Group 1 - The report highlights that domestic equity assets performed well in July 2025, with the risk parity strategy achieving a year-to-date return of 2.65% [2][5][20] - The report provides a summary of various asset allocation strategies, indicating that the domestic asset BL strategy 1 and 2 yielded returns of 2.40% and 2.34% respectively, while the risk parity strategy and macro factor-based strategy returned 2.65% and 2.59% respectively [21][41][42] - The report notes that the domestic equity market saw significant gains, with the CSI 1000 index rising by 4.8% and the Hang Seng Index increasing by 2.78% in July [8][9][10] Group 2 - The report discusses the correlation between different asset classes, indicating that the correlation between the CSI 300 and the total wealth index of government bonds was -38.08%, suggesting a potential for diversification [15][16] - The report outlines the performance of various asset allocation models, with the domestic risk parity strategy showing a maximum drawdown of 0.76% and an annualized volatility of 1.46% [41][42] - The macroeconomic outlook suggests downward risks for growth factors, while inflation expectations may stabilize due to recent policy measures [45][47]

Core Viewpoint - The article outlines the schedule and topics for the 2025 research framework training organized by Guotai Junan Securities, emphasizing a comprehensive approach across various sectors and inviting participation from interested parties [19]. Group 1: Event Schedule - The training sessions are scheduled for August 18-19 and August 25-26, covering a range of topics from macroeconomic research to sector-specific studies [14][19]. - The first two days focus on total, consumption, and financial sectors, while the latter two days will delve into cyclical, pharmaceutical, technology, and manufacturing sectors [19]. Group 2: Research Topics - The training will include sessions on food and beverage research, retail and service research, textile and apparel research, internet applications, home appliances, agriculture, forestry, animal husbandry, and fishery research [15]. - Additional topics will cover macroeconomic research, strategy research, overseas strategy research, fixed income research, fund evaluation, financial engineering, small and medium-sized enterprises, and new stock research [15][16]. - The second week will feature non-metallic building materials, non-ferrous metals, public utilities, biological medicine, cultural communication, electronics, and various engineering and manufacturing studies [16][17].

Quantitative Factors and Construction Factor Name: Stability of Turnover Rate (STR) - **Factor Construction Idea**: The STR factor is designed to evaluate the stability of daily turnover rates. It aims to identify stocks with stable turnover rates, as opposed to focusing solely on low or high turnover rates. This approach addresses the limitations of traditional turnover rate factors, which may misjudge stocks with high turnover but significant future returns [1][8]. - **Factor Construction Process**: - The STR factor is constructed using daily turnover rate data. - The stability of turnover rates is calculated, inspired by the Uniformity of Turnover Rate Distribution (UTD) factor, which measures turnover rate volatility at the minute level. - The STR factor is then adjusted to remove the influence of common market styles and industry effects, ensuring a "pure" factor signal [8]. - **Factor Evaluation**: The STR factor demonstrates strong stock selection capabilities, even after controlling for market and industry influences. It is considered an effective and straightforward factor [6][8]. Traditional Turnover Rate Factor (Turn20) - **Factor Construction Idea**: The Turn20 factor calculates the average daily turnover rate over the past 20 trading days. It assumes that stocks with lower turnover rates are more likely to outperform in the future, while those