Core Viewpoint - The report highlights the performance of various market factors and investment strategies, indicating a mixed performance across different stock pools and strategies, with some factors showing positive returns while others underperformed [4][5][6][7][8][9][10]. Factor Performance - In the overall market stock pool, the momentum factor achieved a positive return of 0.43%, while the Beta factor, market capitalization factor, and non-linear market capitalization factor recorded negative returns of -1.50%, -0.91%, and -0.54% respectively, indicating a small-cap style market performance [4]. - In the CSI 300 stock pool, the best-performing factors included the standard deviation of 5-day trading volume (2.12%), the proportion of downside volatility (1.78%), and the 5-day index moving average of trading volume (1.35%). Conversely, the worst-performing factors were the 5-day reversal (-3.60%), quarterly gross profit margin (-3.43%), and quarterly ROA (-3.38%) [5]. - In the CSI 500 stock pool, the top-performing factors were the inverse of TTM P/E ratio (3.99%), the proportion of downside volatility (3.80%), and the P/E factor (3.17%). The underperforming factors included the 5-day reversal (-1.95%), 5-day average turnover rate (-1.17%), and the 5-day index moving average of trading volume (-1.15%) [5]. - In the liquidity 1500 stock pool, the best-performing factors were the correlation between intraday volatility and trading volume (2.27%), the proportion of downside volatility (1.80%), and the P/B ratio factor (1.51%). The worst-performing factors were quarterly EPS (-1.36%), standardized expected external income (-1.29%), and the 5-day reversal (-1.25%) [5]. Industry Factor Performance - The fundamental factors showed varied performance across industries, with net asset growth rate, net profit growth rate, earnings per share, and TTM operating profit factors yielding consistent positive returns in the non-bank financial sector. Valuation factors such as BP and EP also performed well in the home appliance, comprehensive, and non-bank financial sectors. Residual volatility and liquidity factors showed significant positive returns in the coal industry, while large-cap styles were prominent in the food and beverage, beauty care, and banking sectors [6]. Strategy Performance - The PB-ROE-50 combination achieved positive excess returns in the CSI 500 stock pool, with an excess return of 0.15%. However, it underperformed in the CSI 800 stock pool with an excess return of -1.50% and in the overall market stock pool with an excess return of -2.52% [7]. - The public fund research selection strategy and private fund research tracking strategy both recorded negative excess returns, with the public fund strategy yielding -0.94% relative to the CSI 800 and the private fund strategy yielding -4.83% [8]. - The block trading combination achieved positive excess returns relative to the CSI All Share Index, with an excess return of 1.56% [9]. - The targeted issuance combination also achieved positive excess returns relative to the CSI All Share Index, with an excess return of 1.86% [10].

Quantitative Models and Construction Methods Factor Performance Tracking Single Factor Performance - Factors with the best performance in the CSI 300 stock pool this week include the standard deviation of 5-day trading volume (2.12%), the proportion of downside volatility (1.78%), and the 5-day exponential moving average of trading volume (1.35%) [1][12] - Factors with the worst performance in the CSI 300 stock pool this week include the 5-day reversal (-3.60%), single-quarter total asset gross profit margin (-3.43%), and single-quarter ROA (-3.38%) [1][12] - Factors with the best performance in the CSI 500 stock pool this week include the inverse of the P/E ratio TTM (3.99%), the proportion of downside volatility (3.80%), and the P/E ratio factor (3.17%) [14] - Factors with the worst performance in the CSI 500 stock pool this week include the 5-day reversal (-1.95%), 5-day average turnover rate (-1.17%), and the 5-day exponential moving average of trading volume (-1.15%) [14] - Factors with the best performance in the liquidity 1500 stock pool this week include the correlation between intraday volatility and trading volume (2.27%), the proportion of downside volatility (1.80%), and the P/B ratio factor (1.51%) [16] - Factors with the worst performance in the liquidity 1500 stock pool this week include single-quarter EPS (-1.36%), standardized unexpected income (-1.29%), and the 5-day reversal (-1.25%) [16] Major Factor Performance - In the overall market stock pool this week, the momentum factor achieved a positive return of 0.43%, indicating a momentum effect in the market [18] - The Beta factor, market capitalization factor, and non-linear market capitalization factor achieved negative returns of -1.50%, -0.91%, and -0.54%, respectively, indicating a small-cap style in the market [18] Industry Factor Performance - This week, fundamental factors showed varied performance across industries. The net asset growth rate factor, net profit growth rate factor, net asset per share factor, and operating profit per share TTM factor consistently achieved