Investment Rating - The report does not explicitly state an investment rating for the industry or specific companies [37]. Core Insights - The report highlights that the market is currently favoring high volatility and high momentum stocks, while low momentum and low volatility stocks are also performing well [3][5][20]. - It notes that growth and unexpected growth financial factors are showing positive returns, indicating a preference for stocks with stable growth despite short-term performance challenges [17][22]. - The GRU factor's performance is mixed, with most models showing negative returns, except for the open1d model which has shown positive returns [18][29]. Summary by Sections Style Factor Tracking - The report indicates strong performance in volatility, valuation, and liquidity factors, while non-linear market capitalization, market capitalization, and growth factors are underperforming [15][1]. Overall Market Factor Performance - Basic financial factors show a divergence in returns, with static financial factors yielding negative returns, while growth and unexpected growth factors yield positive returns [17]. - Technical factors are performing positively overall, with high volatility and high momentum stocks leading the performance [17]. CSI 300 Component Stock Factor Performance - Basic financial factors within the CSI 300 show mostly positive returns, with valuation factors underperforming and growth factors performing strongly [20]. - Technical factors show a mixed performance, with momentum factors significantly underperforming while volatility factors are performing positively [20]. CSI 500 Component Stock Factor Performance - Basic financial factors show a divergence in returns, with unexpected growth factors performing well, while static financial factors yield mostly negative returns [22]. - Technical factors show a mixed performance, with momentum factors underperforming and volatility factors performing positively [22]. CSI 1000 Component Stock Factor Performance - Basic financial factors show a divergence in returns, with static financial factors yielding negative returns and unexpected growth factors yielding positive returns [24]. - Technical factors are performing negatively overall, with low momentum and low volatility stocks performing better [25]. Strategy Performance Tracking - The GRU long position strategy has shown strong performance, with excess returns relative to the CSI 1000 index ranging from 0.84% to 1.89% [29]. - The open1d model has shown a strong performance year-to-date, with an excess return of 6.08% relative to the CSI 1000 index [29].

