JPM vs RY

JP Morgan Chase & Co. vs Royal Bank Of Canada — Valuation Comparison 2026

JPM

Banks - Diversified
JP Morgan Chase & Co.
Quality
8.2
out of 10
Value Trap
22
SAFE
Price
$296.73
Last close
Models
10/13
Active
VS

RY

Banks - Diversified
Royal Bank Of Canada
Quality
8.6
out of 10
Value Trap
12
SAFE
Price
$188.89
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType JPM Fair ValueJPM Upside RY Fair ValueRY Upside
Bayesian DCF Intrinsic $189.84 -36.0% $105.39 -44.2%
Earnings Power Value Intrinsic $248.65 -16.2% $148.91 -21.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-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 JPM vs RY — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

JPM vs RY — Which Stock Is More Undervalued?

RY scores higher with a 8.6/10 quality rating vs JPM's 8.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing JP Morgan Chase & Co. (JPM) and Royal Bank Of Canada (RY) 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.

JPM currently trades at $296.73 with a QOC of 8.2/10, while RY trades at $188.89 with a QOC of 8.6/10.

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).