STT vs TDF

State Street Corporation vs Templeton Dragon Fund, Inc. — Valuation Comparison 2026

STT

Asset Management
State Street Corporation
Quality
7.8
out of 10
Value Trap
8
SAFE
Price
$157.61
Last close
Models
11/13
Active
VS

TDF

Asset Management
Templeton Dragon Fund, Inc.
Quality
1.7
out of 10
Value Trap
Price
$11.18
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType STT Fair ValueSTT Upside TDF Fair ValueTDF Upside
Bayesian DCF Intrinsic $202.85 +28.7% $2.96 -73.5%
Earnings Power Value Intrinsic $100.75 -36.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $840.71 +433.4% $43.86 +288.8%
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 STT vs TDF — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

STT vs TDF — Which Stock Is More Undervalued?

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

Comparing State Street Corporation (STT) and Templeton Dragon Fund, Inc. (TDF) 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.

STT currently trades at $157.61 with a QOC of 7.8/10, while TDF trades at $11.18 with a QOC of 1.7/10.

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).