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SP-500 Probability Model (SPM) V1.0

By RecessionALERT on August 28, 2019 in Research Papers

Introduction NOTE : Subsequent to this research note, we also launched an SP-500 Probability Model that measured probability of market tops (peaks). The MARKET-TOP probability model uses the inverse logic of the MARKET-BOTTOM probability model discussed in this research note. In other words, we look at low volatility instead of high volatility from the VIX […]

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The SP-500 200-day average “Goodbye Kiss”

By RecessionALERT on October 22, 2015 in Research Papers

The SP-500 today has met back with its 200-day moving average after spending a “protracted period” of 46 trading days below it. Contrary to popular belief, history since 1972 shows this to be a bearish event, with more downside likely. Many participants on the stock markets like to use the 200-day moving average (200dma) to […]

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Great Trough Detector Improvements

By RecessionALERT on October 9, 2014 in Research Papers

You will notice a new layout for the Great Trough Detector (GTR) chart as shown below. The Signal Panel has been revised to be more intuitive and we have added a new count panel below that: 1. The Signal Panel The first task of this panel is to highlight your two main opportunities: Class-B : […]

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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.