Applying Animation to the Visual Analysis of
Financial Time-Dependent Data
Tatiana Tekušová, Jörn Kohlhammer
Fraunhofer IGD, Darmstadt, Technische Universität Darmstadt
{[email protected], [email protected]}
Abstract
For decades, financial analysts have strived to use modern data visualization tools to improve the timeliness and quality of their analysis. As the amount of data to be processed increases rapidly and requirements on quality of financial analysis rise, the demand for analysis support systems grows. We present a system for the visual analysis of large amounts of time-dependent data using animation. For each data entity, indicators are presented in a scatter-plot framework, displaying the correlation between them. The design of the glyphs illustrates additional data dimensions. The system uses animation to handle the time-dimension of the data. It offers various features, such as focus, zoom, details on demand and time period selection to support the analysis. Financial indicators are used to demonstrate the usability of the system. The animation proves to be a powerful tool for analysing time-dependent processes in cross-sectional data sets and discovering patterns in the data.
Keywords- information visualisation, financial data, time-dependent data, animation, applications.
1. Introduction
Financial analysts need to assess large amounts of numerical data usually under time pressure in order to provide sophisticated investment advice or to make high quality decisions. The analysis is supported by modern statistical and information visualization systems. As the amount of data to be analyzed is increasing at a high pace and requirements on quality and promptness of financial analysis are rising, further development of the visualization systems is needed in order to cope with the higher demands of the analysts for data presentation.
There is a wide spectrum of financial analysis methods. However, the analysis of correlations between financial indicators remains one of the most widely applied techniques. To support the correlation analysis, the relationships between variables are visually presented in form of scatter plots. The standard analysis based on the capital asset pricing model (CAPM) underlying the pricing of securities is commonly conceptualised in this format [19] and [21]. For this reason, financial analysts are educated to think in terms of this presentation and
can easily interpret the observations [1]. However, classical scatter plots can show only correlation either across the time- or cross-section (i.e. observations of many subjects such as firms, stocks or countries/regions at a certain point in time) dimension of the data, whereby the data used in financial analysis often have to be assessed along both dimensions.
As there is a strong need to present both the time and the cross-section component simultaneously when analysing the relationship between indicators, we have chosen a natural way to circumvent the problem – to use animation We have kept the scatter plot as a basic data presentation type, because it reflects the above-mentioned previous user experience in financial analysis, as recommended by [9]. The system animates the glyphs representing the entities of the sample, i.e. it maps the time-dimension of the dataset to animation time.
The main advantage of using animation in a scatter plot is the possibility to show long time series while retaining the focus on cross-section dimensions of the data. This is currently not possible with commonly used applications for data analysis in financial institutions. Animation supports more efficient and timely analysis of large amounts of multi-dimensional data, than the current state-of-the-art applications allow. With the help of our system, different financial indicators can be analysed. The cross-section dimension may, for example, refer either to the companies forming a stock market index, or to the government bond yields with different maturities or economic subjects. The time dimension represents the time period for which the data need to be analysed.
In this paper, we present a visual analysis of daily data stock market data from four countries for the period ranging from 17.5.2005 to 13.11.2006, implying a visualization of more than 70 000 data points. The usability study showed that the animation can reveal new unexpected patterns in the data which cannot be perceived in a static analysis of correlation. Furthermore, animated data visualization better reflects real market movements.
2. Use case – Stock market analysis
Decisions on financial investment are based on a thorough analysis of the indicators for individual stocks and the outlook for the stock market. The main focus of investors is on the return, the liquidity, and the risk of a
stock. The return measures the yield of the stock (dividend plus the change in the price of the stock relative to the price in the previous period) and the traded volume of the stock represents its liquidity. The value of the outstanding stocks, i.e. the size of the market capitalisation, for investors indicates the importance of the company for the market developments. The riskiness of future returns is proxied by the volatility of the return (i.e. the variability of the expected return). It means that the higher the volatility, the higher the risk associated with the stock. Usually higher return is bound to higher risk, therefore the securities can be categorised as shown on the Figure 1 [24]. According to his risk profile, an investor is interested in particular asset categories. However, in general, stocks with higher returns keeping the volatility constant are preferred.
