Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Combination - **Model Construction Idea**: The PB-ROE-50 combination is constructed based on the principle of selecting stocks with low price-to-book (PB) ratios and high return on equity (ROE), aiming to capture value and profitability factors[25] - **Model Construction Process**: - Stocks are selected based on their PB and ROE metrics - The portfolio is rebalanced periodically to maintain the desired exposure to these factors - The construction details are referenced in earlier reports[25][26] - **Model Evaluation**: The model experienced a drawdown in excess returns across all stock pools during the week, indicating potential short-term underperformance[25] --- Model Backtesting Results 1. PB-ROE-50 Combination - **Excess Return**: - CSI 500: -0.23% this week, 2.92% year-to-date - CSI 800: -0.98% this week, 15.82% year-to-date - Full Market: -1.39% this week, 18.21% year-to-date[26] - **Absolute Return**: - CSI 500: -1.49% this week, 30.06% year-to-date - CSI 800: -2.10% this week, 38.80% year-to-date - Full Market: -1.91% this week, 46.11% year-to-date[26] --- Quantitative Factors and Construction Methods 1. Factor Name: Residual Volatility Factor - **Factor Construction Idea**: Captures the residual volatility of stocks after controlling for market and sector effects, aiming to identify stocks with stable performance[20] - **Factor Construction Process**: - Calculate the residual volatility of stock returns after regressing against market and sector returns - Rank stocks based on their residual volatility and construct a portfolio with the desired exposure[20] - **Factor Evaluation**: The factor delivered positive returns this week, indicating its effectiveness in capturing stable stocks during the period[20] 2. Factor Name: Leverage Factor - **Factor Construction Idea**: Measures the financial leverage of companies, aiming to capture the risk-return tradeoff associated with leverage[20] - **Factor Construction Process**: - Calculate the leverage ratio of companies (e.g., debt-to-equity ratio) - Rank stocks based on their leverage and construct a portfolio with the desired exposure[20] - **Factor Evaluation**: The factor delivered positive returns this week, suggesting its relevance in the current market environment[20] 3. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market returns, aiming to capture systematic risk[20] - **Factor Construction Process**: - Calculate the beta of stocks using historical return data - Rank stocks based on their beta and construct a portfolio with the desired exposure[20] - **Factor Evaluation**: The factor delivered negative returns this week, indicating underperformance in the current market environment[20] 4. Factor Name: Size Factor - **Factor Construction Idea**: Captures the size effect by focusing on small-cap stocks, which tend to outperform large-cap stocks over time[20] - **Factor Construction Process**: - Rank stocks based on their market capitalization - Construct a portfolio with a tilt towards smaller-cap stocks[20] - **Factor Evaluation**: The factor delivered negative returns this week, despite the general preference for small-cap stocks in the market[20] 5. Factor Name: Momentum Factor - **Factor Construction Idea**: Captures the momentum effect by focusing on stocks with strong recent performance[20] - **Factor Construction Process**: - Calculate the past returns of stocks over a specific period (e.g., 6 months or 12 months) - Rank stocks based on their momentum and construct a portfolio with the desired exposure[20] - **Factor Evaluation**: The factor delivered negative returns this week, indicating a reversal effect in the market[20] --- Factor Backtesting Results 1. Residual Volatility Factor - Weekly Return: 0.50%[20] 2. Leverage Factor - Weekly Return: 0.36%[20] 3. Beta Factor - Weekly Return: -1.10%[20] 4. Size Factor - Weekly Return: -0.92%[20] 5. Momentum Factor - Weekly Return: -0.70%[20]

Group 1: Market Trends - The market is currently exhibiting a small-cap style, with valuation factors yielding a positive return of 0.40%, while market capitalization factors have negative returns of -0.72% and -0.40% respectively [4] - Momentum and Beta factors also showed negative returns of -0.79% and -0.43%, indicating a reversal effect in the market [4] - The large transaction portfolio achieved a positive excess return of 1.08% relative to the CSI All Share Index [4] Group 2: Fixed Income - The secondary market for publicly listed REITs in China has shown a downward trend, with the weighted REITs index closing at 182.3 and a weekly return of -0.48% [5] - In comparison to other major asset classes, the return rates ranked from high to low are: convertible bonds, crude oil, A-shares, pure bonds, gold, REITs, and US stocks [5] - Credit bonds issued totaled 334, with a total issuance scale of 363.4 billion yuan, reflecting a week-on-week decrease of 7.66% [5] - Industrial bonds accounted for 162 issues, with an issuance scale of 176.9 billion yuan, marking a week-on-week increase of 5.36% [5] Group 3: Oil and Chemical Industry - OPEC+ announced a production increase of 137,000 barrels per day in December, while suspending production increases from January to March 2026, which is expected to alleviate concerns over oil supply [6] - The ongoing geopolitical tensions, particularly the prolonged Russia-Ukraine conflict and increased sanctions on Russia, are likely to provide a risk premium for oil prices [6] Group 4: Basic Chemicals - Strong demand for energy storage and power batteries is tightening the supply-demand situation for iron phosphate, leading to improved prices and profitability for phosphate chemical companies [6] - Limited new capacity for phosphate rock in the short to medium term is expected to maintain high prices for high-grade phosphate rock, benefiting leading companies in the industry [6] Group 5: Semiconductor Industry - In Q3 2025, the company achieved a revenue of 635 million USD, reflecting a year-on-year increase of 20.7% and a quarter-on-quarter increase of 12.2%, driven by increased wafer shipments and ASP growth [7] - The revenue from 8-inch wafers was 259 million USD, showing a year-on-year decrease of 1.6% but a quarter-on-quarter increase of 11.4%, while 12-inch wafers generated 376 million USD, with a year-on-year increase of 43% and a quarter-on-quarter increase of 12.8% [7] Group 6: Healthcare Industry - The company's shareholder return plan has strengthened confidence, further solidifying its position as an industry leader [8] - The "Double Beauty + Double Health" business model has effectively built a high-quality membership system, while the acquisition of the second-largest brand in the industry, Nair, has improved its net profit margin from 6.5% to 10.4% in the first half of 2025 [8]

