AMP vs APO

Ameriprise Financial, Inc. vs Apollo Global Management, Inc. — Valuation Comparison 2026

AMP

Asset Management
Ameriprise Financial, Inc.
Quality
10.0
out of 10
Value Trap
6
SAFE
Price
$439.85
Last close
Models
11/13
Active
VS

APO

Asset Management
Apollo Global Management, Inc.
Quality
9.0
out of 10
Value Trap
18
SAFE
Price
$127.51
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType AMP Fair ValueAMP Upside APO Fair ValueAPO Upside
Bayesian DCF Intrinsic $1412.81 +221.2% $243.74 +91.2%
Earnings Power Value Intrinsic $598.37 +36.0% $56.56 -55.6%
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 AMP vs APO — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AMP vs APO — Which Stock Is More Undervalued?

AMP scores higher with a 10.0/10 quality rating vs APO's 9.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Ameriprise Financial, Inc. (AMP) and Apollo Global Management, Inc. (APO) 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.

AMP currently trades at $439.85 with a QOC of 10.0/10, while APO trades at $127.51 with a QOC of 9.0/10.

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).