- The report tracks the performance of style factors, including momentum, beta, and liquidity factors, which showed strong long positions, while leverage, market capitalization, and valuation factors exhibited strong short positions[3][16] - The report includes the performance of fundamental factors across different stock pools, such as the CSI 300, CSI 500, and CSI 1000, highlighting that low valuation and high growth stocks were generally strong[5][6][7][20][22][25] - Technical factors' performance was mostly positive, with high volatility and long-term momentum stocks performing well, except for the 20-day momentum factor which showed negative performance[4][18][23][26] - The GRU factors' performance was weak overall, with the close1d model showing strong performance, while other models like open1d and barra1d experienced drawdowns[4][5][6][7][18][20][23][26] - The report details the construction and recent performance of the GRU long-only portfolios, noting that the barra1d model outperformed the CSI 1000 index by 0.38%, while the open1d and close1d models underperformed by 0.40%-0.53%[8][31][32] Factor Construction and Performance - **Barra Style Factors**: The report lists several style factors such as Beta, Market Cap, Momentum, Volatility, Non-linear Size, Valuation, Liquidity, Profitability, Growth, and Leverage, with detailed formulas for each[14][15] - **Fundamental Factors**: The report tracks various fundamental factors, including unexpected growth and growth-related financial factors, with mixed performance across different stock pools[4][5][6][7][18][20][22][25] - **Technical Factors**: The report includes several technical factors, such as 20-day momentum, 60-day momentum, 120-day momentum, and various volatility measures, with detailed performance metrics[4][18][23][26] Factor Performance Metrics - **Fundamental Factors**: - Operating Turnover: -1.14% (1 week), 4.19% (1 month), -11.23% (6 months), -11.52% (YTD), -1.86% (3-year annualized), 3.31% (5-year annualized)[19] - ROC: -0.68% (1 week), 0.89% (1 month), -10.51% (6 months), -10.59% (YTD), -13.06% (3-year annualized), -11.85% (5-year annualized)[19] - ROE Growth: 0.36% (1 week), 2.01% (1 month), 10.43% (6 months), 2.27% (YTD), 0.38% (3-year annualized), 2.61% (5-year annualized)[19] - **Technical Factors**: - 20-day Momentum: -0.73% (1 week), 0.66% (1 month), -8.17% (6 months), -12.18% (YTD), -13.19% (3-year annualized), -13.77% (5-year annualized)[19] - Median Deviation: -0.38% (1 week), -3.25% (1 month), -5.83% (6 months), -4.72% (YTD), -15.12% (3-year annualized), -15.62% (5-year annualized)[19] - 60-day Momentum: 0.35% (1 week), -3.31% (1 month), 2.64% (6 months), 5.08% (YTD), -12.82% (3-year annualized), -16.17% (5-year annualized)[19] GRU Model Performance - **GRU Long-Only Portfolios**: - open1d: -0.40% (1 week), -0.20% (1 month), 2.37% (3 months), 6.32% (6 months), 7.16% (YTD)[32] - close1d: -0.53% (1 week), -0.83% (1 month), 4.38% (3 months), 6.80% (6 months), 6.59% (YTD)[32] - barra1d: 0.38% (1 week), -0.25% (1 month), 0.85% (3 months), 2.85% (6 months), 3.78% (YTD)[32] - barra5d: 0.00% (1 week), -0.36% (1 month), 3.59% (3 months), 7.41% (6 months), 8.37% (YTD)[32] - Multi-Factor: -0.38% (1 week), -0.30% (1 month), 1.62% (3 months), 2.54% (6 months), 2.54% (YTD)[32]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The MFE portfolio is designed to maximize the exposure of a single factor while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover limits. This approach ensures that the factor's predictive power is tested under realistic portfolio constraints, making it more applicable in practice [39][40]. **Model Construction Process**: The MFE portfolio is constructed using the following optimization model: $ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. 