Quantitative Models and Construction Methods Model 1: Fund Selection Strategy Based on Trading Motivation Factor and Stock Price Difference Income Factor - **Model Name**: Fund Selection Strategy Based on Trading Motivation Factor and Stock Price Difference Income Factor - **Construction Idea**: The strategy aims to select funds with high stock price difference income, active trading motivation, and low possibility of performance dressing[2] - **Construction Process**: - The strategy combines the trading motivation factor and the stock price difference income factor - The trading motivation factor is constructed by classifying the trading motivations of funds[23] - The stock price difference income factor is derived from the stock price difference income in the fund's income statement[23] - The strategy adopts a semi-annual rebalancing approach, rebalancing at the end of March and August each year[23] - **Evaluation**: The strategy significantly outperformed the Wind Partial Equity Hybrid Fund Index in January 2026[2] Model 2: Fund Selection Strategy Based on Fund Manager's Trading Uniqueness - **Model Name**: Fund Selection Strategy Based on Fund Manager's Trading Uniqueness - **Construction Idea**: The strategy aims to capture the unique trading patterns of fund managers to generate excess returns[3] - **Construction Process**: - Construct a network based on the detailed holdings and transactions of fund managers[31] - Develop an indicator to measure the uniqueness of fund managers' trading[31] - The strategy adopts a semi-annual rebalancing approach, rebalancing at the beginning of April and September each year[31] - **Evaluation**: The strategy outperformed the Wind Partial Equity Hybrid Fund Index in January 2026[3] Model 3: Industry Theme ETF Rotation Strategy Based on Application Information - **Model Name**: Industry Theme ETF Rotation Strategy Based on Application Information - **Construction Idea**: The strategy aims to select industry theme ETFs similar to the applied ETFs to capture market investment hotspots[4] - **Construction Process**: - Conduct event-driven research on the entire issuance process of funds[36] - Construct the industry theme application similarity factor (T+1) based on the information disclosed during the application material public stage[36] - The strategy adopts a monthly rebalancing approach, with a transaction fee rate of 0.1% per side[36] - **Evaluation**: The strategy significantly outperformed the CSI 800 Index in January 2026[4] Model Backtesting Results Fund Selection Strategy Based on Trading Motivation Factor and Stock Price Difference Income Factor - **Monthly Return**: 10.96%[27] - **Annualized Return**: 11.56%[27] - **Annualized Volatility**: 21.60%[27] - **Sharpe Ratio**: 0.54[27] - **Maximum Drawdown**: 48.39%[27] - **Annualized Excess Return**: 3.87%[27] - **Excess Maximum Drawdown**: 19.22%[27] - **Information Ratio (IR)**: 0.64[27] - **Monthly Excess Return**: 3.60%[27] Fund Selection Strategy Based on Fund Manager's Trading Uniqueness - **Monthly Return**: 8.03%[35] - **Annualized Return**: 14.26%[35] - **Annualized Volatility**: 19.47%[35] - **Sharpe Ratio**: 0.73[35] - **Maximum Drawdown**: 37.26%[35] - **Annualized Excess Return**: 5.70%[35] - **Excess Maximum Drawdown**: 10.84%[35] - **Information Ratio (IR)**: 1.10[35] - **Monthly Excess Return**: 0.86%[35] Industry Theme ETF Rotation Strategy Based on Application Information - **Monthly Return**: 22.66%[40] - **Annualized Return**: 22.45%[40] - **Annualized Volatility**: 21.39%[40] - **Sharpe Ratio**: 1.05[40] - **Maximum Drawdown**: 34.89%[43] - **Annualized Excess Return**: 13.84%[43] - **Excess Maximum Drawdown**: 19.07%[43] - **Information Ratio (IR)**: 0.76[43] - **Monthly Excess Return**: 18.18%[43]

