Is PAXS or PDO more undervalued in 2026?

CirclFi compares PIMCO Access Income Fund (PAXS) and PDO (PDO) using 13 institutional-grade valuation models. PAXS has a Quality of Company score of 1.7/10 while PDO scores 1.7/10.

Which is a better buy — PAXS or PDO?

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 PAXS and PDO valuations compare?

PAXS at $14.27 and PDO at $13.00 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

PAXS vs PDO Valuation Comparison 2026 — Intrinsic Value Analysis

PAXS

Asset Management

PIMCO Access Income Fund

Quality

1.7

out of 10

Value Trap

—

Price

$14.27

Last close

Models

6/13

Active

PDO

Asset Management

PDO

Quality

1.7

out of 10

Value Trap

—

Price

$13.00

Last close

Models

6/13

Active

Model-by-Model Comparison

Model	Type	PAXS Fair Value	PAXS Upside	PDO Fair Value	PDO Upside
Bayesian DCF	Intrinsic	$3.78	-73.5%	$3.44	-73.5%
First Chicago	Scenario	$•••.••	••.•%	$•••.••	••.•%
Markov DDM	Intrinsic	$15.65	+11.3%	$13.48	+0.8%
ML-RIV	Intrinsic	$•••.••	••.•%	$•••.••	••.•%
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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PAXS vs PDO — Which Stock Is More Undervalued?

Both PAXS and PDO score 1.7/10 on quality. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing PIMCO Access Income Fund (PAXS) and PDO (PDO) 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.

PAXS currently trades at $14.27 with a QOC of 1.7/10, while PDO trades at $13.00 with a QOC of 1.7/10.

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).