量化周报 1． 资产配置月报 202602：如何衡量黄金的交 易拥挤度？-2026/02/06 2． 量化大势研判 202602：市场△gf 继续保持 扩张-2026/02/04 3． 量化周报：流动性转为下行趋势- 2026/02/01 4． 社融预测月报：2026 年 1 月社融预测： 74432 亿元-2026/02/01 5． 量化专题报告：从基金视角把握"主题" 到"主线"的机会-2026/01/29 市场再次触及阻力线 glmszqdatemark 2026 年 03 月 01 日 [Table_Author] | 叶尔乐 | 分析师 | | --- | --- | | 执业证书： S0590525110059 | | | yeerle@glms.com.cn | 邮箱： | | 关舒丹 | 分析师 | | 执业证书： S0590525110060 | | | guanshudan@glms.com.cn | 邮箱： | | 祝子涵 | 分析师 | | 执业证书： S0590525110061 | | | zhuzihan@glms.com.cn | 邮箱： | | 裴钰琪 | 研究助理 | | ...

港股财务数据处理六问及因子复现手册 glmszqdatemark 2026 年 01 月 25 日 [Table_Author] | 分析师 | 叶尔乐 | | --- | --- | | 执业证书： S0590525110059 | | | 邮箱： | yeerle@glms.com.cn | | 研究助理 | 裴钰琪 | | 执业证书： S0590125110081 | | | 邮箱： | peiyuqi@glms.com.cn | 量化专题报告 相关研究 本公司具备证券投资咨询业务资格，请务必阅读最后一页免责声明 证券研究报告 1 港股与 A 股市场特征存在显著差异，直接影响因子有效性逻辑。本报告聚焦港股 因子投资体系，构建并测试基本面与价量两类核心因子，同时解决财务数据处理 关键问题。港股以机构投资者为主，实行 T+0 交易且无涨跌幅限制，低估值、高 股息标的更受青睐，而 A 股个人投资者占比高，成长股估值弹性更强。报告选取 港股通与全港股（剔除仙股）两个股票池，采用月频调仓、市值行业中性化处理， 回测区间分别为 2014 年 11 月至 2025 年 12 月、2012 年 12 月至 2025 年 ...

Group 1: Market Index and Style Performance Review - Major broad market indices mostly increased this week, with the CSI 500 rising by 2.18%, the Northbound 50 by 1.58%, and the CSI 1000 by 1.27%. However, the Shanghai Composite Index fell by 0.45% and the CSI 300 by 0.57% [2][10]. - Over the past month, all major broad market indices have risen, with the CSI 500 up by 17.59% and the CSI 1000 by 14.64% [10][11]. - In terms of industry performance, sectors such as computer, electronics, media, non-ferrous metals, and machinery performed well, while defense, agriculture, coal, real estate, and non-bank financials lagged behind [14][16]. Group 2: Factor Performance Tracking - In the CSI 300 stock pool, factors such as the 20-day volume variation coefficient, standardized unexpected earnings, and overnight momentum before earnings announcements performed well this week [3][24]. - In the CSI 500 stock pool, the 60-day specificity, 20-day specificity, and 60-day momentum factors showed strong performance [3][26]. - The overall market stock pool saw strong performance from quarterly ROA, quarterly ROE, and quarterly net profit margin factors [3][22]. Group 3: Quantitative Fund Performance - The average excess return for CSI 300 index-enhanced products was 0.58%, while the CSI 500 index-enhanced products had an average excess return of -0.26% [4][12]. - The best-performing active quantitative fund this week was Huian Quantitative Preferred A [4][12]. Group 4: Quantitative Index Enhancement Portfolio Tracking - The CSI 300 index enhancement portfolio achieved an excess return of 0.24% over the past week, while the CSI 500 index enhancement portfolio had an excess return of 0.27% [5][12]. - The CSI 800 index enhancement portfolio recorded an excess return of 0.59% [5].

