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]

Core Viewpoint - The article provides an analysis of market performance, highlighting the positive and negative returns of various factors across different stock pools and industries, indicating a mixed market sentiment and specific investment opportunities [2][3][4]. Group 1: Market Factor Performance - In the overall market stock pool, the Beta factor and valuation factor achieved positive returns of 0.48% and 0.26% respectively, while the market capitalization factor and profit factor recorded negative returns of -0.24% and -0.42%, suggesting a small-cap style market [2]. - The momentum factor yielded a negative return of -0.44%, indicating a reversal style in the market [2]. Group 2: Single Factor Performance - In the CSI 300 stock pool, the best-performing factors included quarterly net profit year-on-year growth rate (1.83%), quarterly operating profit year-on-year growth rate (1.75%), and net profit margin TTM (1.52%) [3]. - In the CSI 500 stock pool, the top factors were price-to-book ratio (2.57%), downside volatility ratio (2.07%), and inverse of price-to-sales ratio TTM (1.33%) [3]. - In the liquidity 1500 stock pool, the leading factors were downside volatility ratio (0.74%), net profit gap (0.49%), and quarterly ROE year-on-year (0.46%) [3]. Group 3: Industry Factor Performance - The fundamental factors showed varied performance across industries, with net asset growth rate, net profit growth rate, earnings per share, and operating profit TTM factors yielding consistent positive returns in the transportation industry [4]. - Among valuation factors, the BP factor performed well, showing significant positive returns in the real estate industry [4]. - Residual volatility and liquidity factors demonstrated notable positive returns in the non-ferrous metals industry [4]. Group 4: Investment Strategy Performance - The PB-ROE-50 combination achieved positive excess returns in the overall market stock pool, while it recorded excess returns of -0.56% in the CSI 500 stock pool and -0.38% in the CSI 800 stock pool [5]. - Public and private fund research selection strategies both gained positive excess returns, with public strategies outperforming the CSI 800 by 0.80% and private strategies by 1.21% [6]. - The block trading combination also achieved positive excess returns relative to the CSI All Index, with an excess return of 1.22% [7]. - The targeted issuance combination gained positive excess returns relative to the CSI All Index, with an excess return of 0.05% [8].

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 - **Model Construction Idea**: The PB-ROE-50 model selects stocks based on a combination of Price-to-Book (PB) ratio and Return on Equity (ROE), aiming to capture value and profitability factors[23] - **Model Construction Process**: - Stocks are ranked based on their PB and ROE metrics - A portfolio is constructed by selecting the top 50 stocks with the best combined PB and ROE scores - The portfolio is rebalanced periodically to maintain the factor exposure[23] - **Model Evaluation**: The model demonstrates the ability to generate excess returns in certain market conditions, particularly in capturing value and profitability factors[23] 2. Model Name: Block Trade Portfolio - **Model Construction Idea**: This model leverages the information embedded in block trades, focusing on stocks with high block trade transaction amounts and low volatility in transaction amounts[29] - **Model Construction Process**: - Identify stocks with high "block trade transaction amount ratio" and low "6-day transaction amount volatility" - Construct a portfolio based on these criteria and rebalance monthly[29] - **Model Evaluation**: The model effectively captures the excess return potential of block trade-related stocks, particularly those with high transaction amounts and low volatility[29] 3. Model Name: Private Placement