a novel explorative visualization tool for financial time ...data without overlaps, and line graph...
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A Novel Explorative Visualization Tool for Financial TimeSeries Data Analysis
Matthias [email protected]
konstanz.de
Franz [email protected]
konstanz.de
Roman Kahl1roman.kahl@uni-
konstanz.deLeishi Zhang1
Tobias Schreck1
Daniel A. Keim1
ABSTRACTAnalyzing high dimensional time series data can be chal-lenging, especially when the data consists of a large numberof long time series. The task involves supporting interac-tive analysis of the data in both global and local fashion.In this paper we introduce a novel visual data analysis tool,which facilitates such type of tasks. The tool advances ex-isting visualization techniques by integrating a pixel basedvisualization technique, which can display a large volume ofdata without overlaps, and line graph visualization, whichprovides an intuitive understanding of patterns and trendsin the time series data.
KeywordsTime Series, Financial Data, Visualization
1. INTRODUCTIONA time series is a sequence of observed values ordered in
time. Such data exist in many application domains such asfinance, communication and science. Extracting trends andpatterns from time series data provides essential knowledgefor many data analysis tasks such as performance analysis,fraud detection and decision support. Various visualizationtechniques have been proposed and applied in many applica-tion scenarios [5][4][10][12][3][11][6][8][1][13] to help extract-ing patterns from time series data. However, challenges stillexist: for example preserving global views of long sequencesof observations while displaying details-on-demand informa-tion, showing multiple time series data while avoiding a clut-tered screen, or supporting a comparison of different timeseries.In this paper we introduce a novel visualization system foranalyzing share performance from historical stock price timeseries and sector indices data. Typically, the stock price datacontains time series which record the share price measuredover a long period of time. Each dataset contains the time
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series of a large number of shares. Analysts often look atthe risk and return of the shares by comparing the perfor-mance of individual share prices against the sector indexwhich records the average performance of all shares in thesector. Performance of different shares is often compared tosupport decision making in financial investments. Duringthe explorative analysis process, the focus of interest mayswitch between the global view of all share performance andperformance of individual shares over a specific time period.To facilitate such analysis tasks, we design our system insuch a way that the users can easily filter the data, switchthe focus of the exploration, compare share performance,and see the volatility (the deviation of the share perfor-mance) of shares. Our tool starts with a pixel based viewwhich shows a global picture of the whole market perfor-mance. Each pixel unit represents the performance of oneshare at one time point and each row represents one share.Analysts can click on a pixel unit to see the detailed viewof the selected share prices as line graph(s) lying on top ofthe colored background. Various user interactions are imple-mented to allow analysts to see the correlations, filter thedata, change time intervals and to zoom into a particulartime period to obtain detailed information.The main contributions are: 1) integration and extensionof pixel based visualization and line graph visualization forthe analysis of share performance; 2) an interactive dataanalysis system for analyzing stock market time series data.The tool is developed for financial time series data analysis,but the proposed visualization techniques can be applied toother time series visualization applications. The paper isorganized as follows: In Section 2 we introduce backgroundand discuss related work, the design and implementation ofthe system is shown in Section 3, and Section 4 shows thebenefits by use cases on real world applications. In Section5 we draw conclusions and discuss future work.
2. RELATED WORKTime series analysis plays an important role in stock mar-
ket trading, where trading prices change every second. Thesechanges are recorded in a database as time series. The datais often used to extract trends and patterns to support de-cision making in trading and investment. Typically, givenhistorical stock market data, analysts want to look at therisk and return of individual shares as well as evaluate theperformance of a share against the average performance ofshares of a similar type. In the past decades various visual-
ization techniques have been proposed to support time se-ries data analysis. For example, spiral techniques [11], clus-ter and calendar based visualization techniques [12], VizTree[6], TreeMap [8] and displays based on Self-Organizing Map-based clusterings [7]. In this section, we focus on three visu-alization techniques, TimeSearcher[1, 2], Recursive Pattern[4] and Growth Matrix [13] which are used to visualize fi-nancial data and closest to the techniques we applied in oursystem design. We discuss the strengths and weaknesses ofeach technique which motivate our system design.TimeSearcher is designed for interactive querying and ex-ploration of time series data. The software applies tradi-tional line graphs to display time series as a river-plot view.The interactive query facilitates the explorative analysis onthe time series, and the pattern based search makes it pos-sible to forecast the development of a time series. Howeverwith large multiple time series the overlap between lines canobscure individual time series and makes it difficult to findpatterns.Recursive Pattern is a pixel based approach which wasdesigned to avoid the over plotting problem when visual-izing large time series. The technique combines the ratiobetween the stock price and the sector index into a singleinput variable and maps this ratio to colors. Each value isvisualized as a colored pixel in the display. The advantage ofthe pixel based technique is its ability to visualize the globalview of a large volume of share performance data withoutcluttering the display. Some patterns can easily be detectedin such a visualization, for example we can observe maximaor minima at certain time stamps. The disadvantages of thistechnique are that it lacks comparison on some detailed in-formation such as the separate share price and sector index,and the static view makes it difficult to compare individualshares.Growth Matrix visualization is proposed to avoid largedata volumes causing an over plotted display. This tech-nique provides an effective way for analyzing financial data,since it allows the identification of strong and weak peri-ods of assets as compared to global market characteristics,and thus allows a more encompassing visual classificationinto “good“ and “poor“ performers than existing chart tech-niques. Like most other pixel based visualization techniques,the drawback of Growth Matrix is the visualization stays asstatic views and the implementation does not support dy-namic queries. The quantities of differences between valuesare hard to estimate.
