Is BHFAM or BHFAN more undervalued in 2026?

CirclFi compares Brighthouse Financial, Inc. - D (BHFAM) and Brighthouse Financial, Inc. - d (BHFAN) using 13 institutional-grade valuation models. BHFAM has a Quality of Company score of 6.5/10 while BHFAN scores 6.1/10.

Which is a better buy — BHFAM or BHFAN?

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 BHFAM and BHFAN valuations compare?

BHFAM at $11.13 and BHFAN at $12.59 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

BHFAM vs BHFAN Valuation Comparison 2026 — Intrinsic Value Analysis

BHFAM

Insurance - Life

Brighthouse Financial, Inc. - D

Quality

6.5

out of 10

Value Trap

—

Price

$11.13

Last close

Models

3/13

Active

BHFAN

Insurance - Life

Brighthouse Financial, Inc. - d

Quality

6.1

out of 10

Value Trap

SAFE

Price

$12.59

Last close

Models

2/13

Active

Model-by-Model Comparison

Model	Type	BHFAM Fair Value	BHFAM Upside	BHFAN Fair Value	BHFAN Upside
First Chicago	Scenario	$55.03	+394.4%	—	—
Markov DDM	Intrinsic	$24.19	+117.4%	$14.19	+12.7%
FTNN Topology	Relative	$39.29	+253.0%	$28.93	+129.8%

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BHFAM vs BHFAN — Which Stock Is More Undervalued?

BHFAM scores higher with a 6.5/10 quality rating vs BHFAN's 6.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Brighthouse Financial, Inc. - D (BHFAM) and Brighthouse Financial, Inc. - d (BHFAN) 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.

BHFAM currently trades at $11.13 with a QOC of 6.5/10, while BHFAN trades at $12.59 with a QOC of 6.1/10.

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).