CRDF vs CSBR

Cardiff Oncology, Inc. vs Champions Oncology, Inc. — Valuation Comparison 2026

CRDF

Biological Products, (No Diagnostic Substances)
Cardiff Oncology, Inc.
Quality
5.9
out of 10
Value Trap
30
LOW
Price
$1.90
Last close
Models
9/13
Active
VS

CSBR

Biological Products, (No Diagnostic Substances)
Champions Oncology, Inc.
Quality
8.1
out of 10
Value Trap
Price
$5.83
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType CRDF Fair ValueCRDF Upside CSBR Fair ValueCSBR Upside
Bayesian DCF Intrinsic $0.57 -70.2% $1.79 -69.4%
Earnings Power Value Intrinsic $0.36 -93.8%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $0.06 -96.2% $0.17 -97.0%
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 CRDF vs CSBR — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CRDF vs CSBR — Which Stock Is More Undervalued?

CSBR scores higher with a 8.1/10 quality rating vs CRDF's 5.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Cardiff Oncology, Inc. (CRDF) and Champions Oncology, Inc. (CSBR) 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.

CRDF currently trades at $1.90 with a QOC of 5.9/10, while CSBR trades at $5.83 with a QOC of 8.1/10.

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).