positive returns in the non-bank financial industry [22] - Among valuation factors, the BP factor and EP factor consistently achieved positive returns in the home appliances, comprehensive, and non-bank financial industries [22] - The residual volatility factor and liquidity factor showed significant positive returns in the coal industry [22] - In terms of market capitalization style, the food and beverage, beauty care, and banking industries showed a significant large-cap style this week [22] Factor Backtesting Results CSI 300 Stock Pool - Standard deviation of 5-day trading volume: 2.12% (1 week), 3.52% (1 month), 8.21% (1 year), 19.07% (10 years) [13] - Proportion of downside volatility: 1.78% (1 week), 0.41% (1 month), -5.44% (1 year), 25.57% (10 years) [13] - 5-day exponential moving average of trading volume: 1.35% (1 week), 1.19% (1 month), 3.70% (1 year), 5.13% (10 years) [13] CSI 500 Stock Pool - Inverse of P/E ratio TTM: 3.99% (1 week), 4.80% (1 month), -5.74% (1 year), 48.40% (10 years) [15] - Proportion of downside volatility: 3.80% (1 week), 1.56% (1 month), -3.09% (1 year), 107.51% (10 years) [15] - P/E ratio factor: 3.17% (1 week), 2.58% (1 month), -4.94% (1 year), 26.11% (10 years) [15] Liquidity 1500 Stock Pool - Correlation between intraday volatility and trading volume: 2.27% (1 week), 3.18% (1 month), 2.59% (1 year), 152.82% (10 years) [17] - Proportion of downside volatility: 1.80% (1 week), 2.97% (1 month), 5.48% (1 year), 114.63% (10 years) [17] - P/B ratio factor: 1.51% (1 week), 3.69% (1 month), -5.28% (1 year), 74.59% (10 years) [17] Portfolio Tracking PB-ROE-50 Portfolio Performance - This week, the PB-ROE-50 portfolio achieved positive excess returns in the CSI 500 stock pool: 0.15% [24] - In the CSI 800 stock pool, the PB-ROE-50 portfolio achieved excess returns of -1.50% [24] - In the overall market stock pool, the PB-ROE-50 portfolio achieved excess returns of -2.52% [24] Institutional Research Portfolio Tracking - This week, the public fund research stock selection strategy and private fund research tracking strategy achieved negative excess returns relative to the CSI 800: -0.94% and -4.83%, respectively [26] Block Trade Portfolio Tracking - This week, the block trade portfolio achieved positive excess returns relative to the CSI All Share Index: 1.56% [30] Private Placement Portfolio Tracking - This week, the private placement portfolio achieved positive excess returns relative to the CSI All Share Index: 1.86% [36]

- **Barra style factors**: The report tracks various style factors including Beta, Market Cap, Momentum, Volatility, Non-linear Market Cap, Valuation, Liquidity, Profitability, Growth, and Leverage. Each factor is constructed using specific financial metrics and formulas. For example, the Profitability factor combines analyst forecast earnings price ratio, inverse price-to-cash flow ratio, and inverse price-to-earnings ratio (TTM), among others. The Growth factor incorporates earnings growth rate and revenue growth rate. These factors are used to evaluate stocks based on their historical and financial characteristics [13][14][15]. - **GRU factors**: GRU factors are derived from different training objectives, such as predicting future one-day close-to-close or open-to-open returns. Examples include `close1d`, `open1d`, `barra1d`, and `barra5d`. These factors are constructed using GRU models trained on historical data to forecast short-term stock movements. GRU factors showed strong performance, with most models achieving positive multi-period returns, except for `barra1d`, which experienced some drawdowns [20][28][32]. - **Factor testing methodology**: Factors are tested using a long-short portfolio approach. At the end of each month, stocks are ranked based on the latest factor values, with the top 10% being long positions and the bottom 10% being short positions. The portfolios are equally weighted, and factors are industry-neutralized before testing. This methodology ensures robust evaluation of factor performance across different market conditions [15][16][31]. - **Factor performance results**: - **Style factors**: Valuation, Profitability, and Leverage factors showed strong long performance, while Beta, Liquidity, and Momentum factors performed well on the short side [15][16]. - **Technical factors**: Across various time windows, low momentum and low volatility stocks generally outperformed, while high volatility and high momentum stocks underperformed. For example, the 60-day momentum factor showed a negative return of -3.11% in the last month but a positive return of 2.12% over the last six months [19][26][30]. - **GRU factors**: GRU models like `barra1d` achieved a year-to-date excess return of 5.22%, while `barra5d` and `open1d` also delivered strong multi-period returns. However, `barra1d` experienced a weekly drawdown of -1.65% [20][32][33]. - **Multi-factor portfolio performance**: The multi-factor portfolio outperformed the benchmark (CSI 1000 Index) by 1.35% over the past week. GRU-based models also showed strong excess returns, ranging from 0.68% to 1.60% over the same period. Year-to-date, the `barra1d` model achieved an excess return of 5.22% [32][33][34].

