Quantitative Models and Construction Methods 1. Model Name: Volume Model - **Construction Idea**: The model uses trading volume data to predict market trends - **Construction Process**: The model analyzes the trading volume of various broad-based indices to determine market sentiment. If the trading volume increases significantly, it indicates a bullish trend - **Evaluation**: The model is effective in capturing short-term market movements based on trading volume[1][14] 2. Model Name: Feature Longhu Board Institution Model - **Construction Idea**: This model uses institutional trading data from the Longhu Board to predict market trends - **Construction Process**: The model tracks the trading activities of institutions listed on the Longhu Board. A higher level of institutional buying indicates a bullish trend - **Evaluation**: The model is useful for understanding the impact of institutional trading on market trends[1][14] 3. Model Name: Feature Volume Model - **Construction Idea**: Similar to the Volume Model, this model uses specific volume features to predict market trends - **Construction Process**: The model analyzes specific volume features such as spikes or drops in trading volume to determine market sentiment - **Evaluation**: The model provides additional insights by focusing on specific volume features rather than overall volume[1][14] 4. Model Name: Intelligent Algorithm Model (CSI 300 and CSI 500) - **Construction Idea**: The model uses machine learning algorithms to predict market trends for the CSI 300 and CSI 500 indices - **Construction Process**: The model employs various machine learning techniques to analyze historical data and predict future trends for the CSI 300 and CSI 500 indices - **Evaluation**: The model is effective in capturing complex patterns and trends in the market using advanced algorithms[1][14] 5. Model Name: Limit Up and Down Model - **Construction Idea**: The model uses the frequency of limit up and down events to predict market trends - **Construction Process**: The model tracks the number of stocks hitting their daily limit up or down to gauge market sentiment. A higher number of limit up events indicates a bullish trend - **Evaluation**: The model is useful for capturing extreme market movements and sentiment[1][15] 6. Model Name: Up and Down Return Difference Model - **Construction Idea**: The model uses the difference between upward and downward returns to predict market trends - **Construction Process**: The model calculates the difference between the returns of stocks moving up and those moving down. A positive difference indicates a bullish trend - **Evaluation**: The model provides a balanced view of market sentiment by considering both upward and downward movements[1][15] 7. Model Name: Calendar Effect Model - **Construction Idea**: The model uses calendar-based patterns to predict market trends - **Construction Process**: The model analyzes historical data to identify recurring patterns based on the calendar, such as monthly or quarterly trends - **Evaluation**: The model is useful for capturing seasonal trends and patterns in the market[1][15] 8. Model Name: Long-term Momentum Model - **Construction Idea**: The model uses long-term momentum to predict market trends - **Construction Process**: The model tracks the long-term momentum of stocks to determine market sentiment. A positive momentum indicates a bullish trend - **Evaluation**: The model is effective in capturing long-term trends and movements in the market[1][16] 9. Model Name: Comprehensive Weapon V3 Model - **Construction Idea**: The model combines multiple indicators and models to provide a comprehensive market prediction - **Construction Process**: The model integrates various short-term, medium-term, and long-term models to generate a comprehensive market outlook - **Evaluation**: The model provides a holistic view of the market by combining multiple indicators and models[1][17] 10. Model Name: Comprehensive National Certificate 2000 Model - **Construction Idea**: Similar to the Comprehensive Weapon V3 Model, this model focuses on the National Certificate 2000 index - **Construction Process**: The model integrates various indicators and models specifically for the National Certificate 2000 index - **Evaluation**: The model is effective in providing a comprehensive outlook for the National Certificate 2000 index[1][17] Model Backtesting Results - **Volume Model**: All broad-based indices are bullish[1][14] - **Feature Longhu Board Institution Model**: Bullish[1][14] - **Feature Volume Model**: Bullish[1][14] - **Intelligent Algorithm Model (CSI 300)**: Bullish[1][14] - **Intelligent Algorithm Model (CSI 500)**: Bullish[1][14] - **Limit Up and Down Model**: Bullish[1][15] - **Up and Down Return Difference Model**: All broad-based indices are bullish[1][15] - **Calendar Effect Model**: Neutral[1][15] - **Long-term Momentum Model**: Neutral[1][16] - **Comprehensive Weapon V3 Model**: Bullish[1][17] - **Comprehensive National Certificate 2000 Model**: Bullish[1][17]

