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Archive | March, 2019

Global Growth Roundup – 4Q2018

By RecessionALERT on March 27, 2019 in Reflections

NOTE : The following charts are extracts from our monthly Global Economy Report available with a standard subscription. Global Economic Activity slowing at the fastest pace since 2011 The cumulative GDP growth for the 4 quarters of 2018 for all 41 major countries covered by the OECD display a stark contrast between the best and worst performers: The G20 and U.S are comfortably above the OECD average, whilst the EU is uncomfortably below average, no doubt due to low growth […]

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U.S Stock Market Valuations continue to warn

By RecessionALERT on March 11, 2019 in Reflections

We have updated the RecessionALERT Valuation Index (RAVI) forecast models for the SP500 using 4Q2018 data. Stock market valuations continue to pose a “clear and present danger” to positive economic and SP500 returns outcomes, and have worsened since our last warning . One and two year SP500 forecasts continue to offer relatively accurate short-run estimates despite their low overall long-term correlations and both are foretelling mediocre returns (click image for larger view): To this end, as is tradition, we offer […]

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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.