Figure 1: Example of asset categories showing relationship between return and volatility
The user case for the visual analysis system presented in this paper is twofold: to facilitate both the monitoring of financial market developments over time and the analysis of the co-movement of stock market indicators. The relationship between risk and return over time is at the centre of the investors’ attention. Changes in risk and return lead to investment decisions and impact future performance of the stocks that are currently held. In case the parameters change rapidly or the market is subject to turbulences, the composition of the portfolio needs to be adjusted in order to achieve the targeted returns and avoid large losses. The current systems used by the financial analysts do not allow for a simultaneous presentation of all these factors through time. Using the available techniques they can see a static picture of the relationship between risk and return but do not see the dynamics of the relationship between the stocks in their portfolio. On the other hand they could display the development of either risk or return over time (i.e. as a line chart) for selected stocks but then it will not be possible to assess the correlation between the two factors. The solution to the problem is to enhance the classical scatterplot display with the additional variables and to map the time dimension into the animation. The necessary information is encoded in glyph appearance (to visualise the country and the capitalisation of the stock).
The co-movement of the stock market is demonstrated by the animation of scatterplot glyphs over time.
3. Related work
Effective data presentation is essential in order for financial analysis to support efficient decision making, placing visualization techniques for financial data at the centre of interest for several decades. We present a brief overview of the current available techniques, which deal with visualizing time-dependent and cross-sectional data. Most current time-dependent data visualization techniques applied to financial and economic data, employ mainly static graphs. Such a static presentation only allows for the display of only a very limited number of data points. The systems currently used by financial analysts are not interactive and do not provide animation of data points (inter alia Excel, E-views, SPlus), although it has been shown that smooth animation increases the accuracy of the decisions ([9]).
A complete overview of the literature on visualisation techniques for presenting time-dependent data, the use of animation in information visualisation and scatterplot based data visualization would exceed the scope of this paper. However, we would like to mention the main techniques discussed. The surveys on systems specialized on time-series can be found in [7], [11] and [26], other interesting visualizations are described in [20].
The use of animation in data visualization overcomes limitations on the number of data points displayed especially for time-dependent data. It is however not a new topic. Several systems have been developed, which use motion to enhance visual analysis of time-series data (see for example [2], [6], [8] and [15])).
In this paper, we concentrate on the visualisation of data in a scatterplot framework as this builds on the user experience with visualisation systems and supports the way the analysts are used to think about the data. The scatterplot technique is not new and the amount of references to literature on scatter plot design and application is extensive. Selected surveys on scatter plot techniques are for example [3], [4], [5], [17], and [22]. However, systems providing scatter plots with animation, with the exception maybe of the presentation shown on gapminder.org, are rare. We do not consider as animated systems those systems that do not animate movement of glyphs in the scatter plots. North at al. [16] represents data changes as “wear marks in the visualization background”. Moere [14] presents a technique for visualizing time-varying datasets using animated “flocking boids” in 3D space. The position and movement of the boids, which represent data on stocks on financial markets, is determined by behaviour rules. Each boid is represented as a trajectory of the previous time-states it has passed with diminishing transparency. Therefore this approach cannot be considered as a scatter plot showing real data in the original sense. Time-series Explorer [6] uses scatter plots to enhance visualisation of
time-series. Glyphs in the scatter plot show development of time-series during a certain time period. They are animated by smoothly adjusting the start and end of the time period. Time-series plot and the corresponding scatter plot in the system offer focus and zoom as well as linking functions. The Time-series Explorer is currently used for analysing biological data.
4. Cognition aspects of motion visualization
and their implications on system design
The human perception plays an important role in the design of visualisation systems. These systems try to make use of the laws of human perception in order to maximize the results of visual queries that are made in the course of system usage. As stated in [23], the “most promising uses of animation seem to be to convey real-time changes and reorientations in real-time and space. “ Animation is therefore evidently well suited for the visualizations of time-dependent data. In this section, we present several issues which have to be taken into consideration when designing motion-based visualisation systems. However, our purpose is not to present a thorough discussion on the perception of motion.
An overview of the main principles which apply to human perception of motion in information visualisation can be found in the section “Patterns in Motion” in the chapter 6 of [25]. With regard to designing the animated visualisation system presented in this paper, the correspondence problem and the issue of perception of motion in context need to be taken into account.
