Report Industry Investment Rating No information provided in the content. Core Viewpoints of the Report The report presents several ETF strategy indices constructed with the help of ETFs, and tracks the performance and positions of these indices on a weekly basis. Each index has its own unique strategy and has achieved different levels of excess returns over different time periods [12]. Summary by Relevant Catalogs 1. ETF Strategy Index Tracking - **Overall Performance Last Week**: The 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 1.73%, with a benchmark (CSI 800) return of 0.20% and an excess return of 1.52% [13]. 1.1. Huabao Research Large - Small Cap Rotation ETF Strategy Index - **Strategy**: Utilizes 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 relative to the market. - **Performance**: As of 2026/1/16, the excess return since 2024 was 27.85%, the excess return in the recent month was 4.13%, and the excess return in the recent week was 1.52%. The index's recent - week return was 1.73%, recent - month return was 10.63%, and return since 2024 was 69.44%, compared with the CSI 800's 0.20%, 6.50%, and 41.59% respectively. - **Positions**: As of 2026/1/16, it held 50% in the CSI 500ETF (fund code: 159922.SZ) and 50% in the CSI 1000ETF (fund code: 512100.SH) [14][15][18]. 1.2. Huabao Research SmartBeta Enhanced ETF Strategy Index - **Strategy**: Uses price - volume indicators to time self - built Barra factors, and then maps the timing signals to ETFs based on the ETFs' 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. - **Performance**: As of 2026/1/16, the excess return since 2024 was 16.89%, the excess return in the recent month was - 4.53%, and the excess return in the recent week was - 1.19%. - **Positions**: As of 2026/1/16, it held 25.23% in the Free Cash Flow ETF800 (fund code: 563580.SH), 25.11% in the Shenzhen Dividend ETF (fund code: 159905.SZ), 24.87% in the Dividend Low - Volatility 100ETF (fund code: 515100.SH), and 24.79% in the High - Dividend ETF (fund code: 563180.SH) [18][19][21]. 1.3. Huabao Research Quantitative Fire - Wheel ETF Strategy Index - **Strategy**: Starts from a multi - factor perspective, including the grasp of medium - to - 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 relative to the market. - **Performance**: As of 2026/1/16, the excess return since 2024 was 39.33%, the excess return in the recent month was 1.80%, and the excess return in the recent week was - 0.03%. - **Positions**: As of 2026/1/16, it held 21.64% in the Non - Ferrous Metals ETF (fund code: 512400.SH), 19.99% in the Chemical ETF (fund code: 159870.SZ), 19.79% in the Penghua Petroleum ETF (fund code: 159697.SZ), 19.43% in the Steel ETF (fund code: 515210.SH), and 19.16% in the E Fund Securities and Insurance ETF (fund code: 512070.SH) [21][23][26]. 1.4. Huabao Research Quantitative Balance Art ETF Strategy Index - **Strategy**: 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 also establishes a prediction model for the market's large - and small - cap styles to adjust the equity market position distribution and comprehensively obtains excess returns relative to the market through timing and rotation. - **Performance**: As of 2026/1/16, the excess return since 2024 was - 11.23%, the excess return in the recent month was - 0.53%, and the excess return in the recent week was 0.77%. - **Positions**: As of 2026/1/16, it held 9.05% in the 10 - Year Treasury Bond ETF (fund code: 511260.SH), 6.50% in the Enhanced 500ETF (fund code: 159610.SZ), 6.38% in the CSI 1000ETF (fund code: 512100.SH), 33.10% in the Enhanced 300 ETF (fund code: 561300.SH), 22.48% in the Short - Term Financing ETF (fund code: 511360.SH), and 22.48% in the Policy Financial Bond ETF (fund code: 511520.SH) [25][26][28]. 1.5. Huabao Research Hot - Spot Tracking ETF Strategy Index - **Strategy**: Based on strategies such as market sentiment analysis, industry major event tracking, investor sentiment and professional opinions, policy and regulatory changes, and historical deduction, it timely tracks and digs out hot - spot index target products to construct an ETF portfolio that can timely capture market hot - spots, providing investors with references for short - term market trends and helping them make more informed investment decisions. - **Performance**: As of 2026/1/16, the excess return in the recent month was 2.67% and the excess return in the recent week was 1.48%. - **Positions**: As of 2026/1/16, it held 41.45% in the Non - Ferrous 50ETF (fund code: 159652.SZ), 21.71% in the Bosera Hong Kong Stock Dividend ETF (fund code: 513690.SH), 19.81% in the E Fund Hong Kong Stock Connect Pharmaceutical ETF (fund code: 513200.SH), and 17.03% in the Short - Term Financing ETF (fund code: 511360.SH) [28][30][31]. 1.6. Huabao Research Bond ETF Duration Strategy Index - **Strategy**: 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 below a certain threshold, it reduces the long - duration positions in the bond investment portfolio to improve the portfolio's long - term returns and drawdown control ability. - **Performance**: As of 2026/1/16, the excess return in the recent month was 0.30% and the excess return in the recent week was 0.20%. - **Positions**: As of 2026/1/16, it held 50.02% in the 10 - Year Treasury Bond ETF (fund code: 511260.SH), 24.99% in the Policy Financial Bond ETF (fund code: 511520.SH), and 24.99% in the 5 - to 10 - Year Treasury Bond ETF (fund code: 511020.SH) [31][32][34].

