A vs BDSX

Agilent Technologies, Inc. vs Biodesix, Inc. — Valuation Comparison 2026

A

Diagnostics & Research
Agilent Technologies, Inc.
Quality
9.5
out of 10
Value Trap
18
SAFE
Price
$135.38
Last close
Models
13/13
Active
VS

BDSX

Diagnostics & Research
Biodesix, Inc.
Quality
6.0
out of 10
Value Trap
30
LOW
Price
$15.99
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType A Fair ValueA Upside BDSX Fair ValueBDSX Upside
Bayesian DCF Intrinsic $72.69 -46.3% $1.93 -88.0%
Earnings Power Value Intrinsic $32.33 -76.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $3.53 -97.4% $0.47 -96.7%
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 A vs BDSX — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

A vs BDSX — Which Stock Is More Undervalued?

A scores higher with a 9.5/10 quality rating vs BDSX's 6.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Agilent Technologies, Inc. (A) and Biodesix, Inc. (BDSX) 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.

A currently trades at $135.38 with a QOC of 9.5/10, while BDSX trades at $15.99 with a QOC of 6.0/10.

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).