September | 2014 | RecessionAlert

Archive | September, 2014

New improvements

By RecessionALERT on September 25, 2014 in Reflections

We have introduced three new improvements to the web site to enhance the user-experience; 1.Progress Loader Some of our pages, particularly the CHARTS page, can take some time to load due to the amount of historical data we allow you to view in the charts. If web site usage is heavy and/or the internet is very busy, this can sometimes lead to up to 10 second delays (US) or even 20 second delays (Asia) before pages are displayed on your screen. […]

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DeMark analysis for SP-500

By RecessionALERT on September 1, 2014 in Research Papers

IMPORTANT NOTICE : Since 2020, we no longer display charts shown below as the Demark Setup methodology we discuss below has been replaced with a far superior Trendex methodology. You can read about our proprietary Trendex Multi-Stop & Probability methodology over here : Trendex Market Timing/Risk Management PREAMBLE A detailed 18-year analysis reveals that the DeMark Setup indicators are suitable for the detection of market tops and bottoms on the SP-500 Index. We have made some modifications to the original algorithms to […]

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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.