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]

Report Summary 1. Investment Rating The provided content does not mention the industry investment rating. 2. Core Viewpoint The report presents several ETF strategy indices constructed by Huabao Research, tracking their performance and positions on a weekly basis. These indices aim to achieve excess returns relative to the market through different quantitative models and strategies [11]. 3. Summary by Index 3.1 Huabao Research Size Rotation ETF Strategy Index - **Strategy**: Uses multi - dimensional technical indicators and a machine - learning model to predict the return difference between the Shenwan Large - Cap Index and the Shenwan Small - Cap Index, determining positions weekly [3][13]. - **Performance**: As of 2025/10/10, the excess return since 2024 is 18.82%, the excess return in the past month is - 1.13%, and the excess return since 2025/9/26 is - 0.18% [3][13]. - **Position**: Holds 100% of the CSI 300 ETF [16]. 3.2 Huabao Research SmartBeta Enhanced ETF Strategy Index - **Strategy**: Utilizes price - volume indicators to time self - built Barra factors and maps timing signals to ETFs based on their exposure to 9 Barra factors, covering mainstream broad - based and style/strategy ETFs [3]. - **Performance**: As of 2025/10/10, the excess return since 2024 is 18.21%, the excess return in the past month is 3.83%, and the excess return since 2025/9/26 is 0.21% [3]. - **Position**: Holds CSI 1000ETF (29.17%), CSI 2000ETF (27.83%), Value 100ETF (26.57%), and CSI 500ETF (16.44%) [20]. 3.3 Huabao Research Quantitative Fire - Wheel ETF Strategy Index - **Strategy**: Considers multiple factors including medium - long - term fundamentals, short - term market trends, and market participant behavior, using valuation and crowding signals to identify industry risks and potential sectors [4][21]. - **Performance**: As of 2025/10/10, the excess return since 2024 is 29.29%, the excess return in the past month is 8.12%, and the excess return since 2025/9/26 is 1.30% [4][21]. - **Position**: Holds New Energy ETF (22.33%), Non - Ferrous Metals ETF (21.31%), Electronic ETF (20.37%), Communication ETF (18.53%), and Logistics ETF (17.45%) [24]. 3.4 Huabao Research Quantitative Balance ETF Strategy Index - **Strategy**: Employs a multi - factor system including economic fundamentals, liquidity, technicals, and investor behavior to construct a quantitative timing system for equity market trend analysis and size - style prediction to adjust positions [4][25]. - **Performance**: As of 2025/10/10, the excess return since 2024 is - 10.98%, the excess return in the past month is - 2.94%, and the excess return since 2025/9/26 is - 0.45% [4][25]. - **Position**: Holds 10 - Year Treasury Bond ETF (9.18%), CSI 500ETF Enhanced (6.27%), CSI 1000ETF (6.01%), 300 Enhanced ETF (32.97%), Short - Term Financing ETF (22.79%), and Policy Financial Bond ETF (22.77%) [26]. 3.5 Huabao Research Hot - Spot Tracking ETF Strategy Index - **Strategy**: Tracks market sentiment, industry events, investor sentiment, policies, and historical trends to identify hot - spot index products and build an ETF portfolio for short - term market trend reference [5][27]. - **Performance**: As of 2025/10/10, the excess return in the past month is 0.23%, and the excess return since 2025/9/26 is 2.95% [5][27]. - **Position**: Holds Non - Ferrous Metals 50ETF (36.47%), Hong Kong Stock Connect Pharmaceutical ETF (22.79%), Hong Kong Stock Dividend ETF (22.40%), and Short - Term Financing ETF (18.33%) [32]. 3.6 Huabao Research Bond ETF Duration Strategy Index - **Strategy**: Uses bond market liquidity and price - volume indicators to select timing factors and predicts bond yields through machine learning, reducing long - duration positions when expected yields are low [5][30]. - **Performance**: As of 2025/10/10, the excess return in the past month is 0.69%, and the excess return since 2025/9/26 is 0.06% [5][30]. - **Position**: Holds Short - Term Financing ETF (49.98%), 10 - Year Treasury Bond ETF (25.03%), 5 - 10 - Year Treasury Bond ETF (12.51%), and Policy Financial Bond ETF (12.48%) [35].

