Core Insights - The dividend index has shown varying performance over the years, outperforming the market from 2016 to 2018 and again from 2022 to 2024, while underperforming from 2019 to 2021 and expected to underperform in 2025 [2]. Group 1 - The A-share market exhibits a characteristic of style rotation, frequently switching between growth and value styles [3]. - A long-term effective strategy may not always yield consistent results, as strategies can become ineffective over two to three years [3]. - Average holding periods for stock funds among general investors are typically only a few weeks, which can lead to suboptimal investment decisions [3].

Group 1: Small and Large Cap Rotation Strategy - The report suggests an overweight position in small-cap stocks for February, based on a quantitative model signal of 0.5 indicating a preference for small-cap [1] - Historically, small-cap stocks have outperformed in February, supporting the recommendation for an overweight allocation [1] - The current market capitalization factor valuation spread is at 0.88, which is below the historical peak range of 1.7 to 2.6, indicating that the market is not overcrowded for small-cap stocks [1] - As of the end of January, the model has achieved a return of 8.16%, outperforming the equal-weight benchmark return of 4.91% by 3.26% [1] Group 2: Value and Growth Style Rotation Strategy - The quantitative model signal for January was 0, suggesting an equal-weight allocation between growth and value styles for February [2] - The model's return as of the end of January was 4.01%, with no excess return compared to the equal-weight benchmark [2] - The long-term outlook favors growth style for the upcoming year [2] Group 3: Style Factor Performance Tracking - Among eight major factors, value and volatility factors showed high positive returns in January, while large-cap and quality factors exhibited negative returns [2] - In January, beta, long-term reversal, and mid-cap factors had high positive returns, whereas large-cap, residual volatility, and industry momentum factors had negative returns [2] - Year-to-date, the same trends in factor performance are observed, with positive returns for beta, long-term reversal, and mid-cap factors, and negative returns for large-cap, residual volatility, and industry momentum factors [2]

Market Overview - The A-share market exhibited a structurally differentiated pattern last week, with major broad indices generally under pressure. The Shanghai Composite Index closed at 4117.95 points, down 0.44% for the week [2] - The ChiNext Index and the North Star 50 both fell over 3.5%, indicating a significant pullback in growth sectors. In contrast, value styles performed relatively strongly, with the Shanghai 50 rising 1.13% and the CSI Dividend Index increasing by 1.58%, reflecting a trend of capital flowing towards undervalued, high-dividend assets [2] - Overall, the market continued to experience style switching amid high-level fluctuations, with value sectors showing relative resilience while growth sectors faced adjustment pressures [2] Industry Performance - Last week, most of the Wind first-level industries declined, with the energy sector leading the gains at 6.4%, benefiting from rising oil prices and geopolitical risk premiums. Defensive sectors such as daily consumption and finance also performed relatively well [10] - In contrast, sectors like discretionary consumption, industrials, and healthcare experienced notable declines, each falling over 3% due to weak demand and pre-holiday risk aversion [10] Fund Issuance - A total of 47 funds were issued last week, including 19 equity funds, 21 mixed funds, 4 bond funds, 2 QDII funds, and 1 FOF fund, with a total fundraising amount of 48.272 billion units [14] Fund Performance - The Wind All-Fund Index fell by 0.49% last week, with ordinary equity funds down 1.07% and equity-mixed funds down 0.76%. Bond funds experienced a slight decline of 0.09%, indicating overall pressure on fund performance, particularly in equity categories [6] Global