Quantitative Models and Construction Methods 1. Model Name: GRU (Generalized Recurrent Unit) - **Model Construction Idea**: GRU models are used to capture temporal dependencies and patterns in financial data, leveraging recurrent neural network structures to predict stock performance or factor returns[3][4][5] - **Model Construction Process**: The GRU model is trained on historical stock data, incorporating features such as price movements, volume, and other technical indicators. Specific GRU-based models mentioned include: - **open1d**: Focuses on daily opening prices - **close1d**: Focuses on daily closing prices - **barra1d**: Integrates Barra-style risk factors for daily predictions - **barra5d**: Extends Barra-style risk factors to a 5-day horizon[5][6][25] - **Model Evaluation**: GRU models show mixed performance, with some models like open1d performing well, while others like barra1d and barra5d experience significant drawdowns in certain market conditions[5][6][25] --- Model Backtesting Results GRU Model Performance - **open1d**: - Weekly excess return: 1.22% - Monthly excess return: 2.58% - Year-to-date excess return: 6.08%[29][30] - **close1d**: - Weekly excess return: 1.89% - Monthly excess return: 2.91% - Year-to-date excess return: 4.14%[29][30] - **barra1d**: - Weekly excess return: 0.85% - Monthly excess return: 1.50% - Year-to-date excess return: 3.48%[29][30] - **barra5d**: - Weekly excess return: 0.84% - Monthly excess return: 2.25% - Year-to-date excess return: 5.59%[29][30] --- Quantitative Factors and Construction Methods 1. Factor Name: Barra Style Factors - **Factor Construction Idea**: Barra factors are designed to capture systematic risk exposures across various dimensions such as size, value, momentum, and volatility[13][14] - **Factor Construction Process**: - **Beta**: Historical beta of the stock - **Size**: Natural logarithm of total market capitalization - **Momentum**: Weighted average of historical excess returns, combining volatility, cumulative deviation, and residual volatility $ Momentum = 0.74 \cdot \text{Volatility} + 0.16 \cdot \text{Cumulative Deviation} + 0.1 \cdot \text{Residual Volatility} $ - **Volatility**: Weighted average of historical residual return volatilities - **Valuation**: Inverse of price-to-book ratio - **Liquidity**: Weighted average of turnover ratios (monthly, quarterly, yearly) - **Profitability**: Weighted average of analyst forecasted earnings yield, cash flow yield, and other profitability metrics - **Growth**: Weighted average of earnings and revenue growth rates - **Leverage**: Weighted average of market leverage, book leverage, and debt-to-asset ratio[14][15] - **Factor Evaluation**: Barra factors demonstrate varying performance across different market conditions, with some factors like volatility and liquidity showing strong returns, while others like size and growth exhibit weaker performance[15][16] 2. Factor Name: Technical Factors - **Factor Construction Idea**: Technical factors aim to capture price and volume-based patterns, focusing on momentum and volatility metrics[17][20][24] - **Factor Construction Process**: - **Momentum**: Calculated over different time horizons (e.g., 20-day, 60-day, 120-day) - **Volatility**: Measured as the standard deviation of returns over specific periods (e.g., 20-day, 60-day, 120-day) - **Median Deviation**: Captures the median absolute deviation of returns[27] - **Factor Evaluation**: High-momentum and high-volatility stocks generally outperform, but certain periods show negative returns for these factors, especially in the 120-day horizon[17][27] 3. Factor Name: Fundamental Factors - **Factor Construction Idea**: Fundamental factors are derived from financial statements, focusing on profitability, growth, and valuation metrics[17][20][24] - **Factor Construction Process**: - **Static Financial Metrics**: Return on equity (ROE), return on assets (ROA), and profit margins - **Growth Metrics**: Earnings growth, revenue growth, and cash flow growth - **Surprise Metrics**: Earnings and revenue surprises relative to analyst expectations[19][21][23] - **Factor Evaluation**: Growth and surprise factors perform well, while static financial metrics like ROA and ROE show weaker performance in certain periods[19][21][23] --- Factor Backtesting Results Barra Factors - **Volatility**: Weekly return: 0.75%, Monthly return: 2.73% - **Liquidity**: Weekly return: 0.68%, Monthly return: 1.37% - **Size**: Weekly return: -1.45%, Monthly return: -3.60%[15][16] Technical Factors - **20-day Momentum**: Weekly return: -1.81%, Monthly return: -6.16% - **60-day Volatility**: Weekly return: -1.79%, Monthly return: -0.74% - **120-day Momentum**: Weekly return: -1.68%, Monthly return: -0.80%[27] Fundamental Factors - **ROA Growth**: Weekly return: 0.23%, Monthly return: 1.31% - **Earnings Surprise**: Weekly return: 0.20%, Monthly return: 1.11% - **Revenue Growth**: Weekly return: 0.17%, Monthly return: 0.77%[19][21][23]

Investment Rating - The report indicates a cautious view on the market with a focus on small-cap stocks as a preferred investment style [1][12]. Core Insights - The A-share market continued to show volatility with a contraction in trading volume, leading to a cautious sentiment in the market. The liquidity remains loose, favoring small-cap stocks, and a "dividend + small-cap" strategy is suggested for relative returns [1][12]. - Major indices showed mixed performance, with the Shanghai Composite Index rising by 0.76% and the ChiNext Index increasing by 1.38%, while the CSI 500 and CSI 1000 experienced slight declines [1][13]. Summary by Sections Overall Market Performance - The report covers the performance of major indices from May 12 to May 16, 2025, highlighting a mixed outcome with the Shanghai Composite Index up by 0.76% and the ChiNext Index up by 1.38% [1][13]. - The report notes that the liquidity environment remains favorable for small-cap stocks, which are expected to continue outperforming [1][12]. Industry Valuation - As of May 16, 2025, the report categorizes the valuation of major indices as "moderate," while the ChiNext Index is classified as "safe." Specific industries such as non-bank financials, transportation, and utilities are also rated as "safe" [1][18][19]. Fund Flow Tracking - The report indicates that institutional interest was highest in stocks like Anji Technology and Hengda, with significant net outflows from ETFs, particularly in the stock-type ETFs [3][56]. - The report highlights that southbound capital saw a net outflow of 86.85 billion HKD during the tracking period [3][56]. Volatility Analysis - The report discusses the cross-sectional volatility of major indices, noting a decrease in the cross-sectional volatility of the CSI 300, CSI 500, and CSI 1000, indicating a deterioration in the short-term alpha environment [2][37]. - Time series volatility for the CSI 300 increased, suggesting an improvement in the alpha environment, while the CSI 500 and CSI 1000 saw declines [2][41].