Core Insights - The article provides an overview of the latest fund net asset values, highlighting the top-performing and underperforming funds in the market [2][3]. Fund Performance Summary - The top 10 funds with the highest net value growth include: 1. Dachen Yixi Positive Pension Target Five-Year Holding Mixed Fund (FOF) with a net value of 1.3297 and a growth of 3.14% 2. Huatai-PineBridge Active Return One-Year Holding Mixed Fund (FOF) A with a net value of 1.1886 and a growth of 2.96% 3. Huatai-PineBridge Active Return One-Year Holding Mixed Fund (FOF) C with a net value of 1.1702 and a growth of 2.96% 4. Green Port Stock Connect Selected Mixed Fund A with a net value of 1.4196 and a growth of 2.85% 5. E Fund Huiyu Active Pension Five-Year Holding Mixed Fund (FOF) A with a net value of 1.4053 and a growth of 2.85% 6. E Fund Huiyu Active Pension Five-Year Holding Mixed Fund (FOF) Y with a net value of 1.4145 and a growth of 2.84% 7. Green Port Stock Connect Selected Mixed Fund C with a net value of 1.4255 and a growth of 2.84% 8. Huaxia Jusheng Preferred One-Year Holding Mixed Fund (FOF) C with a net value of 0.9555 and a growth of 2.81% 9. Huaxia Jusheng Preferred One-Year Holding Mixed Fund (FOF) A with a net value of 0.9707 and a growth of 2.81% 10. Huatai-PineBridge Core Preferred Six-Month Holding Mixed Fund (FOF) C with a net value of 1.2550 and a growth of 2.78% [2]. - The bottom 10 funds with the lowest net value growth include: 1. China Ocean Energy Strategy Mixed Fund with a net value of 0.8707 and a decline of 6.37% 2. Wanjia Cycle View Fund A with a net value of 1.2766 and a decline of 6.35% 3. Galaxy Core Preferred Fund C with a net value of 1.0901 and a decline of 6.35% 4. Wanjia Cycle View Fund C with a net value of 1.2748 and a decline of 6.35% 5. Galaxy Core Preferred Fund A with a net value of 1.1026 and a decline of 6.35% 6. Founder Fubon Strategy Fund C with a net value of 1.5041 and a decline of 6.06% 7. Founder Fubon Strategy Fund A with a net value of 1.5294 and a decline of 6.06% 8. Huaxia Low Carbon Economy Fund C with a net value of 1.0529 and a decline of 5.85% 9. Huaxia Low Carbon Economy Fund A with a net value of 1.0799 and a decline of 5.84% 10. Yinhua Growth Smart Fund A with a net value of 1.2629 and a decline of 5.82% [3]. Market Overview - The Shanghai Composite Index opened lower and experienced horizontal fluctuations, closing with a small decline. The ChiNext Index showed a similar trend, with a total trading volume of 2.19 trillion yuan. The number of rising stocks was 1,618, while declining stocks numbered 3,719, with a ratio of 56 to 23 for stocks hitting the daily limit [5]. Leading Industries and Concepts - The leading industries included daily chemicals and hotel catering, both showing growth of over 3%. Key concepts driving the market included duty-free shopping, internet celebrity economy, short drama concepts, gambling concepts, and pre-made dishes [6]. - The underperforming industries were non-ferrous metals, mineral products, and electrical equipment, all experiencing declines of over 3% [7].

