OBA vs ORIQ

Oxley Bridge Acquisition Limite vs Origin Investment Corp I — Valuation Comparison 2026

OBA

Blank Checks
Oxley Bridge Acquisition Limite
Quality
4.8
out of 10
Value Trap
Price
$10.21
Last close
Models
11/13
Active
VS

ORIQ

Blank Checks
Origin Investment Corp I
Quality
5.7
out of 10
Value Trap
Price
$10.30
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType OBA Fair ValueOBA Upside ORIQ Fair ValueORIQ Upside
Bayesian DCF Intrinsic $0.76 -92.6%
Earnings Power Value Intrinsic $0.55 -94.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $4.42 -56.7% $0.63 -93.9%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $1.07 -89.5% $0.58 -94.4%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

OBA vs ORIQ — Which Stock Is More Undervalued?

ORIQ scores higher with a 5.7/10 quality rating vs OBA's 4.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Oxley Bridge Acquisition Limite (OBA) and Origin Investment Corp I (ORIQ) 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.

OBA currently trades at $10.21 with a QOC of 4.8/10, while ORIQ trades at $10.30 with a QOC of 5.7/10.

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).