Quantitative Models and Construction Methods - **Model Name**: Dividend Enhanced Portfolio **Model Construction Idea**: Focuses on high-dividend stocks, aiming to capture excess returns from dividend-paying assets through a systematic approach[6][15] **Model Construction Process**: 1. Selects stocks with high dividend yields from the market. 2. Constructs two portfolios: "Central SOE High Dividend 30 Portfolio" and "Balanced Growth Dividend 50 Portfolio". 3. Applies a "steady + growth" style for the Central SOE portfolio and a "balanced offensive and defensive" style for the Dividend 50 portfolio[14][21] **Model Evaluation**: The model underperformed the benchmark this week, indicating weaker relative performance in the current market environment[21] - **Model Name**: Electronics Enhanced Portfolio **Model Construction Idea**: Focuses on the electronics sector, leveraging quantitative methods to identify outperforming sub-sectors and stocks[7][14] **Model Construction Process**: 1. Constructs two portfolios: "Electronics Balanced Allocation Enhanced Portfolio" and "Electronics Sector Preferred Enhanced Portfolio". 2. The Balanced Allocation Portfolio adopts a diversified approach across the electronics sector. 3. The Preferred Portfolio targets leading companies in mature sub-sectors of the electronics industry[14][31] **Model Evaluation**: Both portfolios outperformed the electronics sector index, demonstrating strong relative performance[31] Model Backtesting Results - **Dividend Enhanced Portfolio**: - Central SOE High Dividend 30 Portfolio: Weekly return of approximately 4.49%, underperforming the benchmark[15][21] - Balanced Growth Dividend 50 Portfolio: Weekly return of approximately 2.57%, also underperforming the benchmark[15][21] - **Electronics Enhanced Portfolio**: - Electronics Balanced Allocation Enhanced Portfolio: Weekly excess return of approximately 0.59%, ranking in the 15th percentile among active technology funds[7][31] - Electronics Sector Preferred Enhanced Portfolio: Weekly excess return of approximately 0.47%, ranking in the 17th percentile among active technology funds[7][31]

Group 1: Core Insights - The report introduces a quantitative model solution for addressing the value-growth style switching issue based on odds and win rates [1][8] - The recent performance shows that the growth style portfolio achieved a return of 2.54%, while the value style portfolio returned 2.24% [1][8] Group 2: Odds - The relative valuation levels of market styles are key factors influencing expected odds, which are negatively correlated [2][14] - The current estimated odds for the growth style is 1.11, while for the value style it is 1.09 [2][14] Group 3: Win Rates - Among seven win rate indicators, four point to growth and three to value, resulting in a current win rate of 53.87% for growth and 46.13% for value [3][16] Group 4: Investment Expectations and Strategy Returns - The investment expectation for the growth style is calculated at 0.14, while for the value style it is -0.04, leading to a recommendation for the growth style [4][18] - Since 2013, the annualized return of the style rotation model based on investment expectations is 27.62%, with a Sharpe ratio of 1.02 [4][19]

