KITT vs LOAR

Nauticus Robotics, Inc. vs Loar Holdings Inc. — Valuation Comparison 2026

KITT

Aerospace & Defense
Nauticus Robotics, Inc.
Quality
3.3
out of 10
Value Trap
37
LOW
Price
$1.72
Last close
Models
3/13
Active
VS

LOAR

Aerospace & Defense
Loar Holdings Inc.
Quality
7.7
out of 10
Value Trap
5
SAFE
Price
$65.19
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType KITT Fair ValueKITT Upside LOAR Fair ValueLOAR Upside
Bayesian DCF Intrinsic $8.11 -87.6%
Earnings Power Value Intrinsic $0.72 -98.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $0.11 -93.6% $1.44 -97.7%
PWERM Option-Based $5.84 +239.7% $64.61 -0.9%
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 KITT vs LOAR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

KITT vs LOAR — Which Stock Is More Undervalued?

LOAR scores higher with a 7.7/10 quality rating vs KITT's 3.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Nauticus Robotics, Inc. (KITT) and Loar Holdings Inc. (LOAR) 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.

KITT currently trades at $1.72 with a QOC of 3.3/10, while LOAR trades at $65.19 with a QOC of 7.7/10.

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).