Market Overview - Global markets showed significant differentiation last week. Among the three major U.S. indices, the S&P 500 rose slightly by 0.34%, while the Dow Jones fell by 0.42% and the Nasdaq dipped by 0.17%. European markets saw declines in France's CAC40 and Germany's DAX by 0.20% and 1.45%, respectively, while the UK's FTSE 100 rose by 0.79% [4] - In Asia, the Nikkei 225 fell by 0.97%, while the Hang Seng Index rose by 2.38%, and the Korean Composite Index surged by 4.70%, becoming the best-performing index globally [4] Bond Market Overview - The convertible bond index fell by 2.61% last week, while the 10-year government bond futures rose slightly by 0.10%. The 30-year main contract dropped by 0.33%, reflecting a defensive positioning by investors amid unclear policy expectations and rising credit risk concerns [12]

Quantitative Models and Construction Methods 1. **Model Name**: CSI 300 Enhanced Portfolio - **Model Construction Idea**: The model aims to achieve excess returns over the CSI 300 Index by leveraging quantitative strategies and factor-based stock selection - **Model Construction Process**: The model is constructed by selecting stocks from the CSI 300 Index based on specific quantitative factors and optimizing the portfolio to maximize excess returns while managing risk. The exact factors and optimization techniques are not detailed in the report - **Model Evaluation**: The model has shown consistent performance in generating excess returns over the CSI 300 Index in the year-to-date period[5][9][15] 2. **Model Name**: CSI 500 Enhanced Portfolio - **Model Construction Idea**: The model seeks to outperform the CSI 500 Index by utilizing quantitative strategies and factor-based stock selection - **Model Construction Process**: Stocks are selected from the CSI 500 Index based on quantitative factors, and the portfolio is optimized to achieve excess returns while controlling risk. Specific details of the factors and optimization are not provided in the report - **Model Evaluation**: The model's performance has been mixed, with negative excess returns in the year-to-date period[5][9][15] 3. **Model Name**: CSI 1000 Enhanced Portfolio - **Model Construction Idea**: The model aims to generate excess returns over the CSI 1000 Index through quantitative strategies and factor-based stock selection - **Model Construction Process**: Stocks are selected from the CSI 1000 Index using quantitative factors, and the portfolio is optimized to maximize excess returns while managing risk. Specific details of the factors and optimization are not provided in the report - **Model Evaluation**: The model has demonstrated positive excess returns in the year-to-date period[5][9][15] 4. **Model Name**: PB-Profit Combination Portfolio - **Model Construction Idea**: The portfolio combines price-to-book (PB) ratio and profitability factors to identify undervalued stocks with strong earnings potential - **Model Construction Process**: The portfolio is constructed by selecting stocks with low PB ratios and high profitability metrics. The exact methodology for combining these factors is not detailed in the report - **Model Evaluation**: The portfolio has shown strong performance, with significant positive excess returns over the CSI 300 Index in the year-to-date period[5][31][33] 5. **Model Name**: GARP Portfolio - **Model Construction Idea**: The portfolio follows the Growth at a Reasonable Price (GARP) strategy, focusing on stocks with a balance of growth and valuation metrics - **Model Construction Process**: Stocks are selected based on a combination of growth and valuation factors. The specific factors and their weights are not detailed in the report - **Model Evaluation**: The portfolio has achieved significant positive excess returns over the CSI 300 Index in the year-to-date period[5][35] 6. **Model Name**: Small-Cap Value Portfolio 1 - **Model Construction Idea**: The portfolio targets small-cap stocks with value characteristics, aiming to outperform the micro-cap index - **Model Construction Process**: Stocks are selected based on small-cap and value factors. The exact methodology for combining these factors is not detailed in the report - **Model Evaluation**: The portfolio has underperformed the micro-cap index in the year-to-date period[5][37] 7. **Model Name**: Small-Cap Value Portfolio 2 - **Model Construction Idea**: Similar to Small-Cap Value Portfolio 1, this portfolio focuses on small-cap stocks with value characteristics - **Model Construction Process**: Stocks are selected based on small-cap and value factors. The exact methodology for combining these factors is not detailed in the report - **Model Evaluation**: The portfolio has outperformed the micro-cap index in the year-to-date period[5][39] 8. **Model Name**: Small-Cap Growth Portfolio - **Model Construction Idea**: The portfolio targets small-cap stocks with growth characteristics, aiming to outperform the micro-cap index - **Model Construction Process**: Stocks are selected based on small-cap and growth factors. The exact methodology for combining these factors is not detailed in the report - **Model Evaluation**: The portfolio has underperformed the micro-cap index in the year-to-date period[5][41] --- Model Backtesting Results 1. **CSI 300 Enhanced Portfolio** - Weekly return: -0.39% - Weekly excess return: -0.47% - Year-to-date return: 6.85% - Year-to-date excess return: 5.20%[9][15] 2. **CSI 500 Enhanced Portfolio** - Weekly return: -1.74% - Weekly excess return: 0.82% - Year-to-date return: 11.11% - Year-to-date excess return: -1.01%[9][15] 3. **CSI 1000 Enhanced Portfolio** - Weekly return: -0.97% - Weekly excess return: 1.58% - Year-to-date return: 11.99% - Year-to-date excess return: 3.31%[9][15] 4. **PB-Profit Combination Portfolio** - Weekly return: 0.92% - Weekly excess return: 0.84% - Year-to-date return: 6.17% - Year-to-date excess return: 4.52%[31][33] 5. **GARP Portfolio** - Weekly return: 0.95% - Weekly excess return: 0.87% - Year-to-date return: 11.43% - Year-to-date excess return: 9.78%[35] 6. **Small-Cap Value Portfolio 1** - Weekly return: -2.44% - Weekly excess return: -1.29% - Year-to-date return: 7.89% - Year-to-date excess return: -2.83%[37] 7. **Small-Cap Value Portfolio 2** - Weekly return: -1.64% - Weekly excess return: -0.48% - Year-to-date return: 12.37% - Year-to-date excess return: 1.66%[39] 8. **Small-Cap Growth Portfolio** - Weekly return: -2.07% - Weekly excess return: -0.92% - Year-to-date return: 9.13% - Year-to-date excess return: -1.59%[41] --- Quantitative Factors and Construction Methods 1. **Factor Name**: Market Capitalization (Size) Factor - **Construction Idea**: Small-cap stocks tend to outperform large-cap stocks over time - **Construction Process**: Stocks are ranked by market capitalization, and the top 10% (smallest) and bottom 10% (largest) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance - **Evaluation**: The factor has shown mixed performance across different indices and time periods[43][44][45] 2. **Factor Name**: Price-to-Book (PB) Factor - **Construction Idea**: Low PB stocks are expected to outperform high PB stocks - **Construction Process**: Stocks are ranked by PB ratio, and the top 10% (lowest PB) and bottom 10% (highest PB) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance - **Evaluation**: The factor has shown strong performance in the short term but mixed results in the year-to-date period[43][44][45] 3. **Factor Name**: Price-to-Earnings (PE_TTM) Factor - **Construction Idea**: Low PE stocks are expected to outperform high PE stocks - **Construction Process**: Stocks are ranked by PE ratio, and the top 10% (lowest PE) and bottom 10% (highest PE) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance - **Evaluation**: The factor has shown positive short-term performance but mixed year-to-date results[43][44][45] 4. **Factor Name**: Reversal Factor - **Construction Idea**: Stocks with recent underperformance are expected to outperform in the short term - **Construction Process**: Stocks are ranked by recent performance, and the top 10% (worst