Quantitative Models and Construction Methods 1. Model Name: Macro Timing Strategy - **Model Construction Idea**: The model aims to provide signals for equity allocation based on macroeconomic growth and monetary liquidity indicators[26] - **Model Construction Process**: The model uses dynamic macro event factors to construct a stock-bond rotation strategy. The signal strength for economic growth and monetary liquidity is calculated monthly. For April, the signal strength for economic growth is 0%, and for monetary liquidity is 50%[26][27] - **Model Evaluation**: The model has shown a return of 1.06% from the beginning of 2025 to the present, compared to a 1.90% return for the Wind All A index during the same period[26] 2. Model Name: Micro Cap Timing Model - **Model Construction Idea**: The model focuses on timing and rotation signals for micro-cap stocks based on volatility and interest rate indicators[30] - **Model Construction Process**: The model uses two mid-term risk warning indicators: 1) Ten-year government bond yield YoY indicator and 2) Volatility congestion YoY indicator. On October 15, 2024, the volatility congestion indicator fell below the threshold, lifting the risk warning signal. The interest rate YoY indicator was -20.45%, not triggering the risk control threshold of 0.3[30] - **Model Evaluation**: The model has not triggered risk control, suggesting investors continue holding micro-cap stocks[30] Model Backtest Results 1. Macro Timing Strategy - **Economic Growth Signal Strength**: 0%[27] - **Monetary Liquidity Signal Strength**: 50%[27] - **Equity Allocation Recommendation**: 25%[27] - **Return from 2025 to Present**: 1.06%[26] 2. Micro Cap Timing Model - **Ten-year Government Bond Yield YoY**: -28.69%[31] - **Volatility Congestion YoY**: -50.09%[31] Quantitative Factors and Construction Methods 1. Factor Name: Value Factor - **Factor Construction Idea**: The factor aims to capture the value characteristics of stocks based on fundamental metrics[37] - **Factor Construction Process**: The value factor includes metrics such as the latest annual report book value to market value (BP_LR), future 12-month consensus expected net profit to market value (EP_FTTM), and past 12-month operating income to market value (SP_TTM)[47] - **Factor Evaluation**: The value factor performed best in the CSI 300 stock pool last week[37] 2. Factor Name: Size Factor - **Factor Construction Idea**: The factor aims to capture the size characteristics of stocks based on market capitalization[37] - **Factor Construction Process**: The size factor includes metrics such as the logarithm of circulating market capitalization (LN_MktCap)[47] - **Factor Evaluation**: The size factor showed strong positive returns in the CSI 1000 stock pool last week[37] Factor Backtest Results 1. Value Factor - **IC Mean (CSI 300)**: 25.88%[38] - **IC Mean (CSI 500)**: 10.56%[38] - **IC Mean (CSI 1000)**: 6.32%[38] - **Multi-Long Return (CSI 300)**: 10.84%[38] - **Multi-Long Return (CSI 500)**: 10.56%[38] - **Multi-Long Return (CSI 1000)**: 6.32%[38] 2. Size Factor - **IC Mean (CSI 300)**: 3.33%[38] - **IC Mean (CSI 500)**: -3.23%[38] - **IC Mean (CSI 1000)**: -1.84%[38] - **Multi-Long Return (CSI 300)**: 3.33%[38] - **Multi-Long Return (CSI 500)**: -3.23%[38] - **Multi-Long Return (CSI 1000)**: -1.84%[38]

