MLAA vs NMPAR

Mountain Lake Acquisition Corp. vs NMP Acquisition Corp. — Valuation Comparison 2026

MLAA

Blank Checks
Mountain Lake Acquisition Corp.
Quality
4.0
out of 10
Value Trap
Price
$9.93
Last close
Models
7/13
Active
VS

NMPAR

Blank Checks
NMP Acquisition Corp.
Quality
5.9
out of 10
Value Trap
Price
$0.20
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType MLAA Fair ValueMLAA Upside NMPAR Fair ValueNMPAR Upside
Bayesian DCF Intrinsic $2.68 -73.0% $0.16 -21.7%
Earnings Power Value Intrinsic $0.19 -6.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $6.51 -34.4% $0.94 +371.1%
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 MLAA vs NMPAR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MLAA vs NMPAR — Which Stock Is More Undervalued?

NMPAR scores higher with a 5.9/10 quality rating vs MLAA's 4.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Mountain Lake Acquisition Corp. (MLAA) and NMP Acquisition Corp. (NMPAR) 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.

MLAA currently trades at $9.93 with a QOC of 4.0/10, while NMPAR trades at $0.20 with a QOC of 5.9/10.

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).