FDUS vs FINS

Fidus Investment Corporation vs FINS — Valuation Comparison 2026

FDUS

Asset Management
Fidus Investment Corporation
Quality
7.4
out of 10
Value Trap
36
LOW
Price
$18.96
Last close
Models
13/13
Active
VS

FINS

Asset Management
FINS
Quality
1.8
out of 10
Value Trap
Price
$12.82
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType FDUS Fair ValueFDUS Upside FINS Fair ValueFINS Upside
Bayesian DCF Intrinsic $1.51 -91.9% $3.39 -73.5%
Earnings Power Value Intrinsic $1.59 -91.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $62.22 +228.2% $16.57 +29.2%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
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 FDUS vs FINS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FDUS vs FINS — Which Stock Is More Undervalued?

FDUS scores higher with a 7.4/10 quality rating vs FINS's 1.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Fidus Investment Corporation (FDUS) and FINS (FINS) 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.

FDUS currently trades at $18.96 with a QOC of 7.4/10, while FINS trades at $12.82 with a QOC of 1.8/10.

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).