Core Viewpoint - The report highlights the performance of various market factors and investment strategies over the week of January 5 to January 9, 2026, indicating a mixed performance across different factors and sectors, with notable trends in momentum and valuation factors [4][5][6]. Factor Performance - Major factors such as beta, residual volatility, and size factors yielded positive returns of 1.07%, 1.02%, and 0.59% respectively, while the momentum factor showed a significant negative return of -1.08% [4]. - In the CSI 300 stock pool, the best-performing factors included 5-day average turnover rate (4.90%), relative turnover volatility (4.59%), and quarterly revenue growth rate (3.92%), while the worst performers were momentum-adjusted large orders (-1.11%), ROA stability (-1.15%), and ROE stability (-1.43%) [5]. - In the CSI 500 stock pool, the top factors were gross margin TTM (1.29%), quarterly net profit growth rate (1.09%), and total asset growth rate (0.81%), with the worst being price-to-book ratio (-3.51%), TTM price-to-earnings ratio inverse (-4.06%), and price-to-earnings ratio (-4.69%) [5]. - In the liquidity 1500 stock pool, the best factors were gross margin TTM (2.17%), quarterly revenue growth rate (2.14%), and quarterly operating profit growth rate (1.85%), while the worst were the correlation of intraday volatility with transaction amount (-2.64%), price-to-earnings ratio (-3.01%), and TTM price-to-earnings ratio inverse (-3.18%) [5]. Industry Factor Performance - The net asset growth rate factor performed well in the non-bank financial and diversified sectors, while the net profit growth rate factor excelled in the diversified sector [6]. - The per-share net asset factor showed strong performance in the real estate and beauty care sectors, and the per-share operating profit TTM factor performed well in the diversified sector [6]. - The 5-day momentum factor exhibited momentum effects in media, communication, steel, and pharmaceutical sectors, while showing reversal effects in coal and agriculture sectors [6]. - Valuation factors like BP performed well in real estate and leisure services, while EP performed well in banking and non-bank financial sectors [7]. Investment Strategy Performance - The PB-ROE-50 combination achieved significant excess returns in the CSI 800 and overall market stock pools, with excess returns of 1.36% in the CSI 800 and 1.23% in the overall market, but a negative excess return of -2.18% in the CSI 500 stock pool [8]. - The private equity research tracking strategy generated positive excess returns, while the public equity research stock selection strategy had a relative excess return of -0.31% compared to the CSI 800 [9]. - The block trading combination achieved an excess return of 0.69% relative to the CSI All Index [10]. - The targeted issuance combination experienced a pullback in excess returns, with a relative excess return of -1.58% compared to the CSI All Index [11].

