RDNT vs WAT

RadNet, Inc. vs Waters Corporation — Valuation Comparison 2026

RDNT

Diagnostics & Research
RadNet, Inc.
Quality
6.2
out of 10
Value Trap
24
SAFE
Price
$55.31
Last close
Models
12/13
Active
VS

WAT

Diagnostics & Research
Waters Corporation
Quality
5.7
out of 10
Value Trap
23
SAFE
Price
$366.67
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType RDNT Fair ValueRDNT Upside WAT Fair ValueWAT Upside
Bayesian DCF Intrinsic $43.13 -22.0% $15.32 -95.8%
Earnings Power Value Intrinsic $8.29 -85.4% $36.66 -89.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 RDNT vs WAT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

RDNT vs WAT — Which Stock Is More Undervalued?

RDNT scores higher with a 6.2/10 quality rating vs WAT's 5.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing RadNet, Inc. (RDNT) and Waters Corporation (WAT) 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.

RDNT currently trades at $55.31 with a QOC of 6.2/10, while WAT trades at $366.67 with a QOC of 5.7/10.

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).