UPST vs XYF

Upstart Holdings, Inc. vs X Financial — Valuation Comparison 2026

UPST

Credit Services
Upstart Holdings, Inc.
Quality
6.8
out of 10
Value Trap
24
SAFE
Price
$32.69
Last close
Models
12/13
Active
VS

XYF

Credit Services
X Financial
Quality
9.3
out of 10
Value Trap
12
SAFE
Price
$4.73
Last close
Models
4/13
Active

Model-by-Model Comparison

ModelType UPST Fair ValueUPST Upside XYF Fair ValueXYF Upside
Bayesian DCF Intrinsic $3.89 -88.1%
Earnings Power Value Intrinsic $10.98 -66.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $1.64 -95.1% $24.63 +420.8%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $5.09 +7.7%
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 XYF — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

UPST vs XYF — Which Stock Is More Undervalued?

XYF scores higher with a 9.3/10 quality rating vs UPST's 6.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Upstart Holdings, Inc. (UPST) and X Financial (XYF) 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 $32.69 with a QOC of 6.8/10, while XYF trades at $4.73 with a QOC of 9.3/10.

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).