performers) and bottom 10% (best performers) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance - **Evaluation**: The factor has shown positive short-term performance but negative year-to-date results[49][50] 5. **Factor Name**: Turnover Factor - **Construction Idea**: Stocks with lower turnover rates are expected to outperform those with higher turnover rates - **Construction Process**: Stocks are ranked by turnover rate, and the top 10% (lowest turnover) and bottom 10% (highest turnover) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance - **Evaluation**: The factor has shown strong short-term performance but negative year-to-date results[49][50] 6. **Factor Name**: Volatility Factor - **Construction Idea**

Group 1 - The overall change in public fund holdings shows an increase in allocation to non-ferrous metals and communications, while reducing allocation to electronics and pharmaceuticals. The top five industries with increased holdings are non-ferrous metals (up 2.1 percentage points), communications (1.8 percentage points), non-bank financials (0.9 percentage points), chemicals (0.8 percentage points), and machinery (0.7 percentage points). The top five industries with reduced holdings are electronics (-1.6 percentage points), pharmaceuticals (-1.6 percentage points), media (-1.2 percentage points), electric new energy (-0.9 percentage points), and computers (-0.8 percentage points) [1][8][12] Group 2 - The report categorizes funds into four types: increasing, decreasing, adjusting, and extreme style. The increasing funds focus on growth style, adding positions in industrial metals, military electronics, and photovoltaic equipment, while reducing positions in batteries, digital media, and social networks. Decreasing funds are shifting from growth to value, adding positions in components, liquor, and coal mining, while reducing positions in communication equipment, semiconductors, and passenger vehicles. Adjusting funds show a balanced configuration, adding positions in semiconductors, industrial metals, and insurance, while reducing positions in consumer electronics, batteries, and state-owned banks. Extreme style funds make internal adjustments within their styles, adding communication equipment and renovation materials while reducing consumer electronics and bioproducts [7][15][16] Group 3 - The report highlights that the consensus for selling includes bioproducts, internet e-commerce, consumer electronics, social media, batteries, and digital media, while the consensus for buying includes insurance, securities, chemical products, components, photovoltaic equipment, and industrial metals [15][16][18] Group 4 - The analysis indicates that increasing funds prefer large-cap and high-valuation stocks, while decreasing and adjusting funds focus on both growth and profitability. Extreme growth funds tend to hold small-cap, high-valuation stocks with pressured profitability, while extreme value funds focus on low-valuation, large-cap stocks with low earnings growth [7][18][25]

红利国企ETF（510720）跟踪的是上国红利指数（000151），该指数从市场中筛选具备高分红能力与稳 定分红记录的优质企业，覆盖银行、煤炭、交通运输等行业，重点聚焦传统高股息领域。指数通过严格 考察成分股的股息率和分红持续性，并采用跨行业分散配置策略，以有效控制投资风险，反映高股息企 业的整体市场表现。根据基金公告，红利国企ETF可月月评估分红，在上市后的每个月都做到了分红。 1月29日，红利国企ETF（510720）收涨超1.6%，机构称价值风格或占优。 注：分红情况具体详见基金分红公告，基金分红规则以基金法律文件为准，鉴于本基金的特点，本基金 分红不一定来自基金盈利，基金分红并不代表总投资的正回报。提及个股仅用于行业事件分析，不构成 任何个股推荐或投资建议。指数等短期涨跌仅供参考，不代表其未来表现，亦不构成对基金业绩的承诺 或保证。观点可能随市场环境变化而调整，不构成投资建议或承诺。提及基金风险收益特征各不相同， 敬请投资者仔细阅读基金法律文件，充分了解产品要素、风险等级及收益分配原则，选择与自身风险承 受能力匹配的产品，谨慎投资。 平安证券指出，在低利率和中长期资金入市的环境下，红利资产仍具配置价值 ...

文 | 银行螺丝钉 （转载请注明出处） 今天大盘整体微涨，波动不大，截止到收盘，还在3.8星。 港股也整体上涨。 今天港股涨幅比A股还高。 沪深300、中证500等大中盘股上涨。 中证500今天上涨后，也达到了高估。 价值风格大幅上涨。 现金流指数、红利类指数大幅上涨。 这两周价值风格品种也开始补涨了。 低估品种总会有上涨的阶段～ 前两周咱们主动优选做了调仓，止盈了部分高估的成长风格，加仓了价值风格。 刚好匹配最近的市场风格。 今天几个组合整体上涨，主动、指数、 月薪宝 、 365 等都创下历史新高。 恒生指数上涨超2%。 恒生红利低波、港股科技等都上涨明显。 港股中很多也属于人民币类资产，只不过欧美投资者占比高一些。 一般老外看好人民币资产，考虑港股会多一些。 1. 去年螺丝钉也介绍过，这轮牛市的风格，跟2013-2017年比较相似。 （1）2013年，A股上市公司盈利下滑，A股整体也在5点几星的熊市。 （2）2014年下半年，开始了各种刺激政策。人民币利率也大幅下降。 在利率大幅下降的背景下，市场开始大幅上涨。 第一波领涨的品种是证券。 （3）2015年上半年，市场风格切换。第二波领涨的品种，变成了小盘、 ...

