JHG vs MAAS

Janus Henderson Group plc vs Maase Inc. — Valuation Comparison 2026

JHG

Investment Advice
Janus Henderson Group plc
Quality
9.2
out of 10
Value Trap
18
SAFE
Price
$51.71
Last close
Models
13/13
Active
VS

MAAS

Investment Advice
Maase Inc.
Quality
6.1
out of 10
Value Trap
18
SAFE
Price
$11.89
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType JHG Fair ValueJHG Upside MAAS Fair ValueMAAS Upside
Bayesian DCF Intrinsic $69.58 +34.6% $20.50 +112.7%
Earnings Power Value Intrinsic $24.88 -51.9% $3.33 -63.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 JHG vs MAAS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

JHG vs MAAS — Which Stock Is More Undervalued?

JHG scores higher with a 9.2/10 quality rating vs MAAS's 6.1/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Janus Henderson Group plc (JHG) and Maase Inc. (MAAS) 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.

JHG currently trades at $51.71 with a QOC of 9.2/10, while MAAS trades at $11.89 with a QOC of 6.1/10.

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).