RILYG vs RILYZ

BRC Group Holdings, Inc. - 5.00 vs BRC Group Holdings, Inc. - 5.25 — Valuation Comparison 2026

RILYG

Investment Advice
BRC Group Holdings, Inc. - 5.00
Quality
4.8
out of 10
Value Trap
29
LOW
Price
$24.65
Last close
Models
11/13
Active
VS

RILYZ

Investment Advice
BRC Group Holdings, Inc. - 5.25
Quality
5.3
out of 10
Value Trap
42
WARN
Price
$19.92
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType RILYG Fair ValueRILYG Upside RILYZ Fair ValueRILYZ Upside
Bayesian DCF Intrinsic $38.89 +57.8%
Earnings Power Value Intrinsic $25.28 +2.6% $25.49 +36.8%
EROIC Spread Intrinsic $14.01 -43.2% $6.26 -66.4%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

RILYG vs RILYZ — Which Stock Is More Undervalued?

RILYZ scores higher with a 5.3/10 quality rating vs RILYG's 4.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing BRC Group Holdings, Inc. - 5.00 (RILYG) and BRC Group Holdings, Inc. - 5.25 (RILYZ) 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.

RILYG currently trades at $24.65 with a QOC of 4.8/10, while RILYZ trades at $19.92 with a QOC of 5.3/10.

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).