NEAR ETF Filing: Bitwise Targets Spot Market
Bitwise Files for NEAR Spot ETF
Digital asset manager Bitwise has filed with the U.S. Securities and Exchange Commission (SEC) for a spot exchange-traded fund (ETF) tracking the NEAR Protocol token (NEAR). This adds to the increasing number of altcoin ETF applications seeking regulatory approval.
The proposed Bitwise NEAR ETF will track the NEAR token's price, less expenses, using a traditional brokerage. Bitwise's May 6 registration statement details this approach. Coinbase Custody is named as the proposed custodian. The management fee, ticker symbol, and target exchange remain undisclosed.
Bitwise will also submit a Form 19b-4 filing with the SEC to formally initiate the regulatory approval process. This follows their April 28 registration of a trust associated with the NEAR ETF in Delaware.
NEAR Joins Growing Lineup of Spot Crypto ETFs
The SEC currently faces at least a dozen spot crypto ETF applications for review in 2025. These include assets like Litecoin (LTC), Dogecoin (DOGE), Solana (SOL), XRP, Cardano (ADA), Hedera (HBAR), Polkadot (DOT), Chainlink (LINK), Avalanche (AVAX), Aptos (APT), and Sui (SUI).
Bitwise also has pending applications for spot DOGE, SOL, and XRP ETFs. Their approved spot Bitcoin (BTC) and Ether (ETH) ETFs, listed on the NYSE Arca, have accumulated $2.35 billion in net inflows since their launch last year.
NEAR, the native token of the Near layer-1 blockchain, currently holds a $2.73 billion market capitalization (CoinGecko data), ranking 44th among cryptocurrencies.
Once considered a potential "Ethereum killer," NEAR’s proponents position it as a solution to the blockchain trilemma (security, scalability, decentralization). Utilizing Nightshade sharding, NEAR boasts transaction speeds of up to 100,000 transactions per second, secured by 265 active validators (Nearblocks.io data).
The Near ecosystem transitioned its focus from decentralized finance (DeFi) to AI infrastructure in 2024, announcing plans to build the world's largest open-source large language model.
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