Group 1 - The core viewpoint of the article emphasizes the long-term upward trend of stock indices, driven by corporate earnings growth, which ultimately supports index stability and growth [3][6]. - The Shanghai Composite Index has shown significant fluctuations over the years, with historical lows during bear markets, such as around 1,000 points in 2012-2014, 2,500 points in 2018, and around 3,000 points in recent years, indicating a gradual increase in the bear market bottom points [4][5]. - The article highlights that the CSI 300 and CSI 500 indices, which invest in both Shanghai and Shenzhen stocks, reflect a broader market trend, with the overall market index exceeding 4,000 points when including stocks from both exchanges [4][5]. Group 2 - The long-term growth of indices provides a solid foundation for investing in index funds, as it is primarily driven by corporate earnings growth, which is viewed as a "base salary" from the market [6]. - In the event of a bull market, there is potential for significant short-term valuation increases, allowing investors to benefit from both earnings growth and valuation expansion, described as "extra bonuses" from the market [6]. - The article notes that from 2004 to December 2025, the index has risen from 1,000 points to approximately 5,700 points, with dividends potentially pushing the total to 6,000-7,000 points, indicating a strong long-term performance of the market [5].

Quantitative Models and Construction Methods 1. Model Name: Broad-based Index Timing Strategy - **Model Construction Idea**: Utilize macroeconomic variable combinations to predict the future returns of the CSI All Share Index. The strategy involves making long or short decisions based on the predicted values exceeding a threshold[31][34]. - **Model Construction Process**: 1. Combine liquidity and inventory sub-strategies to predict whether the CSI All Share Index will rise. 2. If any predicted value exceeds the threshold (0.6), go long on the CSI All Share Index; otherwise, go short[31]. - **Model Evaluation**: The model effectively captures the impact of macroeconomic variables on the index, providing a systematic approach to timing[34]. 2. Model Name: Dividend Index Timing Strategy - **Model Construction Idea**: Use combinations of inflation and inventory, as well as inventory and credit, to predict the future returns of the Dividend Index. The strategy involves making long or short decisions based on the average predicted values exceeding a threshold[40]. - **Model Construction Process**: 1. Calculate the average predicted value of inflation + inventory and inventory + credit sub-strategies. 2. If the average exceeds the threshold (0.6), go long on the Dividend Index; otherwise, go short[40]. - **Model Evaluation**: The model demonstrates strong defensive characteristics of the Dividend Index, particularly under specific macroeconomic conditions[40]. 3. Model Name: Style Rotation Strategy - **Model Construction Idea**: Leverage macroeconomic factor combinations to predict the future returns of six style indices. Allocate capital to the top two indices with the highest predicted returns[49][54]. - **Model Construction Process**: 1. Use combinations of inflation + inventory and inflation + credit to predict the future returns of six style indices. 2. Smooth the predicted returns and rank them at the end of each month. 3. Allocate capital equally to the top two indices for the next month[54]. - **Model Evaluation**: The strategy effectively captures the differentiated impacts of macroeconomic factors on various styles, providing a robust framework for style rotation[49][54]. --- Model Backtesting Results 1. Broad-based Index Timing Strategy - **Annualized Return**: 15.34% - **Annualized Volatility**: 22.02% - **Sharpe Ratio**: 0.74 - **Maximum Drawdown**: -28.10% - **Excess Return**: 10.31% - **Tracking Error**: 34.16% - **IR**: 0.30 - **Relative Maximum Drawdown**: -50.30%[36]. 2. Dividend Index Timing Strategy - **Annualized Return**: 10.32% - **Annualized Volatility**: 13.74% - **Sharpe Ratio**: 0.75 - **Maximum Drawdown**: -19.92% - **Excess Return**: 7.97% - **Tracking Error**: 9.23% - **IR**: 0.86 - **Relative Maximum Drawdown**: -12.47%[42]. 3. Style Rotation Strategy - **Annualized Return**: 14.79% - **Annualized Volatility**: 21.81% - **Sharpe Ratio**: 0.64 - **Maximum Drawdown**: -45.93% - **Excess Return**: 4.61% - **Tracking Error**: 10.28% - **IR**: 0.52 - **Relative Maximum Drawdown**: -81.71%[59]. --- Quantitative Factors and Construction Methods 1. Factor Name: Macroeconomic Factor Variables - **Factor Construction Idea**: Select significant macroeconomic sub-variables through regression analysis and weight them inversely by their standard deviation over the past year. Use HP filter to adjust for short-term fluctuations and identify long-term trends[2]. - **Factor Construction Process**: 1. Perform regression of macroeconomic indices against broad-based indices and proxy macroeconomic variables. 2. Select sub-variables with significant t-values. 3. Weight the selected variables inversely by their past-year standard deviation. 4. Apply a one-sided HP filter to remove short-term noise and identify long-term trends[2]. - **Factor Evaluation**: The factor construction process effectively integrates macroeconomic trends and states, providing a comprehensive framework for understanding asset price drivers[2]. 2. Factor Name: High-dimensional Macroeconomic Variables - **Factor Construction Idea**: Combine marginal changes and states of macroeconomic variables to address inconsistencies in traditional macroeconomic factor transmission[2][8]. - **Factor Construction Process**: 1. Identify five dimensions of macroeconomic variables: economic prosperity, inflation, interest rates, inventory, and credit. 2. Combine marginal changes and time-series rankings of these variables to construct high-dimensional macroeconomic factors[9]. - **Factor Evaluation**: The high-dimensional approach improves the stability and predictive power of macroeconomic factors, addressing the limitations of single-dimensional indicators[8][9]. --- Factor Backtesting Results 1. Macroeconomic Factor Variables - **Liquidity (Up)**: 70.30% probability of index rise - **Liquidity (Down)**: 58.33% probability of index rise - **Inventory (Up)**: 65.84% probability of index rise - **Inventory (Down)**: 63.91% probability of index rise[37]. 2. High-dimensional Macroeconomic Variables - **Inflation (Up)**: 58.91% probability of index rise - **Inflation (Down)**: 67.33% probability of index rise - **Inventory (Up)**: 64.13% probability of index rise - **Inventory (Down)**: 63.91% probability of index rise[47].

