ASRV vs BAC

AmeriServ Financial Inc. vs Bank of America Corporation — Valuation Comparison 2026

ASRV

National Commercial Banks
AmeriServ Financial Inc.
Quality
6.4
out of 10
Value Trap
12
SAFE
Price
$3.70
Last close
Models
10/13
Active
VS

BAC

National Commercial Banks
Bank of America Corporation
Quality
8.0
out of 10
Value Trap
22
SAFE
Price
$51.60
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType ASRV Fair ValueASRV Upside BAC Fair ValueBAC Upside
Bayesian DCF Intrinsic $0.46 -87.5% $78.37 +51.9%
Earnings Power Value Intrinsic $1.21 -67.3% $8.56 -83.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 ASRV vs BAC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

ASRV vs BAC — Which Stock Is More Undervalued?

BAC scores higher with a 8.0/10 quality rating vs ASRV's 6.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing AmeriServ Financial Inc. (ASRV) and Bank of America Corporation (BAC) 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.

ASRV currently trades at $3.70 with a QOC of 6.4/10, while BAC trades at $51.60 with a QOC of 8.0/10.

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).