The Bitwise NEAR ETF (NRR) — the first U.S. spot exchange-traded product offering direct exposure to the NEAR Protocol (NEAR) token — extended its powerful debut rally, closing at $28.10, up 7.46% from $26.15. Launched September 29 on NYSE Arca, NRR holds NEAR directly, holds roughly 7.2 million tokens, and charges a 0.75% management fee. The fund's second session reflected continued buying interest following a record-setting first day that drew $35.5 million in net inflows.
NRR began trading with unusually strong early demand for a newly listed single-asset crypto fund. On its first day, the fund pulled in $35.5 million in net inflows and reached about $36 million in assets under management, with roughly $15.1 million in trading volume. That debut topped the first-day performance of several prior single-token U.S. crypto products when measured relative to the underlying asset's market value, helping carry positive momentum into the fund's second session.
As a spot fund, NRR moves almost one-for-one with the NEAR token it holds. NEAR climbed roughly 10% on the day, trading near $5.38, extending a rally that has seen the token nearly triple over the past month. That advance — which accelerated as the ETF's listing cleared its final regulatory steps — directly powered the fund's share-price gain and reinforced investor sentiment across the crypto-linked ETF complex.
Bitwise is marketing NEAR as a settlement layer for artificial-intelligence agents — autonomous software that can book, pay for, and swap assets on a user's behalf. Central to that thesis is NEAR Intents, the network's cross-chain transaction protocol, whose cumulative volume has surpassed $32 billion, up from under $1 billion a year earlier. The prospect of machine-to-machine payments routing through NEAR has given the fund a distinct growth story beyond a simple token-price bet.
A scheduled protocol upgrade targeted for early October is expected to remove NEAR's 30% developer gas-fee rebate, meaning a larger share of transaction fees will be burned rather than returned. Combined with the network's roughly 5% staking reward — which Bitwise passes through to NRR shareholders via its in-house staking program — the supply-reduction outlook added another tailwind behind the token and the fund.
NRR is a single-asset fund rather than a diversified basket. Its performance is driven almost entirely by one holding: NEAR, which the fund holds directly and stakes in-house to capture network rewards. Because there are no individual equities or sector constituents to diversify the move, the fund's entire gain traced back to NEAR's roughly 10% advance — a reminder that NRR offers concentrated, high-volatility exposure to a single crypto asset rather than the broad diversification of a traditional index ETF.
NRR traded in a range of $27.62 to $28.99 during its second session, closing near the upper end of that band on moderate volume as the initial launch-day burst of activity began to normalize. The fund's debut-day shares had briefly traded at a modest discount to net asset value, and the second-day strength helped narrow that gap. The move aligned with broader strength in digital assets, as the token's rally coincided with renewed focus on AI-related crypto infrastructure themes rather than a single macroeconomic catalyst.
The key question for NRR is whether early inflows translate into sustained, multi-day demand. Investors will be watching daily net-flow figures, the fund's premium or discount to NAV, and whether NEAR Intents transaction volumes hold at elevated levels. The upcoming network upgrade around October 5 could tighten supply further, while the fund's staking yield adds a layer of return beyond price appreciation. Risks include the token's sharp, rapid gains, the possibility that the AI-agent thesis takes longer to materialize, and the inherent volatility of a concentrated single-asset crypto product.
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Sergey Savastiouk, Ph.D. has a degree in Applied Mathematics from Moscow University and has extensive experience as an entrepreneur, investor, manager, and mathematician. His professional expertise is in applied mathematics, mathematical modeling, system and pattern analysis, and software and hardware system integration. He has served as the CEO of several hi-tech start-up companies and nonprofit organizations, which has given him proven capabilities in business strategy for high-tech start-up companies, market assessment, company formation, team building, product development, marketing, and sales. He has published numerous articles in journals and magazines on related fields. As a retail investor, he spent 15 years developing his proprietary trading and quantitative algorithms (now Tickeron’s A.I.), which brought him significant returns in trading the stock market. His current work and goal in founding Tickeron is to bring professional, sophisticated stock market analysis capabilities to retail investors via an easy-to-use interface.
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