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Portfolio - **Model Construction Idea**: The PB-ROE-50 portfolio is constructed based on the Price-to-Book (PB) ratio and Return on Equity (ROE) metrics, aiming to identify stocks with favorable valuation and profitability characteristics[24] - **Model Construction Process**: - Stocks are selected from the target stock pool (e.g., CSI 800, CSI 500, or the entire market) - The selection criteria prioritize stocks with low PB ratios and high ROE values - The portfolio is rebalanced periodically to maintain the desired characteristics[24][25] - **Model Evaluation**: The PB-ROE-50 portfolio demonstrates significant excess returns in certain stock pools, indicating its effectiveness in capturing valuation and profitability factors[24] 2. Model Name: Block Trade Portfolio - **Model Construction Idea**: This portfolio leverages the information embedded in block trades, focusing on stocks with high block trade transaction ratios and low short-term volatility[31] - **Model Construction Process**: - Stocks with high "block trade transaction ratios" and low "6-day transaction amount volatility" are identified - A monthly rebalancing strategy is applied to construct the portfolio - The methodology is detailed in a prior report dated August 5, 2023[31] - **Model Evaluation**: The portfolio effectively captures the excess information embedded in block trades, as evidenced by its positive performance[31] 3. Model Name: Private Placement Portfolio - **Model Construction Idea**: This portfolio is based on the event-driven strategy of private placements, considering factors such as market capitalization, rebalancing cycles, and position control[37] - **Model Construction Process**: - Stocks involved in private placements are selected, with the shareholder meeting announcement date serving as the event trigger - The portfolio construction incorporates market capitalization adjustments and periodic rebalancing - The methodology is detailed in a prior report dated November 26, 2023[37] - **Model Evaluation**: The portfolio's performance reflects the potential of private placement events to generate excess returns, though it experienced a drawdown in the current week[37] --- Model Backtesting Results 1. PB-ROE-50 Portfolio - **Excess Return (CSI 500)**: -2.18% (weekly)[25] - **Excess Return (CSI 800)**: 1.36% (weekly)[25] - **Excess Return (Entire Market)**: 1.23% (weekly)[25] 2. Block Trade Portfolio - **Excess Return (CSI All Share Index)**: 0.69% (weekly)[32] 3. Private Placement Portfolio - **Excess Return (CSI All Share Index)**: -1.58% (weekly)[38] --- Quantitative Factors and Construction Methods 1. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market movements[20] - **Factor Construction Process**: - Calculated as the covariance of a stock's returns with the market index, divided by the variance of the market index - Formula: $ \beta = \frac{\text{Cov}(R_i, R_m)}{\text{Var}(R_m)} $ where $R_i$ is the stock return, and $R_m$ is the market return[20] - **Factor Evaluation**: The beta factor delivered a weekly return of 1.07%, indicating its positive contribution during the observed period[20] 2. Factor Name: Residual Volatility Factor - **Factor Construction Idea**: Captures the idiosyncratic risk of a stock, independent of market movements[20] - **Factor Construction Process**: - Residual volatility is derived from the standard deviation of the residuals in a stock's regression against the market index - Formula: $ \sigma_{\text{residual}} = \sqrt{\frac{\sum (\epsilon_i^2)}{n-1}} $ where $\epsilon_i$ are the residuals from the regression[20] - **Factor Evaluation**: The residual volatility factor achieved a weekly return of 1.02%, reflecting its effectiveness in the current market environment[20] 3. Factor Name: Size Factor - **Factor Construction Idea**: Reflects the performance difference between small-cap and large-cap stocks[20] - **Factor Construction Process**: - Calculated as the natural logarithm of a stock's market capitalization - Formula: $ \text{Size} = \ln(\text{Market Cap}) $[20] - **Factor Evaluation**: The size factor delivered a weekly return of 0.59%, indicating the dominance of large-cap stocks during the period[20] 4. Factor Name: Momentum Factor - **Factor Construction Idea**: Measures the tendency of stocks with high past returns to continue performing well in the future[20] - **Factor Construction Process**: - Calculated as the cumulative return over a specified look-back period (e.g., 6 months or 12 months) - Formula: $ \text{Momentum} = \prod_{t=1}^{T} (1 + R_t) - 1 $ where $R_t$ is the daily return, and $T$ is the look-back period[20] - **Factor Evaluation**: The momentum factor experienced a significant negative return of -1.08%, indicating a reversal effect during the week[20] --- Factor Backtesting Results 1. Beta Factor - **Weekly Return**: 1.07%[20] 2. Residual Volatility Factor - **Weekly Return**: 1.02%[20] 3. Size Factor - **Weekly Return**: 0.59%[20] 4. Momentum Factor - **Weekly Return**: -1.08%[20]

