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)

A Novel Approach: The Network Cluster

Network clusters were originally developed in the mid-20th century, where they were used to analyze information in fields ranging from data science to archaeology to literature. Their nature allows for an enhanced degree of visualization not seen in other data analysis tools and can present an informative medium to review rendering of information. However, the majority of employment of network clusters has been under static conditions, even though valuable information can be gleaned from the dynamic motion of clusters.

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.

Manifold: A Network Visualization Paradigm

We present an interactive tool that allows individuals unprecedented insight into correlation visualization, resulting in improved asset selection and risk management. Our asset correlation visualization tool pulls live data from financial markets in order to dynamically represent the evolving correlations between equities, indices, and the market as a whole.

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.

The Future of Manifold

The network cluster framework can be applied to additional financial data beyond price, such as liquidity and volume among other measures used as a base for an interactive tool that boasts even greater capabilities. Future updates will not only include financial data but also include data ranging from recent news and development data. The end goal for this product is to create a dynamic and all-encompassing multidimensional network analysis tool. We are also working on adding further infrastructure to analyze risk and parameterize portfolios powered by machine learning techniques. As Manifold continues to improve, we invite you to be a part of the development and share your insight. To learn more about Manifold and our work at Pareto Technologies, do not hesitate to reach out to us.



Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store