LPL vs SONY

LG Display Co, Ltd AMERICAN DEP vs Sony Group Corporation — Valuation Comparison 2026

LPL

Consumer Electronics
LG Display Co, Ltd AMERICAN DEP
Quality
1.9
out of 10
Value Trap
Price
$5.03
Last close
Models
9/13
Active
VS

SONY

Consumer Electronics
Sony Group Corporation
Quality
8.4
out of 10
Value Trap
Price
$21.72
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType LPL Fair ValueLPL Upside SONY Fair ValueSONY Upside
Bayesian DCF Intrinsic $1.48 -70.5% $13.30 -38.8%
Earnings Power Value Intrinsic $1.57 -62.5% $8.17 -63.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 LPL vs SONY — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LPL vs SONY — Which Stock Is More Undervalued?

SONY scores higher with a 8.4/10 quality rating vs LPL's 1.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing LG Display Co, Ltd AMERICAN DEP (LPL) and Sony Group Corporation (SONY) 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.

LPL currently trades at $5.03 with a QOC of 1.9/10, while SONY trades at $21.72 with a QOC of 8.4/10.

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).