TPCS vs VATE

TechPrecision Corporation vs INNOVATE Corp. — Valuation Comparison 2026

TPCS

Fabricated Structural Metal Products
TechPrecision Corporation
Quality
5.7
out of 10
Value Trap
18
SAFE
Price
$3.98
Last close
Models
10/13
Active
VS

VATE

Fabricated Structural Metal Products
INNOVATE Corp.
Quality
6.2
out of 10
Value Trap
17
SAFE
Price
$15.23
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType TPCS Fair ValueTPCS Upside VATE Fair ValueVATE Upside
Bayesian DCF Intrinsic $0.59 -85.3% $74.55 +389.5%
Earnings Power Value Intrinsic $1.41 -66.8% $54.47 +257.6%
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 $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

TPCS vs VATE — Which Stock Is More Undervalued?

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

Comparing TechPrecision Corporation (TPCS) and INNOVATE Corp. (VATE) 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.

TPCS currently trades at $3.98 with a QOC of 5.7/10, while VATE trades at $15.23 with a QOC of 6.2/10.

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).