Core Viewpoint - The report provides a comprehensive analysis of market performance, highlighting the positive and negative returns of various factors and strategies within different stock pools during the specified week [4][5][8]. Group 1: Factor Performance - In the large-cap market, beta, size, and non-linear market capitalization factors yielded positive returns of 1.31%, 0.62%, and 0.58% respectively, while the leverage factor had a negative return of -0.13% [4]. - In the CSI 300 stock pool, the best-performing factors included the early morning return factor (2.16%), year-on-year net profit growth rate (1.75%), and quarterly ROA year-on-year (1.68%), while the worst performers were large net inflow (-1.71%), price-to-book ratio factor (-1.83%), and downside volatility ratio (-2.05%) [5]. - In the CSI 500 stock pool, the top factors were quarterly operating profit growth rate (1.16%), quarterly net profit growth rate (1.11%), and standardized unexpected earnings (1.08%), with the price-to-earnings ratio factor (-2.74%), total asset gross margin TTM (-2.92%), and price-to-book ratio factor (-2.95%) performing poorly [5]. Group 2: Industry Factor Performance - The net asset growth rate factor performed well in the comprehensive and oil & petrochemical industries, while the net profit growth rate factor excelled in the comprehensive industry [6]. - The earnings per share factor showed strong performance in the oil & petrochemical and real estate sectors, and the TTM operating profit factor was notable in the environmental protection industry [6]. - The 5-day momentum factor exhibited momentum effects in the oil & petrochemical and public utilities sectors, while showing reversal effects in the beauty care, leisure services, and food & beverage industries [6][7]. Group 3: Strategy Performance - The PB-ROE-50 combination achieved significant excess returns, with a 1.31% excess return in the CSI 800 stock pool and a 1.36% excess return in the overall market stock pool, while it recorded a -0.62% excess return in the CSI 500 stock pool [8]. - Public and private fund research selection strategies yielded positive excess returns, with public fund strategies achieving a 1.88% excess return relative to the CSI 800 and private fund strategies achieving a 2.14% excess return [9]. - The block trading combination experienced a decline in excess returns, with a -1.94% excess return relative to the CSI All Index [10]. - The targeted issuance combination also faced a decline, with a -1.79% excess return relative to the CSI All Index [11].

Core Viewpoint - The market shows mixed performance with the Shanghai Composite Index rising while the ChiNext Index experiences a pullback, amidst a focus on the performance of the Dividend Low Volatility ETF (512890) which leads its category in trading volume and price stability [1][6] Market Performance - On December 25, the Dividend Low Volatility ETF (512890) increased by 0.08% to a price of 1.178 yuan, with a turnover rate of 0.92% and a trading volume of 246 million yuan, making it the top performer among similar ETFs [1][6] - Over the past five trading days, the ETF has seen a net inflow of 930 million yuan, with a total of 1.72 billion yuan over the last ten days, and 5.81 billion yuan over the last sixty days, indicating strong investor interest [2][7] Institutional Insights - Shenwan Hongyuan Securities anticipates the initiation of a spring market rally in 2026, highlighting that while key sectors like AI may have limited upward potential, alternative themes such as policy-driven investments and high dividend strategies are expected to be active [4][9] - The firm notes two positive short-term factors: sustained liquidity in the spring market and a favorable policy window in the coming months, which historically supports market rebounds before the Spring Festival [4][9] - Open Source Securities emphasizes that "technology first" will be the strongest theme in the upcoming bull market, with a shift towards factor investing focusing on profit growth and return on equity (ROE) [4][9] ETF Historical Performance - The Dividend Low Volatility ETF (512890) was established in December 2018 and has shown robust historical performance, achieving a return of 135.06% as of December 24, 2025, significantly outperforming its benchmark and ranking 80th among 502 similar products [5][10] - The ETF is positioned as a stable investment tool for volatile markets, with recommendations for investors to consider regular investment strategies to mitigate risk [5][10]

Group 1 - The core viewpoint of the article emphasizes that "technology first" is the strongest theme driving the current bull market, supported by three long-term advantageous conditions: (1) relative profit advantage; (2) overseas mapping; (3) global semiconductor cycle resonance upward [1] - The "profit recovery" slow bull market presents cyclical opportunities primarily focused on PPI, with two key indicators providing leading signals for the marginal recovery of PPI [1] - The performance of dividend styles is expected to surpass that of 2025 in 2026, indicating a shift from valuation-driven bull markets to slow bull markets, transitioning from industry Beta investments to factor investments [1] Group 2 - In 2026, it is crucial to focus on factors with the strongest effectiveness in performance periods, particularly the profit factor: marginal changes in profit growth (△g), profit growth, and revenue growth [1] - From an annual effectiveness perspective, revenue growth, profit growth, ROE, and ROIC are identified as the most effective profit factors [1]

