1. Report Industry Investment Rating The provided content does not include information about the industry investment rating, so this part is skipped. 2. Core Viewpoints of the Report - The report constructs multiple ETF portfolios, including macro industry portfolio, macro + momentum industry portfolio, core - satellite portfolio, and trinity style rotation ETF portfolio, aiming to find potential investment opportunities and manage risks [1][5]. - The macro industry portfolio selects ETFs based on the sensitivity scores of economy, liquidity, and credit, and currently leans towards TMT and innovative drugs [1][7]. - The macro + momentum industry portfolio combines macro and momentum methods, with a relatively high proportion of cyclical industries selected by the momentum approach [1][14]. - The core - satellite portfolio uses the CSI 300 as the core and combines it with industry and Smart Beta portfolios, showing relatively stable performance [21]. - The trinity style rotation ETF portfolio constructs a style rotation model centered on macro - liquidity, and the current model leans towards the small - cap growth - high - quality part [6][29]. 3. Summary According to the Directory 3.1 ETF Portfolio Construction Methods 3.1.1 ETF Portfolio Construction Based on Macro - Methods - Calculate the macro - sensitivity of the indices tracked by broad - based, industry - themed, and Smart Beta ETFs according to economic, liquidity, and credit variables, and select ETFs monthly based on the current macro - variable status and index macro - sensitivity [5]. - Traditional cyclical industries are sensitive to the economy, TMT is sensitive to liquidity and insensitive to the economy, and consumption is relatively sensitive to credit. State - owned enterprises and ESG - related themes have low sensitivity to liquidity and credit [5]. - Three ETF portfolios, namely the macro industry portfolio, macro + momentum industry portfolio, and core - satellite industry portfolio, are constructed and rebalanced monthly [5]. 3.1.2 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 [6]. - Construct three types of models: growth/value rotation model, market - cap 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]. - Select ETFs with high exposure to the target style, control the industry exposure of ETFs to be similar to the style portfolio, and set the allocation upper and lower limits of 3% - 20% to obtain the ETF allocation model [6]. 3.2 Macro Industry Portfolio - Select industry - themed indices tracked by ETFs that have been established for more than 1 year and have a current scale of over 200 million. Calculate the sensitivity scores of economy, liquidity, and credit monthly, adjust the score directions according to the latest economic, liquidity, and credit judgment indicators, and sum them up. If liquidity and credit deviate significantly, remove the liquidity score. Select the top 6 industry - themed indices and allocate the corresponding largest - scale ETFs equally [7][8]. - Currently, the economy's leading indicators are falling, liquidity is loose, and credit indicators are tightened. The portfolio is configured with ETFs that are insensitive to the economy, sensitive to liquidity, and insensitive to credit, mainly focusing on TMT and innovative drugs. The March positions include ETFs such as GF China Hong Kong Innovative Drugs ETF and Huaxia CSI 5G Communication Theme ETF [12]. - The portfolio has relatively large fluctuations, and the excess return declined in February [13]. 3.3 Macro + Momentum Industry Portfolio - Combine the macro and momentum methods to form a complementary relationship. The momentum method first groups industry themes into 6 different groups using clustering, and then selects the product with the highest increase in the past 6 months from each group for equal - weight allocation [14]. - The industries selected by the momentum method still have a relatively high proportion of cyclical industries. The March positions include ETFs such as GF China Hong Kong Innovative Drugs ETF and Cathay CSI Semiconductor Materials and Equipment Theme ETF [18]. - The portfolio has performed well this year and continued to outperform in February [19]. 3.4 Core - Satellite Portfolio - Due to the high volatility of industry - themed ETFs and the accelerated industry rotation in the past two years, a "core - satellite" portfolio with the CSI 300 as the core is designed [21]. - Use the macro - sensitivity measurement method to measure the three ETF - tracking index pools of domestic broad - based, industry - themed, and Smart Beta ETFs, construct three stock portfolios, and then weight them at 50%, 30%, and 20% to obtain the final core + satellite portfolio [21]. - The current configuration of broad - based ETFs leans towards the science and technology innovation board and the ChiNext board. The portfolio has performed stably, outperforming in most months except for December, and continued to outperform in February 2026 [26][28]. 3.5 Trinity Style Rotation ETF Portfolio - The current model leans towards the small - cap growth - high - quality part. The factor exposure and historical performance of the model are provided, including factors such as the bond futures - spot spread, US one - year Treasury yield, and trading volume of the Shanghai and Shenzhen stock markets [29][30]. - The March positions include ETFs such as Invesco Great Wall CSI Hong Kong Stock Connect Technology ETF and Invesco Great Wall CSI Guoxin Hong Kong Stock Connect Central State - owned Enterprise Dividend ETF [35]. - The portfolio has achieved certain excess returns in many months [33].