Core Insights - The article highlights the significant growth of gold ETFs in China, particularly the Huaan Gold ETF, which surpassed 100 billion yuan in assets for the first time, reaching 101.81 billion yuan on January 15, 2026 [1][3][15] - The total assets of 14 gold ETFs in the domestic market exceeded 260 billion yuan, marking a nearly threefold increase compared to the previous year [3][15][21] Group 1: Growth of Gold ETFs - The Huaan Gold ETF's assets reached 100.76 billion yuan on January 14, 2026, and continued to grow to 101.81 billion yuan the following day [1][19] - As of January 15, 2026, the total assets of 14 gold ETFs amounted to 2630.61 billion yuan, an increase of over 210 billion yuan from 2415.61 billion yuan on December 31, 2025 [20][21] - Over the past year, the total assets of these gold ETFs increased by more than 190 billion yuan, with a growth rate close to three times [4][21] Group 2: Fund Inflows and Performance - Inflows into gold ETFs have been substantial, with the Huaan Gold ETF, Guotai Gold ETF, and Bosera Gold ETF attracting net inflows of 14.72 billion yuan, 13.78 billion yuan, and 10.86 billion yuan respectively from January 1 to January 15, 2026 [3][18] - The total net inflow for the 14 gold ETFs over the past year was 123.17 billion yuan, with the Huaan Gold ETF leading with 43.79 billion yuan [5][23] - The average return for these gold ETFs exceeded 61% from January 15, 2025, to January 15, 2026, driven by rising international gold prices [6][26] Group 3: Market Dynamics and Future Outlook - The rise in gold prices has been attributed to factors such as declining real interest rates, increased geopolitical risks, and a growing supply-demand gap for gold [7][28] - As of January 16, 2026, spot gold prices approached 4600 USD, reflecting a 6.5% increase since the beginning of the year [9][28] - Fund managers are enhancing liquidity and risk management in response to market conditions, with adjustments to the minimum subscription and redemption units for the gold ETFs [10][29]

Core Viewpoint - The article highlights the significant growth of gold ETFs in China, driven by rising international gold prices and increased investor interest, with the total scale of gold ETFs surpassing 2600 billion yuan, nearly tripling in a year [1][3][4]. Group 1: Growth of Gold ETFs - As of January 14, 2026, the largest commodity ETF in the domestic market, Huaan Gold ETF, reached a scale of 100.76 billion yuan, marking the first time it surpassed the 100 billion yuan threshold [1]. - By January 15, 2026, the scale of Huaan Gold ETF further increased to 101.18 billion yuan, contributing to a total of 14 gold ETFs in the market with a combined scale of 2630.61 billion yuan, up from 2415.61 billion yuan at the end of December 2025 [3][4]. - Over the past year, the total scale of these 14 gold ETFs increased by over 190 billion yuan, with a growth rate close to 300% [4]. Group 2: Fund Inflows and Performance - In the first half of January 2026, major gold ETFs attracted significant net inflows, with Huaan Gold ETF, Guotai Gold ETF, and Bosera Gold ETF receiving net inflows of 1.472 billion yuan, 1.378 billion yuan, and 1.086 billion yuan respectively [3]. - The total net inflow for the 14 gold ETFs reached 123.17 billion yuan over the past year, with Huaan Gold ETF leading with 43.79 billion yuan [4]. - The return rate for these gold ETFs exceeded 61% from January 15, 2025, to January 15, 2026, driven by strong international gold prices influenced by various economic factors [4]. Group 3: Market Dynamics and Investor Behavior - The continuous rise in gold prices, geopolitical tensions, and financial market volatility have led investors to favor gold ETFs as a safe-haven investment [7]. - The convenience and low cost of investing in gold ETFs have attracted a large number of investors, further boosting their popularity [7]. - Adjustments in the minimum subscription and redemption units for gold ETFs by various fund companies indicate a response to market changes and a strategy to ensure stable fund operations [9]. Group 4: Future Outlook - Analysts suggest that while gold prices may slow down in the short term due to reduced uncertainty in U.S.-China trade relations, the long-term outlook remains positive due to ongoing central bank gold accumulation and high demand for gold ETFs [11]. - Investment strategies should focus on maintaining a reasonable allocation to gold, with recommendations for a 10%-20% portfolio allocation to optimize returns and manage risks [10].

