Visualizing Shifting Correlations in Financial Markets

Figure 1: S&P 500 correlation heat map
Figure 2: A common method for understanding correlation between two instruments; here, XLE (energy ETF) and WTIC (Crude Oil)
Figure 3: This basic force-directed graph relates to the interaction between companies in terms of mobile patents suits. The arrows point toward companies that have patent suits placed against them and illustrate the . This graph illustrates the assigned forces from central data points, such as Apple and Microsoft.
Figure 4: This graph illustrates an example of multidimensional scaling, as higher dimensional data is transformed and plotted on a two-dimensional graph. This allows for a better visualization of the level of similarity among individual data points and the swarm of data. The above graphs also exhibit triple encoding, as data correlations are portrayed through color, lines, and highlighted areas, giving the viewer multiple visual differentiation methods that add to their understanding.
Figure 5: This image shows a selection of peripheral assets from the body of assets included in the swarm, thus creating a portfolio that has a large degree of correlation dispersion as seen on the right hand side. This means that this portfolio will be less responsive to general market behavior.
Figure 6: This portfolio was created by selecting assets that significantly outperformed the greater market. With Manifold, users have the ability to not only create maximum-return portfolios but to explore the evolving correlations between the assets that compose them.

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