- The report discusses two main quantitative models: the Diffusion Index Model and the GRU Factor Model[6][7][14][33] Diffusion Index Model 1. **Model Name**: Diffusion Index Model 2. **Model Construction Idea**: The model is based on the principle of price momentum, capturing industry trends by observing the diffusion index of various sectors[6][27] 3. **Model Construction Process**: - Calculate the diffusion index for each industry - Rank industries based on their diffusion index values - Select top industries for investment based on their diffusion index rankings - Formula: $ \text{Diffusion Index} = \frac{\text{Number of advancing stocks}}{\text{Total number of stocks}} $ 4. **Model Evaluation**: The model has shown varying performance over the years, with significant returns in some periods and notable drawdowns in others[26][30] 5. **Model Test Results**: - 2025 YTD excess return: -3.16%[25] - April 2025 excess return: -1.08%[30] - Weekly excess return: 0.43%[30] GRU Factor Model 1. **Model Name**: GRU Factor Model 2. **Model Construction Idea**: The model leverages GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level price and volume data, aiming to capture trading information and trends[7][33] 3. **Model Construction Process**: - Collect minute-level price and volume data - Train a GRU network on historical data to identify patterns - Rank industries based on GRU factor scores - Select top industries for investment based on their GRU factor rankings - Formula: $ \text{GRU Factor} = \text{GRU Network Output} $ 4. **Model Evaluation**: The model has shown strong performance in short cycles but may struggle in long cycles or extreme market conditions[33][36] 5. **Model Test Results**: - 2025 YTD excess return: -3.33%[33] - April 2025 excess return: 0.92%[36] - Weekly excess return: -0.31%[36] Factor Rankings and Performance 1. **Diffusion Index Rankings (as of April 25, 2025)**: - Top industries: Banking (0.986), Non-Banking Financials (0.948), Comprehensive Financials (0.926), Computers (0.873), Retail (0.847), Communication (0.841)[14][27] - Bottom industries: Coal (0.105), Oil & Petrochemicals (0.175), Food & Beverage (0.257), Agriculture (0.396), Steel (0.423), Utilities (0.491)[27][28] 2. **GRU Factor Rankings (as of April 25, 2025)**: - Top industries: Banking (3.81), Transportation (2.77), Non-Banking Financials (2.37), Textiles & Apparel (2.34), Media (1.98), Light Manufacturing (1.81)[7][34] - Bottom industries: Automobiles (-5.31), Agriculture (-4.05), Pharmaceuticals (-4.03), Home Appliances (-3), Coal (-2.67), Defense (-2.64)[34] Weekly and Monthly Performance 1. **Diffusion Index Weekly Performance**: - Top weekly gainers: Construction (0.189), Real Estate (0.187), Building Materials (0.136), Light Manufacturing (0.089), Textiles & Apparel (0.081), Communication (0.069)[29] - Top weekly losers: Steel (-0.111), Utilities (-0.038), Non-Ferrous Metals (-0.018), Coal (0.003), Transportation (0.007), Computers (0.009)[29] 2. **GRU Factor Weekly Performance**: - Top weekly gainers: Banking, Textiles & Apparel, Consumer Services[34] - Top weekly losers: Coal, Automobiles, Construction[34]

摘要：本科就读于北京航空大学机电专业，11年深耕汽车、机械、新能源领域 2025年4月，北京亦庄的马拉松比赛，20支人形机器人队伍与1.2万名来自全国各地的人类选手同场竞 技，最终"天工Ultra"以2小时40分42秒的成绩摘下了桂冠。 机器人是国内先进制造崛起的缩影之一。早在产业趋势出现前，华安基金就启动布局产业跟踪和市场调 研。具有产业学术背景+投研实力的"华安基金科技联盟"，在科技投资的每个细分领域都在不断扩容深 耕与迭代探索。 其中，基金经理熊哲颖本科就读于北京航空大学机电专业，此后11年深耕汽车、机械、新能源领域。当 AI浪潮袭来，她从AI赋能底层机械装备、永续能源等角度，快速挖掘出新科技带来的投资机会，并在 机器人、新能源汽车、机械设备等领域发挥所长。 她构建了一套"抓结构+布节奏+选个股"三位一体的投资框架，将成长股投资体验"更加从容舒适"作为投 资目标。 投资前，熊哲颖会先思考持仓结构，始终不忘"控制回撤"的领先目标。 作为典型的新能源制造+科技成长风格选手，熊哲颖的持仓结构，整体围绕着"先进制造"领域展开，在 维持行业均衡的同时，再通过重仓个股的方式提高锐度，形成了"攻守兼备"的体系。 以其 ...

