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MIB: All-Time Low — Consumer Sentiment Collapses as CPI Stalls the Fed and Semis Soar on Tariff Relief

By RecessionALERT on April 10, 2026 in Reflections

March CPI hit 3.3% (gasoline +21.2%) but core held at 2.6% — Fed stuck. Michigan consumer sentiment crashed to an all-time low of 47.6. Semis surged on Trump tariff exemptions (AVGO +4.69%, AMD +3.55%). TSMC confirmed AI demand is booming: Q1 revenue +35% to $35.7B. Meta locked in $21B with CoreWeave through 2032 (META +3.4%). FICO plunged 14% as the Senate opened a probe into mortgage credit score pricing.

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MIB: February CPI Calm Before Storm, IEA Record Oil Release, Oracle +12% as AI Cloud Demand Holds Firm

By RecessionALERT on March 11, 2026 in Reflections

Oracle surges 12.2% as OCI results blow past estimates (RPO $553B), proving AI cloud demand is war-proof. IEA triggers largest-ever oil reserve release (400M barrels) but WTI holds above $86 — Hormuz still closed. February CPI tame at +2.4% — calm before the storm as energy shock hits March data. Centene (CNC) -14% as CEO warns ACA enrollment collapses 36%. Airlines down 30% YTD; energy sector +25% YTD. All eyes on Adobe Thursday and UMich Friday.

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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.