CCLD vs DRIO

CareCloud, Inc. vs DarioHealth Corp. — Valuation Comparison 2026

CCLD

Health Information Services
CareCloud, Inc.
Quality
8.5
out of 10
Value Trap
24
SAFE
Price
$2.32
Last close
Models
12/13
Active
VS

DRIO

Health Information Services
DarioHealth Corp.
Quality
6.3
out of 10
Value Trap
31
LOW
Price
$7.52
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType CCLD Fair ValueCCLD Upside DRIO Fair ValueDRIO Upside
Bayesian DCF Intrinsic $8.60 +270.5% $1.29 -82.8%
Earnings Power Value Intrinsic $5.65 +143.6% $3.96 -45.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 CCLD vs DRIO — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

CCLD vs DRIO — Which Stock Is More Undervalued?

CCLD scores higher with a 8.5/10 quality rating vs DRIO's 6.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing CareCloud, Inc. (CCLD) and DarioHealth Corp. (DRIO) 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.

CCLD currently trades at $2.32 with a QOC of 8.5/10, while DRIO trades at $7.52 with a QOC of 6.3/10.

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).