ASRT vs AUPH

Assertio Holdings, Inc. vs Aurinia Pharmaceuticals Inc — Valuation Comparison 2026

ASRT

Pharmaceutical Preparations
Assertio Holdings, Inc.
Quality
5.4
out of 10
Value Trap
24
SAFE
Price
$23.44
Last close
Models
9/13
Active
VS

AUPH

Pharmaceutical Preparations
Aurinia Pharmaceuticals Inc
Quality
9.9
out of 10
Value Trap
18
SAFE
Price
$15.33
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType ASRT Fair ValueASRT Upside AUPH Fair ValueAUPH Upside
Bayesian DCF Intrinsic $50.66 +116.1% $15.98 +4.3%
Earnings Power Value Intrinsic $6.74 -56.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $5.55 -69.9% $17.27 +12.6%
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 ASRT vs AUPH — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ASRT vs AUPH — Which Stock Is More Undervalued?

AUPH scores higher with a 9.9/10 quality rating vs ASRT's 5.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Assertio Holdings, Inc. (ASRT) and Aurinia Pharmaceuticals Inc (AUPH) 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.

ASRT currently trades at $23.44 with a QOC of 5.4/10, while AUPH trades at $15.33 with a QOC of 9.9/10.

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).