EBON vs LOGI

Ebang International Holdings In vs Logitech International S.A. - R — Valuation Comparison 2026

EBON

Computer Hardware
Ebang International Holdings In
Quality
2.4
out of 10
Value Trap
Price
$1.88
Last close
Models
10/13
Active
VS

LOGI

Computer Hardware
Logitech International S.A. - R
Quality
10.0
out of 10
Value Trap
6
SAFE
Price
$111.76
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType EBON Fair ValueEBON Upside LOGI Fair ValueLOGI Upside
Bayesian DCF Intrinsic $0.37 -80.4% $119.81 +7.2%
Earnings Power Value Intrinsic $4.25 +72.2% $59.03 -47.2%
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 EBON vs LOGI — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EBON vs LOGI — Which Stock Is More Undervalued?

LOGI scores higher with a 10.0/10 quality rating vs EBON's 2.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Ebang International Holdings In (EBON) and Logitech International S.A. - R (LOGI) 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.

EBON currently trades at $1.88 with a QOC of 2.4/10, while LOGI trades at $111.76 with a QOC of 10.0/10.

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).