup and alphabetic order within each subgroup. An actual user would order the landscape to correspond to particular interests and models of value. Several different forms of mouse and keyboard driven navigation are supported, including zooming to a point of interest, walking, running and moving to preset fixed points of view.

The TSE 300 landscape can be connected to a live data feed, and can display in real time the liquidity of the most significant portion of the Toronto market. With real time data, the landscape bubbles as trades occur and as the bids and asks are updated. Compared to existing quote screens which can display 20 or 30 equities at one time, this landscape provides an order of magnitude more information.

Of course, liquidity is just one attribute of an equity’s performance. Others include net change on the day, volume on the day, volume at a price, trades

at the bid, trades at the offer, etc. For each attribute, it is possible to develop a graphical icon or glyph which will visually and precisely communicate the value of the attribute. These graphical elements can be called “signs” because they are designed to display significance. For example, Color Plate 2 shows a trade by trade sign for each equity.

Color Plate 3 shows a bid/offer sign for an equity with numerical data displayed beside the sign. This is an important requirement for information animation applications. Visual perception can be used to quickly see anomalies and patterns. However, at some point, detailed data is needed. The user must be able to point at signs and retrieve the numerical and textual data behind the signs. This capability is called brushing (Cleveland, 1988).

7 Fixed-Income Trading Analytics

The next prototype, in Color Plate 4, shows the Canadian federal bond market. The green yield curve along the left edge shows the Federal benchmark issues as of early January 1992. The first yield curve in red, along the front edge shows the Federal curve as of early February 1992. Using the slider bar, we can play back in a sequence, the values for each day’s closing yield curve from January to February. Whatever day is currently displayed is shown in the second yield curve (yellow) along the front edge. At the beginning of the sequence the yellow curve is the same as the green curve. At the end, the yellow is the same as the red. Rotating the scene so that we have a 2D view of the yellow yield curve (Color Plate 5), we can play back each day’s yield curve. As we do that, the yellow curve moves and changes over time.

This period of time captures an interesting event - the U.S. Federal Reserve cut interest rates by a full percent. As we playback the data, we see the impact of this event on the Canadian Federal bond market. In the animation, you can see that the curve experiences a large drop, and then recovers to near previous levels.

In the center of the scene, spreads (i.e. differences in yield) are being displayed. The spreads are taken between the start date (green curve) and the current date (yellow curve). Positive spreads are gray, and negative spreads are purple. As we animate the scene, all the spreads move up and down with time.

Several useful conclusions can be made with this prototype. First of all, it becomes readily apparent that the Federal bond market is not an entirely orderly market. Our expectation was that the Federal bond market should be a liquid market with few discontinuities or anomalies. However, as you can see the boundary between positive and negative spreads is ragged. There are positive spreads located among the negative spreads. Further, these anomalies persist for several days at a time.

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It is also interesting to note what is happening in this prototype from a human perceptual and cognitive point of view. This is a 20x20 spread matrix displayed over 20 days for a total of 8000 spreads. Compared to a trader’s traditional quote screen or spreadsheet display which would show at most perhaps 80 spreads, this is a two order of magnitude improvement in displaying information. The display allows the user, in just a few seconds, to pick out the items of interest. Further, these items are presented in a context which supports informed evaluation. Related instrument spreads are shown in the same landscape neighborhood. At any time, the user can click on an anomaly and retrieve numerical and textual data describing the issues and their values.

Another conclusion is that the prototype has the entire field of “spreads” moving. One of the objectives of information animation is to be’ able to imbed information in motion. The user can see waves move across the surface of the spreads. Subtle differences in the waves, which indicate delays and anticipations in several spread regions, can be perceived and detected quickly.

8 Fixed-Income Risk Management

The next example is a fixed income inventory application. Color Plate 6 shows on a single screen a bond inventory with over 3,000 positions in it. Long positions are in green; short positions are in pink. The left axis shows portfolios and trading groups. The front axis shows time to maturity. Height is used to show the value of the positions. Along the front of the landscape is a total line that totals across trading groups. At the rear is a yield curve based on the bid yield of a set of benchmarks.

Color Plate 6 is displaying a profit/loss for each position for a yield curve shift scenario. Other models of asset value can also be displayed (e.g. weighted price value of a basis point or benchmark equivalent).

