ETF Landscape - Bitwise joins Grayscale and Canary in filing for a spot $SUI ETF [1] - The industry anticipates a potentially significant development in 2026 [1] Cryptocurrency Focus - The report centers on a spot @SuiNetwork ETF [1]

New Product Launch - Bands (@bandsdotfun) enables users to launch token-based ETFs on Solana [1] - Users can create their own Creator ETF by naming it and adding assets they believe in [1] - Investors can directly invest in the creator's investment thesis [1] Platform Features - The platform allows users to publish their Creator ETFs on Solana [1] Industry Impact - The launch of token-based ETFs on Solana provides a new investment avenue for users [1]

Platform Update - RT Bands (@bandsdotfun) 在 Solana 上推出 Creator ETF 功能 [1] - 用户可以自定义 ETF 名称，添加资产并发布 [1] - 投资者可以直接投资于创建者的投资理念 [1]

SCARLET: WELCOME TO "ETF IQ." I AM SCARLET FU. KATIE: AND I AM KATIE GREIFELD. IT IS FINALLY HERE.SCARLET: HOLIDAY-SHORTENED TRADING WEEK. KATIE: AND OUR LAST SHOW OF THE YEAR. NEARLY $19 TRILLION GLOBAL ETF INDUSTRY, THE YEAR AS DISCUSSED IS WINDING DOWN AND STOCKS ARE MOVING HIGHER.THE S&P IS UP FOR THE LONGEST WINNING RUN SINCE 2018. WE WILL DISCUSS THE YEAR THAT WAS AND THE YEAR AHEAD. SCARLET: JUST MOMENTS, SHE WILL JOIN US TO DISCUSS THE OUTLOOK FOR 2026.KATIE: WE DISCUSSED THE SHAREHOLDER VOTE TO CON ...

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 and tracks changes in crowding over time[3]. - **Model Construction Process**: The model calculates the crowding level of each industry based on specific metrics (not detailed in the report). It then ranks industries by their crowding levels and highlights those with significant changes in crowding. For example, the report notes that the military and retail industries had high crowding levels, while the computer industry had relatively low levels. Additionally, it tracks main fund flows into and out of industries over recent trading days[3]. - **Model Evaluation**: The model provides actionable insights into industry crowding trends, helping investors identify potential opportunities or risks in specific sectors[3]. 2. Model Name: Premium Rate Z-Score Model - **Model Construction Idea**: This model is used to screen ETF products for potential arbitrage opportunities by calculating the Z-score of their premium rates. It also serves as a warning signal for potential price corrections in ETFs[4]. - **Model Construction Process**: The model involves rolling calculations of the Z-score for the premium rates of various ETFs. The Z-score is calculated as: $ Z = \frac{(P - \mu)}{\sigma} $ where $ P $ is the current premium rate, $ \mu $ is the mean premium rate over a rolling window, and $ \sigma $ is the standard deviation of the premium rate over the same window. ETFs with extreme Z-scores are flagged as potential arbitrage opportunities or correction risks[4]. - **Model Evaluation**: The model is effective in identifying ETFs with significant deviations from their historical premium rates, providing opportunities for arbitrage or risk management[4]. Model Backtesting Results 1. Industry Crowding Monitoring Model - No specific numerical backtesting results are provided for this model in the report[3]. 2. Premium Rate Z-Score Model - No specific numerical backtesting results are provided for this model in the report[4]. Quantitative Factors and Construction Methods No specific quantitative factors are detailed in the report. Factor Backtesting Results No specific backtesting results for factors are detailed in the report.

Group 1 - The core viewpoint of the article highlights the positive performance of the Chinese stock market, with the CSI A500 index rising by 1.2%, and both the CSI A100 and A50 indices increasing by 1% [1] - The A500 ETF from E Fund (159361) saw a trading volume exceeding 7.5 billion yuan, indicating high market attention and significant net subscriptions of over 1.5 billion units [1] - Huaxi Securities notes that the recent interest rate changes by the Federal Reserve and the Bank of Japan have alleviated concerns about arbitrage trading reversals, suggesting a favorable environment for foreign capital inflow and increased insurance fund investments [1] Group 2 - The recent large-scale net subscriptions in stock ETFs and the significant trading volume in multiple broad-based ETFs indicate a trend of incremental capital favoring opportunistic buying at lower prices [1]

12月22日，两市股指盘中强势拉升，沪指重返3900点关口，创业板指、科创50指数大涨逾2%。 12月22日，两市股指盘中强势拉升，沪指重返3900点关口，创业板指、科创50指数大涨逾2%。 ...

LATEST: 📊 BlackRock's Bitcoin ETF ranked sixth in 2025 inflows despite being down 9.6% for the year, making it the only fund in the top 25 with negative returns, according to Bloomberg analyst Eric Balchunas. https://t.co/wbaIt1iVAd ...

Market Overview - The performance of major indices during the sample period shows that SGE Gold 9999, CSI 2000, and S&P 500 had returns of 1.24%, 0.30%, and 0.10% respectively, ranking them at the top [1] - In terms of industry performance, retail trade, non-bank financials, and beauty care sectors led with returns of 6.66%, 2.90%, and 2.87% respectively, while electronics, power equipment, and machinery sectors lagged with returns of -3.28%, -3.12%, and -1.56% [1][15] ETF Product Overview Market Performance - Commodity ETFs had the best average performance with a weighted average return of 0.92%, while QDII ETFs had the worst performance with a return of -2.01% [19] - CSI 2000 and CSI 500 ETFs performed well with weighted average returns of 0.63% and 0.06% respectively, while STAR Market related ETFs had poor performance with returns of -2.55% and -2.48% [19] Fund Flow - Broad-based ETFs saw the highest net inflow of 406.15 billion, while money market ETFs experienced the largest net outflow of -19.43 billion [2][25] - From an industry perspective, technology sector ETFs had the highest net inflow of 100.54 billion, while traditional manufacturing sector ETFs had the lowest net inflow of -29.55 billion [27] Trading Volume - Broad-based ETFs experienced the highest increase in average daily trading volume, with a change rate of 20.12%, while QDII ETFs saw a decrease of -7.34% [31][33] - Financial real estate sector ETFs had the highest increase in average daily trading volume change rate at 15.85%, while the biopharmaceutical sector saw a decrease of -7.10% [37]

Core Insights - The article reports a significant outflow of $644 million from Ethereum spot ETFs during the trading week from December 15 to December 19 [1] - All nine Ethereum ETFs experienced net outflows, with Blackrock's ETF ETHA leading the losses at $558 million for the week [1] - Historically, the total net inflow for ETHA has reached $12.67 billion [1] Summary by Category Market Performance - Ethereum spot ETFs saw a total net outflow of $644 million in the specified week [1] - Blackrock's ETHA ETF recorded the highest weekly net outflow of $558 million [1] Historical Data - The total historical net inflow for Blackrock's ETHA ETF stands at $12.67 billion [1]