Is CLNK or FSOL more undervalued in 2026?

CirclFi compares BITWISE CHAINLINK ETF (CLNK) and Fidelity Solana Fund (FSOL) using 13 institutional-grade valuation models. CLNK has a Quality of Company score of 3.1/10 while FSOL scores 3.0/10.

Which is a better buy — CLNK or FSOL?

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 CLNK and FSOL valuations compare?

CLNK at $16.30 and FSOL at $9.67 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

CLNK vs FSOL Valuation Comparison 2026 — Intrinsic Value Analysis

CLNK

Commodity Contracts Brokers & Dealers

BITWISE CHAINLINK ETF

Quality

3.1

out of 10

Value Trap

—

Price

$16.30

Last close

Models

2/13

Active

FSOL

Commodity Contracts Brokers & Dealers

Fidelity Solana Fund

Quality

3.0

out of 10

Value Trap

—

Price

$9.67

Last close

Models

4/13

Active

Model-by-Model Comparison

Model	Type	CLNK Fair Value	CLNK Upside	FSOL Fair Value	FSOL Upside
ML-RIV	Intrinsic	—	—	$8.36	-13.5%
Dynamic NAV	Asset-Based	$7.98	-51.0%	$4.92	-49.1%
PWERM	Option-Based	$15.04	-7.7%	$9.28	-4.0%
CUCE Ensemble	Ensemble	$•••.••	••.•%	$•••.••	••.•%

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CLNK vs FSOL — Which Stock Is More Undervalued?

CLNK scores higher with a 3.1/10 quality rating vs FSOL's 3.0/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing BITWISE CHAINLINK ETF (CLNK) and Fidelity Solana Fund (FSOL) 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.

CLNK currently trades at $16.30 with a QOC of 3.1/10, while FSOL trades at $9.67 with a QOC of 3.0/10.

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).