Is GOF or GUG more undervalued in 2026?

CirclFi compares Guggenheim Strategic Opportunit (GOF) and GUG (GUG) using 13 institutional-grade valuation models. GOF has a Quality of Company score of 1.7/10 while GUG scores 1.7/10.

Which is a better buy — GOF or GUG?

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 GOF and GUG valuations compare?

GOF at $11.16 and GUG at $16.31 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

GOF vs GUG Valuation Comparison 2026 — Intrinsic Value Analysis

GOF

Asset Management

Guggenheim Strategic Opportunit

Quality

1.7

out of 10

Value Trap

—

Price

$11.16

Last close

Models

6/13

Active

GUG

Asset Management

GUG

Quality

1.7

out of 10

Value Trap

—

Price

$16.31

Last close

Models

6/13

Active

Model-by-Model Comparison

Model	Type	GOF Fair Value	GOF Upside	GUG Fair Value	GUG Upside
Bayesian DCF	Intrinsic	$3.29	-70.5%	$4.32	-73.5%
First Chicago	Scenario	$•••.••	••.•%	$•••.••	••.•%
Markov DDM	Intrinsic	$21.15	+88.5%	$15.15	-5.3%
ML-RIV	Intrinsic	$•••.••	••.•%	$•••.••	••.•%
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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GOF vs GUG — Which Stock Is More Undervalued?

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

Comparing Guggenheim Strategic Opportunit (GOF) and GUG (GUG) 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.

GOF currently trades at $11.16 with a QOC of 1.7/10, while GUG trades at $16.31 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).