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We present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals

A Survey on Visual Analysis Approaches for Financial Data.

Comput. Graph. Forum, no. 3 (2016): 599-617

Cited by: 27|Views73
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Abstract

Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision-making processes based on financial data have been a popular topic in industries. However, analyzing financial data is a non-trivial task due to large volume, di...More

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Introduction
  • The availability of different sources of financial data provides opportunities to market analysts and investors to extract new insights in order to make informed decisions
  • Such analyses guide them in the development of optimal investment and risk management strategies.
  • To facilitate such analysis tasks, the analysts traditionally utilize visual analysis tools that are typically built atop standard statistical methods.
Highlights
  • The availability of different sources of financial data provides opportunities to market analysts and investors to extract new insights in order to make informed decisions
  • We found that there was little information about task requirements and a data exploration model directly derived from financial industry experts
  • Among the five risk analysis projects that we found, two projects [RSE09, SLFE11] show risk data generated from an economic model in user studies in order to find whether investors are able to recognize the risk in their investment and change their investment strategies according to the risk
  • Why state-of-the-art visualization techniques have not been disseminated could be explained based on sparse collaboration between researchers and market participants; researchers c 2016 The Author(s) Computer Graphics Forum c 2016 The Eurographics Association and John Wiley & Sons Ltd
  • We have presented a survey of the research on visual analysis approaches for exploring financial data
  • The fundamental motivation of this work was that there was little information derived from analysts
Methods
  • Various interaction methods have been developed in order to support the different data types and analysis methods to enable users to effectively explore their data.
  • In order to categorize the various interaction methods, a number of surveys are presented with a focus on low level interaction operations [Shn96], tasks [AES05], and benefits and user intentions [YaKSJ07].
  • Note that not all the papers the authors reviewed described interaction methods in their visualizations or systems.
  • The authors briefly present the interaction taxonomy the authors use and examples that the authors considered.
  • These interaction methods consist of: select, explore, reconfigure, encode, abstract/elaborate, filter, and connect as shown in Table 7
Conclusion
  • Financial data analysis is important in that it directly impacts assets and investments.
  • Many business domains remain underutilized that otherwise could inspire new visualization techniques for visual analytics communities.In this work, the authors have presented a survey of the research on visual analysis approaches for exploring financial data.
  • The authors' survey shows that there are trends and preferences in data sources, automated techniques, visualizations, interaction methods and evaluation
  • This implies that there are still many under-examined business domains due to several reasons where research on new techniques and systems are needed as simple line charts are still used with help of aggregation.
  • The authors believe that the lessons from the survey shed light on understanding state-of-the-art visual analytics approaches for financial data analysis
Tables
  • Table1: Table 1
  • Table2: Searched venues for paper collection
  • Table3: Conferences or journals in “Others” in Figure 1. We found only one paper from each conference or journal
  • Table4: Categories for data sources. We derive 6 data sources. It is obvious that stock and fund data are the most popular data in both research communities and financial industries
  • Table5: Categories for automated techniques. We categorize automated techniques used for financial data into 4 categories– clustering, dimensional reduction, statistical, and model- or theory-based techniques. For financial analysis, SOM (selforganizing maps) was used for both clustering and dimensionprojection
  • Table6: Categories for visualization techniques based on the taxonomy in [Kei02]. As we expected standard charts have been a popular technique in analysis. Among advanced visualizations, dense and geometrically-transformed displays were used more than iconic and stacked
  • Table7: Categories for interaction methods. Abstract, Filter and Connect have been the most popular interaction methods for visual financial analysis. The popularity of Connect implies wide adoption of multiple coordinated views
  • Table8: Categories for evaluation methods. Use cases were mostly preferred for evaluation of visualizations or systems followed by case studies. In contrast, user study and evaluation by performance were not popular
Download tables as Excel
Funding
  • This work was supported in part by the U.S Department of Homeland Security’s VACCINE Center under Award Number 2009-ST061-CI0001
  • Ko’s work was supported in part by the 2016 Research Fund (1.160038.01) of UNIST (Ulsan National Institute of Science and Technology)
  • Jang’s work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.R0190-15-2016)
Study subjects and analysis
papers: 50
Table 3 presents the papers in the “Others” category in Figure 1 and only one paper is found in each conference. In the end, we included 50 papers in total for our review. While we were reviewing papers, we found that there was little information available about task requirements that were derived directly from financial analysts

papers: 4
The “Connect” interaction means that data items are highlighted and associated. In general, systems with multiple coordinated views are assumed to provide this interaction, but we found only four papers in our review that utilize this interaction. For this interaction, users can click and select a region to select the data in one or more views

papers: 3
For example, 3D visualization is a good candidate for Reconfigure. However, we found only three papers [DG04, SH05, BM12] that provide information on the interaction methods. Also, when we started our survey, we expected that many financial systems would provide sorting techniques (one implementation of Reconfigure) as shown in Merino et al.’s list of “type of action” [MSK∗06]

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