Group 1: Report Overview - The report focuses on the construction methods and performance of various ETF portfolios in November 2025, including macro-based, macro + momentum, core - satellite, and trinity style rotation portfolios [2] Group 2: ETF Portfolio Construction Methods Based on Macro Approach - Calculate macro - sensitivity of indices tracked by broad - based, industry - themed, and Smart Beta ETFs according to economic, liquidity, and credit variables, and select ETFs monthly. Also consider adding momentum indicators. Traditional cyclical industries are suitable for economic up - periods, TMT for weak - economic but liquid - abundant times, and consumption benefits from credit expansion. Three ETF portfolios are constructed and rebalanced monthly [5] Trinity Style Rotation - Build a mid - to long - term style rotation model centered on macro - liquidity, compared with the CSI 300 index. Combine three models (growth/value, market - cap, and quality) to get 8 style preference results, then screen target - style - exposed ETFs with controlled industry exposure and set allocation limits [6] Group 3: Macro Industry Portfolio - Select industry - themed ETFs with over 1 - year establishment and over 200 million current scale. Calculate sensitivity scores, adjust according to economic, liquidity, and credit indicators, and select the top 6 industry - themed indices. Currently, with economic indicators rising and liquidity/credit tightening, the portfolio turns to value with high bank and cyclical proportions. November holdings are mainly bank and energy - related ETFs, each with a 16.67% weight. The portfolio has large fluctuations and was close to the CSI 300 in October [7][9][11] Group 4: Macro + Momentum Industry Portfolio - Combine macro and momentum methods to address the left - side bias of macro - based strategies. Use clustering to select one product with the highest 6 - month gain from each of 6 industry - themed groups. The portfolio includes many pro - cyclical industries. November holdings have multiple ETFs, with weights like 16.67% for some and 8.33% for others. The portfolio performed well this year and was close to the CSI 300 in October [12][14][15] Group 5: Core - Satellite Portfolio - Designed to address the high volatility and fast industry rotation of industry - themed ETFs. Use the CSI 300 as the core. Construct three sub - portfolios (broad - based, industry, and Smart Beta) and combine them at 50%, 30%, and 20% respectively. November holdings are mainly mid - to large - cap biased. The portfolio performed steadily this year, outperforming the index almost every month, including in October [16][17][21] Group 6: Trinity Style Rotation ETF Portfolio - The model favors small - cap growth + high - quality segments this period. The portfolio's factor exposure and historical performance are provided. November holdings include ETFs such as Southern CSI 500 ETF and Southern CSI 1000 ETF. The portfolio had significant fluctuations in monthly returns and outperformed the index in most months this year, including in October [22][23][26]

Group 1: Report Industry Investment Rating - No industry investment rating is provided in the report. Group 2: Core Viewpoints of the Report - The report constructs multiple ETF portfolios using macro - based methods and a trinity style rotation model, aiming to capture investment opportunities in different market conditions [2][5]. - Different portfolios have different characteristics. For example, the macro - industry portfolio is adjusted monthly based on economic, liquidity, and credit conditions, and currently leans towards growth with more pharmaceutical holdings [8][11]. - The macro + momentum industry portfolio combines macro and momentum methods, and has performed well this year, almost outperforming the index every month [14][19]. - The core - satellite portfolio uses the CSI 300 as the base - position and combines different sub - portfolios to achieve relatively stable performance [20][24]. - The trinity style rotation ETF portfolio uses macro - liquidity as the core to construct a style rotation model and provides 8 style preference results [6][25]. Group 3: Summary According to Relevant Catalogs 1. ETF Portfolio Construction Methods 1.1 Based on Macro Method of ETF Portfolio Construction - Calculate macro - sensitivity of indexes tracked by broad - based, industry - themed, and Smart Beta ETFs according to economic, liquidity, and credit variables. Consider adding momentum indicators for complementarity [5]. - 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]. - Construct three ETF portfolios: macro - industry portfolio, macro + momentum industry portfolio, and core - satellite industry portfolio, and rebalance monthly [5]. 2.2 Trinity Style Rotation ETF Portfolio Construction - Build a medium - to long - term style rotation model centered on macro - liquidity, and compare it with the CSI 300 index. - Construct three types of models: growth/value rotation model, market - capitalization model, and quality model. Combine the results of the three models to get the final style preference, with a total of 8 style preference results [6]. 2. Macro Industry Portfolio - Select industry - themed indexes tracked by ETFs with a listing period of over 1 year and a current scale of over 200 million. Calculate sensitivity scores of economy, liquidity, and credit monthly, adjust the score directions according to the latest indicators, and sum them up. Select the top 6 industry - themed indexes and allocate equally among the corresponding largest - scale ETFs [8]. - Currently, due to the economic downturn, slightly tight liquidity, and good credit, it selects ETFs insensitive to the economy and sensitive to credit, leaning towards growth with more pharmaceutical holdings [11]. 3. Macro + Momentum Industry Portfolio - Combine macro and momentum methods. Use clustering to divide industry - themed indexes into 6 groups, and select the product with the highest 6 - month increase in each group for equal - weight allocation [14]. - Both the macro and momentum parts select many pharmaceutical - sector products, and the gaming and Internet sectors also account for a large proportion. The portfolio has performed well this year, outperforming the index almost every month [16][19]. 4. Core - Satellite Portfolio - Design a "core - satellite" portfolio with the CSI 300 as the base - position to address the high volatility and rapid industry rotation of industry - themed ETFs [20]. - Build three sub - portfolios: a broad - based portfolio, an industry portfolio (using the macro + momentum industry portfolio), and a Smart Beta portfolio. Weight the three sub - portfolios at 50%, 30%, and 20% respectively to get the final portfolio. The portfolio has performed stably this year, also outperforming the index almost every month [21][24]. 5. Trinity Style Rotation ETF Portfolio - The model currently leans towards small - cap growth + high - quality. The factor exposure and historical performance are presented, and the portfolio's monthly returns and August holdings are also provided [25][30].

