We use the Design Study Methodology in a short term project to apply visualization to equity analysis with a major supplier of information to the finance industry. Our interactive prototype is positively received and our work suggests that this kind of design and the application of the DSM to short term visualization projects have potential in stock picking and more broadly in financial analysis

Dykes, J.

Merryweather, A.

Vives, S.

City Research Online

Vives, S., Dykes, J. & Merryweather, A. (2015). Visualization for Equity Analysts: Using the DSM in Stock Picking. Paper presented at the VIS 2015, 25-10-2015 - 30-10-2015, Chicago, USA. City Research OnlineOriginal citation: Vives, S., Dykes, J. & Merryweather, A. (2015). Visualization for Equity Analysts: Using the DSM in Stock Picking. Paper presented at the VIS 2015, 25-10-2015 - 30-10-2015, Chicago, USA. Permanent City Research Online URL: http://openaccess.city.ac.uk/12335/ Copyright & reuseCity University London has developed City Research Online so that its users may access the research outputs of City University London's staff. Copyright © and Moral Rights for this paper are retained by the individual author(s) and/ or other copyright holders.  All material in City Research Online is checked for eligibility for copyright before being made available in the live archive. URLs from City Research Online may be freely distributed and linked to from other web pages. Versions of researchThe version in City Research Online may differ from the final published version. Users are advised to check the Permanent City Research Online URL above for the status of the paper.EnquiriesIf you have any enquiries about any aspect of City Research Online, or if you wish to make contact with the author(s) of this paper, please email the team at publications@city.ac.uk.Visualization for Equity Analysts: Using the DSM in Stock PickingSergi Vives∗F&R UX DesignThomson ReutersJason Dykes†giCentreCity University LondonAndrew Merryweather‡F&R UX DesignThomson ReutersABSTRACTWe use the Design Study Methodology in a short term project toapply visualization to equity analysis with a major supplier of in-formation to the finance industry. Our interactive prototype is pos-itively received and our work suggests that this kind of design andthe application of the DSM to short term visualization projects havepotential in stock picking and more broadly in financial analysis.Index Terms: H.5.2 [User-centred Design]: ;— [J.1]: Adminis-trative Data Processing, Financial—; K.8.1 [Graphics]: —1 CONTEXTThomson Reuters (TR) is a leading multinational media and infor-mation company that provides professionals with high quality infor-mation in a wide variety of fields including finance. Since 1851, TRhas been providing stock market quotations to brokers. The flagshipproduct Eikon is used by 185,000 analysts around the World to findinteresting investment opportunities. We design, develop and eval-uate highly interactive visual interfaces to support equity analystsin stock picking, a creative process that involves vast amounts ofinformation and is currently too complex to automate. We do soin light of existing work on visualization for financial visualization(e.g. [7, 5, 10]) to determine whether a visual approach to dataexploration improves this process, with a view to developing thevisualization capability in Eikon.2 METHODSWe used the Design Study Methodology (DSM) [8] to frame workthat was undertaken through a 3-month internship at TR’s Londonheadquarters at Canary Wharf.2.1 Design and DevelopmentDSM helped to structure the design process by providing the fournested levels of design: domain, data/task abstraction, visual en-coding/interaction idiom and algorithm. Each level defines a set ofactivities and its required output. This division allowed the prob-lem to be analyzed and the solution to be validated at each level al-most independently. However the ‘precondition’ phase took placethroughout – in a non-sequential fashion – as the organization be-came aware of and adopted many of the ideas [9]: Winnowing –activity took place through working on site with close contact withdomain experts. This facilitated rapid discussion and faster feed-back loops informing the design. Data was made fully available;Casting – involved the work supervisor providing access to front-line analysts, who became more accessible as the project gainedmomentum and the data sketches developed became more useful;Discover – unstructured interviews with 7 different domain expertswho were recruited and participated throughout the process.∗e-mail: sergi.vives@thomsonreuters.com†e-mail:j.dykes@city.ac.uk‡e-mail: andrew.merryweather@thomsonreuters.comThe nature of the problem and its organizational setting requiredthat different design alternatives be explored and requirements re-main flexible as [11] different domain experts provided input. AnIncremental Development [4] combined with an Iterative Prototyp-ing approach where found to be the best solution. These workedwell in the flexible DSM context with the four levels of design help-ing structure the project: Domain – initial meetings where held withdomain experts in order to understand the current workflow. Struc-tured interviews followed where initial requirements where vali-dated using mock-ups and sketches; Task & Data Abstraction, Vi-sual Encoding & Interaction – we used the Why?, What?, How?framework [6] to abstract the tasks, explore visualizations and cre-ate interaction paradigms that would fulfil these tasks.After an initial evaluation of different charting libraries and TR’stechnology stack (most products are web-based), D3[1] was consid-ered the most suitable technology.2.2 EvaluationVisualization solutions are best validated with real datasets [2].Thus the evaluation used a real data subset with which users per-formed various tasks derived