The user can point at one of the positions and retrieve fully detailed descriptive numerical and textual details related to that position’s size, issue and issuer..

Animation is used in this landscape to help assess market risk (i.e. risk due to change in interest rates). The yield curve can be moved, in even or uneven shifts, and as it moves, the impact on the inventory’s projected profit and loss can be assessed. Projected P+L values change by increasing or decreasing in size. Users can quickly see where the inventory is hedged (i.e. insensitive to changes in rates) or where it is exposed and by what degree.

Projected P+L is calculated using pre-computed fixed income analytical parameters such as the dollar value of an 01 (i.e. unit value for a change of l/100 of a percent in interest rates) and convexity (i.e. second order approximation for sensitivity due to

change in interest rates). The scenario P+L calculations are done rapidly so that the P+L value changes interactively with a change in the yield curve.

There are several ways a user may interact with this landscape. One or several bond issuers can be selected from a list of all issuers. The corresponding positions in these issues are then highlighted within the inventory so that the user can see what is held and where it is held. The total line then shows the total for the selected set of issuers. The market risk scenarios can be performed on the selected set.

Another type of query example is a filter based on size of position and implemented using a slider bar. The user can filter out all small long and short positions, so that landscape displays only the large magnitude positions.

This risk viewing landscape provides several conclusions. An on-line system could display perhaps 20 to 40 positions per screen. A 3D landscape displays 3,000 or more positions per screen. Using query and filtering, it is possible to highlight patterns that may be hidden in a numerical display. We believe a 3D visual approach provides more insight in minutes than traditional computer numerical displays could provide in hours.

9 Additional Examples

Information animation applications can provide significant value in many areas of the securities industry. The risk viewing application can be expanded to instruments and markets beyond fixed- income, including equities, derivatives, and foreign exchange. In each case, the common elements in the application are positions, models of asset value, parameterized models of risk sensitivity, and scenario projections. Risk viewing starts with being able to quickly see thousands of positions on a single screen. Effective risk visualization requires direct manipulation of such risk parameters as interest rate risk, volatility risk, currency risk, and credit risk. These visual risk models more easily allow control and guidance of risk exposure over a wide variety of scenarios and stress tests. Simple combinations of changes to risk parameters will quickly reveal exposed positions and help suggest more effective risk management strategies.

Another information animation application can be developed for trading in the OTC (over-the- counter) market. NASDAQ level 2 market-maker data provides bid and ask information for all market makers in an equity. Generally, there are 10 to 50 market makers per equity. Numerical equity trading displays now in use are limited to showing market- maker activity for just one equity at a time. Further, the user is able to see only 15- to 20 market makers at once, and must page back and forth to see other market makers. Considering that an individual trader

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makes a market in approximately 10 equities, the trader is left with the choice of either continuously flipping through 20 or 30 screens, or just not being able to see what the other market makers are doing. An information animation landscape can easily show on one screen all the market makers for a watch list consisting of 10 equities. Each market maker’s current activity, their activity since market open, and several other analytics can all be seen simultaneously.

Similar concepts apply to trading on the Toronto and London stock exchanges. Both exchanges publish broker identifications on bids, asks and trades. An information animation landscape can show who is trading what equity and at what price levels. For 50 or more equities, it is possible to see on one screen for every broker, who is trading at the bid or offer, their volume weighted average price (VWAP), and how their trading has changed over the day. This is simply impossible to do any other way. This kind of display can provide a new level of intuitive insight and improved trading performance.

Equity trade execution is another task that could benefit from information animation. In any given asset management firm, hundreds of buy, sell, sell short orders may need to be executed in one day. These orders need to be optimally managed across different sources of liquidity, such as POSIT, DOT, and Reuters Instinet. An information animation landscape allows order aggregation from many portfolios, and then allows those orders to be routed to appropriate trading systems. The status of each order and the quality of execution can be displayed for hundreds of orders simultaneously on one screen.

In fact, information animation can be used in general to unify and simplify diverse sources of trading data. It is possible to integrate on a single screen market data, news, positions, P+L, order management, order routing, portfolio allocations, and historical time series data. Only the expressive power of 4D graphics has the display capacity to do this. Only the human visual perceptual channel has the bandwidth to assimilate this volume of data.

