AGNC vs AGNCL

AGNC Investment Corp. vs AGNC Investment Corp. - Deposit — Valuation Comparison 2026

AGNC

REIT - Mortgage
AGNC Investment Corp.
Quality
7.4
out of 10
Value Trap
6
SAFE
Price
$10.41
Last close
Models
11/13
Active
VS

AGNCL

REIT - Mortgage
AGNC Investment Corp. - Deposit
Quality
7.0
out of 10
Value Trap
6
SAFE
Price
$25.07
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType AGNC Fair ValueAGNC Upside AGNCL Fair ValueAGNCL Upside
Bayesian DCF Intrinsic $7.92 -23.9% $7.26 -71.0%
Earnings Power Value Intrinsic $2.58 -75.2% $2.93 -88.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
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 AGNC vs AGNCL — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AGNC vs AGNCL — Which Stock Is More Undervalued?

AGNC scores higher with a 7.4/10 quality rating vs AGNCL's 7.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing AGNC Investment Corp. (AGNC) and AGNC Investment Corp. - Deposit (AGNCL) 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.

AGNC currently trades at $10.41 with a QOC of 7.4/10, while AGNCL trades at $25.07 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).