with higher turnover rates are more likely to underperform [6][7]. - **Factor Construction Process**: - At the end of each month, the daily turnover rates of all stocks over the past 20 trading days are averaged. - The resulting values are neutralized for market capitalization to eliminate size effects [6]. - **Factor Evaluation**: While the Turn20 factor has historically performed well, its logic has limitations. Specifically, stocks with high turnover rates exhibit significant variability in future returns, leading to potential misjudgments of high-performing stocks within this group [7]. --- Backtesting Results of Factors STR Factor - **Annualized Return**: 40.75% [9][10] - **Annualized Volatility**: 14.44% [9][10] - **Information Ratio (IR)**: 2.82 [9][10] - **Monthly Win Rate**: 77.02% [9][10] - **Maximum Drawdown**: 9.96% [9][10] - **July 2025 Performance**: - Long Portfolio Return: 1.29% [10] - Short Portfolio Return: -0.02% [10] - Long-Short Portfolio Return: 1.32% [10] Turn20 Factor - **Monthly IC Mean**: -0.072 [6] - **Annualized ICIR**: -2.10 [6] - **Annualized Return**: 33.41% [6] - **Information Ratio (IR)**: 1.90 [6] - **Monthly Win Rate**: 71.58% [6]

Core Insights - The report maintains a bullish outlook on commodities such as copper and gold, anticipating that inflation in the U.S. may enter a sustained upward trajectory, despite limited recession risks in the near term [1][2][3]. Group 1: Macroeconomic Environment Outlook - The U.S. job market is expected to continue a trend of moderate slowdown, with recession risks currently deemed limited. Recent non-farm payroll data for July fell short of expectations, and significant downward revisions for May and June have catalyzed market adjustments regarding economic outlook [1][12]. - The unemployment rate remains stable, and wage growth has exceeded expectations, indicating that the slowdown in the job market may be mild [1][12]. - The ISM manufacturing PMI for July showed a decline, primarily due to a significant drop in supplier delivery times, while new orders and production indicators showed marginal improvement, suggesting that supply chain normalization rather than a sharp decline in demand may be at play [1][17]. Group 2: Inflation and Federal Reserve Policy - Inflation trends are likely to play a crucial role in the Federal Reserve's interest rate decisions, with expectations that U.S. inflation may enter a phase of sustained upward surprises [2][18]. - Recent data indicates that the transmission of tariffs to inflation has been weaker than anticipated, but as tariff rates become clearer, the pass-through to consumers may accelerate, increasing the likelihood of inflation exceeding expectations [2][18]. Group 3: Commodity and Asset Allocation Strategy - The report reiterates a positive stance on inflation-hedged commodities, including copper, oil, and gold, in light of resilient U.S. economic conditions and potential inflation surprises [3][18]. - The performance of the asset allocation strategy for July yielded a return of 0.6%, with a one-year return of 9.4% and a maximum drawdown of 2.9%, indicating robust overall performance [4][35]. - The macro scoring model indicates a bullish outlook for A-shares, crude oil, and copper, while suggesting caution regarding domestic bonds due to potential tightening liquidity risks [19][21]. Group 4: Specific Asset Insights - The report maintains a neutral view on U.S. equities, suggesting that the market has not fully priced in the negative effects of tariffs, which may become a focal point in future trading [23]. - The gold market faces short-term constraints due to a reduction in U.S. deficits and slowing central bank purchases, but the medium-term outlook remains positive due to anticipated inflationary pressures [24]. - The crude oil outlook is favorable, with the oil sentiment index rising to 0.61, driven by reduced macro risks and increased inflation expectations [29].

