PWP vs SCHW

Perella Weinberg Partners vs Charles Schwab Corporation (The — Valuation Comparison 2026

PWP

Capital Markets
Perella Weinberg Partners
Quality
5.6
out of 10
Value Trap
24
SAFE
Price
$17.53
Last close
Models
12/13
Active
VS

SCHW

Capital Markets
Charles Schwab Corporation (The
Quality
9.5
out of 10
Value Trap
Price
$85.35
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType PWP Fair ValuePWP Upside SCHW Fair ValueSCHW Upside
Bayesian DCF Intrinsic $3.30 -81.2% $26.19 -69.3%
Earnings Power Value Intrinsic $8.08 -60.8% $48.07 -43.7%
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 PWP vs SCHW — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PWP vs SCHW — Which Stock Is More Undervalued?

SCHW scores higher with a 9.5/10 quality rating vs PWP's 5.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Perella Weinberg Partners (PWP) and Charles Schwab Corporation (The (SCHW) 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.

PWP currently trades at $17.53 with a QOC of 5.6/10, while SCHW trades at $85.35 with a QOC of 9.5/10.

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).