RDIB vs ROKU

Reading International Inc vs Roku, Inc. — Valuation Comparison 2026

RDIB

Entertainment
Reading International Inc
Quality
4.6
out of 10
Value Trap
12
SAFE
Price
$8.57
Last close
Models
8/13
Active
VS

ROKU

Entertainment
Roku, Inc.
Quality
6.2
out of 10
Value Trap
12
SAFE
Price
$131.09
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType RDIB Fair ValueRDIB Upside ROKU Fair ValueROKU Upside
Bayesian DCF Intrinsic $36.28 +263.2% $13.21 -89.9%
Earnings Power Value Intrinsic $19.01 -85.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $19.53 +127.9% $21.93 -83.3%
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 RDIB vs ROKU — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RDIB vs ROKU — Which Stock Is More Undervalued?

ROKU scores higher with a 6.2/10 quality rating vs RDIB's 4.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Reading International Inc (RDIB) and Roku, Inc. (ROKU) 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.

RDIB currently trades at $8.57 with a QOC of 4.6/10, while ROKU trades at $131.09 with a QOC of 6.2/10.

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).