ETF策略指数跟踪周报-20251020

Report Industry Investment Rating No relevant content provided. Core View of the Report The report presents several ETF strategy indices constructed with the help of ETFs, tracking their performance and positions on a weekly basis. These indices aim to obtain excess returns relative to the market through different strategies and models [12]. Summary by Directory 1. ETF Strategy Index Tracking - Overall Performance: A table shows the performance of various ETF strategy indices last week, including their returns, comparison benchmarks, benchmark returns, and excess returns. For example, the Huabao Research Large - Small Cap Rotation ETF Strategy Index had a last - week return of - 2.16%, a benchmark (CSI 800) return of - 3.03%, and an excess return of 0.87% [13]. 1.1. Huabao Research Large - Small Cap Rotation ETF Strategy Index - Strategy: It uses multi - dimensional technical indicator factors and a machine - learning model to predict the return difference between the Shenwan Large - Cap Index and the Shenwan Small - Cap Index. The model outputs signals weekly to predict the strength of the indices in the next week and determines positions accordingly to obtain excess returns [3][14]. - Performance: As of 2025/10/17, the excess return since 2024 was 19.59%, the excess return in the past month was 0.35%, and the excess return in the past week was 0.87% [3][14]. 1.2. Huabao Research SmartBeta Enhanced ETF Strategy Index - Strategy: It uses price - volume indicators to time self - built Barra factors and maps timing signals to ETFs based on their exposure to 9 major Barra factors to obtain returns exceeding the market. The selected ETFs cover mainstream broad - based index ETFs and some style and strategy ETFs [3]. - Performance: As of 2025/10/17, the excess return since 2024 was 15.54%, the excess return in the past month was 0.23%, and the excess return in the past week was - 1.38% [3]. 1.3. Huabao Research Quantitative Windmill ETF Strategy Index - Strategy: It starts from a multi - factor perspective, including the grasp of medium - and long - term fundamental dimensions, the tracking of short - term market trends, and the analysis of the behavior of various market participants. It uses valuation and crowding signals to prompt industry risks and multi - dimensionally digs out potential sectors to obtain excess returns [4][22]. - Performance: As of 2025/10/17, the excess return since 2024 was 26.43%, the excess return in the past month was 2.76%, and the excess return in the past week was - 1.23% [4][22]. 1.4. Huabao Research Quantitative Balance Art ETF Strategy Index - Strategy: It adopts a multi - factor system, including economic fundamentals, liquidity, technical aspects, and investor behavior factors, to build a quantitative timing system for trend analysis of the equity market. It establishes a prediction model for the market's large - and small - cap styles to adjust the position distribution of the equity market and obtains excess returns through comprehensive timing and rotation [25]. - Performance: As of 2025/10/17, the excess return since 2024 was - 9.54%, the excess return in the past month was 0.62%, and the excess return in the past week was 0.97% [25]. 1.5. Huabao Research Hot - Spot Tracking ETF Strategy Index - Strategy: It tracks and mines hot - spot index target products in a timely manner based on strategies such as market sentiment analysis, industry major event tracking, investor sentiment and professional opinions, policy and regulatory changes, and historical deduction, and constructs an ETF portfolio that can capture market hot - spots in time to provide references for investors' short - term market trends and help them make more informed investment decisions [5][29]. - Performance: As of 2025/10/17, the excess return in the past month was 3.44%, and the excess return in the past week was 1.33% [5][29]. 1.6. Huabao Research Bond ETF Duration Strategy Index - Strategy: It uses bond market liquidity indicators and price - volume indicators to screen effective timing factors and predicts bond yields through machine - learning methods. When the expected yield is lower than a certain threshold, it reduces the positions of long - duration bonds in the bond investment portfolio to improve the long - term return and drawdown control ability of the portfolio [5][33]. - Performance: As of 2025/10/17, the excess return in the past month was 0.29%, and the excess return in the past week was - 0.03% [5][33].