NMPAR vs SPEG

NMP Acquisition Corp. vs Silver Pegasus Acquisition Corp — Valuation Comparison 2026

NMPAR

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

SPEG

Blank Checks
Silver Pegasus Acquisition Corp
Quality
4.7
out of 10
Value Trap
Price
$10.24
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType NMPAR Fair ValueNMPAR Upside SPEG Fair ValueSPEG Upside
Bayesian DCF Intrinsic $0.16 -21.7% $3.03 -70.3%
Earnings Power Value Intrinsic $0.19 -6.0%
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 $0.21 +7.2% $6.55 -36.0%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

NMPAR vs SPEG — Which Stock Is More Undervalued?

NMPAR scores higher with a 5.9/10 quality rating vs SPEG's 4.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing NMP Acquisition Corp. (NMPAR) and Silver Pegasus Acquisition Corp (SPEG) 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.

NMPAR currently trades at $0.20 with a QOC of 5.9/10, while SPEG trades at $10.24 with a QOC of 4.7/10.

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).