In times of high volatility it is more important to know what not to do

Avoid high-complexity stocks

 

High Complexity - A New Form of Risk

stocks form complex networks, where hubs concentratE risk

stocks form complex networks, where hubs concentratE risk

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

In times of turbulence these networks are intricate and may contain numerous hubs - concentrations of risk. In order to reduce the exposure of a portfolio, it is paramount to position oneself in a given stock universe away from high-complexity stocks.

Below the arrows indicate two potentially hazardous stocks - hubs - that dominate the structure of the stock universe in their vicinity.

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The region around the hazardous stocks is not the best place from which to pick stocks. The green region, on the other hand, is far less correlated, hence safer.

See example of stock interaction network. The example is the FTSE 100. Large squares represent the hubs, i.e. high-complexity stocks.

The bottom line:

We don’t tell you which stocks to pick or how to build your portfolios - we tell you which stocks to avoid.




 

AI 2.0 For a New Correlation Science

we have developed a new form of cognitive ai to better measure correlations between stocks

we have developed a new form of cognitive ai to better measure correlations between stocks

Correlations play a central role not just in portfolio design or in measuring risk. It is paramount to get them right. However, conventional linear correlations may deliver misleading results. This is because they cannot capture nonlinearities in data. Data very rarely has a linear look and feel and a gaussian distribution.

UR has developed a radically innovative and modern generalized correlation, which takes into account non-linear aspects of data. The method relies on a brand new next-generation AI technology which transforms data into images, emulating an expert actually looking at it.

Data is analyzed by emulating the brain without the need to build math models.

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The method has its roots in quantum physics, nonlinear mechanics and biology.

Based on this technology we are able to determine the true structure of stock correlations and the Complexity Landscape of any universe of stocks.

example of stock complexity Landscape.

The generalized correlation is the correlation of the 21st century. Over the last decade it has been tested and is being utilized in medicine, manufacturing, defence, air traffic management and Business Intelligence.

How It Works

our system is cloud-based and is powered by amazon web services.

our system is cloud-based and is powered by amazon web services.

Complexity rating of stocks in a universe of up to 1000 stocks is possible. The tool is hosted on AMAZON Web Services:

www.assetdynex.com

Users upload anonymized price data in CSV (Comma Separated Values) files. The system returns a text file with stocks ranked in terms of their complexity within the universe.

Download example.

Stocks at the top of the complexity ranking are to be avoided. This is because highly complex stocks induce volatility and exposure, reduce diversification and may affect negatively the performance of a portfolio.

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