FIGX vs FSHP

FIGX Capital Acquisition Corp. vs Flag Ship Acquisition Corp. — Valuation Comparison 2026

FIGX

Blank Checks
FIGX Capital Acquisition Corp.
Quality
4.8
out of 10
Value Trap
Price
$10.22
Last close
Models
11/13
Active
VS

FSHP

Blank Checks
Flag Ship Acquisition Corp.
Quality
5.5
out of 10
Value Trap
6
SAFE
Price
$10.97
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType FIGX Fair ValueFIGX Upside FSHP Fair ValueFSHP Upside
Bayesian DCF Intrinsic $0.69 -93.2% $1.84 -83.2%
Earnings Power Value Intrinsic $0.91 -91.0% $7.09 -35.4%
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 FIGX vs FSHP — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FIGX vs FSHP — Which Stock Is More Undervalued?

FSHP scores higher with a 5.5/10 quality rating vs FIGX's 4.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing FIGX Capital Acquisition Corp. (FIGX) and Flag Ship Acquisition Corp. (FSHP) 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.

FIGX currently trades at $10.22 with a QOC of 4.8/10, while FSHP trades at $10.97 with a QOC of 5.5/10.

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).