MPV vs MSDL

Barings Participation Investors vs MSDL — Valuation Comparison 2026

MPV

Asset Management
Barings Participation Investors
Quality
1.8
out of 10
Value Trap
Price
$17.48
Last close
Models
11/13
Active
VS

MSDL

Asset Management
MSDL
Quality
5.7
out of 10
Value Trap
10
SAFE
Price
$15.28
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType MPV Fair ValueMPV Upside MSDL Fair ValueMSDL Upside
Bayesian DCF Intrinsic $4.63 -73.5% $29.58 +93.6%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $12.94 -26.0%
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 $14.76 -11.3% $52.90 +246.2%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

MPV vs MSDL — Which Stock Is More Undervalued?

MSDL scores higher with a 5.7/10 quality rating vs MPV's 1.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Barings Participation Investors (MPV) and MSDL (MSDL) 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.

MPV currently trades at $17.48 with a QOC of 1.8/10, while MSDL trades at $15.28 with a QOC of 5.7/10.

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).