Core Insights - The article highlights the strong performance of top investment advisors in the A-share market, with significant excess returns compared to the market average, showcasing their capabilities in a volatile market environment [1][3]. Performance Overview - The average return of the top 300 advisors in the stock trading group reached 27.19%, while the top 10 advisors achieved an impressive average return of 47.41% [2][3]. - In June, the three major indices in the A-share market all showed positive performance, with the Shanghai Composite Index rising by 2.9%, the Shenzhen Component Index by 4.23%, and the ChiNext Index by 8.02% [3]. ETF Group Performance - The average return for the top 200 advisors in the ETF group was 17.34%, with the top 10 achieving an average return of 30.93% [10][11]. - Compared to the benchmark indices, the top advisors significantly outperformed, with the Shanghai Composite Index rising by 5.04% and the Shenzhen Component Index by 5.71% during the same period [11]. Advisor Strategies - Advisors from leading firms like Guangfa Securities and CITIC Securities shared their strategies, focusing on growth stocks and utilizing models like "5+30" to identify high-potential sectors [13][14]. - Risk management strategies were emphasized, including controlling drawdowns and diversifying portfolios to mitigate risks during market fluctuations [15][20]. Institutional Strength - Guangfa Securities, CITIC Securities, and China Galaxy Securities led the rankings in terms of the number of advisors participating in the evaluation, indicating their strong institutional capabilities [23][28]. - The competition among institutions reflects a shift towards a client-centric approach in wealth management, emphasizing the importance of professional capabilities [39]. Future Outlook - Advisors are focusing on sectors with high growth potential, such as innovative pharmaceuticals and aerospace, while also considering macroeconomic factors like Federal Reserve policies [17][22]. - The article suggests that as market volatility becomes the norm, the ability of professional advisors to create value will be crucial for their competitive edge [39].

Core Insights - The report emphasizes the construction of ETF portfolios based on industry rotation models, highlighting the potential for enhanced returns through strategic sector allocation [1][2] - The industry rotation model has demonstrated stable excess returns over the backtesting period from January 2019 to April 2025, achieving a total return of 212.87%, significantly outperforming major indices like the CSI 300, CSI 500, and CSI 1000 [9][10] Industry Rotation Model - The model incorporates six factors: momentum, main buying amount, turnover rate change, deviation rate, intra-industry return deviation, and volatility, with a monthly rebalancing frequency [5][6] - The model's performance is evaluated across different market phases, showing varying factor effectiveness, with momentum and main buying amount consistently positive across the tested periods [6][8] ETF Market Overview - As of the end of 2024, the total scale of stock ETFs reached 29,259.35 billion yuan, with industry-themed ETFs accounting for 6,161.25 billion yuan, indicating a growing trend towards sector-specific investment strategies [25][26] - The report notes the increasing feasibility of using ETFs as tools for industry rotation strategies due to the expanding variety of newly issued industry-themed ETFs [25] ETF Portfolio Construction - The report outlines the construction of ETF portfolios based on the industry rotation model, recommending specific ETFs that align closely with the identified sectors [32][34] - The recommended ETF combinations for June 2025 include sectors such as oil and petrochemicals, banking, coal, transportation, steel, and agriculture, reflecting the model's latest insights [18][37]