Core Insights - The investment landscape is undergoing profound changes driven by "technological advancement, new productive forces, and collective consensus" as the new paradigm for asset pricing [2][3] - The traditional valuation models are becoming less effective, necessitating a shift towards quantitative discipline to translate qualitative insights into actionable investment strategies [2][3] Group 1: Investment Philosophy - The investment approach emphasizes the balance between rational calculation and human insight, seeking optimal solutions through a dynamic equilibrium [1][2] - A strategic direction is established through qualitative research, which serves as a guiding compass for investment decisions [2] Group 2: Quantitative Tools - A precise navigation system is essential for executing investment strategies, consisting of two main components: CPPI technology for dynamic risk control and a risk budgeting model for resource allocation [3] - The CPPI technology includes mechanisms for dynamic adjustment of risk exposure based on net value performance and automatic asset allocation during market fluctuations [3] Group 3: Balancing Act - The essence of investment management lies in finding a delicate balance across multiple dimensions, including short-term versus long-term coordination and maintaining flexibility while adhering to core strategies [4][7] - The investment model aims to filter out short-term noise while capturing long-term signals, ensuring that the strategy remains robust against market volatility [6] Group 4: Communication and Adaptation - Clear communication with investors is prioritized, with regular reports to explain performance and investment rationale, helping to set rational expectations [8] - The investment process involves a step-by-step adjustment strategy to minimize market impact while ensuring that asset selection aligns with emerging productive forces [8] Group 5: Continuous Improvement - The investment methodology focuses on building a self-evolving system that withstands the test of time, with quantitative tools playing a crucial role in achieving investment objectives [9] - Each analysis, model optimization, and allocation adjustment is part of a continuous search for better solutions, emphasizing a sustainable approach over chasing short-term trends [9]

Quantitative Models and Construction Methods - **Model Name**: Neutral Constraint Maximum Factor Exposure Portfolio **Construction Idea**: The model aims to maximize the exposure of target factors in the portfolio while maintaining neutrality in industry and style exposures relative to the benchmark index[62][63][64] **Construction Process**: The optimization model is defined as follows: $ \begin{array}{l}\mbox{\it Max}\qquad\quad w^{\prime}\;X_{target}\\ \mbox{\it s.t.}\qquad\quad(w-\;w_{b})^{\prime}X_{ind}=\;0\\ \mbox{\it(w-\;w_{b})}^{\prime}\;X_{Beta}=\;0\\ \mbox{\it|w-\;w_{b}|\leq1\%}\\ \mbox{\it w\geq0}\\ \mbox{\it w^{\prime}B=1}\\ \mbox{\it w^{\prime}1=1}\end{array} $ - **Explanation**: - \( w \): Portfolio weight vector - \( w_b \): Benchmark portfolio weight vector - \( X_{target} \): Factor load matrix for the target factor - \( X_{ind} \): Industry exposure matrix (binary variables) - \( X_{Beta} \): Style factor exposure matrix (e.g., size, valuation, growth) - Constraints ensure neutrality in industry and style exposures, limit deviations from benchmark weights, prohibit short selling, and require full allocation within benchmark constituents[62][63][64] **Evaluation**: The model effectively balances factor exposure maximization with risk control through neutrality constraints[62][63][64] Quantitative Factors and Construction Methods - **Factor Name**: Volatility Factor **Construction Idea**: Captures the performance of stocks with varying volatility levels[16][17] **Construction Process**: - Volatility Factor = \( \frac{DASTD + CMRA + HSIGMA}{3} \) - **Sub-factor Definitions**: - \( DASTD \): Standard deviation of excess returns over 250 trading days, calculated using a half-life of 40 days - \( CMRA \): Cumulative range of log returns over 12 months - \( HSIGMA \): Standard deviation of residuals from beta regression[16][17] **Evaluation**: Demonstrates strong differentiation between high and low volatility stocks, with recent data showing low volatility stocks outperforming high volatility stocks[16][17] - **Factor Name**: Growth Factor **Construction Idea**: Measures growth potential based on revenue and earnings trends[16][17] **Construction Process**: - Growth Factor = \( \frac{SGRO + EGRO}{2} \) - **Sub-factor Definitions**: - \( SGRO \): Regression slope of revenue growth over the past five fiscal