PAPL vs PWP

Pineapple Financial Inc. vs Perella Weinberg Partners — Valuation Comparison 2026

PAPL

Finance Services
Pineapple Financial Inc.
Quality
5.4
out of 10
Value Trap
29
LOW
Price
$1.07
Last close
Models
11/13
Active
VS

PWP

Finance Services
Perella Weinberg Partners
Quality
5.6
out of 10
Value Trap
24
SAFE
Price
$17.16
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType PAPL Fair ValuePAPL Upside PWP Fair ValuePWP Upside
Bayesian DCF Intrinsic $0.65 -38.9% $3.38 -80.3%
Earnings Power Value Intrinsic $1.14 -22.2% $8.08 -60.8%
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 PAPL vs PWP — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

PAPL vs PWP — Which Stock Is More Undervalued?

PWP scores higher with a 5.6/10 quality rating vs PAPL's 5.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Pineapple Financial Inc. (PAPL) and Perella Weinberg Partners (PWP) 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.

PAPL currently trades at $1.07 with a QOC of 5.4/10, while PWP trades at $17.16 with a QOC of 5.6/10.

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).