SPOK vs TMUS

Spok Holdings, Inc. vs T-Mobile US, Inc. — Valuation Comparison 2026

SPOK

Radiotelephone Communications
Spok Holdings, Inc.
Quality
8.9
out of 10
Value Trap
19
SAFE
Price
$10.59
Last close
Models
12/13
Active
VS

TMUS

Radiotelephone Communications
T-Mobile US, Inc.
Quality
9.9
out of 10
Value Trap
5
SAFE
Price
$187.53
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType SPOK Fair ValueSPOK Upside TMUS Fair ValueTMUS Upside
Bayesian DCF Intrinsic $8.24 -22.2% $130.71 -30.3%
Earnings Power Value Intrinsic $3.78 -64.3% $41.82 -77.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 SPOK vs TMUS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SPOK vs TMUS — Which Stock Is More Undervalued?

TMUS scores higher with a 9.9/10 quality rating vs SPOK's 8.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Spok Holdings, Inc. (SPOK) and T-Mobile US, Inc. (TMUS) 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.

SPOK currently trades at $10.59 with a QOC of 8.9/10, while TMUS trades at $187.53 with a QOC of 9.9/10.

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).