QUMS vs RAAQ

Quantumsphere Acquisition Corp. vs Real Asset Acquisition Corp. — Valuation Comparison 2026

QUMS

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

RAAQ

Blank Checks
Real Asset Acquisition Corp.
Quality
5.0
out of 10
Value Trap
Price
$11.35
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType QUMS Fair ValueQUMS Upside RAAQ Fair ValueRAAQ Upside
Bayesian DCF Intrinsic $2.70 -73.6% $0.30 -97.4%
Earnings Power Value Intrinsic $0.84 -92.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $9.55 -6.4% $11.18 -1.5%
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 QUMS vs RAAQ — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

QUMS vs RAAQ — Which Stock Is More Undervalued?

RAAQ scores higher with a 5.0/10 quality rating vs QUMS's 1.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Quantumsphere Acquisition Corp. (QUMS) and Real Asset Acquisition Corp. (RAAQ) 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.

QUMS currently trades at $10.20 with a QOC of 1.8/10, while RAAQ trades at $11.35 with a QOC of 5.0/10.

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).