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DEDeereEarningsIndustrialsCohort

DE Pre-Earnings: A Vol-Sensitive Cohort With Modest Bullish Bias

Chart Library Team··5 min read

Deere Is A VIX-Sensitive Print

Deere's earnings reactions historically track agricultural commodity cycles, but the pre-print cohort surfaces a different dominant feature: VIX level. The cohort_analyze pull on 2026-05-06 retrieved 286 historical analogs with a tightness score of 0.41. The 5-day forward median is +0.20% with IQR [-3.3%, +2.9%], hit-rate 52%. The 10-day median rolls back to -0.17%, IQR [-4.3%, +4.5%].

Modest bullish 5-day bias that fades by day 10 — a print where the immediate reaction is constructive but the continuation isn't durable.

VIX-Positive Feature Interpretation

The top 5-day feature was vix with positive direction (importance 1.16). Higher VIX during the cohort window correlated with stronger DE forward returns. That's the volatility-premium dynamic in industrials — when broad-market vol is elevated, defensive cash-flow generators like Deere tend to benefit from rotation. Current VIX is moderate, sitting near the cohort's median — so the feature contributes a small positive but not a thesis-defining edge.

  • n=286, 5d median +0.20%, IQR [-3.3%, +2.9%], hit-rate 52%
  • 10d median -0.17%, IQR [-4.3%, +4.5%]
  • Cohort tightness 0.41
  • Top 5d feature: vix (positive direction, importance 1.16)

What The Print Resolves

Deere's post-call cohort match weights three signals: production tons forecast for the year (the leading capex indicator), Construction & Forestry segment margin trajectory, and any commentary on credit-portfolio delinquencies (the agricultural recession indicator). When production tons are guided up and credit losses are stable, the post-print cohort tightens to a +3-4% upper band; when both are flagged as deteriorating, the cohort tightens to a -4-5% lower band.

The pre-print bias is mildly positive but not actionable as a directional bet — appropriate sizing is small, with the real signal coming from the print itself.

Search DE on chartlibrary.io after the print for the live agricultural-cycle cohort and feature attribution.

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