TTMI vs WETH

TTM Technologies, Inc. vs Wetouch Technology Inc. — Valuation Comparison 2026

TTMI

Electronic Components
TTM Technologies, Inc.
Quality
8.9
out of 10
Value Trap
Price
$187.79
Last close
Models
12/13
Active
VS

WETH

Electronic Components
Wetouch Technology Inc.
Quality
8.6
out of 10
Value Trap
14
SAFE
Price
$1.40
Last close
Models
3/13
Active

Model-by-Model Comparison

ModelType TTMI Fair ValueTTMI Upside WETH Fair ValueWETH Upside
Bayesian DCF Intrinsic $12.10 -93.6%
Earnings Power Value Intrinsic $14.01 -92.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $29.89 -84.1% $1.63 +16.5%
Regime Cross-Sectional Relative $19.94 -89.4% $8.25 +489.6%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for TTMI vs WETH — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

TTMI vs WETH — Which Stock Is More Undervalued?

TTMI scores higher with a 8.9/10 quality rating vs WETH's 8.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing TTM Technologies, Inc. (TTMI) and Wetouch Technology Inc. (WETH) 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.

TTMI currently trades at $187.79 with a QOC of 8.9/10, while WETH trades at $1.40 with a QOC of 8.6/10.

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).