Report's Investment Rating for the Industry The provided content does not mention the industry investment rating. Core Views of the Report - The report constructs multiple ETF portfolios, including macro industry, macro + momentum industry, core - satellite, and trinity style rotation portfolios, to capture investment opportunities and manage risks in the ETF market [1][5]. - It combines macro - based and momentum - based methods to form complementary strategies, aiming to improve the performance of the portfolios [12]. - The trinity style rotation model uses macro liquidity as the core to build a long - term style rotation model and selects ETFs based on the model's results [6]. Summary by Relevant Catalog 1. ETF Portfolio Construction Methods 1.1 Based on Macro Method - Calculate the macro - sensitivity scores of economic, liquidity, and credit for industry - themed ETFs, and adjust the scores according to the latest indicators. Select the top 6 industry - themed indices and corresponding largest - scale ETFs for equal - weight allocation [1][7]. - Traditional cyclical industries are sensitive to the economy, TMT is sensitive to liquidity, and consumption is sensitive to credit. State - owned enterprises and ESG - related themes have low sensitivity to liquidity and credit [5]. 1.2 Trinity Style Rotation - Build a long - term style rotation model centered on macro liquidity, including growth/value, market capitalization, and quality models. Combine the results of the three models to get the final style preference [6]. - Screen ETFs with high exposure to the target style, control industry exposure, and set allocation limits to obtain the ETF allocation model [6]. 2. Macro Industry Portfolio - Select industry - themed ETFs that have been established for over 1 year and have a current scale of over 200 million. Calculate and adjust sensitivity scores, and remove liquidity scores if there is a significant divergence between liquidity and credit. Then select the top 6 industry - themed indices and corresponding largest - scale ETFs for equal - weight allocation [7][8]. - Currently, with economic forward - looking indicators rising and liquidity and credit indicators tightening, the portfolio is value - oriented with high proportions of banks and cyclical sectors. The December 2025 holdings include Huabao CSI Bank ETF, Cathay CSI Coal ETF, etc. [9]. - The portfolio has large fluctuations and outperformed the benchmark significantly in November 2025 [11]. 3. Macro + Momentum Industry Portfolio - Combine macro - based and momentum - based methods. Use clustering to group industry - themed indices and select the product with the highest 6 - month return from each group for equal - weight allocation [12]. - The December 2025 holdings include Huabao CSI Bank ETF, Cathay CSI Coal ETF, and others. The battery and metal industries selected by momentum have increased [15]. - The portfolio has performed well this year and outperformed the CSI 300 significantly in November 2025 [16]. 4. Core - Satellite Portfolio - Design a "core - satellite" portfolio with the CSI 300 as the core to address the high volatility and rapid industry rotation of industry - themed ETFs [18]. - Calculate macro - sensitivity scores for domestic broad - based, industry - themed, and Smart Beta ETFs, construct three stock portfolios, and weight them at 50%, 30%, and 20% respectively [18]. - The December 2025 holdings include Huatai - Peregrine CSI 300 ETF, Huaxia SSE 50 ETF, etc. The portfolio has been stable this year and outperformed the index almost every month, including in November 2025 [21][23]. 5. Trinity Style Rotation ETF Portfolio - The model currently favors small - cap growth + high - quality segments. The factor exposures and historical performance are presented in the report [24]. - The December 2025 holdings include Southern CSI 500ETF, Southern CSI 1000ETF, etc. [30].

- The report primarily focuses on the weekly performance of major indices in the A-share market, highlighting that the ChiNext Index had the highest increase of 4.74%, while the Shanghai Composite Index experienced the largest decline of 0.26%[10][11] - The financing balance of the Shanghai and Shenzhen stock markets reached 22,811.21 billion yuan on September 2, an increase of 808.89 billion yuan compared to the previous week. The financing balance was 22,650.35 billion yuan, up by 802.55 billion yuan, and the securities lending balance was 160.85 billion yuan, up by 6.34 billion yuan[13][14][16] - The report provides insights into industry-specific financing and securities lending characteristics. For financing, the electronics, communication, and non-bank financial industries had the highest net financing purchases, while the comprehensive, environmental protection, and beauty care industries had the lowest[27][29][31] - Regarding securities lending, the power equipment, pharmaceutical biology, and electronics industries had the highest net securities lending sales, while the basic chemical, computer, and transportation industries had the lowest[32][33][40] - The report highlights the top five ETFs with the highest net financing purchases, including Guotai CSI All Index Communication Equipment ETF, GF CSI Hong Kong Innovative Medicine (QDII-ETF), Bosera STAR Market Artificial Intelligence ETF, Huaan Gold Easy (ETF), and E Fund CSI Hong Kong Securities Investment Theme ETF[42][44][45] - The top five individual stocks with the highest net financing purchases last week were Victory Macro Technology (300476), Zhongji Xuchuang (300308), New Yisheng (300502), East Fortune (300059), and Cambrian (688256)[47][49][50] - The top five individual stocks with the highest net securities lending sales last week were East Fortune (300059), Huagong Technology (000988), Pacific (601099), Xinwanda (300207), and Zhongji Xuchuang (300308)[51][52]