Core Insights - The article provides an overview of the performance of various mutual funds, highlighting the top and bottom performers based on net asset value changes [2][3]. Group 1: Top Performing Funds - The top 10 mutual funds with the highest net value growth on January 20 include: 1. 富国价值发现混合C with a net value of 1.2962 and a growth of 3.92% 2. 富国价值发现混合A with a net value of 1.3122 and a growth of 3.92% 3. 国投瑞银白银期货(LOF)A with a net value of 2.6095 and a growth of 3.88% 4. 富国研究精选灵活配置混合C with a net value of 2.9810 and a growth of 3.65% 5. 富国研究精选灵活配置混合D with a net value of 3.0400 and a growth of 3.61% 6. 景顺长城中国回报混合A with a net value of 1.7070 and a growth of 3.58% 7. 工银价值精选混合C with a net value of 1.1350 and a growth of 3.56% 8. 工银价值精选混合A with a net value of 1.1364 and a growth of 3.55% 9. 景顺长城中国回报混合C with a net value of 1.6810 and a growth of 3.51% 10. 景顺长城资源垄断混合(LOF)C with a net value of 0.5720 and a growth of 3.44% [2]. Group 2: Bottom Performing Funds - The bottom 10 mutual funds with the lowest net value growth on January 20 include: 1. 东财景气驱动C with a net value of 1.6980 and a decline of 6.34% 2. 东财景气驱动A with a net value of 1.7190 and a decline of 6.34% 3. 前海开源沪港深强国产业混合 with a net value of 1.7195 and a decline of 5.83% 4. 东方阿尔法瑞丰混合发起A with a net value of 1.1021 and a decline of 5.21% 5. 东方阿尔法瑞丰混合发起C with a net value of 1.0906 and a decline of 5.21% 6. 平安高端装备混合发起式A with a net value of 1.3687 and a decline of 5.02% 7. 平安高端装备混合发起式C with a net value of 1.3672 and a decline of 5.02% 8. 中加优势企业混合C with a net value of 1.9189 and a decline of 4.98% 9. 中加优势企业混合A with a net value of 2.0087 and a decline of 4.98% 10. 长城久嘉创新成长混合C with a net value of 2.7860 and a decline of 4.77% [3]. Group 3: Market Overview - The Shanghai Composite Index experienced a slight decline after a peak, while the ChiNext Index showed a significant drop. The total trading volume reached 2.80 trillion, with a ratio of advancing to declining stocks at 2233:3102 and a limit-up to limit-down ratio of 62:23. The leading sectors included real estate, chemical fiber, and water utilities, each with gains exceeding 2% [5].

Quantitative Models and Construction Methods 1. Model Name: Fund Selection Strategy Based on Trading Motivation Factor and Stock Spread Income Factor - **Model Construction Idea**: This strategy combines the trading motivation factor and the stock spread income factor to select funds with high stock spread income, active trading motivation, and low likelihood of performance manipulation[2][24] - **Model Construction Process**: - The **trading motivation factor** is derived from fund report data, including fund flows, stock buy/sell amounts, and the proportion of top 20 stocks traded[47] - The **stock spread income factor** is calculated from the stock spread income in the fund's profit statement[47] - The strategy adopts a semi-annual rebalancing approach, adjusting positions at the end of March and August each year, and selects funds from active equity funds after deducting transaction costs[24] - **Model Evaluation**: The strategy has shown long-term outperformance against the Wind Active Equity Hybrid Fund Index, with a fee-adjusted annualized excess return of 3.64% since March 2011[24][28] 2. Model Name: Fund Selection Strategy Based on Fund Manager Trading Uniqueness - **Model Construction Idea**: This strategy evaluates the uniqueness of fund managers' trading behaviors by constructing a network based on their holdings and transactions, aiming to identify funds with distinctive trading styles[3][32] - **Model Construction Process**: - A network is built using detailed fund manager holdings and transaction data - A metric is calculated to measure the uniqueness of each fund manager's trading behavior compared to their peers[48] - The strategy adopts a semi-annual rebalancing approach, adjusting positions in early April and September each year, and selects funds from active equity funds, general stock funds, and flexible allocation funds after deducting transaction costs[32] - **Model Evaluation**: The strategy has demonstrated significant outperformance, achieving a fee-adjusted annualized excess return of 5.66% since its inception[32][36] 3. Model Name: Industry-Themed ETF Selection Strategy Based on Filing Information - **Model Construction Idea**: This strategy leverages the forward-looking information from the public disclosure stage of ETF filing materials to construct an industry-themed filing similarity factor (T+1), aiming to capture market investment hotspots[4][39] - **Model Construction Process**: - The T+1 factor is constructed by calculating the similarity between the indices tracked by newly filed ETFs and existing market indices[48] - The strategy adopts a monthly