Portfolio - **Model Construction Idea**: This model focuses on stocks involved in private placements, aiming to capture the event-driven effects of private placements on stock performance[35] - **Model Construction Process**: - Use the shareholder meeting announcement date as the event trigger - Incorporate market capitalization, rebalancing frequency, and position control into the portfolio construction process - Construct a portfolio based on these parameters[35] - **Model Evaluation**: The model captures the investment opportunities arising from private placement events, though its effectiveness may vary depending on market conditions[35] --- Model Backtesting Results 1. PB-ROE-50 Model - **Excess Return (This Week)**: - CSI 500: -0.56% - CSI 800: -0.38% - All Market: 0.92%[24] - **Year-to-Date Excess Return**: - CSI 500: 2.99% - CSI 800: 6.41% - All Market: 9.28%[24] - **Absolute Return (This Week)**: - CSI 500: 1.39% - CSI 800: 0.73% - All Market: 2.47%[24] - **Year-to-Date Absolute Return**: - CSI 500: 8.41% - CSI 800: 9.44% - All Market: 16.07%[24] 2. Block Trade Portfolio - **Excess Return (This Week)**: 1.22% - **Year-to-Date Excess Return**: 25.89% - **Absolute Return (This Week)**: 2.78% - **Year-to-Date Absolute Return**: 33.71%[30] 3. Private Placement Portfolio - **Excess Return (This Week)**: 0.05% - **Year-to-Date Excess Return**: 8.72% - **Absolute Return (This Week)**: 1.59% - **Year-to-Date Absolute Return**: 15.48%[36] --- Quantitative Factors and Construction Methods 1. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market returns, capturing systematic risk[18] - **Factor Construction Process**: - Calculate the covariance between the stock's returns and market returns - Divide by the variance of market returns to derive the beta coefficient - $ \beta = \frac{\text{Cov}(R_i, R_m)}{\text{Var}(R_m)} $ - Where $R_i$ is the stock return, and $R_m$ is the market return[18] - **Factor Evaluation**: The factor captures systematic risk effectively and is widely used in portfolio construction and risk management[18] 2. Factor Name: Momentum Factor - **Factor Construction Idea**: Measures the tendency of stocks with high past returns to continue performing well in the future[18] - **Factor Construction Process**: - Calculate the cumulative return of a stock over a specific look-back period (e.g., 6 months or 12 months) - Rank stocks based on their cumulative returns and construct a portfolio of top-ranked stocks[18] - **Factor Evaluation**: The factor is effective in capturing trends in stock performance but may underperform in reversal markets[18] 3. Factor Name: Valuation Factor (e.g., PB, PE, PS) - **Factor Construction Idea**: Measures the relative valuation of stocks based on financial metrics like Price-to-Book (PB), Price-to-Earnings (PE), and Price-to-Sales (PS)[18] - **Factor Construction Process**: - Calculate the PB, PE, or PS ratio for each stock - Rank stocks based on these ratios and construct a portfolio of low-ratio stocks (value stocks)[18] - **Factor Evaluation**: Valuation factors are effective in identifying undervalued stocks but may underperform during growth-driven market phases[18] --- Factor Backtesting Results 1. Beta Factor - **Weekly Return**: 0.48%[18] 2. Momentum Factor - **Weekly Return**: -0.44%[18] 3. Valuation Factor - **Weekly Return**: - PB: 2.57% (CSI 500)[14] - PE: 0.37% (CSI 300)[13] - PS: 1.26% (CSI 300)[13]