Group 1 - The core viewpoint of the article emphasizes the growing importance of index investment and the need for investors to establish a robust allocation framework amidst style rotation [1] - The development of index investment in China has significantly progressed, with ETFs becoming mainstream investment tools, surpassing active funds in holdings as of Q3 2024 [4][5] - The total scale of ETFs in China reached approximately 5 trillion yuan, with stock ETFs accounting for around 4 trillion yuan, representing about 3% of the total A-share market capitalization [4] Group 2 - The article discusses the increasing market differentiation, highlighting the performance of the ChiNext index compared to traditional large-cap indices, suggesting that investors should consider using style factor indices to enhance returns [6][8] - Style factor indices, which blend active and passive investment strategies, can provide higher excess returns by breaking the limitations of traditional market-cap-weighted indices [7][8] - The analysis of over 1,000 ETFs indicates that style factor indices exhibit superior mean and variance performance, suggesting better risk-adjusted returns [7] Group 3 - The article outlines a simple and practical configuration logic for utilizing style factors, emphasizing the importance of optimizing stock selection logic and avoiding pitfalls like value traps [9][10] - A recommended strategy for multi-factor combinations is the "constant proportion rebalancing" approach, which can potentially outperform the CSI 300 index through systematic adjustments [10] - The complexity behind index investment is acknowledged, with a focus on the intricate stock selection logic and asset allocation strategies that can lead to excess returns [10] Group 4 - Looking ahead, the article posits that China's capital market has entered a phase of high-quality development in index investment, driven by the maturation of market participants and the application of AI technology [11] - Continuous policy support is expected to enhance market vitality and attract more investors to index investment, particularly in the ETF market [11] - The article aims to encourage a deeper understanding of style factor indices among investors, promoting the construction of resilient investment portfolios in the evolving ETF era [12]

Core Insights - The article discusses the increasing importance of index investment strategies and how to build a robust allocation framework amid style rotation, as highlighted by Lin Weibin, General Manager of the Index Investment Department at E Fund [1] Group 1: Industry Trends - Lin Weibin predicts that the next decade will be a golden period for ETF development in China, estimating that if the total market capitalization of A-shares achieves a 5% annual growth rate, it could reach 200 trillion yuan by 2035 [1] - He references the U.S. market's 10% ETF penetration rate, suggesting that the scale of stock ETFs in China could exceed 20 trillion yuan, and with contributions from bonds, gold, and commodities, the overall ETF market could reach 30 trillion yuan, positioning it among the global leaders [1] Group 2: Style Factors and Investment Logic - Lin Weibin defines style factors as a middle ground between active and purely passive investment, aligning with the Smart Beta concept in overseas markets, which aims to achieve excess returns through clear, rule-based stock selection logic [1] - He emphasizes that the main domestic style factors include dividend, low volatility, growth, value, and quality [2] Group 3: Practical Application of Style Factors - For single-factor usage, Lin suggests optimizing stock selection logic, such as avoiding the value trap by excluding stocks with unstable or negative ROE, and focusing on high dividend and free cash flow indicators [2] - In multi-factor portfolio configuration, he recommends a "constant proportion rebalancing" strategy, such as a 60% value and 40% growth mix, to outperform the CSI 300 index through regular adjustments [2] Group 4: Future of Index Investment - Lin asserts that index investment is not a "fool's investment," as it involves complex stock selection logic and asset allocation strategies [3] - He believes that China's capital market has entered a high-quality development phase for index investment, with participants evolving from simple beta investments to more complex factor investing and multi-asset allocations, further enhanced by AI technology [3]