UPST vs WLTH

Upstart Holdings, Inc. vs Wealthfront Corporation — Valuation Comparison 2026

UPST

Finance Services
Upstart Holdings, Inc.
Quality
6.8
out of 10
Value Trap
24
SAFE
Price
$33.79
Last close
Models
12/13
Active
VS

WLTH

Finance Services
Wealthfront Corporation
Quality
6.1
out of 10
Value Trap
Price
$12.22
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType UPST Fair ValueUPST Upside WLTH Fair ValueWLTH Upside
Bayesian DCF Intrinsic $3.89 -88.5% $12.58 +2.9%
Earnings Power Value Intrinsic $10.98 -67.5% $25.52 +137.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 UPST vs WLTH — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

UPST vs WLTH — Which Stock Is More Undervalued?

UPST scores higher with a 6.8/10 quality rating vs WLTH's 6.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Upstart Holdings, Inc. (UPST) and Wealthfront Corporation (WLTH) 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.

UPST currently trades at $33.79 with a QOC of 6.8/10, while WLTH trades at $12.22 with a QOC of 6.1/10.

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).