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Archive | June, 2012

The SP-500 Great Trough Detector Project

By RecessionALERT on June 19, 2012 in Research Papers

The SP-500 Great Trough Project is a technique where we deploy market breadth to determine favorable points in time for investors to “buy on the dips” on the U.S stock markets, more specifically the SP-500. We make reference to a “Great Trough” as rare, large correction reversals on the SP-500, normally spawning a new bull-market leg, or at least a multi-month gain. There have been 40 Great trough signals since 1987 or roughly 1.6 signals per year or one per 7.5 months. NOTE : THIS […]

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On standby for a Great Trough

By RecessionALERT on June 2, 2012 in Reflections

13 June 2012 UPDATE: This Great Trough is not done yet, we may well re-test prior lows in the bottom-making process. The breadth- index head-faked and fell below 26% yesterday meaning the countdown timer will be reset. We now await for the index to rise above 26 again and attempt a second strike through 87 within 15 trading days. We then issue the BUY signal. For a detailed background, mechanics and statistics of this system read The Great Trough Detector Project on our RESEARCH […]

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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.