Quantitative Models and Construction Methods 1. Model Name: Adjusted PIN Model - **Model Construction Idea**: The model addresses computational bias in the estimation of the Probability of Informed Trading (PIN) by introducing methodological improvements [13] - **Model Construction Process**: - Utilizes a logarithmic likelihood decomposition to resolve numerical instability issues - Implements an intelligent initialization algorithm to avoid local optima - Achieves unbiased estimation of the Adjusted PIN model [11][13] - **Model Evaluation**: The method effectively resolves computational bias and ensures robust estimation [13] 2. Model Name: Elastic String Model for Yield Curve Formation - **Model Construction Idea**: The model simplifies the parameters while maintaining explanatory power for yield curve dynamics [25] - **Model Construction Process**: - Driven by order flow shocks - Implements an elastic string model for the forward rate curve (FRC) - Reduces parameters by 70% while maintaining explanatory power [25] - **Model Evaluation**: The model efficiently captures cross-term structure shock propagation with a delay of ≤3 milliseconds [25] 3. Model Name: Bayesian Black-Litterman Model with Latent Variables - **Model Construction Idea**: Replaces subjective views with data-driven latent variable estimation to enhance portfolio optimization [39] - **Model Construction Process**: - Utilizes data-driven latent variable learning - Provides closed-form solutions for rapid inference - Improves Sharpe ratio by 50% compared to the traditional Markowitz model - Reduces turnover rate by 55% [39] - **Model Evaluation**: The model demonstrates significant improvements in portfolio performance and stability [39] --- Model Backtesting Results 1. Adjusted PIN Model - **Key Metrics**: Not explicitly provided in the report 2. Elastic String Model for Yield Curve Formation - **Key Metrics**: Parameter reduction by 70% while maintaining explanatory power [25] 3. Bayesian Black-Litterman Model with Latent Variables - **Key Metrics**: - Sharpe ratio improvement: +50% - Turnover rate reduction: -55% [39] --- Quantitative Factors and Construction Methods 1. Factor Name: Intangible Asset Factor (INT) - **Factor Construction Idea**: Replaces traditional investment factors to enhance the explanatory power of asset pricing models [10][12] - **Factor Construction Process**: - Introduced as a replacement for traditional investment factors in the five-factor model - Improves the model's ability to explain anomalies in asset pricing [10][12] - **Factor Evaluation**: Demonstrates significant improvement in the explanatory power of the five-factor model [10][12] 2. Factor Name: News-Based Investor Disagreement - **Factor Construction Idea**: Measures investor disagreement based on news sentiment and its impact on stock returns [11][13] - **Factor Construction Process**: - Utilizes the elasticity between trading volume and volatility - Predicts cross-sectional stock returns negatively, aligning with theoretical models [11][13] - **Factor Evaluation**: Effectively predicts stock returns and aligns with theoretical expectations [13] 3. Factor Name: Partially Observable Factor Model (POFM) - **Factor Construction Idea**: Simultaneously processes observable and latent factors to improve model fit and explanatory power [15][16] - **Factor Construction Process**: - Develops a robust estimation method to handle jumps, noise, and asynchronous data - Introduces the HF-UECL framework for unsupervised learning of latent factor contributions - Validates the necessity of latent factors under exogenous settings and their correlation with observable factors under endogenous settings [15][16] - **Factor Evaluation**: Demonstrates the necessity of latent factors and their significant correlation with observable factors [15][16] --- Factor Backtesting Results 1. Intangible Asset Factor (INT) - **Key Metrics**: Improves the explanatory power of the five-factor model for asset pricing anomalies [10][12] 2. News-Based Investor Disagreement - **Key Metrics**: Predicts stock returns negatively, consistent with theoretical models [13] 3. Partially Observable Factor Model (POFM) - **Key Metrics**: - Validates the necessity of latent factors in high-frequency regression residuals - Demonstrates significant correlation between observable and latent factors [15][16]

