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RecessionALERT Valuation Index (RAVI)

By RecessionALERT on December 2, 2014 in Research Papers

PART-1 There are currently 4 mainstream models used to forecast 10-year total returns on the SP-500 (dividends re-invested) The Shiller CAPE ratio (PE10) The Warren Buffet Indicator Tobin’s Q-Ratio Average Investor allocation to stocks The non-linear quarterly correlations between these four models (x-axes) and achieved 10-year future total returns (y-axes) on the SP-500 since 1970 are shown below: Whilst there have been successful variations that improve long-run 10-year return correlations slightly (such as John Hussmanns’ Peak Earnings version of the Shiller […]

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Valuations not at nose-bleed levels yet

By RecessionALERT on November 25, 2014 in Reflections

The recent run in the major U.S stock indices has resulted in Shiller-PE charts being trotted out showing how far we are off the historical mean, implying a nasty pullback is in the works. The problem with a historical mean is that it is a single horizontal value on a chart that fails to take cognisance of any long-term trending the underlying valuation series may be experiencing. The static mean implies there is no valid reason stock market participants would be […]

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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.