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Standard Annual Subscription

For private investors, fund managers, financial advisers, research houses and financial institutions. Internal, single individual use. Includes access to PDF reports of all weekly, monthly and quarterly econometric models, market-timing charts, market breadth, market dashboards and emailed commentary. Sample reports

US$ 399 per annum

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Standard Monthly Subscription

For private investors, fund managers, financial advisers, research houses and financial institutions. Internal, single individual use. Includes access to PDF reports of all weekly, monthly and quarterly econometric models, market-timing charts, market breadth, dashboards and emailed commentary. Cancel anytime with 1 month notice.

US$ 40 per month

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PRO Annual Subscription

Standard subscription PLUS three phone consultations to explain model use & interpretation or to expand on market alerts, commentary or research. PLUS weekly & monthly Excel history files of econometric models with associated diffusions & vintages. PLUS emailed alerts & exclusive research. PLUS ProCharts.

US$ 699 per annum

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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.