Quantitative Models and Construction Methods - **Model Name**: Value Smart Beta Portfolio **Model Construction Idea**: The Value Smart Beta portfolios are constructed based on the goal of achieving high beta elasticity and long-term stable excess returns. The portfolios are designed to capture the value style with low historical correlation to other styles[7] **Model Construction Process**: Two portfolios are constructed under the value style: the "Value 50 Portfolio" and the "Value Balanced 50 Portfolio". These portfolios are selected and weighted to optimize for the value factor while maintaining diversification and minimizing correlation with other factors[7] **Model Evaluation**: The Value Smart Beta portfolios demonstrate a focus on capturing value-oriented excess returns, but their performance is sensitive to market conditions[7] - **Model Name**: Growth Smart Beta Portfolio **Model Construction Idea**: The Growth Smart Beta portfolios aim to capture the growth style with high beta elasticity and long-term stable excess returns. These portfolios are designed to have low historical correlation with other styles[7] **Model Construction Process**: Two portfolios are constructed under the growth style: the "Growth 50 Portfolio" and the "Growth Balanced 50 Portfolio". The portfolios are optimized to emphasize growth characteristics while maintaining diversification[7] **Model Evaluation**: The Growth Smart Beta portfolios are effective in capturing growth-oriented returns but may underperform in value-dominated market conditions[7] - **Model Name**: Small-Cap Smart Beta Portfolio **Model Construction Idea**: The Small-Cap Smart Beta portfolios are designed to capture the small-cap style with high beta elasticity and long-term stable excess returns. These portfolios are constructed to have low historical correlation with other styles[7] **Model Construction Process**: Two portfolios are constructed under the small-cap style: the "Small-Cap 50 Portfolio" and the "Small-Cap Balanced 50 Portfolio". The portfolios are optimized to emphasize small-cap characteristics while maintaining diversification[7] **Model Evaluation**: The Small-Cap Smart Beta portfolios show strong performance in capturing small-cap excess returns, particularly in favorable market environments[7] --- Model Backtesting Results - **Value 50 Portfolio**: - Weekly Absolute Return: -2.12% - Weekly Excess Return: -0.41% - Monthly Absolute Return: 0.20% - Monthly Excess Return: 0.33% - Year-to-Date Absolute Return: 12.44% - Year-to-Date Excess Return: 8.78% - Maximum Relative Drawdown: 2.35%[8] - **Value Balanced 50 Portfolio**: - Weekly Absolute Return: -0.46% - Weekly Excess Return: 1.26% - Monthly Absolute Return: 0.48% - Monthly Excess Return: 0.61% - Year-to-Date Absolute Return: 10.16% - Year-to-Date Excess Return: 6.50% - Maximum Relative Drawdown: 3.99%[8] - **Growth 50 Portfolio**: - Weekly Absolute Return: -1.48% - Weekly Excess Return: 0.68% - Monthly Absolute Return: -0.71% - Monthly Excess Return: -0.31% - Year-to-Date Absolute Return: 4.50% - Year-to-Date Excess Return: 2.38% - Maximum Relative Drawdown: 3.61%[8] - **Growth Balanced 50 Portfolio**: - Weekly Absolute Return: -1.64% - Weekly Excess Return: 0.53% - Monthly Absolute Return: 0.06% - Monthly Excess Return: 0.46% - Year-to-Date Absolute Return: 8.71% - Year-to-Date Excess Return: 6.59% - Maximum Relative Drawdown: 6.11%[8] - **Small-Cap 50 Portfolio**: - Weekly Absolute Return: 1.25% - Weekly Excess Return: 1.43% - Monthly Absolute Return: 1.07% - Monthly Excess Return: 0.85% - Year-to-Date Absolute Return: 36.52% - Year-to-Date Excess Return: 19.90% - Maximum Relative Drawdown: 6.23%[8] - **Small-Cap Balanced 50 Portfolio**: - Weekly Absolute Return: -1.09% - Weekly Excess Return: -0.90% - Monthly Absolute Return: 0.61% - Monthly Excess Return: 0.39% - Year-to-Date Absolute Return: 26.60% - Year-to-Date Excess Return: 9.98% - Maximum Relative Drawdown: 4.56%[8]

Quantitative Models and Construction Methods - **Model Name**: "Offense and Defense Dividend 50 Portfolio" **Model Construction Idea**: This model aims to enhance returns by selecting high-dividend stocks with a balance of growth and stability, outperforming the benchmark dividend indices[6][15] **Model Construction Process**: The portfolio is constructed by combining stocks with high dividend yields, growth potential, and low volatility. The selection process involves filtering stocks based on dividend-related factors and optimizing the portfolio to achieve a balance between growth and defensive characteristics[6][15] **Model Evaluation**: The model demonstrates strong performance, consistently outperforming the