KRMN vs TDG

Karman Holdings Inc. vs Transdigm Group Incorporated — Valuation Comparison 2026

KRMN

Aircraft Parts & Auxiliary Equipment, NEC
Karman Holdings Inc.
Quality
7.7
out of 10
Value Trap
6
SAFE
Price
$57.50
Last close
Models
11/13
Active
VS

TDG

Aircraft Parts & Auxiliary Equipment, NEC
Transdigm Group Incorporated
Quality
9.5
out of 10
Value Trap
12
SAFE
Price
$1258.32
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType KRMN Fair ValueKRMN Upside TDG Fair ValueTDG Upside
Bayesian DCF Intrinsic $22.27 -98.2%
Earnings Power Value Intrinsic $1.53 -97.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $28.51 -50.4% $1299.45 +3.3%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $1.50 -97.4% $972.45 -22.7%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

KRMN vs TDG — Which Stock Is More Undervalued?

TDG scores higher with a 9.5/10 quality rating vs KRMN's 7.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Karman Holdings Inc. (KRMN) and Transdigm Group Incorporated (TDG) 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.

KRMN currently trades at $57.50 with a QOC of 7.7/10, while TDG trades at $1258.32 with a QOC of 9.5/10.

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).