IBM vs III

International Business Machines vs Information Services Group, Inc — Valuation Comparison 2026

IBM

Information Technology Services
International Business Machines
Quality
6.8
out of 10
Value Trap
17
SAFE
Price
$264.22
Last close
Models
12/13
Active
VS

III

Information Technology Services
Information Services Group, Inc
Quality
8.3
out of 10
Value Trap
29
LOW
Price
$4.53
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType IBM Fair ValueIBM Upside III Fair ValueIII Upside
Bayesian DCF Intrinsic $110.88 -58.0% $2.78 -38.7%
Earnings Power Value Intrinsic $10.91 -95.9% $1.20 -73.6%
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 IBM vs III — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

IBM vs III — Which Stock Is More Undervalued?

III scores higher with a 8.3/10 quality rating vs IBM's 6.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing International Business Machines (IBM) and Information Services Group, Inc (III) 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.

IBM currently trades at $264.22 with a QOC of 6.8/10, while III trades at $4.53 with a QOC of 8.3/10.

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).