benchmark dividend indices and ranking in the top percentile among dividend-focused funds[6][21] - **Model Name**: "Central SOE High Dividend 30 Portfolio" **Model Construction Idea**: This model focuses on central state-owned enterprises (SOEs) with high dividend payouts, aiming to capture stable returns from these entities[15] **Model Construction Process**: The portfolio is constructed by selecting 30 central SOEs with the highest dividend yields. The selection criteria emphasize stability and consistent dividend payouts[15] **Model Evaluation**: The model shows stable performance, delivering excess returns over the benchmark dividend indices[15][21] - **Model Name**: "Electronic Sector Enhanced Portfolios" **Model Construction Idea**: These models aim to enhance returns within the electronic sector by focusing on high-growth sub-sectors and leading companies in mature sub-sectors[15][31] **Model Construction Process**: 1. **Balanced Allocation Enhanced Portfolio**: This portfolio is constructed by evenly allocating weights across various electronic sub-sectors to achieve diversification[15] 2. **Sector Leader Enhanced Portfolio**: This portfolio focuses on leading companies in mature sub-sectors, emphasizing their growth potential and market dominance[15][31] **Model Evaluation**: Both portfolios demonstrate positive returns, with the Sector Leader Enhanced Portfolio delivering higher excess returns relative to the electronic sector index[31] Model Backtesting Results - **Offense and Defense Dividend 50 Portfolio**: - Weekly excess return: ~1.41% over the CSI Dividend Total Return Index[6][21] - Year-to-date excess return: ~3.52% over the CSI Dividend Total Return Index[21] - Weekly performance percentile: ~11% among dividend-focused funds[6][21] - **Central SOE High Dividend 30 Portfolio**: - Weekly excess return: ~0.35% over the CSI Dividend Total Return Index[6][21] - **Electronic Sector Enhanced Portfolios**: - **Balanced Allocation Enhanced Portfolio**: Weekly excess return: ~0.89% over the electronic sector index[31] - **Sector Leader Enhanced Portfolio**: Weekly excess return: ~0.89% over the electronic sector index[31] Quantitative Factors and Construction Methods - **Factor Name**: Dividend Quality **Factor Construction Idea**: This factor evaluates the stability and sustainability of a company's dividend payouts[16][18] **Factor Construction Process**: The factor is calculated using metrics such as dividend payout ratio, historical dividend growth rate, and earnings stability. Companies with higher scores on these metrics are ranked higher[16][18] **Factor Evaluation**: The factor demonstrates strong predictive power for identifying high-performing dividend stocks[16][18] - **Factor Name**: Dividend Growth **Factor Construction Idea**: This factor focuses on the growth potential of a company's dividends over time[16][18] **Factor Construction Process**: The factor is derived from the historical growth rate of dividends and projected earnings growth. Companies with consistent and high dividend growth rates are ranked higher[16][18] **Factor Evaluation**: The factor shows significant excess returns compared to pure dividend yield factors[16][18] - **Factor Name**: Low Volatility Dividend **Factor Construction Idea**: This factor targets stocks with high dividend yields and low price volatility[16][18] **Factor Construction Process**: The factor is constructed by combining dividend yield with a volatility measure (e.g., standard deviation of returns). Stocks with high yields and low volatility are ranked higher[16][18] **Factor Evaluation**: The factor provides a defensive characteristic, outperforming during market downturns[16][18] Factor Backtesting Results - **Dividend Quality Factor**: - Weekly excess return: ~1.94% over the CSI Dividend Index[18] - **Dividend Growth Factor**: - Weekly excess return: ~0.92% over the CSI Dividend Index[18] - **Low Volatility Dividend Factor**: - Weekly excess return: ~0.69% over the CSI Dividend Index[18]

证券研究报告|金融工程研究报告 [Table_Date] 2025 年 8 月 3 日 [Table_Title] 7 月份超额收益继续加速——盈利预期期限结构选股月报 202508 [Table_Summary] ► 盈利预期期限结构因子 分析师在某一时点会对上市公司未来多年的盈利做出预 测，我们将预期盈利随未来年度变化的趋势称为盈利预期期 限结构。 我们选择盈利增速、盈利增速加速度综合排名提升最多 的股票，形成的股票组合走势表现优异。 与传统的分析师预期提升策略相比，本方法既体现了年 度间的盈利预期期限结构，又体现了历史业绩成长。 ► 选股组合表现 在沪深 300、中证 500、中证 800、中证 1000 内分别选择 综合因子值排名前 50、50、100、100 名的股票，构成选股组 合。 2025 年 7 月，沪深 300 选股组合、中证 500 选股组合、 中证 800 选股组合、中证 1000 选股组合超额收益继续加速， 大幅跑赢基准，超额收益分别为 2.89%、3.23%、3.39%、 0.99%。 2025 年前 7 个月，沪深 300、中证 500、中证 800、中证 1000 选股组合涨幅 ...