years, normalized by average revenue - \( EGRO \): Regression slope of earnings growth over the past five fiscal years, normalized by average earnings[16][17] **Evaluation**: Provides insights into companies with strong growth trajectories, though sensitivity to financial reporting quality is noted[16][17] Factor Backtesting Results - **Volatility Factor**: - Recent one-week multi-long-short return: -2.90% - Recent one-month multi-long-short return: -1.53%[19][20] - **Growth Factor**: - Recent one-week multi-long-short return: 0.24% - Recent one-month multi-long-short return: 3.27%[19][20] Index Enhancement Portfolio Performance - **Portfolio Name**: CSI 1000 Enhanced Portfolio - Recent one-week excess return: 2.04% - Recent one-month excess return: 2.76% - Recent one-year excess return: 17.07%[57][58] - **Portfolio Name**: CSI 500 Enhanced Portfolio - Recent one-week excess return: 0.03% - Recent one-month excess return: -1.56% - Recent one-year excess return: -8.56%[57][58] - **Portfolio Name**: CSI 800 Enhanced Portfolio - Recent one-week excess return: -0.42% - Recent one-month excess return: -0.26% - Recent one-year excess return: 8.40%[57][58] - **Portfolio Name**: CSI 300 ESG Enhanced Portfolio - Recent one-week excess return: -0.11% - Recent one-month excess return: 0.25% - Recent one-year excess return: 6.90%[57][58] - **Portfolio Name**: CSI 300 Enhanced Portfolio - Recent one-week excess return: -0.71% - Recent one-month excess return: 0.51% - Recent one-year excess return: 10.25%[57][58] Annualized Performance Metrics - **CSI 1000 Enhanced Portfolio**: - Annualized excess return: 15.50% - Information ratio: 2.97[59][60] - **CSI 500 Enhanced Portfolio**: - Annualized excess return: 8.70% - Information ratio: 2.07[59][60] - **CSI 800 Enhanced Portfolio**: - Annualized excess return: 7.11% - Information ratio: 2.18[59][60] - **CSI 300 ESG Enhanced Portfolio**: - Annualized excess return: 5.64% - Information ratio: 1.75[59][60] - **CSI 300 Enhanced Portfolio**: - Annualized excess return: 6.39% - Information ratio: 2.33[59][60]

Quantitative Factors and Models Summary Quantitative Factors and Construction - **Factor Name**: Beta Factor **Construction Idea**: Measures the sensitivity of a stock's returns to market movements, capturing systematic risk **Performance**: Achieved a positive return of 0.73% this week, indicating a preference for high-beta stocks in the market [18] - **Factor Name**: Market Capitalization Factor **Construction Idea**: Captures the size effect, favoring large-cap stocks **Performance**: Delivered a positive return of 0.58%, reflecting a large-cap style in the market this week [18] - **Factor Name**: Growth Factor **Construction Idea**: Identifies stocks with high growth potential based on financial metrics **Performance**: Generated a positive return of 0.21% this week [18] - **Factor Name**: Non-linear Market Capitalization Factor **Construction Idea**: Aims to capture non-linear effects of market capitalization on stock returns **Performance**: Achieved a positive return of 0.21% this week [18] - **Factor Name**: Leverage Factor **Construction Idea**: Measures the financial leverage of a company, often linked to risk and return trade-offs **Performance**: Recorded a negative return of -0.25% this week [18] - **Factor Name**: Total Asset Growth Rate **Construction Idea**: Measures the growth in total assets, indicating expansion and investment **Performance**: Positive returns across multiple stock pools: - 2.41% in CSI 300 [12][13] - 2.12% in CSI 500 [14][15] - 1.09% in Liquidity 1500 [16][17] - **Factor Name**: Total Asset Gross Profit Margin (TTM) **Construction Idea**: Evaluates profitability relative to total assets over a trailing twelve-month period **Performance**: Positive returns across stock pools: - 2.02% in CSI 300 [12][13] - -0.54% in CSI 500 [14][15] - -0.02% in Liquidity 1500 [16][17] - **Factor Name**: ROE Stability **Construction Idea**: Measures the consistency of return on equity over time **Performance**: Positive returns across stock pools: - 1.53% in CSI 500 [14][15] - 1.22% in Liquidity 1500 [16][17] - **Factor Name**: ROA Stability **Construction Idea**: Measures the consistency of return on assets over time **Performance**: Positive returns across stock pools: - 0.76% in CSI 500 [14][15] - 1.89% in Liquidity 1500 [16][17] Quantitative Models and Construction - **Model Name**: PB-ROE-50 Portfolio **Construction Idea**: Combines price-to-book (PB) and return on equity (ROE) metrics to select stocks with strong valuation and profitability characteristics **Construction Process**: - Stocks are ranked based on PB and ROE metrics - Top 50 stocks are selected to form