The correspondence problem of moving objects is characterised by a confusion of a moving object with another object. This phenomenon can be overcome by the smoothing the motion of the object and by visualizing objects differently, for example by using different sizes or colours for the glyphs. Perception of motion is dependent on its context. For example a static dot in a moving box is perceived as moving. Therefore the environment in which glyphs are moving should be static, e.g. the environment is bound by static axes.
Tversky [23] recommends that animations should be “slow and clear enough for observers to perceive movements”. To overcome disadvantages of animations, the application should allow for the control of motion’s speed, allow stopping and reviewing of the animation and zooming in and out. More specifically, Huber and Healey [12] provide some critical measures for direction and velocity of objects in order for that human beings to be able to distinguish difference in motion of glyphs.
Pylyshyn [18] studies the human ability of tracking objects in a multiple object tracking paradigm. He reports that observers can track very precisely four independent but identical moving objects, suggesting a serial tracking process. This would imply, that in our animated scatterplot framework, the users are able to track various object in a serial way. This supports the use
of animation for analytical purposes (i.e. for tracking the dynamics of the stock market and co-movement between individual shares).
Finally, Buehler, et al. [2] summarise a set of recommendations for the design of motion visualisation. We follow selected guidelines for an effective visual data analysis: Motion should characterise changes in the data; visualisation velocity should be proportional to the rate of change of the underlying data, and objects which move similarly should indicate similarity of data changes.
5. Description of the system
The system’s design and functions have been conceived to provide an effective visualisation of multi-dimensional time-dependent financial data across entities. The system presents data in a 2D scatter plot with other data dimensions encoded in the size and colour of the glyphs. Following the user case presented in the paper, the animation is used to facilitate analysis and monitoring of stock market indicators over time. The user can choose to focus and zoom in on the currently visualized data and/or to see further information on selected data points. An export of the visualisation is possible both as a picture of the current view or as a video of the data animation.
5.1 System design
The current system design of our first prototype is based on previous experience with financial analytical systems and on a thorough task analysis carried out in cooperation with financial analysts. Moreover, the system functionality has been adjusted on the basis of user evaluation results. The system components comprise data storage and loading, data calculation and presentation. Interactive features such as focus and zoom, and details on demand are also incorporated in the system. The system also includes animation and animation manipulation modules.
The user interface is designed to suit analytical requirements and to allow an intuitive control of the system features. It is composed of the main visualisation window and the interaction panel. Five panes are used (see Figure 2):
- Pane 1: is used for scatter plot view, which is the main analytical focus. The interaction panel comprises tools for interacting with data and analysis support tools.
- Pane 2: The data pane provides data description and data manipulation tools.
- Pane 3: In the data selection pane, main selection features are available.
- Pane 4: Further data visualization and manipula- tion options are provided.
- Pane 5: The animation pane provides tools for manipulating motion features.
Figure 2: FinMotion System Design
Note: 1: the scatter plot view, 2: the data pane, 3: the data selection pane, 4: the options pane, 5: the animation pane. 5.2 Data visualisation and data manipulation
The system’s main window (see Figure 2, pane 1) presents a scatter plot that visualises the relation between indicators of a set of cross-sectional data. The initial visualisation shows four dimensions of the whole data set at the starting time period. Data are presented as rectangles, where x position, y position, colour and size each encode one dimension of the data. The description of the dimensions as well as the time frame of the loaded data is provided in the pane 2 seen in the Figure 2. Pane 2 also provides functions for loading the data and exporting the visualisations.
In our example, time-varying data on stock volatility and stock return are analysed. As usually presented in financial applications, the x-axis shows the volatility and the y-axis the return of the stocks. The axis minimum and maximum are determined by the data over the whole time period so that the glyphs do not move outside the plot area during animation.
Further data on stock market capitalisation, i.e. the importance of the companies stock for market developments, is encoded in the area of the rectangular glyph. The area instead of side size of the rectangle is used, because humans perceive size as area but not the side of the rectangle. The size of glyphs is normalised by minimum and maximum stock market capitalisation in the sample.
The encoding of a third data dimension as size has been a major drawback with respect to the problem of visualising negative values. The support of negative values is an important issue as negative values are plausible, for example, in case users choose to use
weights in the portfolio as the size dimension. We have decided to show negative values as unfilled rectangles with the size corresponding to the absolute value of the value of the data point (see Figure 3).
