ACA vs AMRC

Arcosa, Inc. vs Ameresco, Inc. — Valuation Comparison 2026

ACA

Engineering & Construction
Arcosa, Inc.
Quality
8.6
out of 10
Value Trap
6
SAFE
Price
$127.14
Last close
Models
13/13
Active
VS

AMRC

Engineering & Construction
Ameresco, Inc.
Quality
7.0
out of 10
Value Trap
30
LOW
Price
$36.56
Last close
Models
10/13
Active

Model-by-Model Comparison

ModelType ACA Fair ValueACA Upside AMRC Fair ValueAMRC Upside
Bayesian DCF Intrinsic $14.28 -88.6%
Earnings Power Value Intrinsic $13.48 -89.4%
EROIC Spread Intrinsic $41.98 -67.0% $2.42 -93.4%
First Chicago Scenario $156.17 +22.8% $59.86 +63.7%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 ACA vs AMRC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ACA vs AMRC — Which Stock Is More Undervalued?

ACA scores higher with a 8.6/10 quality rating vs AMRC's 7.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Arcosa, Inc. (ACA) and Ameresco, Inc. (AMRC) 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.

ACA currently trades at $127.14 with a QOC of 8.6/10, while AMRC trades at $36.56 with a QOC of 7.0/10.

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).