from the requirements and task ab-stractions. The tasks were designed using Visual Data Reasoning(VDAR) [3] to evaluate not only performance efficiency but alsohow the visualization solution could support knowledge generation.Comparison between the new visualization approach and the cur-rent tools on Eikon was performed using an empirical approach.Two types of tasks where designed: benchmark tasks and insightmeasurement tasks [3]. For benchmark tasks error rates and tasktimes where measured. For VDAR tasks the number of findingsabout the data was recorded. These metrics were analysed in thecontext of rich qualitative feedback.Figure 1: Clusters and Arcs – To avoid overlapping and to reducenoise we used a distance-based clustering algorithm. The arcs showthe proportion of items of a given category inside the cluster.3 RESULTS3.1 SystemOur fully functional prototype supports visual data mining for stockdata exploration with linked views that provide complimentary con-current perspectives of the data. Parallel coordinates are used todisplay multi-dimensional information about the stocks and to com-pare and filter. Qualitative data is color-coded enabling stock cat-egories to be distinguished and compared quickly. The scatterplotand mini-map provide a wide overview of the entire dataset andexploration opportunities.Figure 2: Final Prototype – The prototype exposes data in different visualizations with linked views encouraging exploration.3.2 FindingsIn short, the DSM approach with the incremental, iterative, multi-channel methodology helped structure the process and validate de-signs to produce a prototype with really useful features for analysts.Feedback from participants using a new method with little train-ing for some complex tasks support this view and relate directlyto functional requirements: “it’s so much easier comparing stocksin the prototype than with Eikon”; “visually doing the filtering isso much better than manually”; “I can see the data now ... this isreally cool stuff for an analyst.”Not all features were successful, however. A means of visual-izing the quality of an equity was not clearly understood by theanalysts as the visual encoding chosen was deemed ineffective.In the broader context, stock prices in the two companies specif-ically selected by participants as being of investment interest inthe final evaluation subsequently increased (see Fig. 3). Most no-tably, the developer’s subsequent employment as a Data Visualiza-tion Specialist for the F & R Design team at Thomson Reuters isindicative of the level of success felt by the industrial partner.Figure 3: TTWO (purple) & FUN (orange) were picked and in-creased in price – even surpassing the S&P 500 index (green).4 CONCLUSIONAlthough DSM is normally applied in longer-term projects, thefrequent and fast feedback loops allowed activities at each level tobe performed successfully and in a timely manner. The task anddata abstraction work helped us understand the problem, structurethe design and deliver a visualization tool that met analytical needs.The prototype has potential to be developed into a fully functionalapplication included in Eikon to provide a new way to explore andanalyze the stock market data. As stated by the head of Investmentand Management at TR: “some of the features designed could gointo the product”, the prototype “will be taken into account for theset of features being designed for the upcoming year”.Additionally some of the new features envisioned by productmanagers when asked to give feedback about the prototype couldbe implemented in the prototype to establish whether they enhancethe analyst’s workflow – a visual test-bed beyond the initial design.Although the quantitative and qualitative data collected suggeststhat this approach can improve the equity analysis process, a morecomprehensive evaluation is a priority. This evaluation should in-volve real-world users performing real equity analysis.Finally, interactive visual approaches could be beneficial in manyother applications of Eikon: its benefits are not obviously restrictedto stock analysis. Many applications within Eikon could be en-hanced by including such an approach.ACKNOWLEDGMENTSWe wish to thank the AIM team at TR for their support as well asthe F&R UX Design Team for their collaboration.REFERENCES[1] M. Bostock, V. Ogievetsky, and J. Heer. D3 data-driven documents.IEEE TVCG, 17(12):2301–2309, 2011.[2] S. Carpendale. Evaluating information visualizations. In InformationVisualization, pages 19–45. Springer, 2008.[3] H. Lam, E. Bertini, P. Isenberg, C. Plaisant, and S. Carpendale. Em-pirical studies in information visualization: Seven scenarios. IEEETVCG, 18(9):1520–1536, 2012.[4] C. Larman and V. R. Basili. Iterative and incremental development: Abrief history. Computer, (6):47–56, 2003.[5] S. T. Lei and K. Zhang. A visual analytics system for financial time-series data. In Proceedings of the 3rd International Symposium onVisual Information Communication, page 20. ACM, 2010.[6] T. Munzner. Visualization Analysis and Design. CRC Press, 2014.[7] T. Schreck, T. Tekusˇova´, J. Kohlhammer, and D. Fellner. Trajectory-based visual analysis of large financial time series data. ACM SIGKDDExplorations Newsletter, 9(2):30–37, 2007.[8] M. Sedlmair, M. Meyer, and T. Munzner. Design study method-ology: Reflections from the trenches and the stacks. IEEE TVCG,18(12):2431–2440, 2012.[9] J. Wood, R. Beecham, and J. Dykes. Moving beyond sequential de-sign: Reflections on a rich multi-channel approach to data visualiza-tion. IEEE TVCG, 20(12):2171–2180, 2014.[10] H. Ziegler, M. Jenny, T. Gruse, D. Keim, et al. Visual market sectoranalysis for financial time series data. In IEEE VAST, pages 83–90.IEEE, 2010.[11] D. Zowghi and C. Coulin. Requirements elicitation: A survey of tech-niques, approaches, and tools. 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