MFIN vs NNI

Medallion Financial Corp. vs Nelnet, Inc. — Valuation Comparison 2026

MFIN

Credit Services
Medallion Financial Corp.
Quality
8.6
out of 10
Value Trap
20
SAFE
Price
$9.67
Last close
Models
9/13
Active
VS

NNI

Credit Services
Nelnet, Inc.
Quality
8.4
out of 10
Value Trap
12
SAFE
Price
$130.12
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType MFIN Fair ValueMFIN Upside NNI Fair ValueNNI Upside
Bayesian DCF Intrinsic $16.15 +67.0% $175.58 +34.9%
Earnings Power Value Intrinsic $4.18 -56.8%
EROIC Spread Intrinsic $1.28 -85.5% $56.01 -57.0%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 MFIN vs NNI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MFIN vs NNI — Which Stock Is More Undervalued?

MFIN scores higher with a 8.6/10 quality rating vs NNI's 8.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Medallion Financial Corp. (MFIN) and Nelnet, Inc. (NNI) 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.

MFIN currently trades at $9.67 with a QOC of 8.6/10, while NNI trades at $130.12 with a QOC of 8.4/10.

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).