Market Update | RecessionAlert

Tag Archives | Market Update

Market Update 17/10/13 [PUBLIC]

By RecessionALERT on October 17, 2013 in Reflections

The political standoff associated with the debt ceiling and government shutdown has made its impact felt the last 3 weeks on the weekly leading data. Two out of the 5 main components of the WLInr are now firmly planted in recession warning territory with some 30% of all underlying 50 weekly time series also camped there: With the government shutdown now over we hope to restore full reporting service next week as the data starts coming through. We had our […]

Read full story · Comments are closed

Market Update 11/07/2013

By RecessionALERT on July 11, 2013 in Reflections

The SuperIndex has jumped smartly this week from mostly positive data from eLEI and the ISM report. However growth in ETI, GDP and Initial claims has slowed. A few months back on this radio interview we forecast there would be no “Summer Swoon” for the monthly leading data as with the three prior summers. With this new high SuperIndex print, we can put that one to bed. Although the visual upward trend on the monthly leading indicators is obvious, we […]

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.