4. **Constituent Weight Control**: \( B_b \) is a binary vector indicating benchmark constituents, and \( b_l, b_h \) are the lower and upper bounds for constituent weights. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights. 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T} w = 1 \) [39][40][41]. **Model Evaluation**: The MFE portfolio is effective in testing factor performance under realistic constraints, making it a practical tool for portfolio construction [39][40]. Quantitative Factors and Construction Methods - **Factor Name**: DELTAROE **Factor Construction Idea**: Measures the change in return on equity (ROE) over a specific period to capture improvements in profitability [16]. **Factor Construction Process**: $ \text{DELTAROE} = \text{ROE}_{\text{current quarter}} - \text{ROE}_{\text{same quarter last year}} $ Where ROE is calculated as: $ \text{ROE} = \frac{\text{Net Income} \times 2}{\text{Beginning Equity} + \text{Ending Equity}} $ [16]. **Factor Evaluation**: DELTAROE is a profitability factor that has shown strong performance in multiple sample spaces, including CSI 300, CSI 500, and CSI A500 indices [17][19][24]. - **Factor Name**: Pre-expected PEG (Pre-expected Price-to-Earnings Growth) **Factor Construction Idea**: Incorporates analysts' earnings growth expectations to evaluate valuation relative to growth potential [16]. **Factor Construction Process**: $ \text{Pre-expected PEG} = \frac{\text{Forward P/E}}{\text{Expected Earnings Growth Rate}} $ Where forward P/E is based on analysts' consensus earnings estimates [16]. **Factor Evaluation**: This factor has demonstrated strong predictive power in growth-oriented sample spaces such as CSI 300 and CSI A500 indices [17][24]. - **Factor Name**: DELTAROA **Factor Construction Idea**: Measures the change in return on assets (ROA) over a specific period to capture improvements in asset efficiency [16]. **Factor Construction Process**: $ \text{DELTAROA} = \text{ROA}_{\text{current quarter}} - \text{ROA}_{\text{same quarter last year}} $ Where ROA is calculated as: $ \text{ROA} = \frac{\text{Net Income} \times 2}{\text{Beginning Total Assets} + \text{Ending Total Assets}} $ [16]. **Factor Evaluation**: DELTAROA has shown consistent performance across multiple indices, including CSI 1000 and public fund-heavy indices [22][26]. Factor Backtesting Results - **DELTAROE**: - CSI 300: Weekly excess return 0.75%, monthly 2.28%, YTD 8.04% [17]. - CSI 500: Weekly excess return 0.07%, monthly 0.59%, YTD 6.67% [19]. - CSI A500: Weekly excess return 0.68%, monthly 3.61%, YTD 9.20% [24]. - **Pre-expected PEG**: - CSI 300: Weekly excess return 0.72%, monthly 2.10%, YTD 7.22% [17]. - CSI 500: Weekly excess return 0.15%, monthly 1.34%, YTD 9.62% [19]. - CSI A500: Weekly excess return 0.85%, monthly 2.07%, YTD 10.35% [24]. - **DELTAROA**: - CSI 300: Weekly excess return 0.44%, monthly 2.27%, YTD 7.10% [17]. - CSI 1000: Weekly excess return 0.66%, monthly 1.57%, YTD 8.57% [22]. - Public Fund Index: Weekly excess return 0.66%, monthly 1.57%, YTD 8.57% [26].

