Is GJT or PYT more undervalued in 2026?

CirclFi compares Synthetic Fixed-Income Securiti (GJT) and PPlus Tr GSC-2 Tr Ctf Fltg Rate (PYT) using 13 institutional-grade valuation models. GJT has a Quality of Company score of 1.6/10 while PYT scores 1.6/10.

Which is a better buy — GJT or PYT?

Compare intrinsic values from 13 independent models against current market prices. CirclFi also calculates Value Trap scores. This is not financial advice — conduct your own analysis.

How do GJT and PYT valuations compare?

GJT at $23.22 and PYT at $23.49 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

GJT vs PYT Valuation Comparison 2026 — Intrinsic Value Analysis

GJT

Asset-Backed Securities

Synthetic Fixed-Income Securiti

Quality

1.6

out of 10

Value Trap

—

Price

$23.22

Last close

Models

0/13

Active

PYT

Asset-Backed Securities

PPlus Tr GSC-2 Tr Ctf Fltg Rate

Quality

1.6

out of 10

Value Trap

—

Price

$23.49

Last close

Models

0/13

Active

Model-by-Model Comparison

Model	Type	GJT Fair Value	GJT Upside	PYT Fair Value	PYT Upside

🔒

Unlock Full 13-Model Comparison

Access all valuation models for GJT vs PYT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

GJT vs PYT — Which Stock Is More Undervalued?

Both GJT and PYT score 1.6/10 on quality. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Synthetic Fixed-Income Securiti (GJT) and PPlus Tr GSC-2 Tr Ctf Fltg Rate (PYT) 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.

GJT currently trades at $23.22 with a QOC of 1.6/10, while PYT trades at $23.49 with a QOC of 1.6/10.

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).