Complexity Economics

Complexity Rating of Assets

Stock interaction takes the form of large, complex and dynamic networks, that change constantly. The nature and structure of this complexity must be analyzed in order to better understand risk and volatility. This is because complexity is a new and hidden form of risk.

In contexts of high complexity these networks are intricate and may contain numerous hubs, which concentrate risk. But to reduce the exposure of a portfolio, it is important to position onself in a given stock universe away from these hubs. We identify these hubs.

Universal Ratings has developed a novel, complexity-based rating of assets, corporations, market indices, or market sectors and countries.

UR’s stock complexity analysis and rating system measures the complexity of stocks belonging to a given universe (set of stocks) from which one wishes to extract a portfolio. The system analyzes the universe of stocks, determines all the significant correlations between them, and computes the complexity of the universe itself. Finally, this complexity is broken down into components, providing a Complexity Ranking (rating) of the stocks in the context of a particular universe.

What characterizes a hub is a large number of correlations with other stocks and the nature of these correlations. But conventional linear correlation – which is most popular in the financial industry – often provides misleading results. It can strongly overestimate a correlation or miss it altogether. This is because linear correlations cannot capture non-linear aspects of data. Since correlations play a central role in portfolio design or in measuring risk, it is paramount to get them right.

Click here for an interactive Complexity Map of a portfolio

Glide the mouse pointer over the stocks (boxes on diagonal) and off-diagonal links (grey dots) to navigate the map. Click on a stock to investigate stock interdependency.

We have developed a more relevant and modern generalized correlation, which takes into account non-linear aspects of data. The method is based on brand new ‘cognitive AI’ technology which treats scatter plots as images, emulating an expert actually looking at data without the need to build any math models.

In essence, UR’s system ranks stocks based on complexity – this is called Complexity Profiling. The importance of this cannot be overstated. Highly complex stocks in general:

  • are correlated with many other stocks- they are the hubs of the stock universe
  • can be source of high volatility
  • may increase portfolio risk
  • may reduce portfolio performance
  • can behave in unpredictable fashion – this is the essence of high complexity

These are the reasons why

inexperienced investors should stay away from complex financial products

The goal is, therefore, not to indicate which stocks to pick, or how to build portfolios – it is simply to indicate complex and potentially hazardous stocks. Such stocks may be found at the top of the Complexity Ranking.

The tool may be found at www.assetdynex.com

Other examples of interactive portfolio Complexity Maps:

GFCI Global Financial Complexity meta Index

NASDAQ 100

DJIA

STI (Singapore)

HANG SENG

DJI

ZURICH SE

FTSE MIB

DAX30

S&P100

FTSE100

CAC40

Visit http://www.universal-ratings.com/ for more information

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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