报 告 摘 要 一、公募FOF基金2025年一季度概览 截至 2025Q1 ，全市场已成立 FOF 产品数量 512 只，合计规模为 1510.79 亿 元，相比 2024Q4 增加 13.47% 。根据穿透后权益资产占比将 FOF 划分为偏 债型 FOF 、平衡型 FOF 和偏股型 FOF ， 2025Q1 规模分别为 766.59 亿 元、 326.02 亿元、 418.18 亿元， 2025 年一季度收益中位数分别为 0.44% 、 1.57% 、 2.64% 。 二、 2025年一季报中FOF配置最多的基金 为了能更好地观察 FOF 基金经理的投资偏好，本文就市场关注度较高的几类基 金分别统计了 FOF 重仓数量最多和重仓规模最大的基金。 在主动权益基金中，重仓该基金的 FOF 数量最多的三只基金是 大成高鑫 A 、富 国稳健增长 A 、华夏创新前沿 ；重仓规模最大的三只基金是 华夏创新前沿、大 成高鑫 A 、易方达信息行业精选 C 。 在债券型基金中，重仓该基金的 FOF 数量最多的三只基金是 广发纯债 A 、易方 达安悦超短债 A 、富国纯债 AB ；重仓规模最大的三只基金是 易方达安悦超短 债 ...

- The report analyzes the active equity funds' allocation in Q1 2025, highlighting that the median position of active equity funds was 89.55%, which has remained stable around 90% since 2020[1][7] - The top three sectors with the highest allocation in Q1 2025 were machinery, TMT, and Hong Kong stocks, with allocation ratios of 22.30%, 21.01%, and 19.08%, respectively[1][13] - Compared to the previous quarter, the allocation ratio for Hong Kong stocks and pharmaceuticals increased by 4.74% and 0.03%, respectively, while the allocation ratio for machinery, TMT, consumption, finance, others, real estate infrastructure, and cyclical sectors decreased[1][13] - The top five industries with the highest allocation in Q1 2025 were Hong Kong stocks, electronics, pharmaceuticals, power equipment and new energy, and food and beverages, with allocation ratios of 19.08%, 15.29%, 8.63%, 7.91%, and 7.14%, respectively[1][20] - The top five stocks with the largest holdings by active equity funds in Q1 2025 were CATL, Midea Group, Luxshare Precision, Kweichow Moutai, and BYD, with holding values of 54.49 billion yuan, 32.32 billion yuan, 30.82 billion yuan, 37.53 billion yuan, and 23.09 billion yuan, respectively[1][24] - The top five Hong Kong stocks with the largest holdings by active equity funds in Q1 2025 were Tencent Holdings, SMIC, Xiaomi Group-W, Pop Mart, and Meituan-W, with holding values accounting for 4.1%, 1.2%, 1.1%, 0.5%, and 0.6% of the fund's heavy stock value, respectively[1][26][28] - The report defines fund clustering degree as determined by the clustering degree of heavy stocks and their holding ratios, with the calculation method provided[1][30][32] - The report lists the top 20 funds with the highest clustering degree and the top 20 funds with the lowest clustering degree, based on specific screening criteria[1][35][39]

Group 1: Fund Size and Performance - The total net asset value of public funds reached 31.62 trillion yuan at the end of Q1 2025, a decrease of 0.63 trillion yuan compared to the end of Q4 2024 [3][16] - Non-monetary market fund size totaled 18.29 trillion yuan, down 0.35 trillion yuan from the previous quarter, reflecting a 1.87% quarter-on-quarter decline but an 11.84% year-on-year increase [3][16] - The total number of active equity funds was 4,533, with a combined size of 3.81 trillion yuan, showing a 1.10% increase from the previous quarter but a 5.73% decrease year-on-year [5][27] Group 2: Active Equity Funds Analysis - The average holding ratio of active equity funds was 86.36% at the end of Q1 2025, a slight increase from 86.29% in the previous quarter [5][30] - The top ten heavy-weight stocks accounted for an average of 38.63% of the net asset value of active equity funds, down from 40.75% in the previous quarter [5][30] - The concentration of active equity fund management is high, with the top ten fund companies accounting for 44.3% of the total active equity fund size [5][31] Group 3: Fixed Income Plus Funds - As of the end of Q1 2025, there were 1,547 fixed income plus funds with a total size of 1.39 trillion yuan [6][53] - The majority of fixed income plus funds are classified into medium and low elasticity groups, with medium elasticity funds accounting for 49.7% of the total size [6][57] - The investment in non-ferrous metals and banking sectors increased significantly, with respective increases of 3.0% and 1.2% in heavy-weight positions [6][82] Group 4: FOF, ETF, QDII, and Quantitative Funds - The total size of FOF funds reached 1510.79 billion yuan, a quarter-on-quarter increase of 13.5% [7] - The ETF market size was 36,633.88 billion yuan, up 4.15% from Q4 2024 [7] - The QDII market had 257 funds with a total size of 524.80 billion yuan at the end of Q1 2025 [7]

