LATEST: 📈 Multiple US Solana ETFs have launched this week from Fidelity, VanEck, and Canary Capital — some including onchain staking mechanisms — as asset managers test demand for altcoin-focused investment products. https://t.co/ffsvfdeJO3 ...

LATEST: ⚡ Cathie Wood's ARK Invest purchased an additional $10.2M worth of Bullish shares after the crypto exchange's stock dropped to a new all-time low, buying the dip via three of the company's ETF funds. https://t.co/t82xrIAXnu ...

Overview - Canary Capital launches the Canary Marinade Solana ETF (SOLC) [1] - The ETF provides exposure to Solana's native token through Marinade's liquid staking protocol [1] Investment Strategy - The ETF is designed to reflect Solana network performance while earning staking rewards [1] - The ETF aligns with Solana's high-throughput, low-cost characteristics [1] Staking Mechanism - The ETF is backed by MarinadeFinance's automated staking delegation program [1]

Core Insights - The ETF industry is experiencing continuous growth with the introduction of new funds, including the recently launched State Street SPDR S&P Leveraged Loan ETF (LVLN) [1][2] - LVLN aims to provide broad exposure to the investable leveraged loans universe, charging a gross expense ratio of 40 basis points [2] - The fund will track the S&P USD Select Leveraged Loan Index, which includes U.S. dollar-denominated loans with a minimum size of $500 million and applies various liquidity filters and market value weights [2] Company Insights - State Street Investment Management has a broad index-based fixed income lineup and continues to innovate, offering a low-cost alternative to its actively managed SRLN fund [3] - The SRLN fund, launched in 2013, charges a fee of 70 basis points and has returned 8.4% over the last three years, outperforming its ETF Database Category average [3] - The introduction of LVLN adds to the competitive landscape of leveraged loans ETFs, highlighting the reputation of State Street in this space [3]

RT Capital Markets (@capitalmarkets)BREAKING: @Fidelity, with $6.4T AUM, launches its @Solana ETF $FSOL on @NYSE, bringing staking-enabled regulated SOL exposure from one of the world's largest asset managers. https://t.co/QDwu7pxAif ...

Crypto things you might have missed:-Bitcoin dumps below $90k takes down BTCD-Extreme fear in Stock Market-4 XRP ETFs to launch this week-@hashgraphgroup launches DLT helpdesk for @hedera-$4B inflows for XRP + SOL ETFsJOIN THE DAILY 10:30 AM LIVESTREAM ON YOUTUBE AND X ...

New Wave of Solana ETFs Hits the Markets as Fidelity, Canary, and VanEck Roll Out► https://t.co/T8Z4qT7Mdi https://t.co/T8Z4qT7Mdi ...

RT Emmett Gallic (@emmettgallic)Fidelity has launched their Solana ETF $FSOL today seeding it with 23.4K $SOL ($3.3M)They are not using their own custody solution for the ETF and instead are using a combination of Bitgo, Coinbase and Anchorage Digital. https://t.co/xPUTSL2U4e ...

How To Protect Your Portfolio With Crash-Proof ETFs https://t.co/FZEgzOYxdg ...

Quantitative Models and Construction Methods 1. **Model Name**: Industry Crowding Monitoring Model **Model Construction Idea**: This model is designed to monitor the crowding levels of industries on a daily basis, focusing on the Shenwan First-Level Industry Index. It identifies industries with high or low crowding levels to provide actionable insights for investors. [3] **Model Construction Process**: The model calculates the crowding level of each industry based on specific metrics, such as fund flows and other market indicators. The daily crowding levels are ranked, and industries with significant changes in crowding levels are highlighted. For example, the model identified that the crowding levels of military and media industries experienced significant changes recently. [3] **Model Evaluation**: The model provides a useful tool for identifying industry trends and potential investment opportunities by analyzing crowding dynamics. [3] 2. **Model Name**: Premium Rate Z-Score Model **Model Construction Idea**: This model is used to screen ETF products by identifying potential arbitrage opportunities based on the Z-score of their premium rates. [4] **Model Construction Process**: The Z-score is calculated using rolling measurements of the premium rate of ETF products. The model flags ETFs with significant deviations from their historical averages, indicating potential arbitrage opportunities or risks of price corrections. [4] **Model Evaluation**: The model effectively identifies ETFs with potential arbitrage opportunities, but it also warns of potential price correction risks. [4] --- Model Backtesting Results 1. **Industry Crowding Monitoring Model**: No specific numerical backtesting results were provided in the report. [3] 2. **Premium Rate Z-Score Model**: No specific numerical backtesting results were provided in the report. [4] --- Quantitative Factors and Construction Methods No specific quantitative factors were explicitly mentioned or constructed in the report. --- Factor Backtesting Results No specific quantitative factor backtesting results were provided in the report.