3. DESIGN AND IMPLEMENTATIONNone of the existing visualization techniques on its own
solves all the problems in visualizing large-and-long time se-ries. Hence, in the design of the tool we take advantageof several visualization techniques and integrate them suchthat the large-and-long time series can be analyzed in bothglobal and local fashion without losing information or overplotting the display.Performance measure: Given a particular time frame, wedefine the performance measure to be the ratio of the shareprice against the corresponding sector index over the sameperiod of time. This new variable is mapped into the visu-alization by using color. We use a red and green color mapbased on the convention in the financial domain that redindicates negative performance and green indicates positiveperformance. Thus no extra cognitive load is needed while
Figure 1: Overview of the system: GUI interfacewith a global view of stock price time series visual-ization and details-on-demand option for a selectedshare.
examining the graphical representations. The performancemeasures are mapped to the background of the line graph invertical strips corresponding to the respective time stamps(see Figure 1).Visualization of an individual share price series: Weuse a dynamic line graph to visualize a stock price series forgood and easy understanding of trends and changes. The dy-namic line graph visualization allows the analyst to zoom inor out of a particular time period or to change time intervals.The performance measures are mapped to the backgroundof the line graph (see Figure 1). Red colour means that theshare performs worse than the sector average; green meansthat the share performs better than the sector average.
Figure 2: Two different visualizations (trapezoidand triangle slope polygons) for correlation analy-sis and pattern detection.
The correlation within a time stamp between the share priceand the sector index is visualized as yellow or violet “slopepolygons“ which lie on top of the line graph. This design de-cision is taken because yellow and violet are complementarycolors. A yellow polygon means that there is a positive cor-relation between the share and the sector index; in return vi-olet means that there is a negative correlation between thesetwo. Two types of slope polygons are implemented: trape-zoid slope polygons and triangle slope polygons (see Figure2). The triangles have the advantage of visualizing the quan-titative differences between a share price and its sector index(the length of the polygon line from the start to the end ofa time interval indicates the difference). However, when thechange is small, the slope polygons can be too small to see.Hence the trapezoid slope polygons are also provided. Theanalyst can switch between the two slope polygon types tolook for the information they need. For example, in Figure2(b), one can see although the share price has dropped be-
Figure 3: (left): Overview of 222 stocks over 5 years in the oil sector in the World-Datastream Oil and Gasproducers Index with weekly intervals; (right): The line graph appears on top of the pixel based visualizationon a mouse click, here with focus on “Oil & Gas Exploration & Production“.
tween the 5th and the 6th time point (down trend in the linegraph), the performance of the share is still better than thesector index (green background). From 6th to 7th time unit,there is a sharp increase in the share price whilst the sectorindex behaves very differently (big negative correlation).Global and detailed view visualization: In global viewvisualization the performance of each share at each timepoint is shown as a colored pixel. The detailed values ofeach pixel can be seen in a tooltip with mouse over action.The line graphs of stock price time series are laid on top ofthe pixel based visualization. Although the line graphs arehidden in the global picture, the analyst can easily click onany of the pixels and see the details of the price series in theline graph (see Figure 3 (right)). The selected share takesthe major part of the available space and the other sharesare squeezed together, keeping their relative positions, sothat they are still visible and can be considered for a com-parison.Filtering, zooming, and details-on-demand: A col-ormap slider is implemented as a data filter such that theanalyst can select a performance range. The data that falloutside of the range will be removed from the visualization.While filtering, the number of colors in the colormap is re-duced or the colors are renormalized according to the re-maining data values. For example if the performance rangeis set from -50% to -10% by the colormap slider, a sharewith -10% performance measure has the “best“ performance,hence it is colored green. This technique allows the analystto see the relative performance of shares compared to itspeers and amplifies the differences between data values suchthat some hidden patterns or outliers are discovered moreeasily. The system allows the analyst to change time inter-vals and specify time frames.A horizontal zooming function is provided. The analyst canadjust the level of details in the line graph by zooming inand out of the view horizontally. This is especially usefulwhen the analyst needs to study the correlations betweenthe share price and the stock index and see the quantitativedifferences. The analyst can also select a time series in thevisualization panel to expand a particular line graph. Whilemore details of the selected share become visible in the ex-panded line graph and the other parts become smaller (seeFigure 1), the visualization still provides the global pictureof the whole dataset.