报 告 摘 要 一、基金仓位监控 普通股票型基金 仓位中位数为90.62%， 偏股混合型基金 仓位中位数为89.21%，与上一季 度相比基本持平，自2020年以来持续围绕在90%仓位震荡。普通股票型基金仓位处在历史 72.13%分位点，偏股混合型基金仓位处在历史78.69%分位点。 普通股票型和偏股混合型基金 港股仓位 均值分别为12.07%和15.3%，均较上一季度明显提 升。普通股票型配置港股基金数量为233只，偏股混合型配置港股基金数量为1572只，普通 股票型以及偏股混合型基金中配置港股的基金数量占比为57.85%。 二、基金持股集中度监控 基金重仓股占权益配置比重为53.21%，上一期为52.61%，有所提升；另一方面，基金持股 数量在2025年一季度相较于上一季度略有增加，基金经理总体持股数量为2331只，这意味 着，基金经理持仓的股票差异化有所提升。 三、板块配置监控 2025年一季报中披露的主板配置权重为55.08%、创业板配置权重为13.52%、科创板配置权 重为12.27%，港股配置权重为19.12%。 在2025年一季度内， 科技板块配置减仓较多 ，减少了1.73%，而 消费和医药板块配 ...

2025年一季报中披露的主板配置权重为55.08%、创业板配置权重为13.52%、科创板配置权 重为12.27%，港股配置权重为19.12%。 报 告 摘 要 一、基金仓位监控 普通股票型基金 仓位中位数为90.62%， 偏股混合型基金 仓位中位数为89.21%，与上一季 度相比基本持平，自2020年以来持续围绕在90%仓位震荡。普通股票型基金仓位处在历史 72.13%分位点，偏股混合型基金仓位处在历史78.69%分位点。 普通股票型和偏股混合型基金 港股仓位 均值分别为12.07%和15.3%，均较上一季度明显提 升。普通股票型配置港股基金数量为233只，偏股混合型配置港股基金数量为1572只，普通 股票型以及偏股混合型基金中配置港股的基金数量占比为57.85%。 二、基金持股集中度监控 基金重仓股占权益配置比重为53.21%，上一期为52.61%，有所提升；另一方面，基金持股 数量在2025年一季度相较于上一季度略有增加，基金经理总体持股数量为2331只，这意味 着，基金经理持仓的股票差异化有所提升。 三、板块配置监控 绝对市值配置最高 的三只股票为腾讯控股、宁德时代、贵州茅台，其配置绝对市值分别为 68 ...

上周市场表现较弱，主要指数全线回调。截止上周五收盘，沪指收于3238.23点，周跌3.11%；上证50收于2619.58点，周跌1.60%。 中信行业板块方面：农林牧渔、商贸零售、国防军工涨幅居前；电力设备及新能源、通信、传媒跌幅居前。 | | | 估值水平 | 周涨跌幅 | 近一季度 | 41 : | | --- | --- | --- | --- | --- | --- | | | | (PE TTM) | | 涨跌幅 | 涨 b . m | | A股主要 | 科创50 | 81. 80 | -0.63% | 6. 43% | 2. 30% | | | 上证50 | 10. 47 | -1.60% | 2. 32% | -2. 43% | | | 沪深300 | 12. 06 | -2. 87% | 0. 48% | -4. 69% | | | 上证综指 | 13. 80 | -3.11% | 2. 20% | -3. 39% | | 证券指 | 科创创业50 | 34. 76 | -4.19% | -3.52% | -8.32% | | 数 | 中证500 | 27.66 | -4.52% | 3. 9 ...

