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The HiLo Breadth Indexes for SP-500

By RecessionALERT on April 9, 2014 in Research Papers

The HILO breadth index was developed by RecessionALERT for detecting short and medium-term SP-500 stock market peaks in advance. It deploys the following daily breadth data taken from the SP-500 index: New 13-week (quarterly) highs New 13-week (quarterly) lows New 52-week (annual) highs New 52-week (annual) lows The above data is then used to construct two daily components as follows: Net new 13-week highs% = (New 13-week highs LESS New 13-week lows)/issues traded*100 Net new 52-week highs% = (New 52-week […]

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Measuring Selling Pressure for Market Entry Timing

By RecessionALERT on September 10, 2013 in Research Papers

This project uses a composite approach to constructing a broad representation of selling pressure on the SP-500 Index, for the purposes of gauging intensity of corrections, identifying ideal buying points for “buy on the dip” opportunities and provision of warnings of oncoming corrections. The motivation behind using many measurements for selling pressure (as opposed to say how much % the index has corrected for example) is that no single measurement provides safety from false positives(bad signals) and a consensus model can […]

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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.