rebalancing approach, selecting ETFs from industry-themed ETFs with a transaction fee rate of 0.1% per side, using the CSI 800 Index as the benchmark[39] - **Model Evaluation**: The strategy has consistently outperformed the CSI 800 Index since December 2018, with a fee-adjusted annualized excess return of 11.33%[39][44] --- Model Backtesting Results 1. Fund Selection Strategy Based on Trading Motivation Factor and Stock Spread Income Factor - **December 2025 Return**: 1.56% (vs. 3.06% for the benchmark)[28] - **Annualized Return**: 10.85% (vs. 7.33% for the benchmark)[28] - **Annualized Volatility**: 21.62% (vs. 19.97% for the benchmark)[28] - **Sharpe Ratio**: 0.50 (vs. 0.37 for the benchmark)[28] - **Maximum Drawdown**: 48.39% (vs. 45.42% for the benchmark)[28] - **Annualized Excess Return**: 3.64%[28] - **IR**: 0.61[28] - **Excess Maximum Drawdown**: 19.22%[28] - **December 2025 Excess Return**: -1.54%[28] 2. Fund Selection Strategy Based on Fund Manager Trading Uniqueness - **December 2025 Return**: 5.36% (vs. 3.06% for the benchmark)[36] - **Annualized Return**: 13.40% (vs. 7.87% for the benchmark)[36] - **Annualized Volatility**: 19.52% (vs. 18.30% for the benchmark)[36] - **Sharpe Ratio**: 0.69 (vs. 0.43 for the benchmark)[36] - **Maximum Drawdown**: 37.26% (vs. 45.42% for the benchmark)[36] - **Annualized Excess Return**: 5.66%[36] - **IR**: 1.09[36] - **Excess Maximum Drawdown**: 10.84%[36] - **December 2025 Excess Return**: 2.27%[36] 3. Industry-Themed ETF Selection Strategy Based on Filing Information - **December 2025 Return**: 5.84% (vs. 3.31% for the benchmark)[43] - **Annualized Return**: 19.22% (vs. 6.90% for the benchmark)[43] - **Annualized Volatility**: 21.05% (vs. 18.85% for the benchmark)[43] - **Sharpe Ratio**: 0.91 (vs. 0.37 for the benchmark)[43] - **Maximum Drawdown**: 34.89% (vs. 42.96% for the benchmark)[44] - **Annualized Excess Return**: 11.33%[44] - **IR**: 0.64[44] - **Excess Maximum Drawdown**: 19.07%[44] - **December 2025 Excess Return**: 2.53%[44]

Core Viewpoint - The article discusses the recent developments in public fund of funds (FOF) in China, highlighting the growth in the number and scale of FOF products, as well as their investment preferences and directions in the third quarter of 2025 [1]. Group 1: Market Trends and Growth - The FOF market has seen a resurgence in 2025, driven by a recovery in the stock market, leading to increased activity in the fund market [2][3]. - As of September 30, 2025, there are 513 FOF funds, with 50 new funds established in 2025. The total asset scale reached 200.11 billion yuan, an increase of 65.42 billion yuan from the end of 2024 [4]. Group 2: Investment Preferences - FOFs have significantly increased their allocation to short-term bond funds, with nearly half of the top 10 funds held by FOFs being short-term bond funds. The total market value of holdings in the Hai Fu Tong Zhong Zheng Short Bond ETF rose from 1.8 billion yuan at the end of Q2 to 3.3 billion yuan at the end of Q3 [6]. - The shift in FOFs' bond fund allocation from off-market to on-market is noted, with a preference for ETFs among the top holdings [7][9]. Group 3: Gold Investments - FOFs have continued to increase their exposure to gold, with 139 funds holding gold-related investments totaling 2.8 billion yuan by the end of Q3 2025. The Hua An Yi Fu Gold ETF remains the most popular, with a total market value of 1.73 billion yuan [10][11]. Group 4: Equity Fund Allocation - FOFs have shifted their equity fund allocations from value to growth styles, with significant increases in holdings of growth-oriented funds such as Yi Fang Da Ke Rong Mixed Fund and Xin Quan He Run [12][13]. - Notably, several value-oriented funds have been reduced in FOF portfolios, indicating a strategic pivot towards growth sectors like technology and new energy [14]. Group 5: International Investments - FOFs are increasingly utilizing ETFs to gain exposure to overseas markets, with total holdings in QDII funds reaching 4.49 billion yuan by the end of Q3 2025. The focus remains on developed markets such as Hong Kong and the U.S. [17][19]. - The popularity of Hong Kong mutual recognition funds is also highlighted, with a total market value of 1.6 billion yuan held by FOFs, primarily in bond funds [20][22]. Group 6: Insights for Individual Investors - The asset allocation strategies and fund selection approaches of FOFs provide valuable insights for individual investors, emphasizing the importance of diversified portfolios that include commodities and cross-border assets [23]. - A "core + satellite" investment strategy is recommended, prioritizing stable funds for core holdings while incorporating higher-risk, high-growth funds for potential additional returns [24].