Group 1 - The article emphasizes the importance of combining macro quantification with factor momentum to select resonant factors, particularly focusing on growth factors while considering market conditions [1][4] - Current macro indicators show economic decline, slightly loose liquidity, and improving credit indicators, leading to a correction in the direction of economic downturn and tight liquidity [3][4] - The article identifies that the stock pools are still biased towards growth factors, especially in the CSI 300 and CSI 1000 indices, while the CSI 500 leans more towards fundamental factors [4][5] Group 2 - Economic leading indicators suggest a potential slight increase after reaching a short-term bottom in August 2025, despite recent declines in PMI and new orders [6][8] - Various leading indicators are analyzed, indicating that many are in a downward cycle, with expectations for some to reach their bottom by early 2026 [9][10] - The liquidity environment is assessed as slightly loose, with interest rates remaining stable and monetary supply indicators suggesting a continuation of this trend [12][14] Group 3 - Credit indicators are generally weak, but the overall credit environment remains positive, with some signs of recovery in recent months [15][16] - The article recommends increasing stock allocations due to improving equity trends, while reducing allocations in other asset classes [16][17] - The focus remains on liquidity as the most significant variable affecting market dynamics, with credit and inflation also being monitored [18][20] Group 4 - The article suggests industry selection based on economic sensitivity and credit sensitivity, highlighting sectors that are less sensitive to economic downturns but more responsive to credit conditions [20][21] - Industries identified as having high growth potential include electronics, media, and beauty care, which are less affected by economic fluctuations [20][21]

Group 1 - The report emphasizes that growth has become a resonant factor in the current economic environment, with a focus on selecting factors that are insensitive to economic conditions but sensitive to credit [2][7][9] - The report suggests that the current economic indicators are weak, leading to a preference for growth-oriented stocks in the investment strategy, particularly in the CSI 300 and CSI 1000 indices [2][9][10] - The macroeconomic outlook indicates a potential short-term recovery in economic indicators, with a forecasted slight increase in the economic leading indicators in August 2025 [12][13][14] Group 2 - The liquidity environment is described as relatively stable, with interest rates showing slight increases but remaining below historical averages, indicating a slightly loose liquidity condition [19][20][22] - Credit indicators are noted to be weak, with a decline in credit volume and structure, although the overall credit indicators remain positive [23][24] - The report advocates for an increase in stock allocation, reflecting a positive trend in equity markets, while reducing allocations in other asset classes [2][24][25] Group 3 - The report identifies liquidity as the primary focus of market attention, especially following recent market movements driven by liquidity conditions [26][27] - In terms of industry selection, the report recommends focusing on sectors that are less sensitive to economic fluctuations but more responsive to credit conditions, highlighting industries with growth attributes [4][31][28] - The report lists specific industries with high scores for economic insensitivity and credit sensitivity, including electronics, media, and beauty care, indicating a strategic focus on growth-oriented sectors [28][31]

Group 1: Core Insights - The report emphasizes the importance of growth factors in investment strategies, highlighting various indices that represent growth styles [10][12][24] - It identifies three main expressions of growth styles: absolute growth, relative growth, and cash flow growth, each with specific metrics and methodologies for evaluation [15][24][28] Group 2: Growth Factors - The report defines several growth factors, including net profit growth, operating profit growth, and revenue growth, with specific calculation methods outlined for each [15][18] - Correlation analysis among growth factors shows strong relationships, indicating that net profit growth has a 100% correlation with itself and significant correlations with other factors like operating profit growth (94.22%) and revenue growth (52.02%) [18][21] Group 3: Growth Style Performance - Backtesting results indicate that absolute growth composite strategies yield excess returns across various indices, with the highest information ratio observed in the CSI 300 index [21][28] - The report presents performance metrics for different growth styles, showing that the SUE (Surprise Earnings) factor and analyst forecast growth have significant positive impacts on returns [24][28]

- The report tracks the performance of growth factors, highlighting that growth factors performed well across different market capitalizations, with higher excess returns in large-cap stocks[2][42][43] - The report mentions that the growth factor had a strong performance in the past week, with specific factors such as (current consensus forecast rev_FY1 - 3 months ago consensus forecast rev_FY1) / 3 months ago consensus forecast rev_FY1 absolute value, (current ROE - last year's ROE) / last year's ROE absolute value, single-quarter EPS growth rate, R&D to sales ratio, and operating income_TTM / average total assets showing excess returns of over 1% relative to the CSI All Share Index[2][42][43] - The report provides detailed excess returns for various factors over different time periods, with factors like tot_rd_ttm_to_assets, dp_historical, jor, mom3_rating_score_90d, and mom3_rev_fy1 