Is MGRB or MGRE more undervalued in 2026?

CirclFi compares Affiliated Managers Group, Inc. (MGRB) and Affiliated Managers Group, Inc. (MGRE) using 13 institutional-grade valuation models. MGRB has a Quality of Company score of 7.9/10 while MGRE scores 7.9/10.

Which is a better buy — MGRB or MGRE?

Compare intrinsic values from 13 independent models against current market prices. CirclFi also calculates Value Trap scores. This is not financial advice — conduct your own analysis.

How do MGRB and MGRE valuations compare?

MGRB at $16.81 and MGRE at $23.81 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

MGRB vs MGRE Valuation Comparison 2026 — Intrinsic Value Analysis

MGRB

Investment Advice

Affiliated Managers Group, Inc.

Quality

7.9

out of 10

Value Trap

—

Price

$16.81

Last close

Models

3/13

Active

MGRE

Investment Advice

Affiliated Managers Group, Inc.

Quality

7.9

out of 10

Value Trap

—

Price

$23.81

Last close

Models

3/13

Active

Model-by-Model Comparison

Model	Type	MGRB Fair Value	MGRB Upside	MGRE Fair Value	MGRE Upside
EROIC Spread	Intrinsic	$86.70	+415.8%	$86.70	+264.2%
Markov DDM	Intrinsic	$0.42	-97.5%	$0.42	-98.2%
FTNN Topology	Relative	$60.77	+261.5%	$66.55	+179.5%

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MGRB vs MGRE — Which Stock Is More Undervalued?

Both MGRB and MGRE score 7.9/10 on quality. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Affiliated Managers Group, Inc. (MGRB) and Affiliated Managers Group, Inc. (MGRE) 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.

MGRB currently trades at $16.81 with a QOC of 7.9/10, while MGRE trades at $23.81 with a QOC of 7.9/10.

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).