the portfolio - Portfolio is rebalanced periodically [23][24] **Performance**: - 1.04% excess return in CSI 500 - -0.28% excess return in CSI 800 - -0.03% excess return in the overall market [23][24] - **Model Name**: Institutional Research Portfolio **Construction Idea**: Tracks stocks frequently researched by public and private institutions, assuming their research signals potential outperformance **Performance**: - Public research strategy: 2.22% excess return relative to CSI 800 - Private research strategy: 1.51% excess return relative to CSI 800 [25][26] - **Model Name**: Block Trade Portfolio **Construction Idea**: Focuses on stocks with high block trade ratios and low short-term volatility, assuming these characteristics indicate informed trading **Construction Process**: - Stocks are ranked based on block trade ratios and 6-day trading volume volatility - Portfolio is rebalanced monthly [29][30] **Performance**: -0.98% excess return relative to CSI All Share Index [29][30] - **Model Name**: Private Placement Portfolio **Construction Idea**: Leverages event-driven strategies around private placements, considering factors like market capitalization and timing **Construction Process**: - Stocks involved in private placements are selected based on shareholder meeting announcements - Portfolio is adjusted for market capitalization and rebalanced periodically [34][35] **Performance**: -0.21% excess return relative to CSI All Share Index [34][35] Factor Backtesting Results - **Beta Factor**: Weekly return of 0.73% [18] - **Market Capitalization Factor**: Weekly return of 0.58% [18] - **Growth Factor**: Weekly return of 0.21% [18] - **Non-linear Market Capitalization Factor**: Weekly return of 0.21% [18] - **Leverage Factor**: Weekly return of -0.25% [18] - **Total Asset Growth Rate**: - CSI 300: 2.41% [12][13] - CSI 500: 2.12% [14][15] - Liquidity 1500: 1.09% [16][17] - **Total Asset Gross Profit Margin (TTM)**: - CSI 300: 2.02% [12][13] - CSI 500: -0.54% [14][15] - Liquidity 1500: -0.02% [16][17] - **ROE Stability**: - CSI 500: 1.53% [14][15] - Liquidity 1500: 1.22% [16][17] - **ROA Stability**: - CSI 500: 0.76% [14][15] - Liquidity 1500: 1.89% [16][17] Model Backtesting Results - **PB-ROE-50 Portfolio**: - CSI 500: 1.04% excess return - CSI 800: -0.28% excess return - Overall market: -0.03% excess return [23][24] - **Institutional Research Portfolio**: - Public strategy: 2.22% excess return relative to CSI 800 - Private strategy: 1.51% excess return relative to CSI 800 [25][26] - **Block Trade Portfolio**: -0.98% excess return relative to CSI All Share Index [29][30] - **Private Placement Portfolio**: -0.21% excess return relative to CSI All Share Index [34][35]

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Combination - **Model Construction Idea**: This model focuses on selecting stocks with low Price-to-Book (PB) ratios and high Return on Equity (ROE) to construct a portfolio that aims to achieve excess returns[24] - **Model Construction Process**: The portfolio is constructed by screening stocks based on their PB and ROE metrics. Stocks with the lowest PB ratios and highest ROE values are selected to form the top 50 stocks in the portfolio. The portfolio is rebalanced periodically to maintain the selection criteria[24] - **Model Evaluation**: The model demonstrates significant excess returns in the all-market stock pool, though it underperforms in specific indices like the CSI 500 and CSI 800[24][25] 2. Model Name: Public and Private Institutional Research Combination - **Model Construction Idea**: This model leverages the stock selection strategies of public and private institutional research to identify stocks with potential for excess returns[27] - **Model Construction Process**: The portfolio is constructed by tracking the stocks that public and private institutions have recently researched. Stocks with higher research frequency or positive sentiment are included in the portfolio. The portfolio is rebalanced periodically to reflect updated research data[27] - **Model Evaluation**: The public institutional research strategy shows significant excess returns compared to the CSI 800 index, while the private institutional research strategy also achieves positive but smaller excess returns[27][28] 3. Model Name: Block Trade Combination - **Model Construction Idea**: This model identifies stocks with high block trade activity and low volatility, as these characteristics are associated with better subsequent performance[31] - **Model Construction Process**: The portfolio is constructed based on two key metrics: "block trade transaction amount ratio" and "6-day transaction amount volatility." Stocks with