In this paper, we describe solutions how pixel-based visualization techniques can support the decision making process for investors on the financial market. We especially focus on explorative interactive techniques where analysts try to analyze large amounts of financial data for long-term investments, and show how visualization can effectively support an investor to gain insight into large amounts of financial time series data. After presenting methods for improving the traditional performance/risk computation in order to take user-specific regions of interest into account, we present a novel visualization approach that demonstrates how changes in these regions of interest affect the ranking of assets in a long-term investment strategy. Keywords—Visual Data Mining, Financial Data Analysis
Hartmut Ziegler, Tilo Nietzschmann, Daniel A. Keim