TFC vs TMP

Truist Financial Corporation vs Tompkins Financial Corporation — Valuation Comparison 2026

TFC

Banks - Regional
Truist Financial Corporation
Quality
8.4
out of 10
Value Trap
20
SAFE
Price
$47.80
Last close
Models
11/13
Active
VS

TMP

Banks - Regional
Tompkins Financial Corporation
Quality
8.6
out of 10
Value Trap
6
SAFE
Price
$86.52
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType TFC Fair ValueTFC Upside TMP Fair ValueTMP Upside
Bayesian DCF Intrinsic $72.72 +52.1% $3.22 -96.3%
Earnings Power Value Intrinsic $17.16 -64.1% $55.72 -34.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 TFC vs TMP — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

TFC vs TMP — Which Stock Is More Undervalued?

TMP scores higher with a 8.6/10 quality rating vs TFC's 8.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Truist Financial Corporation (TFC) and Tompkins Financial Corporation (TMP) 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.

TFC currently trades at $47.80 with a QOC of 8.4/10, while TMP trades at $86.52 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).