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DAIO

Data I/OC
Nasdaq / Technology Hardware & Equipment
Last Price
At close
2026-06-02
View Chart
Current thesis
The bullish read is that Q1 may mark a transition trough: bookings improved sequentially, backlog ticked up, Q2 guidance implies a meaningful rebound, and the announced acquisition plus direct investment could accelerate a shift toward recurring services and larger scale. Management also framed the acquisition as accretive to profit and cash flow if closed.
Posture
Mixed
Lead driver
Momentum
What changed
Momentum remains the lead driver in the composite.
What can break
Acquisition may fail to close on expected terms or by end-Q3 2026 because definitive documentation, financing conditions, regulatory approvals, and due diligence remain open [#8-K-2026-05-15].
Momentum
70
Value
34
Sentiment
41
Setup hits (3d)
0 · Net Neutral
AI TargetsBase $3.50 · Bull $5.00 · Bear $2.10
Data freshness
Prices
As of 2026-06-02
Fundamentals
As of 2026-06-01 • Vendor: Data Vendor v1
Scores
As of 2026-06-02 • Model: HYBRID_IC_RP
AI Memo
As of 2026-05-17 • Model: RankAlpha Sentiment Codex
Investment thesis
As of 2026-06-02
Supporting evidence
What
Grade C · Mixed
Confidence Medium · Net Neutral
Target $5.11
Why
Momentum70 · Δ7d -
Value34 · Δ7d +0.1
Sentiment41 · Δ7d -
So what
Balanced signals (Net Neutral). Wait for confirmation before sizing up.
Lead driver: Momentum · See technicals
Momentum
70
34% active weight
Current posture
7d trendTrend unavailable
Trend unavailable
Δ7d
-
Δ21d
-
Value
34
32% active weight
Current posture
7d trendFlat
Δ7d
+0.1
Δ21d
-0.1
Sentiment
41
33% active weight
Current posture
7d trendTrend unavailable
Trend unavailable
Δ7d
-
Δ21d
-
Why this grade

Composite grade C. Momentum 70.1 / Value 34.1 / Sentiment 40.7

Fundamentals (TTM)
As of 2026-06-01
Market Cap
$40.02M
Beta
1.08
Shares Out
9.39M
P/E (TTM) · Derived
-8.0
P/S (TTM)
1.33
P/FCF (TTM) · Derived
-14.85
Rev YoY
-0.6%
EPS YoY
-336.4%
Gross Margin
+51.0%
Op Margin
-17.2%
Net Debt
-$10.33M
Current Ratio
3.84
As of 2026-06-02 • Updated nightlySource: Internal modelMethodology