INSE vs ROLR

Inspired Entertainment, Inc. vs High Roller Technologies, Inc. — Valuation Comparison 2026

INSE

Gambling
Inspired Entertainment, Inc.
Quality
7.0
out of 10
Value Trap
12
SAFE
Price
$7.62
Last close
Models
10/13
Active
VS

ROLR

Gambling
High Roller Technologies, Inc.
Quality
5.6
out of 10
Value Trap
8
SAFE
Price
$5.44
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType INSE Fair ValueINSE Upside ROLR Fair ValueROLR Upside
Bayesian DCF Intrinsic $2.39 -66.1% $2.16 -60.4%
Earnings Power Value Intrinsic $5.44 -28.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $6.76 -11.3% $0.88 -83.8%
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 INSE vs ROLR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

INSE vs ROLR — Which Stock Is More Undervalued?

INSE scores higher with a 7.0/10 quality rating vs ROLR's 5.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Inspired Entertainment, Inc. (INSE) and High Roller Technologies, Inc. (ROLR) 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.

INSE currently trades at $7.62 with a QOC of 7.0/10, while ROLR trades at $5.44 with a QOC of 5.6/10.

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).