Group 1 - The core viewpoint of the news highlights a collective strength in sectors such as non-ferrous metals, chemicals, and coal, leading to an upward movement in related indices [1] - As of the midday close, the Guozheng Free Cash Flow Index increased by 2.5%, the Guozheng Value 100 Index rose by 1%, and the Guozheng Growth 100 Index saw a rise of 0.4% [1] Group 2 - The Growth ETF managed by E Fund tracks the Guozheng Growth 100 Index, which consists of 100 stocks with a strong growth style in the A-share market, with over 65% of its composition in the information technology and materials sectors, particularly with a high proportion in information technology [3] - The Value ETF also managed by E Fund tracks the Guozheng Value 100 Index, which is composed of 100 stocks with a strong value style in the A-share market, focusing on consumer discretionary and financial sectors [3]

Core Viewpoint - The article discusses the recognition of various stock indices by institutional investors and their suitability for ordinary investors, particularly beginners. It emphasizes the importance of diversified allocation and rebalancing in index investing [1]. Group 1: Common Stock Index Guidance - The rapid growth of index funds is noted, with projections indicating that by 2025, the total scale of index funds will exceed 5.5 trillion, making it the largest type of stock fund in China [4]. - The introduction of new indices, such as the China Securities A500 index fund launched in September 2024, which reached several hundred billion in scale within just over a year, highlights the increasing variety of index funds available [5]. - The article identifies common stock index guidance suitable for both institutional and ordinary investors, focusing on key indices that can serve as investment references [7][8]. Group 2: Public Fund Performance Benchmark Library - The establishment of a standardized "benchmark library" for public funds aims to address issues of vague performance benchmarks and inconsistent investment strategies among funds [12]. - The current public fund performance benchmark library includes a variety of stock indices, with 69 indices in the first category and 72 in the second category, focusing on strong market representation and high recognition [14]. - The first category includes widely recognized indices such as the CSI 300 and the CSI 500, which are essential for fund managers in developing actively managed funds [14][15]. Group 3: Personal Pension Accounts - The introduction of the personal pension system in 2022 allows individuals to voluntarily open accounts with a maximum annual contribution of 12,000 yuan, which can be deducted from taxable income [17]. - By the end of 2025, the number of pension index funds will expand to 91, covering 16 mainstream indices, indicating a growing focus on retirement investment options [19]. - The first batch of pension index funds includes 85 funds, emphasizing the importance of risk control for new investors [21]. Group 4: Constant Proportion Stock-Bond Indices - Constant proportion stock-bond indices are designed to maintain a fixed ratio of stocks and bonds, with periodic rebalancing to adhere to this ratio [23]. - These indices typically have a higher allocation to bonds, often exceeding 70%, and are characterized by a target risk strategy [28]. - The introduction of these indices aligns with the trend of multi-asset investment strategies, which may include stocks, bonds, and potentially other assets like gold in the future [24]. Group 5: Insurance Company Risk Factor Adjustments - In December 2025, regulatory adjustments reduced the risk factors for insurance companies investing in indices like the CSI 300 and the low-volatility dividend index, allowing for more capital to be allocated to these assets [32]. - The reduction in risk factors from 0.3 to 0.27 for the CSI 300 means that insurance companies can free up more funds for investment, enhancing their capacity to invest in stable assets [38][39]. - The implications of these adjustments are significant for ordinary investors, as they reflect a conservative investment approach focused on long-term value appreciation with manageable volatility [40]. Group 6: Suitable Indices for Beginner Investors - The article identifies the most frequently referenced indices in various guidance categories as suitable for beginner investors, primarily focusing on broad-based indices like the CSI 300 and CSI 500 [67]. - The recommended investment strategy for beginners includes a combination of broad-based indices and growth/value strategies, such as the leading strategy and dividend strategy [68]. - The article suggests that new investors can benefit from diversified exposure to both growth and value styles, which can enhance returns while managing risk [45].