Core Insights - The article provides an objective ranking of fund net asset values, highlighting the top-performing and bottom-performing funds without any subjective bias [1] Fund Performance Summary Top 10 Funds by Net Value Growth - The top-performing funds include: - Huafu Yongxin Flexible Allocation Mixed A with a net value of 1.7137, up by 4.96% [2] - Huafu Yongxin Flexible Allocation Mixed C with a net value of 1.6648, also up by 4.96% [2] - Wanjia Cycle Vision Stock Initiation A with a net value of 0.9939, up by 4.89% [2] - Wanjia Cycle Vision Stock Initiation C with a net value of 0.9931, up by 4.88% [2] - Qianhai Kaiyuan Gold and Silver Jewelry Mixed A with a net value of 2.4180, up by 4.45% [2] - Qianhai Kaiyuan Gold and Silver Jewelry Mixed C with a net value of 2.3630, up by 4.42% [2] - Southern CSI Hong Kong Gold Industry Stock Index Initiation A with a net value of 1.6638, up by 4.26% [2] - Southern CSI Hong Kong Gold Industry Stock Index Initiation C with a net value of 1.6596, up by 4.25% [2] - Yongying CSI Hong Kong Gold Industry ETF Initiation Link C with a net value of 1.8692, up by 4.24% [2] - Yongying CSI Hong Kong Gold Industry ETF Initiation Link A with a net value of 1.8793, up by 4.24% [2] Bottom 10 Funds by Net Value Decline - The underperforming funds include: - Huabao Overseas China Growth Mixed with a net value of 1.4560, down by 3.77% [3] - Bosera Greater China Mixed with a net value of 1.0430, down by 2.89% [3] - AVIC New Start A with a net value of 0.8586, down by 2.85% [3] - AVIC New Start C with a net value of 0.8421, down by 2.85% [3] - Huaan Greater China A with a net value of 2.2590, down by 2.67% [3] - Guofu National Certificate Hong Kong C with a net value of 0.9463, down by 2.66% [3] - Guofu National Certificate Hong Kong A with a net value of 0.9464, down by 2.66% [3] - Huaan Hong Kong Precision A with a net value of 2.8020, down by 2.64% [3] - Huaan Greater China C with a net value of 1.8460, down by 2.64% [3] - Fortune Core Advantage C with a net value of 1.6357, down by 2.57% [3] Market Overview - The Shanghai Composite Index showed slight fluctuations, closing with a minor increase, while the ChiNext Index exhibited similar behavior [5] - The total trading volume reached 1.74 trillion, with a stock rise-to-fall ratio of 1200:4175 [5] Leading Industries and Concepts - Industries with significant gains include: - Shipbuilding, Oil, Insurance, and Non-ferrous Metals, each rising over 2% [6] - Concepts leading the market include: - Aquaculture and Military Trade, both also increasing over 2% [6] Notable Holdings and Fund Strategies - The top holdings in the best-performing fund, Huafu Yongxin Flexible Allocation Mixed A, include: - Shandong Gold, with a daily increase of 5.98% [9] - Other significant holdings include Shandong International and Zijin Mining, contributing to a total holding concentration of 88.53% [9] - Conversely, the underperforming fund, Huabao Overseas China Growth Mixed, has a holding concentration of 38.75%, with major holdings in Jiangxi Copper and Luoyang Molybdenum, which have seen declines [9]

Core Viewpoint - The performance of public FOFs (Fund of Funds) has significantly improved, with some achieving returns as high as 68% this year, surpassing many actively managed equity funds and changing the perception of FOFs as conservative investment products [1][2]. Group 1: Performance and Strategy - The top three performing FOFs this year are Guotai Youxuan Lihang (68%), E Fund Advantage Return (58.33%), and Guotai Industry Rotation (57.47%), all of which have outperformed the average return of actively managed equity funds [2][3]. - FOFs are increasingly focusing on narrow-based industry theme funds, such as ETFs related to gold, batteries, and innovative pharmaceuticals, to enhance their performance [3][4]. - The shift towards selecting funds rather than individual stocks has allowed FOFs to