QSEA vs QUMS

Quartzsea Acquisition Corporati vs Quantumsphere Acquisition Corp. — Valuation Comparison 2026

QSEA

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

QUMS

Blank Checks
Quantumsphere Acquisition Corp.
Quality
1.8
out of 10
Value Trap
Price
$10.20
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType QSEA Fair ValueQSEA Upside QUMS Fair ValueQUMS Upside
Bayesian DCF Intrinsic $1.56 -85.0% $2.70 -73.6%
Earnings Power Value Intrinsic $2.11 -79.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $2.92 -72.2% $9.55 -6.4%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

QSEA vs QUMS — Which Stock Is More Undervalued?

QSEA scores higher with a 4.3/10 quality rating vs QUMS's 1.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Quartzsea Acquisition Corporati (QSEA) and Quantumsphere Acquisition Corp. (QUMS) 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.

QSEA currently trades at $10.49 with a QOC of 4.3/10, while QUMS trades at $10.20 with a QOC of 1.8/10.

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).