SUPV vs TD

Grupo Supervielle S.A. vs Toronto Dominion Bank (The) — Valuation Comparison 2026

SUPV

Commercial Banks, NEC
Grupo Supervielle S.A.
Quality
8.2
out of 10
Value Trap
24
SAFE
Price
$9.74
Last close
Models
12/13
Active
VS

TD

Commercial Banks, NEC
Toronto Dominion Bank (The)
Quality
7.4
out of 10
Value Trap
10
SAFE
Price
$113.58
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType SUPV Fair ValueSUPV Upside TD Fair ValueTD Upside
Bayesian DCF Intrinsic $12.93 +32.8% $5.00 -95.6%
Earnings Power Value Intrinsic $11.97 +22.9% $71.20 -37.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 SUPV vs TD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

SUPV vs TD — Which Stock Is More Undervalued?

SUPV scores higher with a 8.2/10 quality rating vs TD's 7.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Grupo Supervielle S.A. (SUPV) and Toronto Dominion Bank (The) (TD) 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.

SUPV currently trades at $9.74 with a QOC of 8.2/10, while TD trades at $113.58 with a QOC of 7.4/10.

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).