BX vs FHI

Blackstone Inc. vs Federated Hermes, Inc. — Valuation Comparison 2026

BX

Investment Advice
Blackstone Inc.
Quality
8.9
out of 10
Value Trap
6
SAFE
Price
$116.97
Last close
Models
12/13
Active
VS

FHI

Investment Advice
Federated Hermes, Inc.
Quality
9.2
out of 10
Value Trap
18
SAFE
Price
$56.06
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType BX Fair ValueBX Upside FHI Fair ValueFHI Upside
Bayesian DCF Intrinsic $74.17 -36.6% $41.74 -25.6%
Earnings Power Value Intrinsic $5.35 -95.4% $42.62 -24.0%
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 BX vs FHI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

BX vs FHI — Which Stock Is More Undervalued?

FHI scores higher with a 9.2/10 quality rating vs BX's 8.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Blackstone Inc. (BX) and Federated Hermes, Inc. (FHI) 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.

BX currently trades at $116.97 with a QOC of 8.9/10, while FHI trades at $56.06 with a QOC of 9.2/10.

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).