Core Viewpoint - The report highlights the performance of various investment factors and strategies over the week, indicating a mixed market environment with specific factors yielding positive and negative returns [3][4][5]. Factor Performance - BP factor and profit factor achieved positive returns of 0.30% and 0.27% respectively, while non-linear market capitalization factor and size factor showed significant negative returns of -0.31% and -0.29%, indicating a clear small-cap market style [3]. - In the CSI 300 stock pool, the best-performing factors included TTM P/E ratio (0.70%), TTM P/S ratio (0.59%), and 5-minute return skewness (0.57%), while the worst performers were 6-day trading volume moving average (-1.24%), 5-day average turnover rate (-1.44%), and TTM gross profit margin (-1.62%) [4]. - In the CSI 500 stock pool, the top factors were quarterly ROE (1.70%), TTM gross profit margin (1.54%), and 5-day trading volume standard deviation (1.36%), with poor performers including early trading return factor (-0.39%), 5-minute return skewness (-0.44%), and log market capitalization factor (-0.73%) [4]. - In the liquidity 1500 stock pool, the best factors were 5-day reversal (1.62%), quarterly ROE (1.53%), and P/E factor (1.41%), while the worst were 6-day trading volume moving average (-0.61%), early trading return factor (-0.70%), and 5-day index moving average of trading volume (-0.72%) [4]. Industry Factor Performance - The net asset growth rate factor showed significant positive returns in the comprehensive industry, while the net profit growth rate factor performed well across the same sector [5]. - The 5-day momentum factor exhibited strong momentum effects in the comprehensive, steel, and public utility industries, while reversal effects were notable in non-bank financials, non-ferrous metals, and telecommunications [5]. - Valuation factors like BP factor performed well in the comprehensive, steel, and banking industries, while EP factor excelled in the comprehensive, media, and non-bank financial sectors [5]. Strategy Performance - The PB-ROE-50 combination achieved excess returns across various stock pools, with excess returns of 1.17% in the CSI 500 pool, 1.21% in the CSI 800 pool, and 1.36% in the overall market stock pool [6]. - Public fund research selection strategy and private fund research tracking strategy both gained positive excess returns, with public fund strategy achieving 0.02% excess return relative to CSI 800 and private fund strategy achieving 0.25% [7]. - The block trading combination experienced a relative excess return drawdown of -0.24% compared to the CSI All Index [8]. - The directed issuance combination also faced a relative excess return drawdown of -0.69% compared to the CSI All Index [9].

Quantitative Models and Construction Methods - **Model Name**: Neutral Constraint Maximum Factor Exposure Portfolio **Model Construction Idea**: The model aims to maximize the exposure of the target factor in the portfolio while maintaining neutrality in industry and style exposures relative to the benchmark index[60][61] **Model Construction Process**: 1. The objective function is to maximize the portfolio's exposure to the target factor $ \text{Max} \quad w^{\prime} X_{\text{target}} $ 2. Constraints include: - Industry neutrality: $ (w - w_b)^{\prime} X_{\text{inad}} = 0 $ - Style neutrality: $ (w - w_b)^{\prime} X_{\text{Beta}} = 0 $ - Weight deviation limit: $ |w - w_b| \leq 1\% $ - No short selling: $ w \geq 0 $ - Full investment: $ w^{\prime} B = 1 $ and $ w^{\prime} 1 = 1 $ 3. Factors are neutralized to remove correlations with industry and style factors (e.g., size, valuation, growth) 4. Factor directions are adjusted to be positive before optimization[60][62][63] **Model Evaluation**: The model effectively balances factor exposure maximization with risk control, ensuring alignment with the benchmark index[63] --- Quantitative Factors and Construction Methods - **Factor Name**: Valuation Factor (BP) **Factor Construction Idea**: Captures the valuation level of stocks based on book-to-price ratio[14][15] **Factor Construction Process**: - Formula: $ \text{BP} = \frac{\text{Shareholders' Equity}}{\text{Market Capitalization}} $ **Factor Evaluation**: Demonstrates strong performance in capturing undervalued stocks, particularly in defensive market environments[14][15] - **Factor Name**: Growth Factor **Factor Construction Idea**: Measures the growth potential of stocks based