EMIS vs FERA

Emmis Acquisition Corp. vs Fifth Era Acquisition Corp I — Valuation Comparison 2026

EMIS

Blank Checks
Emmis Acquisition Corp.
Quality
5.4
out of 10
Value Trap
Price
$10.13
Last close
Models
12/13
Active
VS

FERA

Blank Checks
Fifth Era Acquisition Corp I
Quality
4.5
out of 10
Value Trap
Price
$10.39
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType EMIS Fair ValueEMIS Upside FERA Fair ValueFERA Upside
Bayesian DCF Intrinsic $0.31 -97.0% $0.52 -95.0%
Earnings Power Value Intrinsic $0.42 -95.9% $0.54 -94.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
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 EMIS vs FERA — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

EMIS vs FERA — Which Stock Is More Undervalued?

EMIS scores higher with a 5.4/10 quality rating vs FERA's 4.5/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Emmis Acquisition Corp. (EMIS) and Fifth Era Acquisition Corp I (FERA) 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.

EMIS currently trades at $10.13 with a QOC of 5.4/10, while FERA trades at $10.39 with a QOC of 4.5/10.

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).