Market Overview - A-shares experienced volatility with the CSI 300 index declining by 0.57% and the ChiNext index increasing by 1% during the week of January 12 to January 16 [1][18] - The Hong Kong market rebounded, with the Hang Seng Index rising by 2.34% [1] ETF Market Dynamics - The ETF market faced a significant downturn, with a loss exceeding 200 billion yuan in a single week, leading to a total scale drop below 4 trillion yuan for stock ETFs [1][19] - Major ETF managers, including Huaxia, E Fund, and Huatai-PB, saw their management scales shrink by over 340 billion yuan due to the outflow of funds from broad-based ETFs [1][19] - Despite the downturn in stock ETFs, cross-border ETFs saw a historic increase, surpassing 1 trillion yuan in total scale for the first time [1][33] ETF Scale Changes - The total market ETF scale decreased by 1,140.38 billion yuan, with stock ETFs alone shrinking by 1,255.76 billion yuan [2][19] - Bond and money market ETFs also faced declines, with reductions of 166.01 billion yuan and 104.97 billion yuan, respectively [2][19] - Conversely, cross-border ETFs added 289.39 billion yuan, marking a significant growth [2][19] ETF Product Performance - The CSI 300 index-linked ETFs saw a dramatic scale reduction of 1,091.49 billion yuan, primarily due to a net outflow of 1,033.66 billion yuan [4][21] - In contrast, the Hong Kong Internet index-linked ETFs experienced a growth of over 100 billion yuan, becoming the "increment king" of the week [4][21] Fund Management Changes - Huaxia Fund's ETF management scale briefly surpassed 1 trillion yuan but fell back below this threshold due to significant fund outflows [15][32] - E Fund and Huatai-PB also reported substantial reductions in their ETF management scales, with E Fund's scale dropping below 900 billion yuan [7][27] Notable ETF Developments - The first gold ETF in the domestic market surpassed 1 trillion yuan, becoming a significant milestone [17][34] - The total number of listed ETFs reached 1,405, with 7 new ETFs launched during the week [1][20]

Market Overview - A-shares experienced a mixed performance with the CSI 300 index declining by 0.57% and the ChiNext index increasing by 1% during the week [1] - The Hong Kong stock market rebounded, with the Hang Seng Index rising by 2.34% [1] ETF Market Dynamics - The ETF market faced a significant downturn, with a total outflow exceeding 200 billion yuan in a single week, leading to a total scale drop below 4 trillion yuan for stock ETFs [1][2] - The major ETF management firms, including Huaxia, E Fund, and Huatai-PB, saw their management scales shrink by over 34 billion yuan each due to the outflow from broad-based ETFs [1][2] ETF Scale Changes - The total ETF market scale decreased by 1140.38 billion yuan, with stock ETFs alone shrinking by 1255.76 billion yuan [2][3] - The broad-based ETFs were the primary contributors to this decline, with a drop of 2070.03 billion yuan [2] Cross-Border and Commodity ETFs - Cross-border ETFs saw a historic increase, adding 289.39 billion yuan to surpass the 1 trillion yuan mark [2][3] - The first domestic gold ETF surpassed 100 billion yuan, marking a significant milestone in the commodity ETF sector [1][19] Major Index-linked ETF Performance - The CSI 300 index-linked ETF experienced a drastic reduction of 1091.49 billion yuan, primarily due to a net outflow of 1033.66 billion yuan [4][6] - Other index-linked ETFs, such as those for the ChiNext and STAR 50, also saw significant reductions, with each losing over 200 billion yuan [4][7] Institutional ETF Management Changes - The top three ETF management firms collectively lost over 340 billion yuan in scale, while some second-tier firms managed to achieve positive growth [8][10] - Huaxia Fund's ETF management scale briefly exceeded 1 trillion yuan but later fell back below this threshold due to significant outflows [16] Notable ETF Products - The top 20 ETFs saw substantial reductions, with eight products losing over 100 billion yuan each, particularly the CSI 300 ETFs managed by Huatai-PB and E Fund [12][15] - The Hong Kong Internet ETF and the gold ETF from Huaxia Fund gained significant scale, with the latter becoming the first gold ETF to exceed 100 billion yuan [13][19]

