UI vs WATT

Ubiquiti Inc. vs Energous Corporation — Valuation Comparison 2026

UI

Communication Equipment
Ubiquiti Inc.
Quality
10.0
out of 10
Value Trap
6
SAFE
Price
$595.01
Last close
Models
13/13
Active
VS

WATT

Communication Equipment
Energous Corporation
Quality
6.6
out of 10
Value Trap
39
LOW
Price
$27.45
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType UI Fair ValueUI Upside WATT Fair ValueWATT Upside
Bayesian DCF Intrinsic $199.12 -66.5% $10.20 -62.8%
Earnings Power Value Intrinsic $135.00 -77.3% $9.35 -72.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 UI vs WATT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

UI vs WATT — Which Stock Is More Undervalued?

UI scores higher with a 10.0/10 quality rating vs WATT's 6.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Ubiquiti Inc. (UI) and Energous Corporation (WATT) 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.

UI currently trades at $595.01 with a QOC of 10.0/10, while WATT trades at $27.45 with a QOC of 6.6/10.

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).