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On the brink of Global Recession

By RecessionALERT on October 31, 2012 in Reflections

The Global Economy is on the brink of a recession with 58% of 29 OECD countries experiencing business cycle contractions. The chart below shows OECD defined global contractions (grey shaded areas) together with the percentage of 29 OECD member countries experiencing slowdowns. It is evident that whenever 50% or more of countries enter contraction (red dotted line) that the odds of global recession are very high. The blue lines show NBER recessions for the U.S and we see that whilst […]

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Can the U.S skirt global recession?

By RecessionALERT on May 1, 2012 in Reflections

With many parts of the Euro-zone entering or already in recession, and the OECD recently putting Australia, Germany and Italy into recession, one has to wonder if the feeble U.S recovery can skirt a global recession. Many mainstream pundits have been pointing to countries around the globe slipping into recession as a reason why a U.S recession in the near future is a done deal. But this is not necessarily the case. The chart below shows which percentage of the 39 OECD countries across the world have their […]

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IMPORTANT DISCLAIMERS: Although many of our models rely heavily on back-testing, optimisation and probability methods, please note that past performance is NO GUARANTEE for future returns. No system devised by man can predict the future, let alone the future of the markets and economies. What we have are sets of mathematical models that use historical data and varying theories to pinpoint places in time when the STATISTICAL LIKELYHOOD of calling the start and end to recessions is the greatest. In the midst of all the best mathematical models, you can have geo-political events, wars, terrorist attacks, natural disasters and even nuclear accidents that tip everything upside down. This is always a risk factor you need to factor in on any recession call no matter how confident you are in a signal. The SuperIndexes are created through mathematical and statistical optimisation techniques that best fit the historical NBER data. There is no guarantee that out-of-sample performance will match that of prior in-sample performance.