A Survey on Visual Analysis Approaches for Financial Data

Ko, Sungahn; Cho, I.; Afzal, Shehzad; Yau, Calvin; Chae, Junghoon; Malik, Abish; Beck, Kristen L.; Jang, Yun; Ribarsky, William; Ebert, David S.

doi:10.1111/cgf.12931

Cited by 59 publications

(49 citation statements)

References 85 publications

(214 reference statements)

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“…In this section, we use the visualization techniques taxonomy defined by Keim [Kei02] and extended further by ko et al [KCA∗16]. Table 5 shows the coding of our surveyed papers according to the taxonomy.…”

Section: Taxonomiesmentioning

confidence: 99%

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Afzal

Hittawe

Ghani

et al. 2019

Computer Graphics Forum

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The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012)

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Section: Taxonomiesmentioning

confidence: 99%

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Afzal

Hittawe

Ghani

et al. 2019

Computer Graphics Forum

Self Cite

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“…Our participants, who represent our primary target user community, are decision‐makers at different levels in police and public safety departments. Of course, with such a limited sample from one specific group, the result may not represent all practice; we have, however, found similar needs in a survey of financial analysts [KCA∗16]: providing context, supporting analyses, allowing comparisons of the details, etc. From our surveys, we identified a few key points in the current practices that identify the limitations of this communication process between data analysts and decision‐makers and derived the requirements of our design in Section 1.…”

Section: Survey: Communicating Insights To Stakeholdersmentioning

confidence: 92%

Bridging the Data Analysis Communication Gap Utilizing a Three‐Component Summarized Line Graph

Yau

Karimzadeh

Surakitbanharn

et al. 2019

Computer Graphics Forum

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Communication‐minded visualizations are designed to provide their audience—managers, decision‐makers, and the public—with new knowledge. Authoring such visualizations effectively is challenging because the audience often lacks the expertise, context, and time that professional analysts have at their disposal to explore and understand datasets. We present a novel summarized line graph visualization technique designed specifically for data analysts to communicate data to decision‐makers more effectively and efficiently. Our summarized line graph reduces a large and detailed dataset of multiple quantitative time‐series into (1) representative data that provides a quick takeaway of the full dataset; (2) analytical highlights that distinguish specific insights of interest; and (3) a data envelope that summarizes the remaining aggregated data. Our summarized line graph achieved the best overall results when evaluated against line graphs, band graphs, stream graphs, and horizon graphs on four representative tasks.

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“…Sorenson et al [49] developed a time series visualization in Bloomberg Inc. by combining the discrete glyph-based events, which assists in price fluctuation analysis. Additionally, there are outstanding surveys towards financial data visualization, like FinanceVis.net [18] and towards visual analysis [32].…”

Section: Financial Multivariate Time Series Data Visualizationmentioning

confidence: 99%

sPortfolio: Stratified Visual Analysis of Stock Portfolios

Yue

Bai

Liu

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. With sPortfolio, we can observe the management and strategy of stock portfolios from different perspectives. A) The portfolio cluster view gives an overview of all stock portfolios within a given time-period. B) The factor correlation view reveals the return correlations of risk factors, which can be used to validate the effectiveness of the factors in a multi-factor model. C) The comparison view is designed to compare the risk preference and sector position of portfolios, which reveals their strategies. D) The individual portfolio view illustrates the stock holdings and trading style of a specific portfolio.Abstract-Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates enormous quantities of factor data, through which even experienced portfolio managers find it difficult to navigate. This has led to portfolio analysis and factor research being limited by a lack of intuitive visual analytics tools. Previous portfolio visualization systems have mainly focused on the relationship between the portfolio return and stock holdings, which is insufficient for making actionable insights or understanding market trends. In this paper, we present sPortfolio, which, to the best of our knowledge, is the first visualization that attempts to explore the factor investment area. In particular, sPortfolio provides a holistic overview of the factor data and aims to facilitate the analysis at three different levels: a Risk-Factor level, for a general market situation analysis; a Multiple-Portfolio level, for understanding the portfolio strategies; and a Single-Portfolio level, for investigating detailed operations. The system's effectiveness and usability are demonstrated through three case studies. The system has passed its pilot study and is soon to be deployed in industry.

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A Survey on Visual Analysis Approaches for Financial Data

Cited by 59 publications

References 85 publications

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Bridging the Data Analysis Communication Gap Utilizing a Three‐Component Summarized Line Graph

sPortfolio: Stratified Visual Analysis of Stock Portfolios

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