November | 2014 | RecessionAlert

Archive | November, 2014

Valuations not at nose-bleed levels yet

By RecessionALERT on November 25, 2014 in Reflections

The recent run in the major U.S stock indices has resulted in Shiller-PE charts being trotted out showing how far we are off the historical mean, implying a nasty pullback is in the works. The problem with a historical mean is that it is a single horizontal value on a chart that fails to take cognisance of any long-term trending the underlying valuation series may be experiencing. The static mean implies there is no valid reason stock market participants would be […]

Read full story · Comments are closed

Fingerprints of a short-term market top

By RecessionALERT on November 18, 2014 in Research Papers

The SP-500 has rallied sharply since the 15th October bottom, recovering all her losses in a shorter time than it took to incur them. It was a very rare display of the opposite behavior to the norm where “the bull climbs the staircase and the bear comes down the elevator”, since in this case, the bear came down the elevator but the bull climbed up on a rocket. The question that now naturally arises is if this rally has gone […]

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.