1. Report Industry Investment Rating - Not provided in the given content 2. Core Viewpoints of the Report - The report focuses on constructing multiple ETF portfolios, including macro industry, macro + momentum industry, core - satellite, and trinity style rotation portfolios, aiming to find better investment opportunities by combining macro factors, momentum factors, and style rotation [4][5]. - Different industries have different sensitivities to economic, liquidity, and credit factors. For example, traditional cycle industries are sensitive to the economy, TMT is sensitive to liquidity, and consumption is sensitive to credit [4]. 3. Summary According to Relevant Catalogs 3.1 ETF Portfolio Construction Methods 3.1.1 Based on Macro Method - Calculate macro - sensitivities of broad - based, industry - themed, and Smart Beta ETFs based on economic, liquidity, and credit variables. Combine with momentum indicators for complementary analysis [4]. - Traditional cycle industries are suitable for economic up - periods, TMT for weak - economy but loose - liquidity periods, and consumption for credit - expansion periods. State - owned enterprises and ESG - related themes have low sensitivities to liquidity and credit [4]. - Construct three ETF portfolios (macro industry, macro + momentum industry, and core - satellite) and adjust positions monthly [4]. 3.1.2 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 [5]. - Construct three types of models (growth/value rotation, market - cap, and quality models) by screening macro, fundamental, and market - sentiment factors. The model has 8 style - preference results [5]. - Select ETFs with high exposure to the target style, control industry exposure, and set allocation limits to get the final ETF allocation model [5]. 3.2 Macro Industry Portfolio - Select industry - themed ETFs with over 1 - year establishment and over 200 million current scale. Calculate sensitivity scores of economic, liquidity, and credit factors monthly, adjust scores according to the latest indicators, and sum them up. If liquidity and credit deviate significantly, remove the liquidity score. Select the top 6 industry - themed indices and corresponding largest - scale ETFs for equal - weight allocation [6][7]. - Currently, with falling economic leading indicators, loose liquidity, and tightened credit, the portfolio is biased towards TMT and consumption. The February positions are shown in Table 1 [8]. - The portfolio has large fluctuations and outperformed the benchmark significantly in January [11]. 3.3 Macro + Momentum Industry Portfolio - Combine macro and momentum methods to address the left - side nature of macro - based strategies (low win - rate but high odds). Use clustering to group industry - themed indices and select the highest - rising product in each group in the past 6 months for equal - weight allocation [12]. - The momentum - selected industries still have a high proportion of cyclical industries. The February positions are shown in Table 3 [16]. - The portfolio has performed well this year and outperformed the CSI 300 significantly in January [17]. 3.4 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 [19]. - Calculate macro - sensitivities for broad - based, industry - themed, and Smart Beta ETFs, construct three stock portfolios, and weight them at 50%, 30%, and 20% respectively [19]. - The current allocation of broad - based ETFs is biased towards the Sci - tech Innovation Board and the ChiNext. The portfolio has performed stably, outperforming the benchmark in most months except December, and had significant excess returns in January 2026 [23][24]. 3.5 Trinity Style Rotation ETF Portfolio - The model currently favors the small - cap growth - high - quality segment. The factor exposures and historical performance are shown in Table 7 [26]. - The February positions are shown in Table 9 [31]. - The portfolio has achieved certain excess returns, especially in some months such as August 2025 and January 2026 [29].

