Is INV or IRAB more undervalued in 2026?

CirclFi compares Innventure, Inc. (INV) and Iris Acquisition Corp II (IRAB) using 13 institutional-grade valuation models. INV has a Quality of Company score of 5.4/10 while IRAB scores 3.3/10.

Which is a better buy — INV or IRAB?

Compare intrinsic values from 13 independent models against current market prices. CirclFi also calculates Value Trap scores. This is not financial advice — conduct your own analysis.

How do INV and IRAB valuations compare?

INV at $5.33 and IRAB at $9.93 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

INV vs IRAB Valuation Comparison 2026 — Intrinsic Value Analysis

INV

Blank Checks

Innventure, Inc.

Quality

5.4

out of 10

Value Trap

SAFE

Price

$5.33

Last close

Models

4/13

Active

IRAB

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Iris Acquisition Corp II

Quality

3.3

out of 10

Value Trap

—

Price

$9.93

Last close

Models

6/13

Active

Model-by-Model Comparison

Model	Type	INV Fair Value	INV Upside	IRAB Fair Value	IRAB Upside
Bayesian DCF	Intrinsic	$0.19	-96.4%	$2.63	-73.5%
First Chicago	Scenario	$•••.••	••.•%	$•••.••	••.•%
ML-RIV	Intrinsic	$0.31	-95.0%	$4.10	-58.7%
PWERM	Option-Based	$3.78	-29.1%	$9.27	-6.7%
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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INV vs IRAB — Which Stock Is More Undervalued?

INV scores higher with a 5.4/10 quality rating vs IRAB's 3.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Innventure, Inc. (INV) and Iris Acquisition Corp II (IRAB) 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.

INV currently trades at $5.33 with a QOC of 5.4/10, while IRAB trades at $9.93 with a QOC of 3.3/10.

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).