ASTC vs BNGO

Astrotech Corporation vs Bionano Genomics, Inc. — Valuation Comparison 2026

ASTC

Laboratory Analytical Instruments
Astrotech Corporation
Quality
4.9
out of 10
Value Trap
24
SAFE
Price
$49.80
Last close
Models
10/13
Active
VS

BNGO

Laboratory Analytical Instruments
Bionano Genomics, Inc.
Quality
6.0
out of 10
Value Trap
39
LOW
Price
$1.24
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType ASTC Fair ValueASTC Upside BNGO Fair ValueBNGO Upside
Bayesian DCF Intrinsic $0.83 -98.3% $0.25 -79.6%
Earnings Power Value Intrinsic $4.69 +58.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $6.80 -86.3% $0.50 -59.6%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

ASTC vs BNGO — Which Stock Is More Undervalued?

BNGO scores higher with a 6.0/10 quality rating vs ASTC's 4.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Astrotech Corporation (ASTC) and Bionano Genomics, Inc. (BNGO) 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.

ASTC currently trades at $49.80 with a QOC of 4.9/10, while BNGO trades at $1.24 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).