RTAC vs SBXD

Renatus Tactical Acquisition Co vs SilverBox Corp IV — Valuation Comparison 2026

RTAC

Blank Checks
Renatus Tactical Acquisition Co
Quality
5.2
out of 10
Value Trap
Price
$10.43
Last close
Models
12/13
Active
VS

SBXD

Blank Checks
SilverBox Corp IV
Quality
4.7
out of 10
Value Trap
Price
$10.80
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType RTAC Fair ValueRTAC Upside SBXD Fair ValueSBXD Upside
Bayesian DCF Intrinsic $0.28 -97.4% $0.91 -91.6%
Earnings Power Value Intrinsic $0.50 -95.2% $1.07 -90.1%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

RTAC vs SBXD — Which Stock Is More Undervalued?

RTAC scores higher with a 5.2/10 quality rating vs SBXD's 4.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Renatus Tactical Acquisition Co (RTAC) and SilverBox Corp IV (SBXD) 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.

RTAC currently trades at $10.43 with a QOC of 5.2/10, while SBXD trades at $10.80 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).