VST vs WAVE

Vistra Corp. vs Eco Wave Power Global AB (publ) — Valuation Comparison 2026

VST

Electric Services
Vistra Corp.
Quality
7.3
out of 10
Value Trap
18
SAFE
Price
$160.23
Last close
Models
12/13
Active
VS

WAVE

Electric Services
Eco Wave Power Global AB (publ)
Quality
1.7
out of 10
Value Trap
Price
$8.82
Last close
Models
6/13
Active

Model-by-Model Comparison

ModelType VST Fair ValueVST Upside WAVE Fair ValueWAVE Upside
Bayesian DCF Intrinsic $100.74 -36.4% $2.12 -75.9%
Earnings Power Value Intrinsic $2.78 -98.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $119.53 -25.4% $0.09 -98.9%
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 VST vs WAVE — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

VST vs WAVE — Which Stock Is More Undervalued?

VST scores higher with a 7.3/10 quality rating vs WAVE's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Vistra Corp. (VST) and Eco Wave Power Global AB (publ) (WAVE) 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.

VST currently trades at $160.23 with a QOC of 7.3/10, while WAVE trades at $8.82 with a QOC of 1.7/10.

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).