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]

Core Insights - The report investigates the performance of active equity funds, specifically whether they can outperform the broader A-share market, represented by a market capitalization-weighted portfolio of most A-share stocks [11][12] - The analysis reveals that while most active fund portfolios can outperform the market, the excess returns are not statistically significant, with annualized excess returns ranging from 0.216% to 3.05% across various fund sizes [1][2] Fund Performance Analysis - Active funds show a preference for large-cap stocks and high-valuation stocks, with significant positive exposure to high-valuation stocks impacting performance negatively when considering size and value factors [2][3] - Under a three-factor model, all fund portfolios exhibit positive excess returns, ranging from 1.54% to 6.37%, with small and mid-cap funds showing significant excess returns [2][3] - The growth factor demonstrates a high annualized return of 9.91%, with excess returns reaching 10.5% to 12.5%, indicating a strong correlation between short-term earnings growth and future stock returns [3][4] Factor Contributions - The report highlights that aside from the market factor, the value factor has a significant negative contribution, while the growth factor contributes positively to performance [3][4] - In a five-factor model, the market factor contributes between 6.03% and 6.48%, while the value factor contributes negatively between -3.16% and -2.83%, and the growth factor contributes positively between 1.98% and 2.49% [3][4] Fund Composition and Trends - The report notes a structural shift in the composition of active equity funds, with a significant increase in the number of mixed equity funds since 2015, reflecting a change in regulatory requirements [20][21] - The number of active equity funds has grown from 207 at the end of 2008 to 5508 by the end of 2024, with a compound annual growth rate of 22.8% [15][21] - The report also discusses the impact of market conditions on fund performance, noting that the active equity fund's net asset value reached a peak in 2007 and has only recently surpassed that level [22][21]

Quantitative Factors and Construction Methods Factor Name: BP (Book-to-Price Ratio) - **Construction Idea**: Measures the valuation of a stock by comparing its book value to its market value [13] - **Construction Process**: - Formula: $ BP = \frac{\text{Current Book Value}}{\text{Current Market Value}} $ [13] Factor Name: BP Three-Year Percentile - **Construction Idea**: Evaluates the relative valuation of a stock over the past three years [13] - **Construction Process**: - Formula: BP Three-Year Percentile = Percentile rank of the current BP within the last three years [13] Factor Name: Quarterly EP (Earnings-to-Price Ratio) - **Construction Idea**: Measures the profitability of a stock relative to its market price [13] - **Construction Process**: - Formula: $ \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**: - Formula: Quarterly EP One-Year Percentile = Percentile rank of the current Quarterly EP within the last year [13] Factor Name: Quarterly SP (Sales-to-Price Ratio) - **Construction Idea**: Measures the revenue generation capability of a stock relative to its market price [13] - **Construction Process**: - Formula: $ \text{Quarterly SP} = \frac{\text{Quarterly Revenue}}{\text{Net Assets}} $ [13] Factor Name: Quarterly SP One-Year Percentile - **Construction Idea**: Tracks the relative revenue generation capability of a stock over the past year [13] - **Construction Process**: - Formula: Quarterly SP One-Year Percentile = Percentile rank of the current Quarterly SP within the last year [13] Factor Name: Small Market Cap - **Construction Idea**: Captures the size effect by focusing on smaller companies [13] - **Construction Process**: - Formula: $ \text{Small Market Cap} = \log(\text{Market Capitalization}) $ [13] Factor Name: 1-Month Reversal - **Construction Idea**: Captures the short-term reversal effect in stock prices [13] - **Construction Process**: - Formula: $ \text{1-Month Reversal} = \text{Cumulative Return over the Last 20 Trading Days} $ [13] Factor Name: Fama-French Three-Factor 1-Month Residual Volatility - **Construction Idea**: Measures the idiosyncratic risk of a stock based on the Fama-French three-factor model [13] - **Construction Process**: - Formula: $ \text{Residual Volatility} = \text{Standard Deviation of Residuals from Fama-French Three-Factor Regression over the Last 20 Trading Days} $ [13] --- Factor Backtesting Results IC Performance - **BP**: Weekly IC = -4.17%, Monthly IC = 0.88%, Yearly IC = 1.86%, Historical IC = 2.19% [9] - **BP Three-Year Percentile**: Weekly IC = -1.08%, Monthly IC = -0.99%, Yearly IC = 2.58%, Historical IC = 1.58% [9] - **Quarterly EP**: Weekly IC = 2.10%, Monthly IC = -0.48%, Yearly IC = -0.46%, Historical IC = 1.18% [9] - **Quarterly EP One-Year Percentile**: Weekly IC = 4.23%, Monthly IC = 3.81%, Yearly IC = 0.98%, Historical IC = 1.73% [9] - **Quarterly SP**: Weekly IC = 0.79%, Monthly IC = 0.93%, Yearly IC = 0.53%, Historical IC = 0.74% [9] - **Quarterly SP One-Year Percentile**: Weekly IC = 4.80%, Monthly IC = 2.82%, Yearly IC = 2.87%, Historical IC = 1.83% [9] - **Small Market Cap**: Weekly IC = 10.49%, Monthly IC = 8.17%, Yearly IC = 3.61%, Historical IC = 2.05% [9] - **1-Month Reversal**: Weekly IC = 7.22%, Monthly IC = 1.22%, Yearly IC = 3.40%, Historical IC = 2.22% [9] - **Fama-French Three-Factor 1-Month Residual Volatility**: Weekly IC = 3.60%, Monthly IC = 1.11%, Yearly IC = 3.49%, Historical IC = 2.48% [9] Excess Return Performance (Long-Only Portfolio) - **BP**: Weekly Excess Return = -0.83%, Monthly Excess Return = -1.04%, Yearly Excess Return = 3.02%, Historical Cumulative Excess Return = 28.90% [11] - **BP Three-Year Percentile**: Weekly Excess Return = -0.58%, Monthly Excess Return = -1.51%, Yearly Excess Return = 0.97%, Historical Cumulative Excess Return = -3.21% [11] - **Quarterly EP**: Weekly Excess Return = 0.57%, Monthly Excess Return = 1.10%, Yearly Excess Return = 1.44%, Historical Cumulative Excess Return = 30.83% [11] - **Quarterly EP One-Year Percentile**: Weekly Excess Return = -0.01%, Monthly Excess Return = 0.51%, Yearly Excess Return = 3.23%, Historical Cumulative Excess Return = 34.69% [11] - **Quarterly SP**: Weekly Excess Return = -0.01%, Monthly Excess Return = 0.49%, Yearly Excess Return = 0.70%, Historical Cumulative Excess Return = -2.69% [11] - **Quarterly SP One-Year Percentile**: Weekly Excess Return = 0.09%, Monthly Excess Return = 1.25%, Yearly Excess Return = 7.91%, Historical Cumulative Excess Return = 2.23% [11] - **Small Market Cap**: Weekly Excess Return = 0.96%, Monthly Excess Return = 2.76%, Yearly Excess Return = 18.31%, Historical Cumulative Excess Return = 62.57% [11] - **1-Month Reversal**: Weekly Excess Return = 0.83%, Monthly Excess Return = 0.76%, Yearly Excess Return = 3.54%, Historical Cumulative Excess Return = 1.57% [11] - **Fama-French Three-Factor 1-Month Residual Volatility**: Weekly Excess Return = 0.28%, Monthly Excess Return = 0.75%, Yearly Excess Return = 8.69%, Historical Cumulative Excess Return = 18.67% [11]

