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AMSF

AMERISAFEC
Nasdaq / Insurance
Last Price
At close
2026-07-22
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AI scenario view

RankAlpha Sentiment CodexPost-earnings T+1
B+
Bull case
20%
Probability
Target price
$41.00
+31.6% vs current
Most likely
B
Base case
45%
Probability
Target price
$35.00
+12.4% vs current
B-
Bear case
35%
Probability
Target price
$27.00
-13.3% vs current

AI sentiment snapshot

Latest data as of 2026-07-22
Recent news sentiment (30D)
+28.5
Positive
Company
-
Unavailable
Macro
+28.5
Positive
Pulse
-
Unavailable
Sentiment proxy
+53.8
Score

AI commentary

News tone is mixed: strong premium growth and capital returns contrast with weaker operating profitability and underwriting metrics. The live quote snapshot showed approximately a 2.4% decline on July 22 after the release. Social coverage, options data, short interest, employee sentiment, and analyst revision detail are unavailable; confidence remains moderate and the setup is monitoring-oriented.

RankAlpha Sentiment Codex - 2026-07-22
Open post-earnings memo

Evidence flagged

No evidence quality warning is currently attached to this memo.

Impact
standard
Confidence
-

AI events

2026-10-20eventQuarterly dividend maintainedMedium impact

The board declared a $0.41 quarterly dividend payable September 25, 2026; the company also repurchased $5.6 million of stock during Q2. [#8-K-2026-07-21]

2026-10-20catalystPost-print underwriting pressureMedium impact

Q2 net premiums earned rose 11.4%, but operating EPS fell 17.0%, underwriting profit declined 37.9%, and the combined ratio worsened to 95.4% from 91.7%. Favorable reserve development also moderated year over year. [#SEC-8K-2026-07-21]

2027-07-22catalystSustained specialty-insurance growthHigh impact

Management reported a ninth consecutive quarter of top-line growth, supported by renewal retention, while emphasizing underwriting discipline and capital management. [#SEC-8K-2026-07-21]

View full catalyst timeline

Recommendation

N/A

No formal recommendation provided.

Open AI Memo
As of 2026-07-22 • Updated nightlySource: Internal modelMethodology