Is RFMZ or RMI more undervalued in 2026?

CirclFi compares RFMZ (RFMZ) and RiverNorth Opportunistic Munici (RMI) using 13 institutional-grade valuation models. RFMZ has a Quality of Company score of 1.7/10 while RMI scores 1.7/10.

Which is a better buy — RFMZ or RMI?

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 RFMZ and RMI valuations compare?

RFMZ at $13.24 and RMI at $15.39 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

RFMZ vs RMI Valuation Comparison 2026 — Intrinsic Value Analysis

RFMZ

Asset Management

RFMZ

Quality

1.7

out of 10

Value Trap

—

Price

$13.24

Last close

Models

6/13

Active

RMI

Asset Management

RiverNorth Opportunistic Munici

Quality

1.7

out of 10

Value Trap

—

Price

$15.39

Last close

Models

6/13

Active

Model-by-Model Comparison

Model	Type	RFMZ Fair Value	RFMZ Upside	RMI Fair Value	RMI Upside
Bayesian DCF	Intrinsic	$3.51	-73.5%	$4.07	-73.5%
First Chicago	Scenario	$•••.••	••.•%	$•••.••	••.•%
Markov DDM	Intrinsic	$10.54	-19.4%	$9.62	-37.5%
ML-RIV	Intrinsic	$•••.••	••.•%	$•••.••	••.•%
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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RFMZ vs RMI — Which Stock Is More Undervalued?

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

Comparing RFMZ (RFMZ) and RiverNorth Opportunistic Munici (RMI) 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.

RFMZ currently trades at $13.24 with a QOC of 1.7/10, while RMI trades at $15.39 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).