PLMK vs QSEA

Plum Acquisition Corp. IV vs Quartzsea Acquisition Corporati — Valuation Comparison 2026

PLMK

Blank Checks
Plum Acquisition Corp. IV
Quality
4.7
out of 10
Value Trap
Price
$10.64
Last close
Models
11/13
Active
VS

QSEA

Blank Checks
Quartzsea Acquisition Corporati
Quality
4.3
out of 10
Value Trap
Price
$10.49
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType PLMK Fair ValuePLMK Upside QSEA Fair ValueQSEA Upside
Bayesian DCF Intrinsic $0.96 -91.0% $1.56 -85.0%
Earnings Power Value Intrinsic $1.26 -88.1% $2.11 -79.7%
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 $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

PLMK vs QSEA — Which Stock Is More Undervalued?

PLMK scores higher with a 4.7/10 quality rating vs QSEA's 4.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Plum Acquisition Corp. IV (PLMK) and Quartzsea Acquisition Corporati (QSEA) 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.

PLMK currently trades at $10.64 with a QOC of 4.7/10, while QSEA trades at $10.49 with a QOC of 4.3/10.

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).