higher transaction ratios and lower volatility are selected. The portfolio is rebalanced monthly[31] - **Model Evaluation**: The model experienced a drawdown in the past week, with negative excess returns relative to the CSI All Share Index[31][32] 4. Model Name: Directed Issuance Combination - **Model Construction Idea**: This model focuses on stocks involved in directed issuance events, which are analyzed for their potential investment value based on event-driven factors[37] - **Model Construction Process**: The portfolio is constructed by identifying stocks with directed issuance announcements. Factors such as market capitalization, rebalancing cycles, and position control are considered. The portfolio is rebalanced periodically to reflect new issuance events[37] - **Model Evaluation**: The model experienced a drawdown in the past week, with negative excess returns relative to the CSI All Share Index[37][38] --- Model Backtesting Results 1. PB-ROE-50 Combination - **Weekly Excess Return**: All-market stock pool: +0.79%; CSI 500: -0.57%; CSI 800: -0.02%[24][25] - **Year-to-Date Excess Return**: All-market stock pool: +22.30%; CSI 500: +3.00%; CSI 800: +16.16%[25] - **Weekly Absolute Return**: All-market stock pool: +2.87%; CSI 500: +2.79%; CSI 800: +1.89%[25] - **Year-to-Date Absolute Return**: All-market stock pool: +48.27%; CSI 500: +28.59%; CSI 800: +36.42%[25] 2. Public and Private Institutional Research Combination - **Weekly Excess Return**: Public research: +3.82%; Private research: +0.51%[27][28] - **Year-to-Date Excess Return**: Public research: +8.10%; Private research: +12.02%[28] - **Weekly Absolute Return**: Public research: +5.81%; Private research: +2.44%[28] - **Year-to-Date Absolute Return**: Public research: +26.96%; Private research: +31.56%[28] 3. Block Trade Combination - **Weekly Excess Return**: -1.77%[31][32] - **Year-to-Date Excess Return**: +0.26%[32] - **Weekly Absolute Return**: Not explicitly stated - **Year-to-Date Absolute Return**: +62.65%[32] 4. Directed Issuance Combination - **Weekly Excess Return**: -1.71%[37][38] - **Year-to-Date Excess Return**: -0.77%[38] - **Weekly Absolute Return**: Not explicitly stated - **Year-to-Date Absolute Return**: +20.29%[38] --- Quantitative Factors and Construction Methods 1. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market movements, capturing systematic risk[20] - **Factor Construction Process**: Beta is calculated using regression analysis of a stock's returns against the market index over a specified period[20] - **Factor Evaluation**: Demonstrated significant positive returns in the past week, indicating a preference for high-beta stocks[20] 2. Factor Name: Momentum Factor - **Factor Construction Idea**: Captures the tendency of stocks with strong past performance to continue performing well in the short term[20] - **Factor Construction Process**: Momentum is calculated based on the cumulative returns of a stock over a specific lookback period, such as 1 month or 5 days[20][22] - **Factor Evaluation**: Significant positive returns were observed, with notable momentum effects in sectors like media, real estate, and agriculture[20][22] 3. Factor Name: Scale Factor - **Factor Construction Idea**: Reflects the size effect, where larger-cap stocks tend to outperform smaller-cap stocks in certain market conditions[20] - **Factor Construction Process**: Scale is measured using market capitalization, with adjustments for sector and industry effects[20] - **Factor Evaluation**: Demonstrated positive returns, indicating a preference for large-cap stocks in the past week[20] --- Factor Backtesting Results 1. Beta Factor - **Weekly Return**: +0.70%[20] 2. Momentum Factor - **Weekly Return**: +0.46%[20] 3. Scale Factor - **Weekly Return**: +0.16%[20]

Quantitative Models and Construction PB-ROE-50 Model - **Model Name**: PB-ROE-50 - **Model Construction Idea**: The model combines Price-to-Book (PB) and Return on Equity (ROE) metrics to identify stocks with strong profitability and reasonable valuation[24] - **Model Construction Process**: The PB-ROE-50 portfolio is constructed by selecting 50 stocks based on a combination of PB and ROE metrics. The portfolio is rebalanced periodically to maintain the desired exposure to these factors[24] - **Model Evaluation**: The model demonstrates consistent positive excess returns across different stock pools, indicating its effectiveness in capturing value and profitability signals[24] --- Quantitative Factors and Construction Standardized Unexpected Earnings (SUE) - **Factor Name**: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by historical earnings volatility[12] - **Factor Construction Process**: $ SUE = \frac{E_{actual} - E_{expected}}{\sigma_{earnings}} $ Where: $E_{actual}$ = Actual earnings $E_{expected}$ = Expected earnings $\sigma_{earnings}$ = Standard deviation of historical earnings[12] - **Factor Evaluation**: Demonstrates strong positive returns in the CSI 300 stock pool, indicating its ability to capture earnings surprises effectively[12] Single-Quarter ROE YoY Growth - **Factor Name**: Single-Quarter ROE YoY Growth - **Factor Construction Idea**: Measures the year-over-year growth in Return on Equity (ROE) for a single quarter[14] - **Factor Construction Process**: $ ROE_{YoY} = \frac{ROE_{current\_quarter} - ROE_{same\_quarter\_last\_year}}{ROE_{same\_quarter\_last\_year}} $ Where: $ROE_{current\_quarter}$ = ROE for the current quarter $ROE_{same\_quarter\_last\_year}$ = ROE for the same quarter in the previous year[14] - **Factor Evaluation**: Shows strong positive returns in the CSI 500 stock pool, highlighting its effectiveness in identifying growth trends[14] Total Asset Growth Rate - **Factor Name**: Total Asset Growth Rate - **Factor Construction Idea**: Measures the growth rate of total assets over a specific period[16] - **Factor Construction Process**: $ Growth_{assets} = \frac{Assets_{current} - Assets_{previous}}{Assets_{previous}} $ Where: $Assets_{current}$ = Total assets in the current period $Assets_{previous}$ = Total assets in the previous period[16] - **Factor Evaluation**: Demonstrates strong positive returns across multiple stock pools, indicating its robustness in capturing growth signals[16] --- Backtesting Results of Models PB-ROE-50 Model - **Excess Return (Weekly)**: - CSI 500: 0.47% - CSI 800: 0.25% - All Market: 1.02%[25] - **Excess Return (YTD)**: - CSI 500: 3.22% - CSI 800: 11.76% - All Market: 14.28%[25] --- Backtesting Results of Factors Standardized Unexpected Earnings (SUE) - **Excess Return (Weekly)**: - CSI 300: 4.12% - CSI 500: 0.34% - Liquidity 1500: 1.16%[12][15][17] Single-Quarter ROE YoY Growth - **Excess Return (Weekly)**: - CSI 300: 0.84% - CSI 500: 2.28% - Liquidity 1500: 1.27%[12][15][17] Total Asset Growth Rate - **Excess Return (Weekly)**: - CSI 300: 2.39% - CSI 500: 0.50% - Liquidity 1500: 2.12%[12][15][17]

Core Viewpoint - The article emphasizes the potential of smart beta index funds, which utilize more sophisticated stock selection rules compared to traditional index funds, to achieve long-term excess returns in the market [3][4][11]. Group 1: Smart Beta Index Funds - Smart beta index funds represent a small portion of the market, with only 1.7 trillion yuan, accounting for approximately 0.5% of the total public fund size of 32.24 trillion yuan in China by the end of 2024 [2]. - These funds employ stock selection based on proven financial metrics or price characteristics, rather than just market capitalization [4][5]. - Common factors used in smart beta strategies include dividend yield, quality, value, low volatility, and momentum [15]. Group 2: Performance of Smart Beta Strategies - Historical data from 1927 to 2023 indicates that smart beta strategies can outperform the market, with various factors showing significant annualized returns above the overall market return of 9.5% [17][18]. - The long-term performance of factor-based strategies demonstrates that almost all factor long portfolios yield returns significantly higher than the market index, suggesting that holding a good smart beta fund is likely to provide better returns than traditional indices like the CSI 300 [20][23]. Group 3: Challenges and Considerations - Despite the effectiveness of smart beta strategies, they can experience prolonged periods of underperformance, which may lead to investor skepticism [24][26]. - Historical data shows that some factors can have long periods of underperformance, with the longest being four years for several factors [28][29]. - Diversifying across multiple factors can help mitigate risks associated with individual factor underperformance, as different factors may perform well at different times [30]. Group 4: Insights from Historical Data - Long-term data supports the reliability of smart beta index funds, indicating that missing out on these investment opportunities could be regrettable [32]. - Investors are advised to construct multi-factor portfolios to balance risk and return, incorporating defensive and aggressive strategies [35]. - A long-term investment horizon is essential for realizing the excess returns from smart beta strategies, as they may require enduring periods of underperformance [37][39]. - Risk management is crucial, as smart beta funds are still subject to market fluctuations and can decline during bear markets [40][41].

