OST vs RAL

Ostin Technology Group Co., Ltd vs Ralliant Corporation — Valuation Comparison 2026

OST

Electronic Components
Ostin Technology Group Co., Ltd
Quality
2.3
out of 10
Value Trap
Price
$1.70
Last close
Models
10/13
Active
VS

RAL

Electronic Components
Ralliant Corporation
Quality
7.4
out of 10
Value Trap
Price
$62.34
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType OST Fair ValueOST Upside RAL Fair ValueRAL Upside
Bayesian DCF Intrinsic $0.34 -80.2% $45.02 -27.8%
Earnings Power Value Intrinsic $151.93 +143.7%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $6.36 +275.2% $67.40 +8.1%
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 OST vs RAL — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

OST vs RAL — Which Stock Is More Undervalued?

RAL scores higher with a 7.4/10 quality rating vs OST's 2.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Ostin Technology Group Co., Ltd (OST) and Ralliant Corporation (RAL) 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.

OST currently trades at $1.70 with a QOC of 2.3/10, while RAL trades at $62.34 with a QOC of 7.4/10.

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).