Group 1 - The core viewpoint of the article emphasizes the growing importance of index investment and the need for investors to establish a robust allocation framework amidst style rotation [1] - The development of index investment in China has significantly progressed, with ETFs becoming mainstream investment tools, surpassing active funds in holdings as of Q3 2024 [4][5] - The total scale of ETFs in China reached approximately 5 trillion yuan, with stock ETFs accounting for around 4 trillion yuan, representing about 3% of the total A-share market capitalization [4] Group 2 - The article discusses the increasing market differentiation, highlighting the performance of the ChiNext index compared to traditional large-cap indices, suggesting that investors should consider using style factor indices to enhance returns [6][8] - Style factor indices, which blend active and passive investment strategies, can provide higher excess returns by breaking the limitations of traditional market-cap-weighted indices [7][8] - The analysis of over 1,000 ETFs indicates that style factor indices exhibit superior mean and variance performance, suggesting better risk-adjusted returns [7] Group 3 - The article outlines a simple and practical configuration logic for utilizing style factors, emphasizing the importance of optimizing stock selection logic and avoiding pitfalls like value traps [9][10] - A recommended strategy for multi-factor combinations is the "constant proportion rebalancing" approach, which can potentially outperform the CSI 300 index through systematic adjustments [10] - The complexity behind index investment is acknowledged, with a focus on the intricate stock selection logic and asset allocation strategies that can lead to excess returns [10] Group 4 - Looking ahead, the article posits that China's capital market has entered a phase of high-quality development in index investment, driven by the maturation of market participants and the application of AI technology [11] - Continuous policy support is expected to enhance market vitality and attract more investors to index investment, particularly in the ETF market [11] - The article aims to encourage a deeper understanding of style factor indices among investors, promoting the construction of resilient investment portfolios in the evolving ETF era [12]

1. Report Industry Investment Rating - No relevant content provided 2. Core Viewpoints of the Report - The report presents several ETF strategy indices constructed with the help of ETFs and tracks their performance and positions on a weekly basis, including the Huabao Research Small - Large Cap Rotation ETF Strategy Index, Huabao Research SmartBeta Enhanced ETF Strategy Index, etc [12] 3. Summary by Relevant Catalogs 3.1 ETF Strategy Index Tracking - **Performance of ETF Strategy Indices Last Week**: The Huabao Research Small - Large Cap Rotation ETF Strategy Index had a return of 1.65%, with a benchmark return of 2.04% and an excess return of - 0.39%; the Huabao Research SmartBeta Enhanced ETF Strategy Index had a return of 5.13%, with a benchmark return of 2.04% and an excess return of 3.10%; the Huabao Research Quantitative Fire - Wheel ETF Strategy Index had a return of 0.58%, with a benchmark return of 2.04% and an excess return of - 1.46%; the Huabao Research Quantitative Balancing Act ETF Strategy Index had a return of 0.84%, with a benchmark return of 1.64% and an excess return of - 0.80%; the Huabao Research Hot - Spot Tracking ETF Strategy Index had a return of 1.98%, with a benchmark return of 2.82% and an excess return of - 0.84%; the Huabao Research Bond ETF Duration Strategy Index had a return of - 0.16%, with a benchmark return of - 0.26% and an excess return of 0.09% [13] 3.2 Huabao Research Small - Large Cap Rotation ETF Strategy Index - **Strategy Principle**: 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 based on the results to obtain excess returns relative to the market [14] - **Performance**: As of 2025/11/28, the excess return since 2024 was 19.93%, the excess return in the past month was 0.50%, and the excess return in the past week was - 0.39%. The return in the past week was 1.65%, - 2.39% in the past month, and 51.22% since 2024 [14][15] - **Position**: The position was 100% in the CSI 300ETF (fund code: 510300.SH) [18] 3.3 Huabao Research SmartBeta Enhanced ETF Strategy Index - **Strategy Principle**: It uses price - volume indicators to time self - built Barra factors and maps the timing signals to ETFs based on the exposure of ETFs 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 [19] - **Performance**: As of 2025/11/28, the excess return since 2024 was 24.67%, the excess return in the past month was 7.70%, and the excess return in the past week was 3.10%. The return in the past week was 5.13%, 4.81% in the past month, and 55.96% since 2024 [19][20] - **Position**: The positions included 25.12% in the Science and Technology Innovation 100 ETF (fund code: 588220.SH), 25.03% in the Full Science and Technology Innovation Index ETF (fund code: 589600.SH), 24.96% in the ChiNext 200 ETF (fund code: 159270.SZ), and 24.90% in the ChiNext Comprehensive ETF (fund code: 159541.SZ) [23] 3.4 Huabao Research Quantitative Fire - Wheel ETF Strategy Index - **Strategy Principle**: It starts from multiple factors, including the grasp of medium - and long - term fundamental dimensions, the tracking of short - term market trends, and the analysis of the behaviors 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 relative to the market [23] - **Performance**: As of 2025/11/28, the excess return since 2024 was 33.97%, the excess return in the past month was 2.00%, and the excess return in the past week was - 1.46%. The return in the past week was 0.58%, - 0.89% in the past month, and 65.27% since 2024 [23][26] - **Position**: The positions included 21.02% in the Bank ETF (fund code: 512800.SH), 20.57% in the Oil and Gas ETF (fund code: 159697.SZ), 19.59% in the Securities and Insurance ETF (fund code: 512070.SH), 19.57% in the Power ETF (fund code: 159611.SZ), and 19.25% in the New Energy ETF (fund code: 516160.SH) [27] 3.5 Huabao Research Quantitative Balancing Act ETF Strategy Index - **Strategy Principle**: It uses a multi - factor system including economic fundamentals, liquidity, technical aspects, and investor behaviors to build a quantitative timing system for trend analysis of the equity market. It also establishes a prediction model for the market's small - and large - cap styles to adjust the position distribution in the equity market and comprehensively obtains excess returns relative to the market through timing and rotation [27] - **Performance**: As of 2025/11/28, the excess return since 2024 was - 9.55%, the excess return in the past month was 1.23%, and the excess return in the past week was - 0.80%. The return in the past week was 0.84%, - 1.23% in the past month, and 22.38% since 2024 [27][28] - **Position**: The positions included 9.38% in the Ten - Year Treasury Bond ETF (fund code: 511260.SH), 5.87% in the 500ETF Enhanced (fund code: 159610.SZ), 5.83% in the CSI 1000ETF (fund code: 512100.SH), 32.27% in the 300 Enhanced ETF (fund code: 561300.SH), 23.35% in the Policy - Financial Bond ETF (fund code: 511520.SH), and 23.30% in the Short - Term Financing ETF (fund code: 511360.SH) [30] 3.6 Huabao Research Hot - Spot Tracking ETF Strategy Index - **Strategy Principle**: It uses strategies such as market sentiment analysis, tracking of major industry events, investor sentiment and professional opinions, policy and regulatory changes, and historical deduction to timely track and dig out hot - spot index target products, construct an ETF portfolio that can capture market hot - spots, provide investors with references for short - term market trends, and help them make wiser investment decisions [30] - **Performance**: As of 2025/11/28, the excess return in the past month was 1.93%, and the excess return in the past week was - 0.84%. The return in the past week was 1.98%, - 0.36% in the past month [30][33] - **Position**: The positions included 35.39% in the Non - Ferrous Metals 50ETF (fund code: 159652.SZ), 24.30% in the Bosera Hong Kong Stock Dividend ETF (fund code: 513690.SH), 21.43% in the Hong Kong Stock Connect Pharmaceutical ETF (fund code: 513200.SH), and 18.88% in the Short - Term Financing ETF (fund code: 511360.SH) [34] 3.7 Huabao Research Bond ETF Duration Strategy Index - **Strategy Principle**: It uses bond market liquidity and price - volume indicators to select 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 the long - term return and drawdown control ability of the portfolio [34] - **Performance**: As of 2025/11/28, the excess return in the past month was 0.24%, and the excess return in the past week was 0.09%. The return in the past week was - 0.16%, - 0.15% in the past month, 9.14% since 2024, and 23.25% since its establishment [34][35] - **Position**: The positions included 49.99% in the Ten - Year Treasury Bond ETF (fund code: 511260.SH), 25.01% in the Policy - Financial Bond ETF (fund code: 511520.SH), and 25.00% in the 5 - to 10 - Year Treasury Bond ETF (fund code: 511020.SH) [37]