on sales and earnings growth rates[14][15] **Factor Construction Process**: - Formula: $ \text{Growth Factor} = \frac{\text{SGRO} + \text{EGRO}}{2} $ - $ \text{SGRO} $: Regression slope of past 5 years' annual sales divided by average sales - $ \text{EGRO} $: Regression slope of past 5 years' annual earnings divided by average earnings[14][15] **Factor Evaluation**: Useful in identifying high-growth stocks, though performance may vary across market cycles[14][15] - **Factor Name**: Beta Factor **Factor Construction Idea**: Reflects the sensitivity of a stock's returns to market movements[14][15] **Factor Construction Process**: - Formula: $ \text{Beta} = \text{Regression Coefficient of Stock Returns on Market Returns} $ - Regression uses 252 trading days with a half-life of 63 days[14][15] **Factor Evaluation**: Effective in capturing market risk preferences, with low-beta stocks outperforming in risk-averse environments[14][15] - **Factor Name**: Momentum Factor (RSTR) **Factor Construction Idea**: Identifies stocks with strong relative strength over a specific period[14][15] **Factor Construction Process**: - Formula: $ \text{RSTR} = \text{Cumulative Returns over 504 Days (Excluding Last 21 Days)} $ - Returns are weighted using a half-life of 126 days[14][15] **Factor Evaluation**: Performs well in trending markets but may underperform during reversals[14][15] --- Factor Backtesting Results - **Valuation Factor (BP)**: - Recent 1-week long-short return: 1.90% - Recent 1-month long-short return: -0.58%[17] - **Growth Factor**: - Recent 1-week long-short return: -0.79% - Recent 1-month long-short return: -0.99%[17] - **Beta Factor**: - Recent 1-week long-short return: -2.89% - Recent 1-month long-short return: 5.63%[17] - **Momentum Factor (RSTR)**: - Recent 1-week long-short return: -1.03% - Recent 1-month long-short return: -2.31%[17] --- Index Enhancement Portfolio Backtesting Results - **CSI 300 Enhanced Portfolio**: - 1-week excess return: 0.63% - 1-month excess return: 2.22% - 1-year excess return: 2.43%[57][58] - **CSI 500 Enhanced Portfolio**: - 1-week excess return: 0.10% - 1-month excess return: -1.25% - 1-year excess return: -2.90%[57][58] - **CSI 800 Enhanced Portfolio**: - 1-week excess return: 0.70% - 1-month excess return: 1.47% - 1-year excess return: 1.03%[57][58] - **CSI 1000 Enhanced Portfolio**: - 1-week excess return: 0.16% - 1-month excess return: 0.21% - 1-year excess return: 12.99%[57][58] - **CSI 300 ESG Enhanced Portfolio**: - 1-week excess return: 0.36% - 1-month excess return: 2.64% - 1-year excess return: 7.88%[57][58]

- The Beta factor showed a significant positive return of 6.95% this week, indicating a strong market preference for high Beta stocks [10] - The Liquidity factor also performed well with a return of 5.53%, reflecting increased demand for highly liquid assets [10] - The Volatility factor improved significantly with a return of 4.19%, showing heightened market interest in high-volatility assets [10] - The Trend factor experienced a notable decline, with a return of -1.76%, indicating a reduced market preference for trend-following strategies [11] - The Size factor showed a significant drop with a return of -3.30%, indicating a decreased market focus on small-cap stocks [11] - The Value factor also declined sharply, with a return of -3.55%, reflecting a reduced market preference for value investment strategies [11] - The one-year momentum factor performed well across various indices, including the CSI 500 and CSI 1000, indicating strong performance in the past year [7][24][30] - The DELTAROE factor showed strong performance in indices like the CSI 800 and CSI 2000, indicating robust profitability growth [27][33] - The three-month reversal factor also performed well in multiple indices, reflecting a strong short-term reversal trend [7][24][27] - The UMR factors, including one-month, three-month, and six-month UMR, generally performed poorly across various indices, indicating weak momentum [7][24][27][30] - The public fund index enhancement products for the CSI 300, CSI 500, and CSI 1000 showed varying levels of excess returns, with the CSI 300 products generally outperforming the others [7][46][48][50] - The MFE (Maximized Factor Exposure) portfolio construction method was used to evaluate the effectiveness of individual factors under various constraints, ensuring controlled industry and style exposures [51][52][54][55]

