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Archive | June, 2020

US enters 2nd wave of Covid19 infections

By RecessionALERT on June 26, 2020 in Reflections

Note : Most charts shown below are available to subscribers in the COVID19 analytics section. Summary Economic mobility in US increasing at a slower pace than Covid-19 infections, contrary to rest of G7. At a state level, New York, New Jersey, Massachusetts & Michigan are leading economic mobility recovery vs infections. US has moved from a peaked scenario to join a host of second-wave countries struggling to contain infections after lockdowns. Stock market is going to struggle to post new […]

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NBER declares 2020 recession dates

By RecessionALERT on June 12, 2020 in Reflections

The National Bureau for Economic Research (NBER) has announced official start dates for the 2020 US recession. It is very rare for such quick pronouncements (they are normally made 9-12 months after the fact) but the fact that 90% of the economy came to a sudden halt, has led to such deep declines in their metrics that they could make an early pronunciation without risk of being proven wrong later. The monthly economic peak was declared as February 2020 (first […]

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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.