Quantitative Models and Construction Methods Style Factors 1. **Factor Name**: Beta Factor - **Construction Idea**: Captures the market sensitivity of stocks - **Construction Process**: - Calculate the daily returns of individual stocks and the market index (CSI All Share Index) over the past 252 trading days - Perform an exponentially weighted regression with a half-life of 63 trading days - The regression coefficient is taken as the Beta factor - **Evaluation**: High Beta stocks outperformed low Beta stocks in the recent week, indicating a preference for market-sensitive stocks[15][16] 2. **Factor Name**: Leverage Factor - **Construction Idea**: Measures the financial leverage of companies - **Construction Process**: - Calculate three sub-factors: Market Leverage (MLEV), Debt to Assets (DTOA), and Book Leverage (BLEV) - MLEV = Non-current liabilities / Total market value - DTOA = Total liabilities / Total assets - BLEV = Non-current liabilities / Shareholders' equity - Combine the three sub-factors equally to form the Leverage factor - **Evaluation**: Low leverage companies outperformed high leverage companies, indicating a market preference for financially stable companies[15][16] 3. **Factor Name**: Growth Factor - **Construction Idea**: Measures the growth potential of companies - **Construction Process**: - Calculate two sub-factors: Sales Growth (SGRO) and Earnings Growth (EGRO) - SGRO = Regression slope of past five years' annual sales per share divided by the average sales per share - EGRO = Regression slope of past five years' annual earnings per share divided by the average earnings per share - Combine the two sub-factors equally to form the Growth factor - **Evaluation**: The Growth factor showed a negative return, indicating a decline in market preference for high-growth stocks[15][16] Stock Selection Factors 1. **Factor Name**: Single Quarter ROA - **Construction Idea**: Measures the return on assets for a single quarter - **Construction Process**: - Single Quarter ROA = Net income attributable to parent company / Total assets - **Evaluation**: Performed well in the CSI 300 stock pool over the past week[21][24] 2. **Factor Name**: 240-Day Skewness - **Construction Idea**: Measures the skewness of daily returns over the past 240 trading days - **Construction Process**: - Calculate the skewness of daily returns over the past 240 trading days - **Evaluation**: Performed well in the CSI 300 stock pool over the past week[21][24] 3. **Factor Name**: Single Quarter ROE - **Construction Idea**: Measures the return on equity for a single quarter - **Construction Process**: - Single Quarter ROE = Net income attributable to parent company / Shareholders' equity - **Evaluation**: Performed well in the CSI 300 stock pool over the past week[21][24] Factor Backtesting Results 1. **Beta Factor**: Weekly long-short return: 1.86%, Monthly long-short return: 1.64%[17] 2. **Leverage Factor**: Weekly long-short return: -3.07%, Monthly long-short return: -1.58%[17] 3. **Growth Factor**: Weekly long-short return: -1.73%, Monthly long-short return: -5.13%[17] Stock Selection Factor Backtesting Results 1. **Single Quarter ROA**: Weekly excess return: 0.98%, Monthly excess return: 2.61%, Annual excess return: 9.49%, Ten-year annualized return: 3.69%[22] 2. **240-Day Skewness**: Weekly excess return: 0.75%, Monthly excess return: 2.48%, Annual excess return: 6.40%, Ten-year annualized return: 2.85%[22] 3. **Single Quarter ROE**: Weekly excess return: 0.74%, Monthly excess return: 1.55%, Annual excess return: 8.96%, Ten-year annualized return: 3.46%[22]