Quantitative Models and Construction Methods 1. Model Name: Volume Timing Signal - **Model Construction Idea**: The model uses volume-based signals to determine market timing, identifying bullish or cautious market views based on volume trends[23][24] - **Model Construction Process**: 1. Analyze the volume trends of major broad-based indices 2. Assign a "bullish" or "cautious" signal based on the volume dynamics 3. For example, as of April 11, 2025, the Beixin 50 index showed a "bullish" signal, while other indices like the Shanghai Composite and CSI 300 showed "cautious" signals[23][24] - **Model Evaluation**: The model provides a straightforward and intuitive approach to market timing but may lack robustness in highly volatile markets[23][24] 2. Model Name: Momentum Sentiment Indicator - **Model Construction Idea**: This model captures market sentiment by analyzing the proportion of stocks with positive returns in the CSI 300 index over a specific period[24][25] - **Model Construction Process**: 1. Calculate the proportion of CSI 300 constituent stocks with positive returns over the past N days $ \text{CSI 300 N-day Upward Proportion} = \frac{\text{Number of stocks with positive returns in N days}}{\text{Total number of stocks in CSI 300}} $ 2. Smooth the indicator using two moving averages with different windows (N1 and N2, where N1 > N2) 3. Generate signals: - If the short-term moving average (fast line) exceeds the long-term moving average (slow line), the market is considered bullish - If the fast line falls below the slow line, the market sentiment is turning cautious[27] - **Model Evaluation**: The indicator is effective in capturing upward opportunities but may fail to avoid risks in declining markets[25][27] 3. Model Name: Moving Average Sentiment Indicator - **Model Construction Idea**: This model uses an eight-moving-average system to assess the trend state of the CSI 300 index[32][33] - **Model Construction Process**: 1. Calculate the closing prices of the CSI 300 index for eight moving averages (parameters: 8, 13, 21, 34, 55, 89, 144, 233) 2. Assign values to the indicator based on the number of moving averages above or below the current price: - If the current price exceeds five or more moving averages, the market is considered bullish - Otherwise, the market is neutral or bearish[32][33] - **Model Evaluation**: The model provides a clear trend-following signal but may lag in rapidly changing markets[35] --- Model Backtesting Results 1. Volume Timing Signal - Beixin 50 Index: Bullish signal[23][24] - Other indices (e.g., Shanghai Composite, CSI 300): Cautious signal[23][24] 2. Momentum Sentiment Indicator - CSI 300 Upward Proportion: Approximately 53% in the most recent week[25] 3. Moving Average Sentiment Indicator - CSI 300 Index: Currently in a non-bullish sentiment zone[35] --- Quantitative Factors and Construction Methods 1. Factor Name: Cross-Sectional Volatility - **Factor Construction Idea**: Measures the dispersion of stock returns within an index to assess the alpha environment[37] - **Factor Construction Process**: 1. Calculate the cross-sectional volatility of constituent stocks in indices like CSI 300, CSI 500, and CSI 1000 2. Compare the recent quarter's average volatility with historical periods to determine the alpha environment[41] - **Factor Evaluation**: Higher cross-sectional volatility indicates a better alpha environment for stock selection[37][41] 2. Factor Name: Time-Series Volatility - **Factor Construction Idea**: Measures the volatility of index returns over time to assess the alpha environment[41][44] - **Factor Construction Process**: 1. Calculate the time-series volatility of indices like CSI 300, CSI 500, and CSI 1000 2. Compare the recent quarter's average volatility with historical periods to evaluate the alpha environment[44] - **Factor Evaluation**: Higher time-series volatility suggests a favorable alpha environment for active strategies[41][44] --- Factor Backtesting Results 1. Cross-Sectional Volatility - CSI 300: 1.90% (recent quarter), 77.80% of the past six months' percentile[41] - CSI 500: 2.16% (recent quarter), 42.06% of the past six months' percentile[41] - CSI 1000: 2.52% (recent quarter), 64.54% of the past six months' percentile[41] 2. Time-Series Volatility - CSI 300: 0.63% (recent quarter), 78.84% of the past six months' percentile[44] - CSI 500: 0.48% (recent quarter), 55.56% of the past six months' percentile[44] - CSI 1000: 0.29% (recent quarter), 70.12% of the past six months' percentile[44]