showing significant excess returns over the past week and month[2][44] - The report also analyzes factor performance across different indices (CSI 300, CSI 500, CSI 1000, and CSI 2000), noting that factors such as fix_ratio, sue1, peg, yoy_roe, and yoy_eps_q performed well across all indices, with better performance in large-cap indices[2][45][46] - The report evaluates the performance of quantitative portfolios, noting that the enhanced portfolios based on financial report coverage for CSI 300, CSI 500, and CSI 1000 achieved positive absolute and excess returns over the past week, month, and year[2][47][48] - The report provides specific performance metrics for the enhanced portfolios, including absolute returns, relative returns, and annualized excess returns, with the CSI 300 enhanced portfolio achieving an absolute return of 230.84% and an annualized excess return of 10.89% since 2015[2][48][49] - The report lists the top 30 holdings for each enhanced portfolio, including stocks like JinkoSolar, Inspur Information, and Weir Group for the CSI 300 enhanced portfolio, and stocks like Shanghai Jahwa, Kedali, and Lianlong for the CSI 500 enhanced portfolio[2][58]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure (MFE) Portfolio **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of individual factors under realistic constraints, such as industry exposure, style exposure, stock weight limits, and turnover constraints. This approach ensures that the factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[41][42]. **Model Construction Process**: The MFE portfolio is constructed using the following optimization model: $ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f^{T} w \) represents the weighted exposure of the portfolio to the factor \( f \), and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) represents the factor exposure matrix for stocks, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style factor exposure[42]. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviations[42]. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviations relative to the benchmark[42]. 4. **Constituent Weight**: \( B_b \) is a binary vector indicating whether a stock is part of the benchmark, and \( b_l, b_h \) are the lower and upper bounds for constituent weights[42]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \)[42]. 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T} w = 1 \)[43]. - **Implementation**: 1. Define constraints for style, industry, and stock weights. For example, for CSI 500 and CSI 300 indices, industry exposure is neutralized, and stock weight deviations are capped at 1%[45]. 2. Construct the MFE portfolio at the end of each month based on the constraints[45]. 3. Backtest the portfolio, accounting for transaction costs (0.3% per side), and calculate performance metrics relative to the benchmark[45]. **Model Evaluation**: The MFE portfolio effectively tests factor performance under realistic constraints, making it a robust tool for evaluating factor predictability in practical scenarios[41][42]. Quantitative Factors and Construction Methods - **Factor Name**: DELTAROA **Factor Construction Idea**: Measures the change in return on assets (ROA) compared to the same quarter in the previous year, capturing improvements in asset utilization efficiency[16]. **Factor Construction Process**: $ DELTAROA = ROA_{current\ quarter} - ROA_{same\ quarter\ last\ year} $ Where \( ROA = \frac{Net\ Income}{Total\ Assets} \)[16]. **Factor Evaluation**: DELTAROA is a growth-oriented factor that has shown strong performance in multiple sample spaces, particularly in the CSI A500 index[19][25]. - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings, to capture earnings surprises[16]. **Factor Construction Process**: $ SUE = \frac{Actual\ Earnings - Expected\ Earnings}{Standard\ Deviation\ of\ Expected\ Earnings} $[16]. **Factor Evaluation**: SUE is a profitability factor that performs well in growth-oriented indices like CSI 1000 and CSI A500[19][23][25]. - **Factor Name**: One-Year Momentum **Factor Construction Idea**: Captures the trend-following behavior of stocks by measuring price momentum over the past year, excluding the most recent month[16]. **Factor Construction Process**: $ Momentum = \frac{Price_{t-12} - Price_{t-1}}{Price_{t-1}} $ Where \( t-12 \) and \( t-1 \) represent the stock price 12 months and 1 month ago, respectively[16]. **Factor Evaluation**: Momentum is a widely used factor that has shown consistent performance in large-cap indices like CSI 300 and CSI 500[19][21]. Factor Backtesting Results - **CSI 300 Sample Space**: - **Best-Performing Factors (1 Week)**: Single-quarter revenue growth, DELTAROA, single-quarter ROE[19]. - **Worst-Performing Factors (1 Week)**: Three-month volatility, one-month volatility, three-month turnover[19]. - **CSI 500 Sample Space**: - **Best-Performing Factors (1 Week)**: One-year momentum, standardized unexpected revenue, standardized unexpected earnings[21]. - **Worst-Performing Factors (1 Week)**: SPTTM, single-quarter SP, dividend yield[21]. - **CSI 1000 Sample Space**: - **Best-Performing Factors (1 Week)**: Three-month reversal, standardized unexpected revenue, single-quarter surprise magnitude[23]. - **Worst-Performing Factors (1 Week)**: Dividend yield, one-month volatility, BP[23]. - **CSI A500 Sample Space**: - **Best-Performing Factors (1 Week)**: DELTAROA, standardized unexpected earnings, single-quarter ROA[25]. - **Worst-Performing Factors (1 Week)**: Three-month volatility, one-month turnover, one-month volatility[25]. - **Public Fund Heavyweight Index Sample Space**: - **Best-Performing Factors (1 Week)**: One-year momentum, standardized unexpected revenue, expected net profit QoQ[27]. - **Worst-Performing Factors (1 Week)**: Dividend yield, one-month volatility, three-month volatility[27].