金融工程 | 金工定期报告 金融工程 证券研究报告 戴维斯双击本周超额基准 3.76% 戴维斯双击策略 今年以来，策略累计绝对收益 29.82%，超额中证 500 指数 21.30%，本周策 略超额中证 500 指数 3.76%。本期组合于 2025-05-06 日开盘调仓，截至 2025-08-01 日，本期组合超额基准指数 7.50%。 净利润断层策略 净利润断层策略是基本面与技术面共振双击下的选股模式，其核心有两点： "净利润"，指通常意义上的业绩超预期；"断层"，指盈余公告后的首个交 易日股价出现向上跳空，该跳空通常代表市场对盈余报告的认可程度。 策略在 2010 年至今取得了年化 29.83%的收益，年化超额基准 27.67%。本 年组合累计绝对收益 35.44%，超额基准指数 26.93%，本周超额收益 0.43%。 沪深 300 增强组合 根据对优秀基金的归因，投资者的偏好可以分为：GARP 型，成长型以及 价值型。GARP 型投资者希望以相对低的价格买入盈利能力强、成长潜力 稳定的公司。以 PB 与 ROE 的分位数之差构建 PBROE 因子，寻找估值低并 且盈利能力强的股票；以 PE 与增速 ...

- The RPV factor (Renewed Correlation of Price and Volume) integrates intraday and overnight information by dividing price-volume into four quadrants. It leverages monthly IC averages to identify reversal and momentum effects. The factor incorporates "volume" information in correlation form, optimizing intraday and overnight price-volume relationships to create a robust selection factor[6][1][7] - The SRV factor (Smart Renewed Volume) splits intraday price movements into morning and afternoon segments, calculating minute-level "smart" indicators. It identifies the 20% of afternoon minutes with the highest "smart" indicator values as informed trading periods. The factor uses turnover rates during these periods and replaces overnight turnover rates with the last 30-minute turnover rate of the previous day, combining the best-performing intraday and overnight price-volume correlation factors[6][1][7] - The RPV factor achieved an annualized return of 14.44%, annualized volatility of 7.71%, IR of 1.87, monthly win rate of 72.46%, and maximum drawdown of 10.63% during the backtest period from January 2014 to July 2025[7][10] - The SRV factor achieved an annualized return of 17.15%, annualized volatility of 6.49%, IR of 2.64, monthly win rate of 74.64%, and maximum drawdown of 3.74% during the backtest period from January 2014 to July 2025[7][10] - In July 2025, the RPV factor's 10-group long portfolio returned 5.18%, the short portfolio returned 5.58%, and the long-short portfolio returned -0.39%[10] - In July 2025, the SRV factor's 10-group long portfolio returned 5.66%, the short portfolio returned 5.81%, and the long-short portfolio returned -0.15%[10]