Visual Explorations in Finance
Visual Explorations in Finance
CHI '99 Extended Abstracts on Human Factors in Computing Systems
Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data
VIS '95 Proceedings of the 6th conference on Visualization '95
ThemeRiver: Visualizing Theme Changes over Time
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Interactive graphical querying of time series and linear sequence data sets
Interactive graphical querying of time series and linear sequence data sets
Proceedings of the 35th conference on Winter simulation: driving innovation
Visualising changes in fund manager holdings in two and a half-dimensions
Information Visualization
A spectral visualization system for analyzing financial time series data
EUROVIS'06 Proceedings of the Eighth Joint Eurographics / IEEE VGTC conference on Visualization
Advanced visual analytics interfaces
Proceedings of the International Conference on Advanced Visual Interfaces
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Visual data analysis has received a lot of research interest in recent years, and a wide variety of new visualization techniques and applications have been developed to improve insight into the various application domains. In financial data analysis, however, analysts still primarily rely on a set of statistical performance parameters in combination with traditional line charts in order to evaluate assets and to make decisions, and information visualization is only very slowly entering this important domain. In this paper, we analyze some of the standard statistical measures for technical financial data analysis and demonstrate cases where they produce insufficient and misleading results that do not reflect the real performance of an asset. We propose a technique for visualizing financial time series data that eliminates these inadequacies, offering a complete view on the real performance of an asset. The technique is enhanced by relevance and weighting functions according to the users' preferences in order to emphasize specific regions of interest. Based on these principles we redefine some of the standard performance measures. We apply our technique on real world financial data sets and combine it with higher-level financial analysis techniques such as performance/risk analysis, dominance evaluation, and efficiency curves in order to show how traditional techniques from economics can be improved by modern visual data analysis techniques.