Quantitative Models and Construction Methods - **Model Name**: Value Smart Beta Portfolio **Model Construction Idea**: The model is based on selecting stocks with low historical correlation and aims to achieve high beta elasticity and long-term stable excess returns[5] **Model Construction Process**: The Value Smart Beta Portfolio includes two sub-portfolios: Value 50 Portfolio and Value Balanced 50 Portfolio. These portfolios are constructed by selecting stocks that align with the value style and optimizing for beta elasticity and excess return stability. The detailed construction process is referenced in a prior report[5] **Model Evaluation**: The Value Balanced 50 Portfolio outperformed the Value 50 Portfolio in terms of weekly, monthly, and yearly returns, demonstrating its superior performance in capturing value style excess returns[3][6] - **Model Name**: Growth Smart Beta Portfolio **Model Construction Idea**: Similar to the value model, this portfolio focuses on stocks with low historical correlation, targeting high beta elasticity and stable excess returns in the growth style[5] **Model Construction Process**: The Growth Smart Beta Portfolio includes Growth 50 Portfolio and Growth Balanced 50 Portfolio. Stocks are selected based on growth style characteristics, and the portfolios are optimized for beta elasticity and excess return stability. The detailed methodology is referenced in a prior report[5] **Model Evaluation**: The Growth Balanced 50 Portfolio consistently outperformed the Growth 50 Portfolio, indicating its effectiveness in capturing growth style excess returns[3][6] - **Model Name**: Small-Cap Smart Beta Portfolio **Model Construction Idea**: This portfolio targets small-cap stocks with low historical correlation, aiming for high beta elasticity and stable excess returns[5] **Model Construction Process**: The Small-Cap Smart Beta Portfolio includes Small-Cap 50 Portfolio and Small-Cap Balanced 50 Portfolio. Stocks are selected based on small-cap style characteristics, and the portfolios are optimized for beta elasticity and excess return stability. The detailed methodology is referenced in a prior report[5] **Model Evaluation**: The Small-Cap Balanced 50 Portfolio outperformed the Small-Cap 50 Portfolio, showcasing its ability to capture small-cap style excess returns effectively[3][6] --- Model Backtesting Results - **Value Smart Beta Portfolio** - **Value 50 Portfolio**: Weekly return: 1.38%, Monthly return: -0.11%, Yearly return: -0.11%, Maximum relative drawdown: 2.43%[6] - **Value Balanced 50 Portfolio**: Weekly return: 4.59%, Monthly return: 8.27%, Yearly return: 8.27%, Maximum relative drawdown: 0.56%[6] - **Growth Smart Beta Portfolio** - **Growth 50 Portfolio**: Weekly return: -0.37%, Monthly return: 5.13%, Yearly return: 5.13%, Maximum relative drawdown: 1.30%[6] - **Growth Balanced 50 Portfolio**: Weekly return: 3.79%, Monthly return: 10.01%, Yearly return: 10.01%, Maximum relative drawdown: 1.33%[6] - **Small-Cap Smart Beta Portfolio** - **Small-Cap 50 Portfolio**: Weekly return: 3.71%, Monthly return: 11.57%, Yearly return: 11.57%, Maximum relative drawdown: 3.08%[6] - **Small-Cap Balanced 50 Portfolio**: Weekly return: 4.27%, Monthly return: 12.88%, Yearly return: 12.88%, Maximum relative drawdown: 2.38%[6]