点击注册小程序 查看完整报告 特别申明： 本订阅号中所涉及的证券研究信息由光大证券研究所编写，仅面向光大证券专业投资者客户，用作新媒体形势下研究 信息和研究观点的沟通交流。非光大证券专业投资者客户，请勿订阅、接收或使用本订阅号中的任何信息。本订阅号 难以设置访问权限，若给您造成不便，敬请谅解。光大证券研究所不会因关注、收到或阅读本订阅号推送内容而视相 关人员为光大证券的客户。 今 日 聚 焦 【策略】A股牛市历史复盘及前景展望——解密牛市系列之一 基本面回升是牛市形成的核心驱动力，流动性宽松与产业趋势往往形成共振效应。当基本面全面改善时， 通常催生全面牛市，而在基本面结构性改善阶段，若与流动性宽松、产业趋势形成共振，同样可能孕育牛 市。展望未来，基本面修复进程或将呈现温和且渐进的特征，宏微观流动性共振与产业升级有望驱动市场 上涨。 （张宇生/郭磊） 2025-05-18 您可点击今日推送内容的第1条查看 【金工】小市值风格仍占优——金融工程市场跟踪周报20250519 上周A股延续震荡表现，主要宽基指数量能有所收缩。交易情绪方面，伴随量能收缩，截至上周五 （2025.05.16，下同）主要宽基指数量能择时指标 ...

Core Viewpoint - The A-share market continued to show a fluctuating performance with reduced trading volume, indicating a cautious sentiment among investors. The market is experiencing a "profit-taking" state, with net outflows from ETFs [2]. Market Performance Summary - The major indices showed mixed results: the Shanghai Composite Index rose by 0.76%, the SSE 50 increased by 1.22%, the CSI 300 gained 1.12%, while the CSI 500 and CSI 1000 fell by 0.10% and 0.23% respectively. The ChiNext Index rose by 1.38%, and the Northbound 50 Index increased by 3.13% [2]. - As of May 16, 2025, the valuation percentiles for major indices were categorized as "moderate" for the Shanghai Composite, SSE 50, CSI 300, CSI 500, and CSI 1000, while the ChiNext Index was rated as "safe" [2]. Sector Valuation Analysis - According to the CITIC industry classification, sectors such as non-ferrous metals, electric power and utilities, home appliances, food and beverage, agriculture, non-bank financials, and transportation are rated as "safe" in terms of valuation [2]. Volatility Analysis - The cross-sectional volatility of the CSI 300, CSI 500, and CSI 1000 indices decreased compared to the previous week, indicating a deterioration in the short-term Alpha environment. However, the time series volatility for the CSI 300 increased, suggesting an improvement in the Alpha environment, while the CSI 500 and CSI 1000 saw a decline [3]. Fund Flow Tracking - The top five stocks attracting institutional attention were Anji Technology (241 institutions), Hengerd (237), Naipu Mining Machine (122), Haimu Star (113), and Diaowei (94) [4]. - During the period from May 12 to May 16, 2025, southbound funds experienced a net outflow of 8.685 billion HKD, with net inflows of 4.910 billion HKD in the Shanghai Stock Connect and net outflows of 13.595 billion HKD in the Shenzhen Stock Connect [4]. ETF Performance - The median return for stock ETFs was 0.74% with a net outflow of 25.370 billion CNY. The median return for Hong Kong stock ETFs was 1.27% with a net outflow of 6.696 billion CNY. Cross-border ETFs had a median return of 3.80% with a net outflow of 1.081 billion CNY, while commodity ETFs had a median return of -4.71% with a net outflow of 4.308 billion CNY [6].

