THW vs TPVG

Tekla World Healthcare Fund Sha vs TriplePoint Venture Growth BDC — Valuation Comparison 2026

THW

Asset Management
Tekla World Healthcare Fund Sha
Quality
1.7
out of 10
Value Trap
Price
$12.90
Last close
Models
5/13
Active
VS

TPVG

Asset Management
TriplePoint Venture Growth BDC
Quality
6.1
out of 10
Value Trap
28
LOW
Price
$5.55
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType THW Fair ValueTHW Upside TPVG Fair ValueTPVG Upside
Bayesian DCF Intrinsic $3.42 -73.5% $0.50 -90.9%
Earnings Power Value Intrinsic $1.41 -74.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $15.75 +23.1% $4.66 -16.0%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 THW vs TPVG — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

THW vs TPVG — Which Stock Is More Undervalued?

TPVG scores higher with a 6.1/10 quality rating vs THW's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Tekla World Healthcare Fund Sha (THW) and TriplePoint Venture Growth BDC (TPVG) 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.

THW currently trades at $12.90 with a QOC of 1.7/10, while TPVG trades at $5.55 with a QOC of 6.1/10.

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).