10 Conclusions

Rows and columns of numbers are a representation appropriate for machine processing but do not draw upon human perceptual and cognitive strengths. With 3D and 4D graphical representations, people can see more information, more quickly, with more comprehension. These 4D graphical applications are a significant technological advance and can be thought of as a new type of decision support medium. Information animation applications combine large amounts of rapidly changing data with interactive decision-making models. The most effective landscapes encapsulate and simplify complex decision making process models. Information

animation technology will evolve into a powerful new decision-making medium, which will unify disparate sources of data and disconnected task processes.

At this point, we can summarize a number of key components used in any information animation application.

Signs. A sign is a graphical geometrical object that represents a set of related data elements, and is transformed as the data changes. For example, the current bid and ask of an equity can be represented by one sign. Each sign needs to be optimally designed to portray the significance of the data. “Sign” is preferred over the word “glyph” because it stresses the need for effective illustration.

Controller signs. This is a sign that maps a set of related input/output process control parameters to a geometrical object. For example, an interactive yield curve used to assess market risk on an inventory is a controller sign.

Landscapes. Each information application is based on one or more landscapes. A landscape consists of an arrangement in xyz space of signs and interactive controller signs. Effective landscapes reinforce - or make apparent - information attributes, decision models and the task process.

Navigation. Information animation applications require new user interface methods to allow users to move through the landscapes. These include zooming in and out, walking and running along an xyz vector, returning to predetermined fixed points of view, and a local heads-up capability to allow quick controlled viewing in the immediate neighborhood of a landscape.

Brushing. Users need to be able to point the cursor at a sign and retrieve the precise numerical and textual information behind it.

Queries. Landscapes need to be searchable, and to allow the display of the results of those searches. Users must be able to select any data attribute, whether it is price movement or size of position, and filter the landscape so that only those qualifying signs are displayed in the landscape. Query results can be displayed either by removing any signs that do not meet the search criteria, or graphically highlighting just the signs that do meet the criteria.

As with any new technology, there are a number of challenges in developing information animation applications. Perhaps the most significant is the choice of the geometrical representations for the signs, and the layout within an application landscape. The semantic challenge is to illustrate large,

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multidimensional and multivariate data sets without compromising the dimensionality or granularity of the data. Every variable for every data point must be able to be shown without invoking summary or reduction methods. The design challenge is to clearly and intuitively present the data.

Another challenge in building landscape applications is to effectively incorporate process models and decision models. Landscapes support decision making tasks, and much labour intensive knowledge engineering is required to identify and represent the underlying task domain. To bypass this development bottleneck, information animation tools will need to be developed which will allow end users to create their own landscapes.

Two other technology challenges are worth mentioning. A high level of animation performance is required. A large amount of 3D geometry in the landscape needs to be updated with new data and redrawn in subseconds to achieve animation frame rates of at least four frames per second. Considerable effort and insight into new data structures and rendering algorithms were required in the prototypes to reach the frame rate achieved. Even higher performance levels are required as larger volumes of data are input into landscapes, as 3D landscapes become larger in scope, and as signs make more use of motion as a representation medium.

Finally, ease of use and user interface design are crucial to application success. Information animation is a new way of working with information and requires innovative user interface techniques. Several new techniques have been developed in the prototypes, and more are being tested. Much more work needs to be done in this area. The expressive power of information animation provides an opportunity to dramatically simplify user interfaces.

Information animation has an important role within organizations. With significant investments made in computing infrastructure over the last decade, organizations have vast amounts of business data available to support decision making. So much data, in fact, that some current conditions might be described as information overload. Used effectively, information animation can accelerate perception, provide insight and control, and allow this flood of valuable data to be harnessed for competitive advantage in business decision making.

References

[l] Cleveland, W.S., and M.E. McGill, eds. Dynamic Graphics for Statistics, Belmont, CA, Wadsworth Inc., 1988.

[2] Feiner, S., and C. Beshers, “Worlds within Worlds - Metaphors for Exploring n- Dimensional Virtual Worlds”, ACM Symposium on User Interface Software, 1990.

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[lo] Zeki, S., “The Visual Image in Mind and Brain”, Scientific American, September, 1992.

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