查看完整报告 特别申明： 本订阅号中所涉及的证券研究信息由光大证券研究所编写，仅面向光大证券专业投资者客 户，用作新媒体形势下研究信息和研究观点的沟通交流。非光大证券专业投资者客户，请勿 订阅、接收或使用本订阅号中的任何信息。本订阅号难以设置访问权限，若给您造成不便， 敬请谅解。光大证券研究所不会因关注、收到或阅读本订阅号推送内容而视相关人员为光大 证券的客户。 报告摘要 点击注册小程序 本周，基本面因子在多数行业表现较差，其中净利润增长率因子在煤炭行业正收益显著。估值类因子中， BP因子在综合行业正收益显著。流动性因子在交通运输、美容护理、化工、商业贸易和轻工制造行业正收 益显著。市值风格上，本周多数行业表现为小市值风格。 PB-ROE-50组合跟踪： 本周PB-ROE-50组合在中证500、中证800股票池中获取正超额收益。中证500股票池中获得超额收益 0.88%，中证800股票池中获得超额收益0.43%，全市场股票池中获得超额收益-0.02%。 量化市场跟踪 大类因子表现： 本周全市场股票池中，残差波动率因子和盈利因子分别获取正收益0.55%、0.26%；市值因子和非线性市值 因子分别获取负收益-0. ...

- The report tracks the performance of various factors in different stock pools, including the CSI 300, CSI 500, and Liquidity 1500 stock pools[1][2][3] - In the CSI 300 stock pool, the best-performing factors this week were single-quarter net profit year-on-year growth rate (1.02%), single-quarter EPS (1.00%), and PE ratio factor (0.89%)[12][13] - In the CSI 500 stock pool, the best-performing factors this week were EPTTM percentile (1.30%), PB ratio factor (1.07%), and operating cash flow ratio (0.97%)[14][15] - In the Liquidity 1500 stock pool, the best-performing factors this week were post-morning return factor (2.27%), momentum spring factor (1.43%), and PE TTM reciprocal (1.33%)[16][17] - The PB-ROE-50 portfolio achieved positive excess returns in the CSI 500 and CSI 800 stock pools this week, with excess returns of 0.88% and 0.43% respectively[24][25] - The institutional research portfolio tracking strategy achieved positive excess returns this week, with the private equity research tracking strategy achieving an excess return of 0.22% relative to the CSI 800[26][27] - The block trading portfolio achieved a positive excess return of 0.36% relative to the CSI All Share Index this week[30][31] - The directed issuance portfolio achieved a positive excess return of 0.48% relative to the CSI All Share Index this week[35][36]