Quantitative Models and Construction Methods - **Model Name**: Neutral Constraint Maximum Factor Exposure Portfolio **Construction Idea**: Maximize the exposure of the target factor in the portfolio while maintaining neutrality in industry and style exposures relative to the benchmark index[60][61][63] **Construction Process**: 1. **Objective Function**: Maximize portfolio exposure to the target factor $ \text{Max}\quad w^{\prime}X_{\text{target}} $ 2. **Constraints**: - Industry neutrality: $ (w - w_b)^{\prime}X_{\text{inad}} = 0 $ - Style neutrality: $ (w - w_b)^{\prime}X_{\text{Beta}} = 0 $ - Weight deviation limit: $ |w - w_b| \leq 1\% $ - No short selling: $ w \geq 0 $ - Full allocation: $ w^{\prime}1 = 1 $ - Constituents from benchmark index: $ w^{\prime}B = 1 $ **Evaluation**: The model ensures that the portfolio remains neutral to industry and style biases while maximizing factor exposure[60][61][63] Factor Construction and Definitions - **Factor Name**: Beta Factor **Construction Idea**: Capture the sensitivity of individual stock returns to market returns[14][15] **Construction Process**: - Calculate the regression coefficient of individual stock daily returns against the market index (CSI All Share Index) over the past 252 trading days using a half-life weighting of 63 days **Formula**: $ \text{Beta} = \text{Regression Coefficient} $ **Evaluation**: Reflects market risk sensitivity, useful for identifying high-risk or low-risk stocks[14][15] - **Factor Name**: Book-to-Price (BP) **Construction Idea**: Measure valuation by comparing book equity to market capitalization[14][15] **Construction Process**: - $ \text{BP} = \frac{\text{Shareholders' Equity}}{\text{Market Capitalization}} $ **Evaluation**: Indicates undervaluation or overvaluation of stocks, commonly used in value investing[14][15] - **Factor Name**: Sales Growth (SGRO) **Construction Idea**: Assess growth potential by analyzing historical revenue trends[14][15] **Construction Process**: - Perform regression on annual revenue data from the past five fiscal years - Divide the regression slope by the average revenue to calculate growth rate **Formula**: $ \text{SGRO} = \frac{\text{Regression Slope}}{\text{Average Revenue}} $ **Evaluation**: Useful for identifying companies with strong growth trajectories[14][15] Factor Backtesting Results - **Beta Factor**: Weekly long-short return of 7.50%, monthly return of 8.74%[16] - **Book-to-Price (BP)**: Weekly return of -0.27%, monthly return of 0.39%[21][26][30] - **Sales Growth (SGRO)**: Not explicitly tested in the report[14][15] Portfolio Backtesting Results - **Neutral Constraint Maximum Factor Exposure Portfolio**: - **CSI 300 Enhanced Portfolio**: Weekly excess return of 0.03%, monthly return of 1.91%, annual return of 1.34%[57][58] - **CSI 500 Enhanced Portfolio**: Weekly excess return of -1.29%, monthly return of -1.24%, annual return of -2.54%[57][58] - **CSI 800 Enhanced Portfolio**: Weekly excess return of -0.32%, monthly return of 1.68%, annual return of 1.19%[57][58] - **CSI 1000 Enhanced Portfolio**: Weekly excess return of -0.95%, monthly return of 1.33%, annual return of 13.01%[57][58] - **CSI 300 ESG Enhanced Portfolio**: Weekly excess return of 0.51%, monthly return of 2.44%, annual return of 7.72%[57][58] Factor Performance in Different Stock Pools - **CSI 300 Stock Pool**: - Weekly top-performing factors: Log Market Cap (0.83%), Single Quarter Operating Profit Growth (0.72%), 