HPE vs IBM

Hewlett Packard Enterprise Comp vs International Business Machines — Valuation Comparison 2026

HPE

Computer & office Equipment
Hewlett Packard Enterprise Comp
Quality
7.1
out of 10
Value Trap
8
SAFE
Price
$43.04
Last close
Models
13/13
Active
VS

IBM

Computer & office Equipment
International Business Machines
Quality
6.7
out of 10
Value Trap
17
SAFE
Price
$297.80
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType HPE Fair ValueHPE Upside IBM Fair ValueIBM Upside
Bayesian DCF Intrinsic $3.61 -90.4% $103.67 -65.2%
Earnings Power Value Intrinsic $6.74 -84.3% $10.91 -96.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for HPE vs IBM — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

HPE vs IBM — Which Stock Is More Undervalued?

HPE scores higher with a 7.1/10 quality rating vs IBM's 6.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Hewlett Packard Enterprise Comp (HPE) and International Business Machines (IBM) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

HPE currently trades at $43.04 with a QOC of 7.1/10, while IBM trades at $297.80 with a QOC of 6.7/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).