ETF策略指数跟踪周报-20260202

1. Report Industry Investment Rating - Not provided in the content 2. Core Viewpoints - The report presents several ETF strategy indices constructed by Huabao Research and tracks their performance and positions on a weekly basis, aiming to help investors convert quantitative models or subjective views into practical investment strategies [11] 3. Summary by Relevant Catalog 3.1 ETF Strategy Index Tracking - Overall Performance: The table shows the performance of various ETF strategy indices last week. The Huabao Research Quantitative Windmill ETF Strategy Index had the highest weekly excess return of 2.56%, while the Huabao Research SmartBeta Enhanced ETF Strategy Index had the lowest weekly excess return of -2.76% [12] 3.1.1 Huabao Research Size 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. It outputs weekly signals to predict the strength of the indices in the next week and determines positions accordingly to obtain excess returns [13] - Performance: As of 2026/1/30, the excess return since 2024 was 29.34%, the excess return in the past month was 5.89%, and the excess return in the past week was - 1.86%. The index's positions include 50% in the CSI 500ETF and 50% in the CSI 1000ETF [13][17] 3.1.2 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 achieve market - outperforming returns. The selected ETFs cover mainstream broad - based index ETFs and some style and strategy ETFs [17] - Performance: As of 2026/1/30, the excess return since 2024 was 20.15%, the excess return in the past month was - 2.11%, and the excess return in the past week was - 2.76%. The index's positions are mainly in several science - innovation and growth - style ETFs [17] 3.1.3 Huabao Research Quantitative Windmill ETF Strategy Index - Strategy: It starts from a multi - factor perspective, including the grasp of medium - to - long - term fundamentals, tracking of short - term market trends, and analysis of the behavior of various market participants. It uses valuation and crowding signals to indicate industry risks and multi - dimensionally digs out potential sectors to obtain excess returns [20] - Performance: As of 2026/1/30, the excess return since 2024 was 51.39%, the excess return in the past month was 6.51%, and the excess return in the past week was 2.56%. The index's positions are mainly in commodity - related and financial - related ETFs [20][25] 3.1.4 Huabao Research Quantitative Balance ETF Strategy Index - Strategy: It adopts a multi - factor system, including economic fundamentals, liquidity, technical analysis, and investor behavior factors, to construct a quantitative timing system for trend analysis of the equity market. It also builds a prediction model for market large - and small - cap styles to adjust the equity market position distribution and obtain excess returns through comprehensive timing and rotation [24] - Performance: As of 2026/1/30, the excess return since 2024 was - 10.24%, the excess return in the past month was 0.48%, and the excess return in the past week was - 0.36%. The index's positions include bonds and equity - based ETFs [24][27] 3.1.5 Huabao Research Hot - Spot Tracking ETF Strategy Index - Strategy: It uses strategies such as market sentiment analysis, tracking of major industry events, investor sentiment and professional opinions, policy and regulatory changes, and historical analysis to track and dig out hot - spot index target products in a timely manner, constructing an ETF portfolio that can capture market hot spots and providing short - term market trend references for investors [27] - Performance: As of 2026/1/30, the excess return in the past month was 6.21%, and the excess return in the past week was 3.21%. The index's positions are mainly in commodity, Hong - Kong - stock, and short - term financing ETFs [27][30] 3.1.6 Huabao Research Bond ETF Duration Strategy Index - Strategy: It uses bond market liquidity and price - volume indicators to screen effective timing factors and predicts bond yields through machine - learning methods. When the expected yield is below a certain threshold, it reduces the long - duration positions in the bond investment portfolio to improve long - term returns and drawdown control [30] - Performance: As of 2026/1/30, the excess return in the past month was 0.40%, and the excess return in the past week was 0.14%. The index's positions are mainly in bond - related ETFs [30][33]