- **Quantitative factors tracked** - Single factor performance: In the CSI 300 stock pool, the best-performing factors this week include PE TTM inverse (3.05%), PE factor (2.30%), and PB factor (2.06%) [12][13] - In the CSI 500 stock pool, the best-performing factors include PE TTM inverse (2.71%), PB factor (2.07%), and PE factor (1.74%) [14][15] - In the liquidity 1500 stock pool, the best-performing factors include PE TTM inverse (1.74%), PE factor (1.68%), and PB factor (1.34%) [16][17] - **Sector-specific factor performance** - Fundamental factors such as net asset growth rate, net profit growth rate, per-share net asset factor, and per-share operating profit TTM factor achieved positive returns in the oil and petrochemical sector [21][22] - Valuation factors like BP factor performed well across most industries [21][22] - Residual volatility factor and liquidity factor showed significant positive returns in the comprehensive industry [21][22] - **Factor classification and market trends** - Broad market factor performance: Valuation factors achieved positive returns of 0.40%, while market capitalization factors and non-linear market capitalization factors recorded negative returns of -0.72% and -0.40%, respectively, indicating a small-cap style market trend [18][20] - Momentum factor and Beta factor recorded negative returns of -0.79% and -0.43%, respectively, reflecting a reversal effect in the market [18][20] - **Quantitative portfolio tracking** - PB-ROE-50 portfolio: This week, the portfolio achieved excess returns of 1.00% in the CSI 500 stock pool, 0.48% in the CSI 800 stock pool, and -2.00% in the broad market stock pool [23][24] - Institutional research portfolio: The public fund research stock selection strategy achieved excess returns of 0.00% relative to the CSI 800, while the private fund research tracking strategy recorded excess returns of -1.96% relative to the CSI 800 [25][26] - Block trading portfolio: Constructed based on the principle of "high transaction volume, low volatility," this portfolio achieved excess returns of 1.08% relative to the CSI All Share Index this week [29][30] - Private placement portfolio: Built around the event-driven strategy of targeted placements, this portfolio achieved excess returns of 1.93% relative to the CSI All Share Index this week [35][36] - **Performance metrics of quantitative portfolios** - PB-ROE-50 portfolio: Weekly excess return of 1.00% in CSI 500, 0.48% in CSI 800, and -2.00% in the broad market [24] - Institutional research portfolio: Weekly excess return of 0.00% for public fund research stock selection and -1.96% for private fund research tracking [26] - Block trading portfolio: Weekly excess return of 1.08% [30] - Private placement portfolio: Weekly excess return of 1.93% [36]

Group 1 - The core argument of the article emphasizes the importance of index composition adjustments, which serve as an internal elimination mechanism to ensure the vitality and health of the index over time [1][6] - The S&P 500 index has undergone significant changes since its inception, with 917 adjustments made to its constituent stocks from 1957 to 2003, averaging 20 changes per year [3][4] - Holding the original S&P 500 stocks from 1957 to 2003 yielded a higher return than the continuously updated index, with an initial investment of $1,000 growing to $157,029 at an annualized return of 11.40%, compared to $124,522 and 10.85% for the index [4][5] Group 2 - The article discusses the rationale behind the performance of newly added companies in the index, which tend to have better quality and growth potential, although they may be more expensive at the time of inclusion [6][7] - The index's methodology includes removing underperforming stocks, ensuring that the remaining constituents have good liquidity and stable performance [8][9] - The article highlights that the continuous updating of index constituents is crucial for maintaining the index's representativeness in the market [10] Group 3 - The article introduces the concept of dividend indices, which prioritize stocks with high dividend yields, contrasting with the S&P 500's focus on market capitalization and liquidity [11][12] - Historical data shows that the performance of the dividend index significantly outperforms that of its original constituents, with an initial investment of 100,000 yuan growing to 546,100 yuan at an annualized return of 13.03% compared to 135,300 yuan and 1.76% for the original stocks [14] - The core of dividend investing is to identify companies with sustainable high dividends, which can be assessed using expected dividend yields that factor in future earnings potential [15][16] Group 4 - The article mentions the establishment of the "CETC Central State-Owned Enterprise Dividend Index," which selects stocks based on expected dividend yields from central state-owned enterprises [16] - This index aims to reflect the overall performance of high expected dividend yield stocks among central state-owned enterprises, providing a new investment avenue for interested investors [16]

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]