Core Viewpoint - The personal pension fund list in China has expanded, with 7 new products added, bringing the total to 309 funds as of the end of last year [1][3]. Group 1: Fund Expansion - The latest list of personal pension funds includes 309 products, an increase of 7 from the end of the third quarter last year [3]. - The newly added products consist of ordinary index funds, enhanced index funds, stable pension target funds, and target date funds [3]. Group 2: Sales Institutions - The number of sales institutions for personal pension funds remains unchanged at 52 [3]. - Securities companies lead with 25 institutions, followed by commercial banks with 19, and independent fund sales institutions with 8 [3]. Group 3: Performance and Scale - The personal pension funds have seen significant performance and scale growth, with an average net value growth rate of 17.54% for 279 funds that have been established for over a year [5]. - As of the end of the third quarter of 2025, the total scale of personal pension fund Y shares reached 15.107 billion yuan, marking a 65.32% increase from the end of 2024 [5]. Group 4: Industry Insights - Public funds are recognized as pioneers in the personal pension business and play a crucial role in the personal pension ecosystem [6]. - The industry is expected to enhance product diversity, demonstrate long-term investment value, and foster collaborative efforts among market participants to promote effective operation of personal pensions [6].

Report Industry Investment Rating No relevant content provided. Core Views - PB-ROE framework's ETF rotation strategy recommends next week to focus on the communication, agriculture, forestry, animal husbandry, and transportation industries, corresponding to their industry ETFs; the ETF redemption sentiment indicator model suggests focusing on the Science and Technology Innovation 50 ETF, SSE 50 ETF, Medical ETF, Photovoltaic ETF, and Robot ETF [9][40] - Combining PB and ROE for industry configuration may be a better choice; the third quadrant's high PB high ROE and the fifth quadrant's low PB medium ROE are key focus areas; combining the third and fifth quadrants to construct a comprehensive PB-ROE strategy has an annualized return of 11.93% and an annualized excess return of 13.22% [32][33] Summary by Directory 1. Recent Market Overview (January 12 - January 16, 2026) - Index performance: Shanghai Composite Index closed at 4101.91, down 0.45% for the week; Shenzhen Component Index closed at 14281.08, up 1.14%; ChiNext Index closed at 3361.02, up 1.00%; Beijing Stock Exchange 50 closed at 1548.33, up 1.58%; Hang Seng Index closed at 26844.96, up 2.34%. The average daily trading volume of the Shanghai and Shenzhen stock markets was 34250.96 billion yuan, and the total trading volume for the week was 17.13 trillion yuan [12] - Industry performance: Among 31 Shenwan primary industries, 13 industries rose and 18 fell. The top three gainers were computer (up 3.82%), electronics (up 3.77%), and non-ferrous metals (up 3.03%); the top three losers were national defense and military industry (down 4.92%), real estate (down 3.52%), and agriculture, forestry, animal husbandry, and fishery (down 3.27%) [5][12] - Main funds: Main funds had net outflows for 5 trading days and no net inflows, with a total net outflow of 2752.39 billion yuan for the week. The industries with more net inflows were banks, public utilities, and coal; the industries with more net outflows were national defense and military industry, power equipment, and computer [5][13] 2. Recent ETF Market Performance (January 12 - January 16, 2026) - Overall situation: As of January 16, 2026, there were 1411 ETFs in the Shanghai and Shenzhen stock markets, with a total asset management scale of 60766.01 billion yuan. There were 1101 equity ETFs (38892.41 billion yuan), 53 bond ETFs (7479.66 billion yuan), 27 money market ETFs (1529.88 billion yuan), 17 commodity ETFs (2751.84 billion yuan), 207 cross-border ETFs (10070.46 billion yuan), and 6 unlisted ETFs (41.76 billion yuan) [20] - Newly listed and established ETFs: 8 ETFs were newly listed, all equity ETFs; 7 ETFs were newly established, with a total issuance scale of 51.24 billion yuan [21] - Equity ETFs: The median weekly increase or decrease was 0.59%. Science and