Figure 3: Visualisation example of glyph size - negative values shown as unfilled rectangles
With an increasing number of cross-sectional entities, the overlap of glyphs and labels will pose problems for visualizing the data. In order to separate the glyphs, we firstly have chosen to use a different colour for the glyph’s borders from its “filling” colour and secondly to position glyphs on the back/foreground of the screen according to their size. The border colour highlights contours of the glyphs and back/foreground positions controls for overdrawing smaller glyphs with bigger glyphs
A further dimension of the data is represented by the colour of the glyphs. For example, German stocks are coloured red and French stocks are in blue. It enables users to compare the development of the two stock categories more easily.
5.3 Animation
Animation is the major feature of the system. The main advantage of animation is that users can see the time dependent changes in the data while remaining focussed on the cross-sectional diversity of the data. The linear movement of the glyphs represents the data change between two subsequent points in time. The animation steps are smooth in order to support the accuracy of decision making [9].
There are different options for the choice of interpolation technique which can be used for animating scatter plot glyphs. We have employed a simple linear movement between two subsequent data points, because this motion is intuitive for users and easy to follow. In linear interpolation, the speed of motion is proportional to the rate of change in the data between two subsequent time points, as advised by [2]. It is also important for analysis purposes, as analysts can see, for example, which financial instruments exhibit larger price movements.
The animation of the glyphs is a powerful tool for showing data changes. However, it obscures the history of past data points. Therefore, the system provides users with the option to draw trajectory paths of the items during animation. This feature is particularly important for users interested in analysing and comparing the history of data items in more detail, when viewing a “static picture” of the data (see Figure 4). In the example below, we see a trajectory of the dynamics of three German car producers during one week in June 2005. A priori, the user might suppose that they move in sync. The trajectory feature highlights, that the movements in return and risk of Volkswagen (VOW) and BMW were indeed similar, whereas the evolution for DaimlerChrysler (DCX) was quite different. This would reflect that the risk and return changes of the two former companies were highly correlated in the period, whereas the factors affecting DCX were of a different nature.
Figure 4: The animation trajectory visualisation
The user can choose to focus on a certain time period by choosing the start and end date of the animation and by choosing to visualise certain points in time via the time slider (see Figure 2, pane 5). The animation speed can be adjusted, in order to better concentrate on changes in glyph positions during the play. Furthermore, the user can save the current view as
a picture or can decide to save the animation as a video. The video and picture export function allows the user to view saved results of his or her analysis at a later time or to use it in other reporting applications.
5.4 Focus and Zoom
As financial analysts need to examine large datasets, selecting and zooming features are required in order to provide users with the possibility to concentrate the analysis on interesting data. The system provides conventional focus and zoom functions for scatter plots together with special tools in response to the animation feature.
As can be seen in the Figure 2, pane 3, users can choose among several focus options. The selected items are displayed in their original colour and the other items are coloured in grey (see Figure 5). Apart from focusing on stocks from the chosen countries, she can highlight items that satisfy a user defined threshold on the dimensions represented by the x- and y-axis or the rectangle size. The highlighting of stocks according to size allows to compare the dynamics of different stock groups - big companies versus small companies. The x and y threshold shows whether the companies satisfying the risk and return criteria in one time period keep on satisfying these limits over time. Furthermore, a free selection option allows the highlighting of user selected items by mouse-clicks or by using a search tool for the stock symbol. This is useful for the objective of monitoring, as it enables defining “target stocks” from a group of stocks and allows tracking their development over time while comparing their relative performance to the rest of the stocks shown. The user can show or hide those items which are not selected, depending on whether she wants to concentrate on the selected group only or to keep the “comparing view” to the rest of the stocks displayed.
Zooming is provided either by delimiting an area of the scatter plot using the mouse or via clicking on the axis labels and determining minimum and maximum values of the axis. We implemented both zoom types as users are accustomed to these kinds of zooming interaction in financial applications. The system saves the zooming history. The user can return to the previous selection sequences by pressing the back button.
5.5 Details on demand
As users may wish more information on the visualised data, a legend and further information on demand are provided. The legend tool visualises further information chosen by the user for currently selected glyphs. More detailed information for a chosen data point is shown in a pop-up window (see Figure 6).