Investment Rating - The industry investment rating is "Outperform the Market," indicating an expected industry index increase of over 5% within six months [19]. Core Insights - Recent performance of factors shows that small-cap, book-to-price (bp), and one-month illiquidity shock factors performed well in the last week, while Beta, consensus expected net profit compound growth rate, and average executive compensation factors performed poorly [8][10]. - Over the past month, factors such as one-month average turnover rate and quarterly net profit year-on-year growth have shown strong performance, while Beta and small-cap factors lagged [10]. - In the past year, factors like Fama-French three-factor one-month residual volatility and one-month excess return volatility have performed well, while one-year momentum and consensus expected net profit compound growth rate have underperformed [8][10]. Factor Tracking Summary Factor IC Performance - In the last week, small-cap, bp, and one-month illiquidity shock factors showed positive IC performance, while Beta and consensus expected net profit growth were negative [8][9]. - Over the last month, the one-month average turnover rate and one-month illiquidity shock factors performed well, while Beta and small-cap factors were negative [8][9]. - For the past year, Fama-French three-factor one-month residual volatility and one-month excess return volatility were strong, while one-year momentum and consensus expected net profit growth were weak [8][9]. Long Position Factor Performance - In the last week, factors such as the combined shareholding ratio of the top five shareholders and quarterly asset turnover rate performed well, while one-month reversal and Beta factors were weak [10][11]. - Over the last month, standardized unexpected earnings based on consensus expectations and the combined shareholding ratio of the top five shareholders showed strong performance, while Beta and small-cap factors lagged [10][11]. - In the past year, small-cap and quarterly net profit year-on-year growth factors performed well, while one-year momentum and 90-day net upward revision ratio were weak [10][11]. Factor Introduction - The factors used in the analysis are categorized into valuation, profitability, growth, dividends, reversal, turnover rate, volatility, and analyst factors, with specific calculations provided for each category [12][13].

Group 1 - The A-share market showed mixed performance with the Shanghai Composite Index relative to the growth index declining by 0.45%, closing at 2675.86 points and a trading volume of 171.84 billion [1] - The Shanghai Composite Index relative to the growth index has decreased by 5.59% over the past month, increased by 0.79% over the past three months, and has declined by 1.15% year-to-date [1] - The Shanghai Composite Index style index series is based on the Shanghai Composite Index, calculating style scores based on growth and value factors, selecting the top 150 listed companies for the growth and value indices [1] Group 2 - The top ten holdings of the Shanghai Composite Index relative to the growth index include Kweichow Moutai (12.65%), Zijin Mining (3.77%), and others, with the Shanghai Stock Exchange accounting for 100% of the holdings [2] - The industry composition of the holdings in the Shanghai Composite Index relative to the growth index includes Information Technology (19.80%), Industrials (19.31%), Consumer Staples (18.67%), and others [2] Group 3 - The index sample is adjusted every six months, with adjustments occurring on the next trading day after the second Friday of June and December, with a sample adjustment ratio generally not exceeding 20% [3] - In special circumstances, the index may undergo temporary adjustments, and companies that are delisted will be removed from the index sample [3]