Complexity Economics

Rating the Resilience of Major Markets, 31-st December, 2015

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Resilience Ratings of major markets are reported in the table below. Analysis performed on 31/12/2015. The results are presented without comments.

 

MAjor_Markets_31_21_2015

 

Resilience is the capacity to absorb shocks or destabilizing events, such as financial contagion, stock market collapses, market bubbles, natural disasters or geopolitical events. Opposite of fragility, resilience provides an indication of how stable a portfolio or market is and how it will react in the presence of the said events. It is therefore important to measure the resilience of portfolios and markets as the global economy and markets are dominated by increasingly frequent and intense instabilities. In a turbulent and complex economy driven by uncertainty and discontinuities, resilient portfolios provide the basis for more sustainable investments.

Resilience ranges from 0% to 100%. Higher values correspond to a better resilience rating (four or five stars) while low values point to fragility (one or two stars).

Low resilience does not necessarily imply low performance.

Analysis is performed over a 50 trading-day horizon and the results do not represent a static property of the markets.

 

The above information is available on a daily basis and free of charge here.

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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