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PFIS

Peoples Financial ServicesB
Nasdaq / Banks
Last Price
At close
2026-06-02
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AI scenario view

RankAlpha Sentiment Codex
B+
Bull case
30%
Probability
Target price
$64.50
+8.8% vs current
Most likely
B
Base case
45%
Probability
Target price
$58.50
-1.3% vs current
B-
Bear case
25%
Probability
Target price
$48.50
-18.2% vs current

AI sentiment snapshot

Latest data as of 2026-06-02
Recent news sentiment (30D)
+16.9
Positive
Company
+23.6
Positive
Macro
+17.9
Positive
Pulse
-34.8
Negative
Sentiment proxy
+46.3
Score

AI commentary

Low coverage and no fresh analyst revision data keep this in monitoring mode. The primary-source earnings release was constructive, but the visible target median (57.5) is only slightly below the 58.04 anchor, so upside depends on sustaining loan growth and keeping provisions and expenses under control.

RankAlpha Sentiment Codex - 2026-06-02
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Evidence flagged

No evidence quality warning is currently attached to this memo.

Impact
standard
Confidence
-

AI events

2026-05-01eventQ1 earnings showed stable profits with stronger loan growthMedium impact

On the 2026-04-30 earnings release, PFIS reported Q1 net income of $14.7M ($1.47/share) and revenue net of interest expense of $49.8M, which topped Street forecasts; management said higher provision for credit losses and higher non-interest expense were partly offset by stronger net interest income and non-interest income. [#SEC-8K-2026-04-30] [#PR-EARNINGS-2026-05-01]

2026-09-30catalystBalance-sheet repositioning supports loan deployment and book valueMedium impact

The company sold $31.9M of agency MBS in Q1, redeployed about half of the proceeds into the AFS portfolio and used the rest to fund loan demand; book value per share rose to $52.50 and tangible book value per share to $42.29. That supports capital flexibility and loan funding, though the gain from repositioning is not automatically repeatable. [#PR-EARNINGS-2026-05-01]

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Recommendation

N/A

No formal recommendation provided.

Open AI Memo
As of 2026-06-02 • Updated nightlySource: Internal modelMethodology