ING vs MUFG

ING Group, N.V. vs Mitsubishi UFJ Financial Group, — Valuation Comparison 2026

ING

Banks - Diversified
ING Group, N.V.
Quality
1.7
out of 10
Value Trap
Price
$30.84
Last close
Models
12/13
Active
VS

MUFG

Banks - Diversified
Mitsubishi UFJ Financial Group,
Quality
7.8
out of 10
Value Trap
30
LOW
Price
$18.82
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType ING Fair ValueING Upside MUFG Fair ValueMUFG Upside
Bayesian DCF Intrinsic $10.28 -66.7%
Earnings Power Value Intrinsic $12.67 -54.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $33.99 +11.9% $2.04 -89.2%
Markov DDM Intrinsic $93.11 +226.1% $9.77 -48.1%
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 ING vs MUFG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ING vs MUFG — Which Stock Is More Undervalued?

MUFG scores higher with a 7.8/10 quality rating vs ING's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing ING Group, N.V. (ING) and Mitsubishi UFJ Financial Group, (MUFG) 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.

ING currently trades at $30.84 with a QOC of 1.7/10, while MUFG trades at $18.82 with a QOC of 7.8/10.

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).