IBKR vs IPST

Interactive Brokers Group, Inc. vs IP Strategy Holdings, Inc. — Valuation Comparison 2026

IBKR

Capital Markets
Interactive Brokers Group, Inc.
Quality
9.2
out of 10
Value Trap
36
LOW
Price
$83.11
Last close
Models
12/13
Active
VS

IPST

Capital Markets
IP Strategy Holdings, Inc.
Quality
4.9
out of 10
Value Trap
18
SAFE
Price
$4.97
Last close
Models
7/13
Active

Model-by-Model Comparison

ModelType IBKR Fair ValueIBKR Upside IPST Fair ValueIPST Upside
Bayesian DCF Intrinsic $18.91 -77.2%
Earnings Power Value Intrinsic $19.92 -76.0% $31.13 +466.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $3.65 -95.5% $3.50 -42.8%
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 IBKR vs IPST — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

IBKR vs IPST — Which Stock Is More Undervalued?

IBKR scores higher with a 9.2/10 quality rating vs IPST's 4.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Interactive Brokers Group, Inc. (IBKR) and IP Strategy Holdings, Inc. (IPST) 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.

IBKR currently trades at $83.11 with a QOC of 9.2/10, while IPST trades at $4.97 with a QOC of 4.9/10.

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).