FIS vs GLE

Fidelity National Information S vs Global Engine Group Holding Lim — Valuation Comparison 2026

FIS

Information Technology Services
Fidelity National Information S
Quality
7.8
out of 10
Value Trap
32
LOW
Price
$42.22
Last close
Models
12/13
Active
VS

GLE

Information Technology Services
Global Engine Group Holding Lim
Quality
6.3
out of 10
Value Trap
21
SAFE
Price
$0.45
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType FIS Fair ValueFIS Upside GLE Fair ValueGLE Upside
Bayesian DCF Intrinsic $86.30 +104.4% $0.19 -57.5%
Earnings Power Value Intrinsic $10.99 -74.0% $0.03 -91.1%
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 FIS vs GLE — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

FIS vs GLE — Which Stock Is More Undervalued?

FIS scores higher with a 7.8/10 quality rating vs GLE's 6.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Fidelity National Information S (FIS) and Global Engine Group Holding Lim (GLE) 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.

FIS currently trades at $42.22 with a QOC of 7.8/10, while GLE trades at $0.45 with a QOC of 6.3/10.

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).