Similar to the problem of the overlapping of glyphs, the legend placement is a challenge for the system design. The legend currently overplots all displayed items and its positioning is done similarly to the algorithm proposed in [10]. The legend position is bound to the glyph rectangle as shown in Figure 7, where it is not restricted to only the four positions shown but can be placed in the whole area around the glyph (the grey area). The algorithm used in the system enables all legend items to be placed, although the issue of overlapping of legend items cannot be overcome completely.
Figure 6: Data label placement and details on demand visualisation Glyph Legend text Legend text Legend text Legend text Glyph Legend text Legend text Legend text Legend text
Figure 7: Possible positioning of glyph legend
text around the glyph (shown as grey area)
6. Qualitative user evaluation
We have conducted a usability survey both with experienced users from the financial industry and with users that are not familiar with financial analysis. We have asked ten financial specialists and several persons not working in the financial industry to answer questions about design and usability of the system. The questions for the second user group concentrated on design and ease of function control. The experienced users also
answered questions on the value added by the new software in their analytical work and possible usage in their business areas. Users also provided input for further functionalities of the system.
The general impression of all users was very positive. The users liked the ease of application of the software. They were able to control the system without any previous explanation. Upon user feedback we added the option to see the visualisation pane in full screen as some analysts’ display areas are limited.
The data presentation which encodes many dimensions was deemed valuable by users allowing one data display to illustrate all the needed data characteristics. In contrast, currently used data presentations only show a few dimensions. The users currently produce various visual representations of the same data using only a limited number of dimensions, and then compare them across the different visual representations. This can be avoided in the new system.
The animation feature was very positively ranked. The experienced users claimed it was very useful for their analysis. The comparison with other tools used in financial analysis shows that the animation is an interesting new feature which cannot be found in the currently used systems. In the analysis process using available tools, the users are forced to examine the data either across time or across entities, but not both at the same time. This poses major limitations for the analysis process and makes it cumbersome. The animation offers the possibility to better interpret market parameter changes of individual entities over time. The users would have appreciated the possibility of showing labels also during animation which was not available during the tests. The trajectory feature helps analysing the development of indicators of individual stocks during time also on static picture. However, some users found this feature confusing when too many stocks are shown, as it can be difficult to follow individual trajectories The record of video output and the possibility of taking screenshots were seen as useful for later comparative analyses and for decision making support and documentation.
The selection feature has proven to be very helpful. The users especially liked the threshold selection feature as it helps them easily identify stocks which are “good” or ”bad” (i.e. low volatility and high return vs. high volatility and low return) and to observe their behaviour over a longer period of time. The size threshold helps them to compare the data performance of small and big companies. Upon user request we also added the stock symbol search so that users can easily highlight stocks which interest them.
Zoom and details on demand are standard tools and have proven necessary for the users in order to view more detailed information on the current state of the market.
In general, the tool adds dynamic data change presentation and an easy presentation as well as interpretation of analysis results. It was overall deemed a
useful tool for decision making support and analysis of market movements.
6. Discussion and future work
We have presented a system for the visual analysis of financial time-dependent data with an extensive number of entities. We have used a scatter plot framework to display relationships between two entity indicators including additional dimensions of the data encoded by a suitable glyph design. The choice of scatterplot framework is based on previous user experience in analysing financial indicators. The system uses animation of glyphs to handle the time-dimension of the data which responds to the user task to analyse the relationship between stock market indicators over time. Further features of the application include for example focus and zoom, details on demand, and time period selection.
The usability of the system has been demonstrated on stock market data example. The daily data comprised various financial variables of stocks from several countries over a period of about 1½ years. User experience suggests the usability of the system and especially the value added by the animation feature in the analysis process. The animation better reflects real behaviour of the stock market. Further features, as for example stock symbol and threshold selection, are deemed very useful because the analysts can then better focus on the examination of interesting stocks.
In the future, we would like to implement modules for search in news feeds of stock market developments. Such news can help to explain market movements observed by the user in this analysis tool. Furthermore, semantic information can be used to enhance zoom and search in the stock market data [13]. It could also answer the scalability issue by semantic-based display of aggregated entities (i.e. stock market indices). The application can be used I n various other business analysis fields, for example in macro-economic analysis, consumer behaviour analysis, insurance analysis, or in industrial or medical applications. The user survey showed that the calculation and display of the “efficient frontier” and of sharp ratio would improve analytical features of the software. These will be implemented in the near future.
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