Quantitative Factors and Models Summary Quantitative Factors and Construction Process - **Factor Name**: Intraday Skewness Factor **Construction Idea**: This factor captures the skewness of intraday stock returns, reflecting the asymmetry in return distribution[13][16][18] **Construction Process**: The factor is calculated based on the third moment of intraday return distribution, normalized by the cube of standard deviation. The detailed methodology is referenced in the report "Stock Selection Factor Series Research (19) - High-Frequency Factors on Stock Return Distribution Characteristics"[13][16][18] - **Factor Name**: Downside Volatility Proportion Factor **Construction Idea**: This factor measures the proportion of downside volatility in the total realized volatility of a stock[18][19][20] **Construction Process**: The factor is derived by decomposing realized volatility into upside and downside components. The methodology is detailed in the report "Stock Selection Factor Series Research (25) - High-Frequency Factors on Realized Volatility Decomposition"[18][19][20] - **Factor Name**: Post-Open Buying Intention Proportion Factor **Construction Idea**: This factor quantifies the proportion of buying intention in the early trading period after market open[22][23][24] **Construction Process**: The factor is constructed using high-frequency data to identify and aggregate buying signals in the post-open period. The methodology is detailed in the report "Stock Selection Factor Series Research (64) - Low-Frequency Applications of High-Frequency Data Based on Intuitive Logic and Machine Learning"[22][23][24] - **Factor Name**: Post-Open Buying Intensity Factor **Construction Idea**: This factor measures the intensity of buying activity in the early trading period after market open[27][28][29] **Construction Process**: Similar to the proportion factor, this factor aggregates the magnitude of buying signals during the post-open period, normalized by trading volume[27][28][29] - **Factor Name**: Post-Open Large Order Net Buying Proportion Factor **Construction Idea**: This factor captures the proportion of large order net buying in the early trading period after market open[32][34][35] **Construction Process**: The factor is calculated by summing the net buying of large orders during the post-open period and dividing by total trading volume[32][34][35] - **Factor Name**: Post-Open Large Order Net Buying Intensity Factor **Construction Idea**: This factor measures the intensity of large order net buying in the early trading period after market open[37][39][40] **Construction Process**: The factor aggregates the net buying of large orders during the post-open period, normalized by the total number of large orders[37][39][40] - **Factor Name**: Improved Reversal Factor **Construction Idea**: This factor captures the reversal effect in stock returns, adjusted for high-frequency data characteristics[40][43][44] **Construction Process**: The factor is constructed by identifying stocks with extreme short-term returns and measuring their subsequent reversal performance[40][43][44] - **Factor Name**: Deep Learning Factor (Improved GRU(50,2)+NN(10)) **Construction Idea**: This factor leverages a deep learning model combining GRU and neural networks to predict stock returns[63][65][66] **Construction Process**: The model uses 50 GRU units and 10 neural network layers, trained on historical high-frequency data to predict short-term stock returns[63][65][66] - **Factor Name**: Deep Learning Factor (Residual Attention LSTM(48,2)+NN(10)) **Construction Idea**: This factor employs an LSTM model with residual attention mechanisms to enhance prediction accuracy[65][66][68] **Construction Process**: The model uses 48 LSTM units and 10 neural network layers, incorporating residual connections to capture long-term dependencies in high-frequency data[65][66][68] - **Factor Name**: Multi-Granularity Model Factor (5-Day Label) **Construction Idea**: This factor predicts stock returns over a 5-day horizon using a multi-granularity deep learning model[68][69][70] **Construction Process**: The model is trained using bidirectional AGRU (Attention-Gated Recurrent Unit) to capture multi-scale temporal patterns in stock data[68][69][70] - **Factor Name**: Multi-Granularity Model Factor (10-Day Label) **Construction Idea**: Similar to the 5-day label factor, this factor predicts stock returns over a 10-day horizon[69][70][71] **Construction Process**: The model uses the same AGRU architecture as the 5-day label factor but is trained with a 10-day prediction horizon[69][70][71] Factor Backtesting Results - **Intraday Skewness Factor**: - IC: 0.024 (2025), 0.019 (historical) - e^(-RankMAE): 0.327 (2025), 0.324 (historical) - Long-Short Return: 16.90% (2025 YTD), -0.66% (last week) - Long-Only Excess Return: 1.84% (2025 YTD), -0.79% (last week)[9][10][13] - **Downside Volatility Proportion Factor**: - IC: 0.020 (2025), 0.016 (historical) - e^(-RankMAE): 0.325 (2025), 0.323 (historical) - Long-Short Return: 12.93% (2025 YTD), -1.19% (last week) - Long-Only Excess Return: -0.12% (2025 YTD), -1.07% (last week)[9][10][18] - **Post-Open Buying Intention Proportion Factor**: - IC: 0.026 (2025), 0.026 (historical) - e^(-RankMAE): 0.322 (2025), 0.321 (historical) - Long-Short Return: 13.98% (2025 YTD), 0.27% (last week) - Long-Only Excess Return: 7.20% (2025 YTD), 0.28% (last week)[9][10][22] - **Post-Open Buying Intensity Factor**: - IC: 0.029 (2025), 0.030 (historical) - e^(-RankMAE): 0.327 (2025), 0.326 (historical) - Long-Short Return: 18.53% (2025 YTD), 0.05% (last week) - Long-Only Excess Return: 7.09% (2025 YTD), 0.43% (last week)[9][10][27] - **Post-Open Large Order Net Buying Proportion Factor**: - IC: 0.027 (2025), 0.036 (historical) - e^(-RankMAE): 0.319 (2025), 0.322 (historical) - Long-Short Return: 18.25% (2025 YTD), 0.31% (last week) - Long-Only Excess Return: 9.48% (2025 YTD), 0.43% (last week)[9][10][32] - **Post-Open Large Order Net Buying Intensity Factor**: - IC: 0.019 (2025), 0.025 (historical) - e^(-RankMAE): 0.318 (2025), 0.321 (historical) - Long-Short Return: 10.50% (2025 YTD), 0.31% (last week) - Long-Only Excess Return: 7.08% (2025 YTD), 0.24% (last week)[9][10][37] - **Improved Reversal Factor**: - IC: 0.025 (2025), 0.031 (historical) - e^(-RankMAE): 0.331 (2025), 0.330 (historical) - Long-Short Return: 17.44% (2025 YTD), 0.12% (last week) - Long-Only Excess Return: 6.14% (2025 YTD), 0.33% (last week)[9][10][40] - **Deep Learning Factor (Improved GRU(50,2)+NN(10))**: - IC: 0.045 (2025), 0.066 (historical) - e^(-RankMAE): 0.335 (2025), 0.336 (historical) - Long-Short Return: 28.86% (2025 YTD), 1.36% (last week) - Long-Only Excess Return: 2.19% (2025 YTD), 0.06% (last week)[9][10][63] - **Deep Learning Factor (Residual

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]