20-Day Specificity (0.71%)[21][23] - Monthly top-performing factors: Single Quarter EP (3.19%), EP_TTM (2.93%), Single Quarter ROE (2.63%)[24] - **CSI 500 Stock Pool**: - Weekly top-performing factors: 20-Day Specificity (1.39%), 60-Day Volume Ratio (1.13%), 60-Day Reversal (1.00%)[26][28] - Monthly top-performing factors: Single Quarter Revenue Growth (3.31%), Single Quarter Operating Profit Growth (2.73%), Single Quarter ROE Growth (2.72%)[28] - **CSI 800 Stock Pool**: - Weekly top-performing factors: Log Market Cap (1.59%), Single Quarter ROE Growth (1.20%), Single Quarter Operating Profit Growth (1.06%)[30][32] - Monthly top-performing factors: Single Quarter EP (4.36%), Single Quarter ROE Growth (3.90%), Single Quarter ROE (3.90%)[33] - **CSI 1000 Stock Pool**: - Weekly top-performing factors: 60-Day Reversal (1.40%), Single Quarter SP (1.30%), SP_TTM (1.29%)[35][37] - Monthly top-performing factors: Log Market Cap (3.66%), 60-Day Reversal (3.43%), Single Quarter Net Profit Growth (3.24%)[38] - **CSI 300 ESG Stock Pool**: - Weekly top-performing factors: Log Market Cap (1.05%), 20-Day Volume Ratio (0.63%), 20-Day Specificity (0.60%)[40][41] - Monthly top-performing factors: Log Market Cap (4.20%), Single Quarter ROE (2.55%), EP_TTM (2.49%)[42] - **All-Market Stock Pool**: - Weekly top-performing factors: Log Market Cap (24.81% Rank IC), 20-Day Specificity (21.07% Rank IC), 60-Day Reversal (19.50% Rank IC)[44][45] - Monthly top-performing factors: 20-Day Specificity (11.25% Rank IC), 20-Day Three-Factor Model Residual Volatility (10.96% Rank IC), 60-Day Specificity (10.73% Rank IC)[45]

Core Viewpoint - The article emphasizes the importance of effectively controlling risks and reducing volatility in asset management, advocating for a "macro factor" risk parity strategy that adapts to changing macroeconomic environments, contrasting it with traditional asset risk parity models [1][13][15]. Group 1: Macro Factor Risk Parity Framework - The construction of a macro factor risk parity framework involves four steps: selecting factors, calculating risk exposure, determining target risk exposure, and matching target risk exposure to asset weights [2][16][17]. - The mainstream methods for constructing macro factors include using low-frequency economic data, principal component analysis (PCA), and regression methods to fit higher-frequency macro factors [3][18][19]. Group 2: Factor Construction and High-Frequency Transformation - The article outlines a refined approach to factor construction, summarizing it as "defining dimensions, screening assets, and high-frequency transformation," which combines the advantages of various methods [3][18][19]. - The transformation of low-frequency macro factors into high-frequency factors is achieved through factor mimicking, which involves regression analysis to identify strong correlations with asset prices [5][29][31]. Group 3: Risk Exposure and Asset Sensitivity - A risk exposure matrix is created to show the sensitivity of assets to different macro variables, using robust OLS regression to capture dynamic features [6][33][34]. - The analysis reveals that large-cap stocks are more sensitive to economic growth, while mid-cap stocks are more sensitive to liquidity conditions [6][35][38]. Group 4: Performance of Different Strategies - The "lightweight" strategy, focusing on growth and inflation factors, has shown an annualized return of 7.7% with a volatility of 5.4% since 2016, outperforming traditional asset risk parity strategies [7][40][41]. - The "three-dimensional" strategy, incorporating M1, BCI, and PPI, has yielded an annualized return of 9.0% with a volatility of 7.8%, indicating a more diversified asset allocation [8][9]. - The "broad-spectrum" strategy, which includes multiple macro factors, has achieved an annualized return of 7.5% with a lower volatility of 4.0%, demonstrating a higher Sharpe ratio compared to simpler models [9][10].