Quantitative Factors and Construction Methods Factor Name: bp - **Construction Idea**: Represents the valuation level of a stock by comparing its book value to its market value [13] - **Construction Process**: Calculated as: $ bp = \frac{\text{Current Net Asset}}{\text{Current Total Market Value}} $ [13] Factor Name: bp three-year percentile - **Construction Idea**: Measures the relative valuation of a stock over the past three years [13] - **Construction Process**: Represents the percentile rank of the current bp value within the last three years [13] Factor Name: Quarterly ep - **Construction Idea**: Evaluates profitability by comparing quarterly net profit to net assets [13] - **Construction Process**: Calculated as: $ \text{Quarterly ep} = \frac{\text{Quarterly Net Profit}}{\text{Net Assets}} $ [13] Factor Name: Quarterly ep one-year percentile - **Construction Idea**: Tracks the relative profitability of a stock over the past year [13] - **Construction Process**: Represents the percentile rank of the current quarterly ep value within the last year [13] Factor Name: Quarterly sp - **Construction Idea**: Measures operational efficiency by comparing quarterly revenue to net assets [13] - **Construction Process**: Calculated as: $ \text{Quarterly sp} = \frac{\text{Quarterly Revenue}}{\text{Net Assets}} $ [13] Factor Name: Quarterly sp one-year percentile - **Construction Idea**: Tracks the relative operational efficiency of a stock over the past year [13] - **Construction Process**: Represents the percentile rank of the current quarterly sp value within the last year [13] Factor Name: Quarterly net profit YoY growth - **Construction Idea**: Measures the growth rate of quarterly net profit compared to the same period last year [13] - **Construction Process**: Calculated as: $ \text{Quarterly Net Profit YoY Growth} = \frac{\text{Current Quarter Net Profit} - \text{Last Year Same Quarter Net Profit}}{\text{Last Year Same Quarter Net Profit}} $ [13] Factor Name: Standardized unexpected earnings - **Construction Idea**: Quantifies the deviation of current earnings from expected levels based on historical trends [13] - **Construction Process**: Calculated as: $ \text{Standardized Unexpected Earnings} = \frac{\text{Current Quarter Net Profit} - (\text{Last Year Same Quarter Net Profit} + \text{Average YoY Growth of Last 8 Quarters})}{\text{Standard Deviation of YoY Growth of Last 8 Quarters}} $ [13] Factor Name: Dividend yield - **Construction Idea**: Measures the return to shareholders through dividends relative to the stock's market value [13] - **Construction Process**: Calculated as: $ \text{Dividend Yield} = \frac{\text{Last Year Dividend}}{\text{Current Market Value}} $ [13] --- Factor Backtesting Results IC Performance - **bp**: Weekly IC = 7.22%, Monthly IC = 3.46%, Yearly IC = 1.87%, Historical IC = 2.34% [9] - **bp three-year percentile**: Weekly IC = -1.28%, Monthly IC = 1.67%, Yearly IC = 2.48%, Historical IC = 1.68% [9] - **Quarterly ep**: Weekly IC = 6.27%, Monthly IC = 0.71%, Yearly IC = -0.44%, Historical IC = 1.09% [9] - **Quarterly ep one-year percentile**: Weekly IC = 7.04%, Monthly IC = 2.84%, Yearly IC = 0.95%, Historical IC = 1.72% [9] - **Quarterly sp**: Weekly IC = 2.97%, Monthly IC = 0.68%, Yearly IC = 0.50%, Historical IC = 0.72% [9] - **Quarterly sp one-year percentile**: Weekly IC = -1.88%, Monthly IC = 2.56%, Yearly IC = 2.85%, Historical IC = 1.83% [9] - **Quarterly net profit YoY growth**: Weekly IC = 7.35%, Monthly IC = 2.60%, Yearly IC = 0.60%, Historical IC = 1.28% [9] - **Standardized unexpected earnings**: Weekly IC = 7.52%, Monthly IC = 3.04%, Yearly IC = 0.60%, Historical IC = 