technology semiconductor ETFs and semiconductor equipment ETFs performed well, with the Science and Technology Semiconductor ETF Peng Hua rising the most at 12.46%; aerospace and high-end equipment ETFs performed poorly, with the Aerospace ETF falling the most at 6.88%. The average weekly share change was a decrease of 19.4716 million shares. Software ETFs and media ETFs had more share increases, while the Science and Technology Innovation 50 ETF and CSI 300 ETF had more share decreases [24] - Bond ETFs: The median weekly increase or decrease of 53 bond ETFs was 0.12%. The convertible bond ETF had the highest increase of 0.91%, while the science and technology innovation bond ETF had the highest decrease of 0.00%. As of January 16, 2026, the Haifutong CSI Short-term Financing ETF had the largest scale of 631.50 billion yuan [27] - Cross-border ETFs: The median weekly increase or decrease was 1.18%. The China-South Korea Semiconductor ETF and Hong Kong Stock Connect Internet ETF had the highest increases, with the China-South Korea Semiconductor ETF rising 6.11%; the Hong Kong Securities ETF and Nasdaq Biotechnology ETF had the highest decreases, with the Hong Kong Securities ETF falling 2.28%. Since the beginning of the year, the median increase or decrease was 3.82%, with the China-South Korea Semiconductor ETF and Hong Kong Medical ETF having higher increases, and the Nasdaq ETF and Nasdaq Technology ETF having higher decreases [29] 3. PB-ROE Framework's ETF Rotation Strategy Tracking - Factor effectiveness: PB factor and PB quantile factor show certain stratification ability, and PB quantile factor is more effective; ROE factor's effectiveness declined after 2018; using ROE factor is better than ROE quantile factor; expected ROE factor is better than expected ROE year-on-year factor. Combining PB and ROE for industry configuration may be a better choice [32] - Key quadrants: The third quadrant's high PB high ROE and the fifth quadrant's low PB medium ROE are key focus areas. From 2017 to February 2024, the compound annualized excess returns of the third and fifth quadrant portfolios were 4.27% and 1.55% respectively [32] - Strategy improvement: After supplementing the PB-ROE framework with four dimensions, the annualized excess returns of the third and fifth quadrant strategies were 4.78% and 3.94% respectively. Combining the two strategies, the annualized return was 11.93% and the annualized excess return was 13.22% [33] - Recent performance: This week, the strategy focused on the communication, agriculture, forestry, animal husbandry, and transportation industries, with a cumulative return of -0.86%, and an excess return of -0.29% compared to the CSI 300 Index [8][34] - Performance since 2023: The cumulative return was 26.03%, with an excess return of 3.81% compared to the CSI 300 Index [8][36] - Performance since 2022: The cumulative return was 7.77%, with an excess return of 11.99% compared to the CSI 300 Index [39] 4. Investment Recommendations - PB-ROE framework: Focus on the communication, agriculture, forestry, animal husbandry, and transportation industries next week, corresponding to their industry ETFs [9][40] - ETF redemption sentiment indicator model: Focus on the Science and Technology Innovation 50 ETF, SSE 50 ETF, Medical ETF, Photovoltaic ETF, and Robot ETF next week [9][40]

Core Viewpoint - The article discusses the significant inflow of funds into public offerings, particularly focusing on "fixed income +" and Fund of Funds (FOF) products, as a response to the upcoming maturity of a large volume of fixed deposits, estimated to be between 30 trillion to 60 trillion yuan by 2026, with an average forecast of around 50 trillion yuan [4]. Group 1: Performance of Multi-Asset Products - The "fixed income +" and FOF products have shown impressive performance, with the total management scale of public FOF funds reaching 238.3 billion yuan by the end of 2025, marking a historical high with an annual growth of 100 billion yuan [5]. - The "fixed income +" funds reached a scale of 2.53 trillion yuan, growing over 700 billion yuan within the year, indicating a strong market demand for these products [5][20]. - Notable performances include the "fixed income +" fund from China Universal, which achieved a