BRAG vs BRSL

Bragg Gaming Group Inc. vs Brightstar Lottery PLC Trading — Valuation Comparison 2026

BRAG

Gambling
Bragg Gaming Group Inc.
Quality
5.9
out of 10
Value Trap
35
LOW
Price
$1.69
Last close
Models
11/13
Active
VS

BRSL

Gambling
Brightstar Lottery PLC Trading
Quality
2.5
out of 10
Value Trap
Price
$11.18
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType BRAG Fair ValueBRAG Upside BRSL Fair ValueBRSL Upside
Bayesian DCF Intrinsic $4.78 +182.9% $2.48 -77.8%
Earnings Power Value Intrinsic $5.59 +230.7%
EROIC Spread Intrinsic $1.12 -33.7% $5.45 -57.4%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 BRAG vs BRSL — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

BRAG vs BRSL — Which Stock Is More Undervalued?

BRAG scores higher with a 5.9/10 quality rating vs BRSL's 2.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Bragg Gaming Group Inc. (BRAG) and Brightstar Lottery PLC Trading (BRSL) 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.

BRAG currently trades at $1.69 with a QOC of 5.9/10, while BRSL trades at $11.18 with a QOC of 2.5/10.

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).