Is BAVA or BSOL more undervalued in 2026?

CirclFi compares BITWISE AVALANCHE ETF (BAVA) and Bitwise Solana Staking ETF (BSOL) using 13 institutional-grade valuation models. BAVA has a Quality of Company score of 1.6/10 while BSOL scores 2.5/10.

Which is a better buy — BAVA or BSOL?

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 BAVA and BSOL valuations compare?

BAVA at $23.98 and BSOL at $11.09 are compared across Bayesian DCF, EPV, EROIC Spread, First Chicago, Markov DDM, and 8 additional models.

BAVA vs BSOL Valuation Comparison 2026 — Intrinsic Value Analysis

BAVA

Commodity Contracts Brokers & Dealers

BITWISE AVALANCHE ETF

Quality

1.6

out of 10

Value Trap

—

Price

$23.98

Last close

Models

0/13

Active

BSOL

Commodity Contracts Brokers & Dealers

Bitwise Solana Staking ETF

Quality

2.5

out of 10

Value Trap

—

Price

$11.09

Last close

Models

2/13

Active

Model-by-Model Comparison

Model	Type	BAVA Fair Value	BAVA Upside	BSOL Fair Value	BSOL Upside
Dynamic NAV	Asset-Based	—	—	$5.48	-50.6%
PWERM	Option-Based	—	—	$10.60	-4.4%

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BAVA vs BSOL — Which Stock Is More Undervalued?

BSOL scores higher with a 2.5/10 quality rating vs BAVA's 1.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing BITWISE AVALANCHE ETF (BAVA) and Bitwise Solana Staking ETF (BSOL) 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.

BAVA currently trades at $23.98 with a QOC of 1.6/10, while BSOL trades at $11.09 with a QOC of 2.5/10.

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).