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Combination - **Model Construction Idea**: This model combines Price-to-Book (PB) and Return on Equity (ROE) metrics to identify stocks with strong valuation and profitability characteristics[25] - **Model Construction Process**: - The PB-ROE-50 combination is constructed by selecting stocks based on their PB and ROE metrics, emphasizing stocks with favorable valuation and profitability profiles - The portfolio is rebalanced periodically to maintain the desired exposure to these factors[25] - **Model Evaluation**: The model demonstrates significant excess returns across multiple stock pools, indicating its effectiveness in capturing valuation and profitability signals[25] 2. Model Name: Block Trade Combination - **Model Construction Idea**: This model leverages the "high transaction, low volatility" principle to identify stocks with favorable post-trade performance based on block trade characteristics[31] - **Model Construction Process**: - Stocks are selected 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 included in the portfolio - The portfolio is rebalanced monthly to capture updated signals[31] - **Model Evaluation**: The model effectively extracts excess information from block trades, yielding consistent positive returns relative to the benchmark[31] 3. Model Name: Private Placement Combination - **Model Construction Idea**: This model focuses on the event-driven effects of private placements, considering factors such as market value and timing of announcements[37] - **Model Construction Process**: - Stocks involved in private placements are selected based on the announcement date of shareholder meetings - Adjustments are made for market value factors, rebalancing cycles, and position control to optimize the portfolio[37] - **Model Evaluation**: The model captures the investment opportunities associated with private placements, delivering notable excess returns over the benchmark[37] --- Model Backtesting Results 1. PB-ROE-50 Combination - **Excess Return**: - CSI 500: 0.45% (weekly), 2.64% (YTD)[26] - CSI 800: 1.87% (weekly), 3.86% (YTD)[26] - All Market: 3.35% (weekly), 5.86% (YTD)[26] - **Absolute Return**: - CSI 500: 2.38% (weekly), 3.30% (YTD)[26] - CSI 800: 2.20% (weekly), 2.83% (YTD)[26] - All Market: 4.72% (weekly), 7.74% (YTD)[26] 2. Block Trade Combination - **Excess Return**: 0.41% (weekly), 23.89% (YTD)[32] - **Absolute Return**: 1.89% (weekly), 26.09% (YTD)[32] 3. Private Placement Combination - **Excess Return**: 1.97% (weekly), 6.08% (YTD)[38] - **Absolute Return**: 3.48% (YTD)[38] --- Quantitative Factors and Construction Methods 1. Factor Name: Total Asset Growth Rate - **Factor Construction Idea**: Measures the growth in total assets to capture expansion potential[12] - **Factor Construction Process**: - Calculated as the percentage change in total assets over a specified period - Adjusted for industry and market capitalization effects to isolate the factor signal[12] - **Factor Evaluation**: Demonstrates strong positive returns across multiple stock pools, indicating its effectiveness in identifying growth opportunities[12][18] 2. Factor Name: Single-Quarter ROA - **Factor Construction Idea**: Reflects the profitability of assets on a quarterly basis[12] - **Factor Construction Process**: - Calculated as net income divided by total assets for a single quarter - Adjusted for industry and market capitalization effects to enhance signal clarity[12] - **Factor Evaluation**: Consistently positive performance across stock pools, highlighting its robustness in capturing profitability signals[12][18] 3. Factor Name: Single-Quarter Revenue Growth Rate - **Factor Construction Idea**: Tracks the growth in revenue on a quarterly basis to identify companies with improving top-line performance[12] - **Factor Construction Process**: - Calculated as the percentage change in revenue compared to the same quarter in the previous year - Adjusted for industry and market capitalization effects to ensure comparability[12] - **Factor Evaluation**: Strong positive returns in multiple stock pools, validating its ability to capture growth momentum[12][18] --- Factor Backtesting Results 1. Total Asset Growth Rate - **Excess Return**: - CSI 300: 2.23% (weekly)[12] - CSI 500: 1.26% (weekly)[14] - Liquidity 1500: 2.67% (weekly)[18] 2. Single-Quarter ROA - **Excess Return**: - CSI 300: 1.58% (weekly)[12] - CSI 500: -0.44% (weekly)[15] - Liquidity 1500: 0.88% (weekly)[19] 3. Single-Quarter Revenue Growth Rate - **Excess Return**: - CSI 300: 1.78% (weekly)[12] - CSI 500: 0.58% (weekly)[15] - Liquidity 1500: 2.13% (weekly)[19]