return of 37% in 2025, and the FOF fund from Guotai, which returned 66.14% [6][17]. Group 2: Trends in Public Fund Products - There are two prominent trends in public fund products: the toolization of products, particularly ETFs, and the multi-asset allocation strategy that aims for stable performance across different market conditions [9][10]. - The toolization trend is evident in both equity and bond products, with a significant increase in bond ETFs, which have surpassed 700 billion yuan [9]. - The multi-asset allocation strategy seeks to balance investments between stocks and bonds based on market conditions, enhancing the potential for stable returns [10][12]. Group 3: Market Dynamics and Future Outlook - The growth of multi-asset products is expected to continue, driven by the increasing acceptance of "fixed income +" and FOF products among retail and institutional investors [22]. - The public fund industry is witnessing a shift towards more systematic and quantitative asset allocation strategies, moving away from reliance on subjective judgment [24][27]. - The competitive landscape is changing, with companies like China Universal and Invesco Great Wall rapidly expanding their multi-asset offerings, indicating a potential shift in market leadership [38][41].

Core Viewpoint - The precious metals market has seen significant price increases since the beginning of 2026, leading to heightened interest and investment in related ETFs, with public funds actively launching products in this sector [1][2]. Group 1: Market Performance - Since the beginning of 2025, the precious metals index has increased by nearly 107% [3]. - As of January 16, 2026, related ETFs have attracted a total of 242.93 billion yuan in investments this year [3]. - The Southern Precious Metals ETF has seen a growth of 129.9 billion yuan, reaching a total size of 335.50 billion yuan [4]. Group 2: Fund Launches - A total of 15 precious metals-related fund products have been reported since early December 2025, with major fund companies like Huatai-PineBridge, Huaxia, and Ping An among those launching new ETFs [3]. - The focus on upstream rare resources in the precious metals mining sector has been highlighted as a strong performer [3]. Group 3: Market Dynamics - Short-term volatility in the precious metals sector is expected to increase, driven by high market sentiment and rising margin balances [5][6]. - Despite recent price corrections, the long-term value proposition of the sector remains intact, supported by expectations of interest rate cuts and strong demand from energy transition and digital infrastructure [7]. Group 4: Supply and Demand Factors - Supply constraints are evident due to declining ore grades, insufficient capital expenditure, and geopolitical risks, while demand is bolstered by the explosive growth in electric vehicles and renewable energy sectors [7]. - The competition for resources in high-end manufacturing, including AI and semiconductors, is expected to further support metal prices [7]. Group 5: Risks and Uncertainties - Investors are advised to be cautious of multiple uncertainties, including potential volatility from high valuations and geopolitical tensions affecting supply chains [9]. - The market is also sensitive to changes in monetary policy and economic growth rates, which could impact the sector's performance [9].

Group 1 - The A-share broad-based ETF market has seen a significant increase in trading volume, with multiple products experiencing substantial turnover [1][3] - On January 16, the Huaxia CSI 300 ETF recorded a trading volume of 22.7 billion yuan, while the Huatai-PB CSI 300 ETF reached 25.9 billion yuan on the same day [3] - The average daily trading volume of ETFs in the two markets has exceeded 550 billion yuan since 2026, indicating a strong market trend [1][3] Group 2 - The total trading volume of 370 broad-based ETFs in the market has reached 1.251 trillion yuan year-to-date, reflecting a year-on-year growth of 64.03% [3] - Several ETFs, including the Huaxia CSI 500 ETF and Huatai-PB A500 fund, have each surpassed 100 billion yuan in trading volume within the month [3] - As of January 16, 86 ETF products have shown a net value increase of over 10%, highlighting strong performance in the sector [3]