Group 1: Report Information - Report Date: October 10, 2025 [1] - Report Title: Shenwan Hongyuan Gold ETF Portfolio 202510 [1] - Analysts: Shen Siyi, Deng Hu [3] - Research Support: Bai Haotian [3] - Contact: Shen Enyi [3] Group 2: Investment Ratings - No industry investment ratings are provided in the report. Group 3: Core Views - The report constructs four ETF portfolios, including the macro industry portfolio, macro + momentum industry portfolio, core - satellite portfolio, and trinity style rotation ETF portfolio, based on macro - sensitivity and momentum analysis, aiming to capture investment opportunities in different market environments [5][8]. - The current economic leading indicators are rising, liquidity indicators are slightly tight, and credit indicators remain positive. The portfolios are shifting towards a more balanced allocation, with an increased proportion of consumer sectors [5]. - The trinity style rotation model combines macro - liquidity, fundamental, and market sentiment factors to construct a medium - to long - term style rotation model, providing insights into market style preferences [5][9]. Group 4: ETF Portfolio Construction Methods 4.1 Based on Macro - Method - Calculate macro - sensitivity for broad - based, industry - theme, and Smart Beta ETFs based on economic, liquidity, and credit variables. Traditional cyclical industries are sensitive to the economy, TMT to liquidity, and consumption to credit [8]. - Construct three ETF portfolios (macro industry, macro + momentum industry, and core - satellite) using macro - sensitivity and momentum, and rebalance monthly [8]. 4.2 Trinity Style Rotation ETF Portfolio - Build a medium - to long - term style rotation model centered on macro - liquidity, comparing with the CSI 300 index. Screen macro, fundamental, and market sentiment factors to construct three types of models (growth/value, market - cap, and quality) [9]. Group 5: Portfolio Details 5.1 Macro Industry Portfolio - Select the top 6 industry - theme indices based on macro - sensitivity scores, and equally weight the largest - scale corresponding ETFs. Currently, the portfolio is more balanced with an increased consumer proportion [5][10]. - October 2025 holdings include ETFs related to tourism, home appliances, chemicals, etc. [14]. - In 2025, the portfolio had varying monthly excess returns, with positive excess returns in September [15]. 5.2 Macro + Momentum Industry Portfolio - Combine macro and momentum methods. The pharmaceutical sector's weight is further reduced, and rare earth and battery sectors are selected on the momentum side [5][16]. - October 2025 holdings include multiple industry - themed ETFs [18]. - The portfolio performed well in 2025, with positive excess returns in September after a drawdown in August [19]. 5.3 Core - Satellite Portfolio - Use the CSI 300 as the core and combine broad - based, industry, and Smart Beta portfolios. Weight them at 50%, 30%, and 20% respectively [20][21]. - October 2025 holdings include a mix of broad - based and industry - themed ETFs [24][25]. - The portfolio performed steadily in 2025, outperforming the index almost every month [25]. 5.4 Trinity Style Rotation ETF Portfolio - The model currently favors small - cap growth and high - quality styles. The portfolio's factor exposure and historical performance are presented [26][27]. - October 2025 holdings include ETFs related to small - cap indices and high - growth sectors [31]. - The portfolio has shown certain performance since 2021, with positive excess returns in September 2025 [30].

Core Viewpoint - The article discusses the construction and testing of index congestion indicators to enhance the performance of ETF portfolios by removing ETFs corresponding to highly congested indices, thereby reducing the impact of market reversals on the ETF portfolio [1][2][3]. Group 1: Index Congestion Indicators - The construction of congestion indicators is based on six dimensions: trading volume, volatility level, financing balance, financing increment, fund holdings, and capital flow [2][23]. - The effectiveness of these congestion indicators is tested by comparing the performance of a congestion index portfolio against the average performance of sample equity indices [25][55]. - The overall correlation between different congestion indicators is low, indicating that a multi-indicator congestion index portfolio performs more stably [2][55]. Group 2: ETF Portfolio Construction - The article outlines the process of constructing relative return index portfolios by excluding indices with two or more congestion indicators from the top-scoring indices [3][63]. - Backtesting results show that the constructed portfolios outperform those that do not consider index congestion, with a cumulative return of 355.05% from December 31, 2016, to June 30, 2025 [12][64]. - The annualized return of the relative return index portfolio combined with index congestion is 19.75%, compared to 17.67% for the standard relative return portfolio [64][66]. Group 3: A-share Market ETF Development - Since Q4 2018, the number of equity ETFs in the A-share market has increased from 133 to 972 by Q2 2025, with total assets rising from 0.27 trillion yuan to 3.03 trillion yuan [7]. Group 4: Backtesting Results - The cumulative return of the relative return ETF portfolio from December 31, 2016, to June 30, 2025, is 201.79%, significantly higher than the benchmark portfolio's return of 33.38% [12][70]. - The performance of the relative return ETF portfolio shows significant excess returns in most years during the backtesting period [12][66]. Group 5: Individual Congestion Indicators - The article details the performance of individual congestion indicators, such as trading volume and beta, showing that portfolios based on these indicators generally underperform compared to sample equity indices [26][33][38]. - For example, the trading volume congestion index portfolio had a cumulative return of -11.66% compared to 41.03% for the sample equity index portfolio [26][33]. Group 6: Multi-Indicator Combination - A multi-indicator congestion index portfolio is constructed by combining the six different congestion indicators, which shows a low average correlation among them [55][58]. - Backtesting results indicate that multi-indicator portfolios generally underperform compared to sample equity indices, particularly those with two or more congestion indicators [59][70].