IBIO vs IDYA

iBio, Inc. vs IDEAYA Biosciences, Inc. — Valuation Comparison 2026

IBIO

Pharmaceutical Preparations
iBio, Inc.
Quality
3.6
out of 10
Value Trap
12
SAFE
Price
$1.90
Last close
Models
10/13
Active
VS

IDYA

Pharmaceutical Preparations
IDEAYA Biosciences, Inc.
Quality
6.4
out of 10
Value Trap
6
SAFE
Price
$29.47
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType IBIO Fair ValueIBIO Upside IDYA Fair ValueIDYA Upside
Bayesian DCF Intrinsic $0.99 -47.8% $7.23 -75.5%
Earnings Power Value Intrinsic $16.44 -46.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $1.08 -43.1% $9.32 -68.4%
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 IBIO vs IDYA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

IBIO vs IDYA — Which Stock Is More Undervalued?

IDYA scores higher with a 6.4/10 quality rating vs IBIO's 3.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing iBio, Inc. (IBIO) and IDEAYA Biosciences, Inc. (IDYA) 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.

IBIO currently trades at $1.90 with a QOC of 3.6/10, while IDYA trades at $29.47 with a QOC of 6.4/10.

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).