MPLT vs MRVI

MapLight Therapeutics, Inc. vs Maravai LifeSciences Holdings, — Valuation Comparison 2026

MPLT

Biotechnology
MapLight Therapeutics, Inc.
Quality
5.0
out of 10
Value Trap
Price
$29.78
Last close
Models
7/13
Active
VS

MRVI

Biotechnology
Maravai LifeSciences Holdings,
Quality
6.4
out of 10
Value Trap
Price
$4.64
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType MPLT Fair ValueMPLT Upside MRVI Fair ValueMRVI Upside
Bayesian DCF Intrinsic $8.54 -71.3% $15.68 +238.0%
Earnings Power Value Intrinsic $0.05 -98.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $7.03 -76.4% $0.39 -91.6%
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 MPLT vs MRVI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

MPLT vs MRVI — Which Stock Is More Undervalued?

MRVI scores higher with a 6.4/10 quality rating vs MPLT's 5.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing MapLight Therapeutics, Inc. (MPLT) and Maravai LifeSciences Holdings, (MRVI) 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.

MPLT currently trades at $29.78 with a QOC of 5.0/10, while MRVI trades at $4.64 with a QOC of 6.4/10.

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).