Core Viewpoint - The article highlights the strong performance of the GF Quantitative Multi-Factor Mixed Fund (005225), which has achieved a cumulative return of 109.93% since its inception on March 21, 2018, significantly outperforming its benchmark across various time frames [1]. Group 1: Fund Performance - The GF Quantitative Multi-Factor Fund has a high equity position of 91.75%, with a diversified portfolio that includes six stocks with a total market capitalization below 10 billion, accounting for 8.35% of the fund's net asset value [2]. - Over the past year, the GF Quantitative Multi-Factor Fund has outperformed the National Securities 2000 Index by 30 percentage points, achieving a return of 54.33% compared to the index's performance [2]. - The fund's monthly win rate against the National Securities 2000 Index is 81%, with an average monthly excess return of 1.20% since the current fund managers took over [3]. Group 2: Investment Strategy - The fund employs a dual-engine model combining traditional quantitative multi-factor models with advanced machine learning techniques to enhance factor discovery and integration [4][5]. - The fund managers utilize AI tools to identify hidden pricing patterns and improve the efficiency of alpha factor extraction, addressing the limitations of traditional models [5]. Group 3: Other Quantitative Funds - The article also discusses other quantitative funds under GF, such as the GF Multi-Factor Mixed Fund (002943), which has consistently outperformed major indices over the past seven years [6][7]. - GF has a diverse range of quantitative products, including Smart Beta strategies, which focus on small-cap style enhancement [7]. Group 4: Dividend and Value Strategies - The GF Stable Strategy Fund (006780) employs a combination of subjective and quantitative approaches to capture dividend opportunities, achieving a return of 25.93% in 2024, outperforming the benchmark by 7.17% [10]. - The GF High Dividend Preferred Fund (008704) focuses on high-dividend, low-valuation stocks, achieving a year-to-date return of 12.10%, significantly surpassing the performance of the benchmark indices [14][15].

Group 1 - The report highlights the performance of various ETF strategy indices, indicating that the Huabao Research Large and Small Cap Rotation ETF Strategy Index achieved an excess return of 17.33% since the beginning of 2024, with a weekly return of 0.29% [14][18] - The Huabao Research SmartBeta Enhanced ETF Strategy Index reported an excess return of 17.02% since the beginning of 2024, with a recent monthly return of -2.18% [18][21] - The Huabao Research Quantitative Fire Wheel ETF Strategy Index has shown an excess return of 3.01% since the beginning of 2024, with a weekly return of -0.09% [22][24] Group 2 - The Huabao Research Quantitative Balance ETF Strategy Index has recorded an excess return of -0.42% since the beginning of 2024, with a recent weekly return of -0.87% [26][28] - The Huabao Research Hotspot Tracking ETF Strategy Index has a recent monthly excess return of -0.68% and a weekly return of -1.09% [30][31] - The Huabao Research Bond ETF Duration Strategy Index reported a recent monthly excess return of -0.10% and a weekly return of -0.05% [34][36]

Group 1 - The report highlights a trend of increasing credit risk in the convertible bond market over the past five years, with a significant rise in the number of downgrades from 7 in 2020 to 49 in 2024, while upgrades remain scarce [1][11][23] - There is a notable seasonal clustering in rating adjustments, particularly during Q1 and Q4, with Q1 2022 seeing a peak where 73% of downgrades occurred, indicating a concentration of risk exposure during financial disclosures [11][12] - Structural differentiation is evident across industries, with social services and textiles experiencing significantly higher downgrade ratios, while sectors like coal and steel show no downgrades, reflecting their cash flow stability [17][18] Group 2 - A comprehensive rating factor system is essential for predicting credit ratings, categorized into five main factors: conversion pressure, debt repayment pressure, profitability and operational efficiency, corporate governance, and market performance [2][28] - The conversion pressure factor indicates that indicators such as bond balance to underlying stock market value and recent stock price trends are positively correlated with rating downgrades, while conversion value shows a negative correlation [29][30] - The debt repayment pressure factor reveals that a higher debt-to-asset ratio correlates positively with downgrades, while metrics like EBITDA to interest-bearing debt show a negative correlation, indicating the importance of long-term repayment capacity [40][41] Group 3 - The profitability and operational efficiency factor assesses the issuer's ability to generate cash flow, with continuous losses and financial delisting risks showing a strong positive correlation with downgrades, while metrics like earnings per share exhibit a negative correlation [46][51] - Corporate governance factors, such as the type of audit opinion, significantly influence credit ratings, with non-standard audit opinions correlating positively with downgrades, indicating potential financial uncertainties [58][60] - Market performance factors reflect real-time investor sentiment towards the issuer's creditworthiness, with indicators like market price and earnings ratios showing significant correlations with rating changes [3][61]