February | 2013 | RecessionAlert

Archive | February, 2013

World plunges into recession in Q42012

By RecessionALERT on February 15, 2013 in Reflections

With the disappointing initial GDP releases for Q42012 from Europe out, the “world” as defined by 41 OECD countries across the globe, has plunged into recession. We define “recession” through two alternative definitions for our comparison, either the presence of a single negative quarter-on-quarter growth or the more traditional two consecutive negative quarterly growths. Whichever way you look at it, the […]

Read full story · Comments are closed

Personal Incomes to decline sharply for January

By RecessionALERT on February 2, 2013 in Reflections

Personal Incomes, less Transfer Payments and deflated by Personal Consumption Expenditures, is used as one of the four co-incident components of the NBER Recession Model. There was a huge jump in this indicator for the months of November and December 2012 quite possibly as a result of incomes being pulled forward as a result of the […]

Read full story · Comments are closed

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.