0.97% [9] - **Dividend yield**: Weekly IC = 3.43%, Monthly IC = 0.78%, Yearly IC = -0.36%, Historical IC = 0.61% [9] Long-only Portfolio Performance - **bp**: Weekly Excess Return = 0.39%, Monthly Excess Return = 0.53%, Yearly Excess Return = 1.50%, Historical Cumulative Excess Return = 31.88% [11] - **bp three-year percentile**: Weekly Excess Return = -0.16%, Monthly Excess Return = -1.08%, Yearly Excess Return = 0.42%, Historical Cumulative Excess Return = -2.91% [11] - **Quarterly ep**: Weekly Excess Return = 0.56%, Monthly Excess Return = 1.22%, Yearly Excess Return = 3.02%, Historical Cumulative Excess Return = 30.83% [11] - **Quarterly ep one-year percentile**: Weekly Excess Return = 0.24%, Monthly Excess Return = 0.71%, Yearly Excess Return = 3.76%, Historical Cumulative Excess Return = 32.90% [11] - **Quarterly sp**: Weekly Excess Return = -0.25%, Monthly Excess Return = 0.10%, Yearly Excess Return = 1.18%, Historical Cumulative Excess Return = -2.98% [11] - **Quarterly sp one-year percentile**: Weekly Excess Return = -0.43%, Monthly Excess Return = 0.20%, Yearly Excess Return = 8.26%, Historical Cumulative Excess Return = -0.57% [11] - **Quarterly net profit YoY growth**: Weekly Excess Return = 0.47%, Monthly Excess Return = 1.56%, Yearly Excess Return = 9.60%, Historical Cumulative Excess Return = 36.36% [11] - **Standardized unexpected earnings**: Weekly Excess Return = 0.57%, Monthly Excess Return = 0.97%, Yearly Excess Return = -3.21%, Historical Cumulative Excess Return = 7.84% [11] - **Dividend yield**: Weekly Excess Return = 0.63%, Monthly Excess Return = 1.27%, Yearly Excess Return = -4.27%, Historical Cumulative Excess Return = 12.82% [11]

Quantitative Models and Construction - **Model Name**: Three-dimensional Timing Framework **Construction Idea**: The model integrates liquidity, divergence, and prosperity indicators to assess market timing and risk levels[7][14][16] **Construction Process**: 1. **Liquidity Index**: Tracks market liquidity trends[22] 2. **Divergence Index**: Measures market disagreement levels[20] 3. **Prosperity Index**: Evaluates industrial prosperity trends[26] 4. Combines these three dimensions to form a comprehensive timing framework[14] **Evaluation**: Demonstrates stable performance in identifying market timing opportunities[16] - **Model Name**: Financing-Active Large Order Flow Intersection Strategy **Construction Idea**: Combines financing and large order flows to identify industries with strong capital inflows[34][40] **Construction Process**: 1. **Financing Flow Factor**: Neutralizes market capitalization and calculates the net financing buy-sell difference over a 50-day average[40] 2. **Active Large Order Flow Factor**: Neutralizes transaction volume and ranks net inflows over the past year, using a 10-day average[40] 3. Filters extreme industries and integrates both factors to enhance stability[40] **Evaluation**: Achieves stable annualized excess returns with reduced drawdowns compared to other strategies[40] Quantitative Models Backtesting Results - **Three-dimensional Timing Framework**: Historical performance shows stable risk assessment and timing capabilities[16] - **Financing-Active Large Order Flow Intersection Strategy**: - Annualized excess return: 13.5% - IR: 1.7[40] - Weekly absolute return: -1.6% - Weekly excess return: -0.1%[40] Quantitative Factors and Construction - **Factor Name**: Valuation Factors **Construction Idea**: Focuses on valuation metrics such as earnings yield and book-to-market ratios[46][47] **Construction Process**: 1. **Earnings Yield (ep_fy3)**: $ ep\_fy3 = \frac{1}{PE\_FY3} $ 2. **Book-to-Market Ratio (bp)**: $ bp = \frac{Shareholder\_Equity}{Market\_Value} $ 3. Neutralizes industry and market capitalization effects[46][48] **Evaluation**: Demonstrates strong performance across multiple timeframes and indices[46][48] - **Factor Name**: Growth Factors **Construction Idea**: Captures growth metrics such as revenue and profit growth rates[46][49] **Construction Process**: 1. **Revenue Growth (yoy_or)**: $ yoy\_or = \frac{Current\_Revenue - Previous\_Revenue}{Previous\_Revenue} $ 2. **Profit Growth (yoy_np)**: $ yoy\_np = \frac{Current\_Net\_Profit - Previous\_Net\_Profit}{Previous\_Net\_Profit} $ 3. Neutralizes industry and market capitalization effects[46][50] **Evaluation**: Performs better in large-cap indices and shows consistent excess returns[49][50] Quantitative Factors Backtesting Results - **Valuation Factors**: - Weekly excess return: 1.5%-2.18% - Monthly excess return: 1.46%-3.85%[48] - **Growth Factors**: - Weekly excess return: 1.52%-3.89% - Monthly excess return: 0.79%-3.02%[50] Quantitative Portfolios and Construction - **Portfolio Name**: Index Enhancement Portfolios **Construction Idea**: Adjusts factor selection based on research coverage to enhance index performance[51] **Construction Process**: 1. Divides stocks into high and low research coverage domains[51] 2. Applies suitable factors for each domain to optimize portfolio construction[51] **Evaluation**: Outperforms original index selection methods in terms of excess returns[51] Quantitative Portfolios Backtesting Results - **Index Enhancement Portfolios**: - **HS300**: - Weekly absolute return: -0.89% - Weekly excess return: 0.03% - Annualized excess return: 7.77%[52] - **CSI500**: - Weekly absolute return: 0.16% - Weekly excess return: 0.40% - Annualized excess return: 9.82%[52] - **CSI1000**: - Weekly absolute return: -0.58% - Weekly excess return: -0.74% - Annualized excess return: 9.26%[52]

Quantitative Models and Factor Analysis Quantitative Models and Construction - **Model Name**: Macro Timing Strategy **Model Construction Idea**: This model evaluates macroeconomic signals to determine optimal equity allocation levels. It incorporates economic growth and monetary liquidity signals to generate recommended equity positions[27][28] **Model Construction Process**: 1. The model assigns weights to two dimensions: economic growth and monetary liquidity. 2. Signal strength for each dimension is calculated as a percentage. 3. The final equity allocation recommendation is derived based on the combined signal strength. **Evaluation**: The model is designed for stable and moderately bullish configurations, with a focus on balancing growth and liquidity signals[27][28] - **Model Name**: Micro-Cap Timing Model **Model Construction Idea**: This model uses risk warning indicators to assess the timing for micro-cap stock investments. It incorporates volatility congestion and interest rate changes as key metrics[30] **Model Construction Process**: 1. **Volatility Congestion**: Measured as the year-over-year change in volatility. A threshold of 0.55 is used to trigger risk warnings. 2. **Interest Rate Change**: Measured as the year-over-year change in the 10-year government bond yield. A threshold of 0.30 is used to trigger risk warnings. 