RJF vs RMM

Raymond James Financial, Inc. vs RiverNorth Managed Duration Mun — Valuation Comparison 2026

RJF

Asset Management
Raymond James Financial, Inc.
Quality
8.9
out of 10
Value Trap
6
SAFE
Price
$142.00
Last close
Models
12/13
Active
VS

RMM

Asset Management
RiverNorth Managed Duration Mun
Quality
1.7
out of 10
Value Trap
Price
$14.59
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType RJF Fair ValueRJF Upside RMM Fair ValueRMM Upside
Bayesian DCF Intrinsic $242.41 +70.7% $3.86 -73.5%
Earnings Power Value Intrinsic $138.49 -2.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $263.87 +85.8% $9.25 -34.8%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for RJF vs RMM — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RJF vs RMM — Which Stock Is More Undervalued?

RJF scores higher with a 8.9/10 quality rating vs RMM's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Raymond James Financial, Inc. (RJF) and RiverNorth Managed Duration Mun (RMM) 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.

RJF currently trades at $142.00 with a QOC of 8.9/10, while RMM trades at $14.59 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).