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RGR

Sturm RugerD
NYSE / Consumer Durables & Apparel
Last Price
At close
2026-06-02
View Chart

AI scenario view

RankAlpha Sentiment AI
B+
Bull case
20%
Probability
Target price
$50.40
+29.8% vs current
Most likely
B
Base case
60%
Probability
Target price
$41.21
+6.1% vs current
B-
Bear case
20%
Probability
Target price
$31.00
-20.2% vs current

AI sentiment snapshot

Latest data as of 2026-03-03
Recent news sentiment (30D)
-
Unavailable
Company
-
Unavailable
Macro
-
Unavailable
Pulse
-
Unavailable
Sentiment proxy
+54.5
Score

AI commentary

Market tone is mixed-to-cautious with the analyst median modestly above the current anchor; sources show a spread with highs near $50.40 and lows near $31.00 [#SERP-1], [#SERP-2]. Near-term focus is on upcoming earnings and seasonal demand drivers that could prompt revisions; the scenario methodology (base=median, bull=high, bear=low) is used as a sanity check against the anchor and current trading [#SERP-4].

RankAlpha Sentiment AI - 2026-03-03
Open full AI memo

Evidence flagged

No evidence quality warning is currently attached to this memo.

Impact
standard
Confidence
-

AI events

2026-06-01catalystNext quarterly earnings release (estimated)Medium impact

Upcoming quarterly report likely to drive near-term volatility; analyst consensus and targets may update around the release [#SERP-4], [#SERP-1].

2026-11-30eventSeasonal retail demand / hunting season impact (assumed window end)High impact

Seasonal demand patterns (hunting/retail cycles) can materially affect sales and inventory; using the window end for timing assumption [assumption: window end used] [#SERP-3].

2027-03-03catalyst12-month analyst target revisions / consensus re-ratingHigh impact

Analyst target updates over the next 12 months could re-rate the stock; current collected targets show dispersion (low–high) used for scenario sanity checks [#SERP-2], [#SERP-6].

View full catalyst timeline

Recommendation

N/A

No formal recommendation provided.

Open AI Memo
As of 2026-03-03 • Updated nightlySource: Internal modelMethodology