3. If neither indicator exceeds its threshold, the model suggests continuing to hold micro-cap stocks[30][31] **Evaluation**: The model is effective in identifying risk levels and provides clear signals for long-term investors[30] Model Backtesting Results - **Macro Timing Strategy**: - Equity allocation recommendation: 45% for June[27][28] - Signal strength: Economic growth at 50%, monetary liquidity at 40%[27][28] - Year-to-date return: 1.06%, compared to Wind All-A return of 1.90%[27] - **Micro-Cap Timing Model**: - Volatility congestion: -50.09%, below the 0.55 threshold[31] - Interest rate change: -28.69%, below the 0.30 threshold[31] --- Quantitative Factors and Construction - **Factor Name**: Value Factor **Factor Construction Idea**: Measures the relative valuation of stocks based on financial metrics such as book-to-market ratio and earnings yield[43] **Factor Construction Process**: 1. **Book-to-Market Ratio (BP_LR)**: Calculated as the latest book value divided by market capitalization. 2. **Earnings Yield (EP_FTTM)**: Calculated as the forward 12-month consensus earnings divided by market capitalization. 3. **Sales-to-Enterprise Value (Sales2EV)**: Calculated as the past 12-month revenue divided by enterprise value[43] **Evaluation**: The value factor consistently delivers strong excess returns, particularly in large-cap stocks[34][35] - **Factor Name**: Quality Factor **Factor Construction Idea**: Evaluates the financial health and operational efficiency of companies[43] **Factor Construction Process**: 1. **Operating Cash Flow to Current Debt (OCF2CurrentDebt)**: Measures the ratio of operating cash flow to average current liabilities over the past 12 months. 2. **Gross Margin (GrossMargin_TTM)**: Measures the gross profit margin over the past 12 months. 3. **Revenue-to-Asset Ratio (Revenues2Asset_TTM)**: Measures the revenue generated per unit of average total assets over the past 12 months[43] **Evaluation**: The quality factor is a key driver of excess returns, particularly in mid-cap and small-cap stocks[34][35] - **Factor Name**: Growth Factor **Factor Construction Idea**: Focuses on companies with strong earnings and revenue growth potential[43] **Factor Construction Process**: 1. **Quarterly Revenue Growth (Revenues_SQ_Chg1Y)**: Measures the year-over-year growth in quarterly revenue. 2. **Quarterly Operating Income Growth (OperatingIncome_SQ_Chg1Y)**: Measures the year-over-year growth in quarterly operating income. 3. **Return on Equity (ROE_FTTM)**: Measures the forward 12-month consensus net income divided by average shareholder equity[43] **Evaluation**: The growth factor performs well in mid-cap stocks, particularly in the China A-share market[34][35] Factor Backtesting Results - **Value Factor**: - IC mean: 0.23 in the CSI 300 pool[34] - Multi-long-short return: 1.75% in the CSI 300 pool[34] - **Quality Factor**: - IC mean: 0.0702 in the CSI 500 pool, 0.064 in the CSI 1000 pool[34] - Multi-long-short return: 1.45% in the All A-share pool[34] - **Growth Factor**: - IC mean: 0.11 in the CSI 500 pool[34] - Multi-long-short return: 2.83% in the CSI 500 pool[34] - **Other Factors**: - Momentum and low-volatility factors showed weaker performance, with negative returns in some pools[34][35] --- Convertible Bond Factors and Construction - **Factor Name**: Convertible Bond Valuation Factor **Factor Construction Idea**: Evaluates convertible bonds based on their valuation relative to underlying stocks and market conditions[39] **Factor Construction Process**: 1. **Parity-Premium Ratio**: Measures the premium of the convertible bond price over its parity value. 2. **Underlying Stock Factors**: Incorporates stock-specific factors such as growth, quality, and valuation metrics[39] **Evaluation**: The valuation factor is effective in identifying mispriced convertible bonds[39] Convertible Bond Factor Backtesting Results - **Convertible Bond Valuation Factor**: - Multi-long-short return: 0.97% last week[39] - Other stock-related factors (e.g., growth, quality) showed mixed performance, with growth factor declining by 0.35%[39]