Is CCIF or CFND more undervalued in 2026?

CirclFi compares CCIF (CCIF) and C1 Fund Inc. (CFND) using 13 institutional-grade valuation models. CCIF has a Quality of Company score of 1.8/10 while CFND scores 1.7/10.

Which is a better buy — CCIF or CFND?

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 CCIF and CFND valuations compare?

CCIF at $3.13 and CFND at $3.53 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

CCIF vs CFND Valuation Comparison 2026 — Intrinsic Value Analysis

CCIF

Asset Management

CCIF

Quality

1.8

out of 10

Value Trap

—

Price

$3.13

Last close

Models

7/13

Active

CFND

Asset Management

C1 Fund Inc.

Quality

1.7

out of 10

Value Trap

—

Price

$3.53

Last close

Models

5/13

Active

Model-by-Model Comparison

Model	Type	CCIF Fair Value	CCIF Upside	CFND Fair Value	CFND Upside
Bayesian DCF	Intrinsic	$0.83	-73.5%	$0.93	-73.5%
First Chicago	Scenario	$3.99	+23.1%	$4.31	+23.1%
Markov DDM	Intrinsic	$6.29	+101.1%	—	—
ML-RIV	Intrinsic	$•••.••	••.•%	$•••.••	••.•%
Sentiment SOTP	Hybrid	$•••.••	••.•%	$•••.••	••.•%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%
FTNN Topology	Relative	$•••.••	••.•%	$•••.••	••.•%

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CCIF vs CFND — Which Stock Is More Undervalued?

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

Comparing CCIF (CCIF) and C1 Fund Inc. (CFND) 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.

CCIF currently trades at $3.13 with a QOC of 1.8/10, while CFND trades at $3.53 with a QOC of 1.7/10.

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).