Quantitative Models and Construction Methods - **Model Name**: PB-ROE-50 **Model Construction Idea**: The model selects stocks based on a combination of Price-to-Book (PB) ratio and Return on Equity (ROE), aiming to capture value and profitability factors[23] **Model Construction Process**: The portfolio is constructed by ranking stocks based on PB and ROE metrics, selecting the top 50 stocks, and rebalancing periodically. Details of the construction process are referenced in earlier reports[23] **Model Evaluation**: The model underperformed this week, delivering negative excess returns across all stock pools, indicating potential challenges in the current market environment[23] - **Model Name**: Public and Private Institution Research Portfolios **Model Construction Idea**: These portfolios are based on stocks that receive significant attention from public and private institutional research, leveraging the informational advantage of institutional focus[25] **Model Construction Process**: Stocks are selected based on the frequency and intensity of institutional research coverage. The portfolios are rebalanced periodically to reflect updated research trends[25] **Model Evaluation**: The public research portfolio achieved positive excess returns this week, while the private research portfolio remained flat, suggesting varying effectiveness of institutional research strategies[25] - **Model Name**: Block Trade Portfolio **Model Construction Idea**: This model identifies stocks with high block trade activity and low volatility, hypothesizing that such stocks exhibit superior subsequent performance[29] **Model Construction Process**: Stocks are ranked based on "block trade transaction ratio" and "6-day transaction volatility." A portfolio is constructed by selecting stocks with high transaction ratios and low volatility, rebalanced monthly[29] **Model Evaluation**: The portfolio delivered strong positive excess returns this week, highlighting the effectiveness of the "high transaction, low volatility" principle[29] - **Model Name**: Private Placement Portfolio **Model Construction Idea**: This model focuses on stocks involved in private placement events, aiming to capture event-driven investment opportunities[34] **Model Construction Process**: Stocks are selected based on private placement announcements, considering factors such as market capitalization, rebalancing frequency, and position control. The portfolio is rebalanced periodically[34] **Model Evaluation**: The portfolio achieved modest positive excess returns this week, indicating the continued relevance of private placement events in generating alpha[34] Model Backtesting Results - **PB-ROE-50 Model**: - Excess return (CSI 500): -0.26% - Excess return (CSI 800): -0.83% - Excess return (All Market): -1.00%[24] - **Public Research Portfolio**: - Excess return (CSI 800): 0.81%[26] - **Private Research Portfolio**: - Excess return (CSI 800): 0.00%[26] - **Block Trade Portfolio**: - Excess return (CSI All Share): 1.55%[30] - **Private Placement Portfolio**: - Excess return (CSI All Share): 0.19%[35] Quantitative Factors and Construction Methods - **Factor Name**: Momentum Factor **Factor Construction Idea**: Captures the momentum effect by identifying stocks with strong recent performance[18] **Factor Construction Process**: Stocks are ranked based on their recent price performance, and the factor is constructed by taking long positions in high-momentum stocks and short positions in low-momentum stocks[18] **Factor Evaluation**: The factor delivered positive returns this week, indicating the presence of momentum effects in the market[18] - **Factor Name**: Nonlinear Market Cap Factor **Factor Construction Idea**: Measures the nonlinear relationship between market capitalization and stock returns[18] **Factor Construction Process**: The factor is derived by fitting a nonlinear regression model to market cap and return data, isolating the nonlinear component[18] **Factor Evaluation**: The factor underperformed this week, reflecting the dominance of small-cap stocks in the market[18] - **Factor Name**: Residual Volatility Factor **Factor Construction Idea**: Identifies stocks with low residual volatility, hypothesizing that such stocks exhibit superior risk-adjusted returns[18] **Factor Construction Process**: Residual volatility is calculated by regressing stock returns on market returns and measuring the standard deviation of residuals. Stocks with low residual volatility are favored[18] **Factor Evaluation**: The factor delivered negative returns this week, suggesting a challenging environment for low-volatility strategies[18] Factor Backtesting Results - **Momentum Factor**: Weekly return: 0.69%[18] - **Nonlinear Market Cap Factor**: Weekly return: -0.58%[18] - **Residual Volatility Factor**: Weekly return: -0.64%[18]

点击注册小程序 特别申明： 本订阅号中所涉及的证券研究信息由光大证券研究所编写，仅面向光大证券专业投资者客户，用作新媒体形势下研究 信息和研究观点的沟通交流。非光大证券专业投资者客户，请勿订阅、接收或使用本订阅号中的任何信息。本订阅号 难以设置访问权限，若给您造成不便，敬请谅解。光大证券研究所不会因关注、收到或阅读本订阅号推送内容而视相 关人员为光大证券的客户。 报告摘要 沪深300股票池中，本周表现较好的因子有动量调整大单 (2.40%)、大单净流入 (2.32%)、动量调整小单 (1.82%)。表现较差的因子有单季度总资产毛利率(-2.60%)、总资产增长率(-2.48%)、单季度ROA (-2.14%)。 中证500股票池中，本周表现较好的因子有动量调整小单(2.99%)、市盈率因子(2.89%)、市净率因子 (2.75%)。表现较差的因子有5日成交量的标准差(-0.57%)、净利润率TTM (-0.48%)、营业利润率TTM (-0.27%)。 流动性1500股票池中，本周表现较好的因子有市盈率因子(2.79%)、市盈率TTM倒数(2.10%)、日内波动率 与成交金额的相关性 (1.89%)。表现较差 ...

Group 1 - The core viewpoint of the article emphasizes that the spring market trend is expected to continue, driven by policy and economic data catalysts, with a focus on growth and consumer sectors [4] - The A-share market has experienced significant volatility, with major indices declining, particularly the ChiNext Index, while growth stocks and small-cap stocks are expected to outperform [5] - The oil and chemical sectors are poised for recovery due to easing geopolitical tensions, benefiting downstream refining companies from reduced cost pressures [7][8] Group 2 - The low-altitude economy is projected to reach a market size of 1.5 trillion yuan in 2025, with significant growth in the humanoid robot market, expected to grow from approximately 1.19 million units to 60.57 million units by 2030 [9] - The Hong Kong stock market has seen increased trading activity since September 2024, leading to record financial performance, with anticipated boosts from mainland China's stimulus policies [10]