IVZ vs KKRS

Invesco Ltd vs KKR Group Finance Co. IX LLC 4. — Valuation Comparison 2026

IVZ

Investment Advice
Invesco Ltd
Quality
8.3
out of 10
Value Trap
33
LOW
Price
$28.46
Last close
Models
12/13
Active
VS

KKRS

Investment Advice
KKR Group Finance Co. IX LLC 4.
Quality
8.6
out of 10
Value Trap
30
LOW
Price
$16.22
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType IVZ Fair ValueIVZ Upside KKRS Fair ValueKKRS Upside
Bayesian DCF Intrinsic $18.70 -34.3%
Earnings Power Value Intrinsic $51.80 +82.0% $11.52 -29.0%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $20.32 -28.6% $49.52 +205.3%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 IVZ vs KKRS — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

IVZ vs KKRS — Which Stock Is More Undervalued?

KKRS scores higher with a 8.6/10 quality rating vs IVZ's 8.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Invesco Ltd (IVZ) and KKR Group Finance Co. IX LLC 4. (KKRS) 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.

IVZ currently trades at $28.46 with a QOC of 8.3/10, while KKRS trades at $16.22 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).