achieve high performance, indicating a new trend in the capital market and public fund industry [2][3]. Group 2: Future Investment Directions - Resource industry funds are becoming popular choices for FOFs, with managers identifying potential recovery opportunities in cyclical industries, particularly in the metal and financial real estate sectors [4][5]. - FOF managers are also looking to increase defensive positions by focusing on the most undervalued sectors within growth and cyclical industries, suggesting that bank, resource, and photovoltaic theme funds may become core investment targets [5][6]. - The current momentum in the global AI industry and improvements in the renewable energy sector are driving FOFs to adjust their allocations, increasing exposure to technology and resource theme funds while reducing financial asset allocations [5][6].

Core Insights - A total of 28,873 funds have updated their net asset values, indicating a significant level of activity in the fund market [2] Group 1 - The article highlights the performance of various funds, identifying the top performers and the underperformers within the market [2]

- The "Style Rotation Fund Selection Strategy" is based on the dimensions of growth value and market capitalization, constructing an absolute active rotation indicator to identify whether a fund is a style rotation fund or a style stable fund. The strategy uses a semi-annual rebalancing approach, adjusting positions at the end of March and August each year, and the fund selection range includes equity-biased hybrid funds and ordinary stock funds, with transaction costs deducted[4][46][51] - The "Comprehensive Fund Selection Strategy Based on Fund Characteristics and Capabilities" constructs selection factors from multiple dimensions such as fund size, holder structure, fund performance momentum, stock selection ability, hidden trading ability, and gold content, and performs equal-weight synthesis. The strategy uses a quarterly rebalancing approach, adjusting positions at the end of January, April, July, and October each year, with transaction costs deducted[5][55][60] - The "Fund Selection Strategy Based on Trading Motivation Factors and Stock Spread Income Factors" combines the trading motivation factors of funds with the stock spread income factors from the fund's profit statement. The strategy aims to select funds with high stock spread income, active trading motivation, and low likelihood of performance manipulation. The strategy uses a semi-annual rebalancing approach, adjusting positions at the end of March and August each year, with transaction costs deducted[6][61][66] - The "Fund Manager Holding Network Trading Uniqueness Fund Selection Strategy" constructs a network based on the details of fund managers' holdings and transactions, and constructs an indicator of the uniqueness of fund managers' transactions. The strategy uses a semi-annual rebalancing approach, adjusting positions at the beginning of April and September each year, with transaction costs deducted[7][67][74] - The "Style Rotation Fund Selection Strategy" achieved a return of -0.08% in May 2025, with an excess return of -1.11% relative to the Wind equity-biased hybrid fund index[4][46][51] - The "Comprehensive Fund Selection Strategy Based on Fund Characteristics and Capabilities" achieved a return of 0.18% in May 2025, with an excess return of -0.88% relative to the Wind equity-biased hybrid fund index[5][55][60] - The "Fund Selection Strategy Based on Trading Motivation Factors and Stock Spread Income Factors" achieved a return of -0.96% in May 2025, with an excess return of -1.98% relative to the Wind equity-biased hybrid fund index[6][61][66] - The "Fund Manager Holding Network Trading Uniqueness Fund Selection Strategy" achieved a return of -0.06% in May 2025, with an excess return of -1.09% relative to the Wind equity-biased hybrid fund index[7][67][74]