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].