Is FSCO or FTHY more undervalued in 2026?

CirclFi compares FSCO (FSCO) and FTHY (FTHY) using 13 institutional-grade valuation models. FSCO has a Quality of Company score of 1.7/10 while FTHY scores 1.8/10.

Which is a better buy — FSCO or FTHY?

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 FSCO and FTHY valuations compare?

FSCO at $5.07 and FTHY at $13.75 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

FSCO vs FTHY Valuation Comparison 2026 — Intrinsic Value Analysis

FSCO

Asset Management

FSCO

Quality

1.7

out of 10

Value Trap

—

Price

$5.07

Last close

Models

6/13

Active

FTHY

Asset Management

FTHY

Quality

1.8

out of 10

Value Trap

—

Price

$13.75

Last close

Models

6/13

Active

Model-by-Model Comparison

Model	Type	FSCO Fair Value	FSCO Upside	FTHY Fair Value	FTHY Upside
Bayesian DCF	Intrinsic	$1.34	-73.5%	$3.64	-73.5%
First Chicago	Scenario	$•••.••	••.•%	$•••.••	••.•%
Markov DDM	Intrinsic	$6.96	+39.2%	$13.11	-4.6%
ML-RIV	Intrinsic	$•••.••	••.•%	$•••.••	••.•%
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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FSCO vs FTHY — Which Stock Is More Undervalued?

FTHY scores higher with a 1.8/10 quality rating vs FSCO's 1.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing FSCO (FSCO) and FTHY (FTHY) 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.

FSCO currently trades at $5.07 with a QOC of 1.7/10, while FTHY trades at $13.75 with a QOC of 1.8/10.

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).