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HPE

Hewlett Packard EnterpriseA
NYSE / Technology Hardware & Equipment
Last Price
At close
2026-07-18
View Chart
Current thesis
The core bull case is that HPE's June 1 release showed unusually strong operating leverage: record revenue, record gross margin, record non-GAAP diluted EPS for the quarter, and sharply improved cash flow, with management explicitly saying the company is two years ahead of its prior fiscal 2028 financial plan [#SEC-8K-2026-06-01].
Posture
Constructive
Lead driver
Momentum
What changed
Momentum remains the lead driver in the composite, 7D delta -1.5.
What can break
Networking growth is a major driver, but the segment's operating margin was 21.6% versus 25.0% a year earlier, leaving risk that mix or integration costs offset some revenue strength [#SEC-8K-2026-06-01].
Momentum
94
Value
61
Sentiment
56
Setup hits (3d)
0 · Net Neutral
AI TargetsBase $57.00 · Bull $66.00 · Bear $41.00
Data freshness
Prices
As of 2026-07-18
Fundamentals
As of 2026-07-17 • Vendor: Data Vendor v1
Scores
As of 2026-07-18 • Model: HYBRID_IC_RP
AI Memo
As of 2026-07-13 • Model: RankAlpha Sentiment Codex
Investment thesis
As of 2026-07-18
Supporting evidence
What
Grade A · Constructive
Confidence Medium · Net Neutral
Target $68.59
Why
Momentum94 · Δ7d -1.5
Value61 · Δ7d +0.4
Sentiment56 · Δ7d +25.4
So what
Strength-led posture (Net Neutral). Favor watchlist adds and disciplined entries.
Lead driver: Momentum · See technicals
Momentum
94
26% active weight
Current posture
7d trendImproving
Δ7d
-1.5
Δ21d
-2.9
Value
61
39% active weight
Current posture
7d trendFlat
Δ7d
+0.4
Δ21d
-0.7
Sentiment
56
34% active weight
Current posture
7d trendImproving
Δ7d
+25.4
Δ21d
+6.7
Why this grade

Composite grade A. Momentum 93.7 / Value 60.7 / Sentiment 56.1

Fundamentals (TTM)
As of 2026-07-17
Market Cap
$60.68B
Beta
1.34
Shares Out
1.32B
P/E (TTM)
28.0
P/S (TTM)
0.94
P/FCF (TTM)
23.28
Rev YoY
+40.6%
EPS YoY
-150.9%
Gross Margin
+31.4%
Op Margin
-0.7%
Net Debt
$18.22B
Current Ratio
1.08
As of 2026-07-18 • Updated nightlySource: Internal modelMethodology