4. APPLICATIONS
4.1 Comparison in European banking sectorIn this section we show the effectiveness of the system
through its application in the analysis of two real world datasets. In the first use case we study the performance of theDeutsche Bank share which belongs to the European banksector. The sector includes the shares of 54 big Europeanbanks (STOXX, see [9]). We have the Deutsche Bank shareprice time series over 5 years (from 01.08.2005 to 01.08.2010)on a daily basis. Figure 4 shows a screenshot of our systemdisplaying the weekly aggregated data compared to the Eu-ropean bank index. There is an obvious turbulent patternbetween the end of 2008 and the beginning of 2009 when theglobal crisis in the finance market took place. There werenumerous transactions in the market during that period. Ona daily basis the Deutsche Bank share sometimes developedvery differently from the sector index.
Figure 4: Deutsche Bank and STOXX Europe 600Banks Index five years performance on a weekly ag-gregated basis from 01.08.2005 to 01.08.2010; (lowerleft): Performance of Deutsche Bank comparedagainst this Index over the period aggregated quar-terly: it has similar performance to the sector ex-cept for the first quarter of 2009: here the the stockrecords a +6.3% performance which means that theshare outperformed the sector index by 25%.
The aggregation provided by the tool allows us to analyzethe data at different levels by changing the time interval.Figure 4 (lower left) shows the line graph of the same data
on a quarterly basis. By changing from a small time intervalto a bigger time interval, some price fluctuations in the smalltime interval are filtered out and thus the global trends canbe perceived more easily. From the higher level performancegraph we can see an interesting pattern which is hidden inthe previous visualization: During the first quarter of 2009,Deutsche Bank performed much better than the sector av-erage (see the strong green color in the background, whichmeans the share performed well and the performance has anegative correlation to the sector index). The good perfor-mance of the Deutsche Bank share during the first quarterof year 2009 indicates that the bank had a rapid recoveryfrom the financial crisis and the speed of recovery is fasterthan the sector average.
4.2 Explorative analysis of share performancein international oil and gas sector
In this use case study, we look at international oil andsector stock market data over 5 years (from 01.08.2005 to02.08.2010) to see the shares with best and worst perfor-mance. To see the shares with bad performance, we set thedisplayed range to performances smaller than -10%. Fig-ure 5 gives a global view of the filtering result. The per-formance values are renormalized after filtering; hence theperformance of -10% is mapped to green and the maximalnegative performance is mapped to red. As shown in the Fig-ure 5, the share with the biggest loss (worst performance)stands out (the red pixel in blue circle in Figure 5). Byclicking on the red pixel we get a details-on-demand viewof the share (see Figure 3). We see that there was a sharpfall in the share price which caused the extreme negativeperformance value. The same filter function can be used foranalyzing shares with best performance.
Figure 5: Limited to performances below 10% com-pared to the index. Shares with very bad perfor-mance emerge clearly, for example see the red pixelmarked with a blue circle.
5. CONCLUSIONS AND FUTURE WORKVisualizing large numbers of long time series faces great
challenges in terms of display space, effective visual repre-sentations and interactive visual interfaces. In this paper wepresent a visualization system that combines different visualtechniqes for historical stock data analysis. Two novel vi-sual representations, triangle and trapezoid polygons, areproposed to visualize correlation between individual shareperformance and the sector index. A colormap slider is de-signed to help filtering data base on share performance. Thetwo use cases analyzing the Deutsche Bank share and Oiland Gas sector data demonstrate that the system is able tosuccessfully highlight some interesting patterns in the data
and the visualization techniques scale well to large data sets.The current version of the system implements a basic sortingfunction which allows the analyst to see the shares orderedby their volatility. We want to further extend this analysisby implementing a similarity measure based on volatility val-ues and use this measure to group shares that have similarperformance patterns. This will be useful in pattern detec-tion and time series prediction. An evaluation with real endusers will be carried out to improve the design and func-tionalities of the system. We also want to integrate morestatistics and data mining methods such as clustering, asso-ciation rules and believe network models to support higherlevel data analysis tasks.
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