Core Viewpoint - The report provides a comprehensive analysis of market performance, highlighting the positive and negative returns of various factors across different stock pools, indicating a mixed market sentiment and potential investment opportunities in specific sectors [3][4][5][6]. Group 1: Market Factor Performance - The overall market showed a positive return of 0.49% for the Beta factor, while momentum and liquidity factors experienced negative returns of -0.60% and -0.49% respectively, suggesting a reversal effect in the market [3]. - In the CSI 300 stock pool, the best-performing factors included quarterly operating profit growth rate (2.40%), price-to-book ratio (2.30%), and turnover rate relative volatility (2.19%), while the worst performers were operating profit margin TTM (-0.95%), total asset gross margin TTM (-0.76%), and net profit margin TTM (-0.71%) [4]. - The CSI 500 stock pool saw strong performance from the downside volatility ratio (3.85%), intraday volatility and trading volume correlation (3.44%), and inverse price-to-earnings ratio TTM (2.31%), with poor performance from quarterly ROE (-1.66%), post-opening return factor (-1.42%), and ROIC enhancement factor (-1.31%) [4]. Group 2: Liquidity and Industry Performance - In the liquidity 1500 stock pool, the best-performing factors were price-to-book ratio (1.67%), inverse price-to-earnings ratio TTM (1.20%), and price-to-earnings ratio (0.97%), while the worst performers included 5-day reversal (-2.11%), post-opening return factor (-1.69%), and logarithmic market value factor (-1.69%) [5]. - 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 non-ferrous metals, beauty care, and diversified industries [6]. - Valuation factors, particularly the BP factor, performed well in the coal and diversified industries, while residual volatility and liquidity factors showed significant positive returns in agriculture, forestry, animal husbandry, and beauty care sectors [6]. Group 3: Strategy Performance Tracking - The PB-ROE-50 combination achieved positive excess returns in the overall market stock pool, with excess returns of -0.57% in the CSI 500 stock pool and -0.45% in the CSI 800 stock pool, while the overall market stock pool saw an excess return of 0.06% [7]. - Public and private fund research selection strategies yielded positive excess returns, with public research selection strategy outperforming the CSI 800 by 1.02% and private research tracking strategy outperforming by 2.72% [8]. - The block trading combination achieved positive excess returns relative to the CSI All Index, with an excess return of 0.83% [9]. - The targeted issuance combination, however, recorded negative excess returns relative to the CSI All Index, with an excess return of -0.46% [10].

- The report tracks the performance of various factors in different stock pools, including the CSI 300, CSI 500, and Liquidity 1500 stock pools[1][2][3] - In the CSI 300 stock pool, the best-performing factors this week were single-quarter net profit year-on-year growth rate (1.02%), single-quarter EPS (1.00%), and PE ratio factor (0.89%)[12][13] - In the CSI 500 stock pool, the best-performing factors this week were EPTTM percentile (1.30%), PB ratio factor (1.07%), and operating cash flow ratio (0.97%)[14][15] - In the Liquidity 1500 stock pool, the best-performing factors this week were post-morning return factor (2.27%), momentum spring factor (1.43%), and PE TTM reciprocal (1.33%)[16][17] - The PB-ROE-50 portfolio achieved positive excess returns in the CSI 500 and CSI 800 stock pools this week, with excess returns of 0.88% and 0.43% respectively[24][25] - The institutional research portfolio tracking strategy achieved positive excess returns this week, with the private equity research tracking strategy achieving an excess return of 0.22% relative to the CSI 800[26][27] - The block trading portfolio achieved a positive excess return of 0.36% relative to the CSI All Share Index this week[30][31] - The directed issuance portfolio achieved a positive excess return of 0.48% relative to the CSI All Share Index this week[35][36]

Quantitative Models and Factor Construction Multi-Horizon Factor - **Factor Name**: Multi-Horizon Factor - **Construction Idea**: This factor captures short-term reversal, medium-term momentum, and long-term reversal effects by analyzing moving average (MA) data across multiple time horizons [2][14][21] - **Construction Process**: - Calculate moving averages for different time horizons \( L = [3, 5, 10, 20, 30, 60, 90, 120, 180, 240, 270, 300] \) using the formula: \[ A_{j t,L} = \frac{P_{j,\,d-L+1}^{t} + \cdots + P_{j,d}^{t}}{L} \] where \( P_{j,d}^t \) represents the price of stock \( j \) at time \( t \) [21] - Standardize the moving average factor: \[ \tilde{A}_{j t,\,L} = \frac{A_{j t,\,L}}{P_{j}^{t}} \] [22] - Perform cross-sectional regression of stock returns on lagged standardized moving average factors: \[ r_{j,t} = \beta_{0,t} + \Sigma_{i}\beta_{i,t}\tilde{A}_{j t-1,L_{i}} + \epsilon_{j,t} \] [23] - Predict next-period regression coefficients by averaging the past 25 weeks' coefficients: \[ E\left[\beta_{i,\,t+1}\right] = \frac{1}{25}\,\sum_{m=1}^{25}\,\beta_{i,t+1-m} \] [24] - Use predicted coefficients and new factor values to estimate next-period returns: \[ E\left[r_{j,t+1}\right] = \Sigma_{i}\,E\left[\beta_{i,\,t+1}\right]\tilde{A}_{j t,\,L_{i}} \] [25] - Rank stocks by predicted returns and construct long-short portfolios [26] - **Evaluation**: The factor demonstrates strong predictive power for stock returns across different market segments, with positive IC values dominating [30][32] LLT Trend Factor - **Factor Name**: LLT Trend Factor - **Construction Idea**: To address the lagging sensitivity of MA, the LLT (Low-Lag Trendline) indicator is used as a replacement. LLT reduces delay and better captures momentum and reversal effects [14][76] - **Construction Process**: - LLT is calculated using a second-order linear filter with the recursive formula: \[ LLT = \begin{cases} P(T), & T=1,2 \\ (2-2\alpha)LLT(T-1) - (1-\alpha)^2LLT(T-2) + \left(\alpha-\frac{\alpha^2}{4}\right)P(T) \\ + \left(\frac{\alpha^2}{2}\right)P(T-1) - \left(\alpha-\frac{3}{4}\alpha^2\right)P(T-2), & \text{else} \end{cases} \] where \( \alpha = \frac{2}{1+N} \) and \( N \) is the smoothing parameter [76] - Replace MA with LLT in the multi-horizon factor construction process [76] - **Evaluation**: LLT-based factors outperform MA-based factors in terms of IC mean, positive IC ratio, and predictive power for asset returns [82][84] --- Backtesting Results Multi-Horizon Factor - **Annualized Return**: 25.40% [3][48] - **Annualized Volatility**: 14.12% [48] - **Maximum Drawdown**: 13.31% [48] - **IR**: 1.81 [48] LLT Trend Factor - **Annualized Return**: 29.58% [4][103] - **Annualized Volatility**: 10.46% [103] - **Maximum Drawdown**: 11.57% [103] - **IR**: 2.51 [103]