New Probability Models

By RecessionALERT on October 14, 2019 in Research Papers

Following on from extensive client feedback since the launch of the SP-500 trough probabilities and SP-500 Trendex trend-following model, we have decided to target the models at the six largest investable U.S Exchange Traded Fund (ETF) categories by assets under management (AUM) as depicted below, for a total of 50.1% coverage of the total U.S ETF market by AUM:

Except for the SP-500, the largest ETF’s by AUM are used in the models to proxy the respective sectors in question.

All models and charts are updated daily and shown in the PRO menu as depicted below:

Remember it costs just $75 to upgrade your existing annual subscription to PRO. Just log in, go to REPORTS and click the [Upgrade] link for instant activation.

About RecessionALERT

Dwaine has a Bachelor of Science (BSc Hons) university degree majoring in computer science, math & statistics and is a full-time trader and investor. His passion for numbers and keen research & analytic ability has helped grow RecessionALERT into a company used by hundreds of hedge funds, brokerage firms and financial advisers around the world.

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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.