FIGX vs GIW

FIGX Capital Acquisition Corp. vs GigCapital8 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

GIW

Blank Checks
GigCapital8 Corp.
Quality
5.5
out of 10
Value Trap
Price
$10.05
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType FIGX Fair ValueFIGX Upside GIW Fair ValueGIW Upside
Bayesian DCF Intrinsic $0.69 -93.2% $0.21 -97.9%
Earnings Power Value Intrinsic $0.91 -91.0% $0.29 -97.1%
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 FIGX vs GIW — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FIGX vs GIW — Which Stock Is More Undervalued?

GIW 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 GigCapital8 Corp. (GIW) 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 GIW trades at $10.05 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).