- The report constructs macro factor variables by regressing macro indices on broad-based indices and proxy macro variables, selecting significant sub-macro variables, and weighting them by the inverse of the past year's standard deviation. The macroeconomic data is adjusted using a one-sided HP filter to eliminate short-term fluctuations and identify long-term trends. Based on the filtered variables, macro trends (upward, downward) are divided using factor momentum, and macro states (high, medium, low) are divided using time series percentiles[2] - The necessity of elevating macro factors is highlighted, as the price transmission of macro factor A to broad-based, style, and industry indices varies with different marginal changes of A. Additionally, the impact of macro factor A on returns is different under various states of macro factor B. The combination of marginal changes and states of macro variables is required to comprehensively consider the trend and time series ranking of macro variables[2] - The small-cap all-index timing strategy, based on a combination of macro variables, achieved an annualized return of 16.56% from the end of January 2012 to January 31, 2026, with an excess return of 10.19% relative to the CSI All Index[3] - The dividend index timing strategy, based on a combination of macro variables, achieved an annualized return of 10.97% from the end of January 2012 to January 31, 2026, with an excess return of 8.49% relative to the dividend index[3] - The style rotation strategy, based on a combination of macro variables, achieved an annualized return of 14.79% from September 30, 2014, to January 31, 2026, with an excess return of 4.61% relative to the equal-weighted style index[3] Model and Factor Construction 1. **Macro Factor Variables** - **Construction Idea**: Regress macro indices on broad-based indices and proxy macro variables, select significant sub-macro variables, and weight them by the inverse of the past year's standard deviation[2] - **Construction Process**: - Regress macro indices on broad-based indices and proxy macro variables - Select sub-macro variables with significant t-values - Weight selected variables by the inverse of the past year's standard deviation - Adjust macroeconomic data using a one-sided HP filter to eliminate short-term fluctuations - Divide macro trends using factor momentum and macro states using time series percentiles[2] - **Evaluation**: The necessity of elevating macro factors is highlighted due to the varying price transmission of macro factors under different marginal changes and states[2] 2. **Small-Cap All-Index Timing Strategy** - **Construction Idea**: Use a combination of macro variables to predict the highest returns when inventory is at a medium upward level[3] - **Construction Process**: - Combine macro variables to construct the timing strategy - Evaluate the strategy's performance from January 2012 to January 31, 2026[3] - **Evaluation**: The strategy achieved significant excess returns relative to the CSI All Index[3] 3. **Dividend Index Timing Strategy** - **Construction Idea**: Use a combination of macro variables to construct the dividend index allocation strategy[3] - **Construction Process**: - Combine macro variables to construct the timing strategy - Evaluate the strategy's performance from January 2012 to January 31, 2026[3] - **Evaluation**: The strategy achieved significant excess returns relative to the dividend index[3] 4. **Style Rotation Strategy** - **Construction Idea**: Use a combination of macro variables to construct the style rotation allocation strategy[3] - **Construction Process**: - Combine macro variables to construct the style rotation strategy - Evaluate the strategy's performance from September 30, 2014, to January 31, 2026[3] - **Evaluation**: The strategy achieved significant excess returns relative to the equal-weighted style index[3] Model Backtest Results 1. **Small-Cap All-Index Timing Strategy** - **Annualized Return**: 16.56% - **Excess Return**: 10.19%[3] 2. **Dividend Index Timing Strategy** - **Annualized Return**: 10.97% - **Excess Return**: 8.49%[3] 3. **Style Rotation Strategy** - **Annualized Return**: 14.79% - **Excess Return**: 4.61%[3]

- The report does not contain any specific quantitative models or factors, nor does it provide details on their construction or evaluation [1][2][3] - The report primarily focuses on tracking and analyzing the performance of broad-based indices in the A-share market, including metrics such as daily returns, moving averages, turnover rates, risk premiums, PE-TTM, dividend yields, and price-to-book ratios [5][6][19][28][40][49][55] - The analysis includes comparisons of indices against their historical averages, standard deviations, and percentile rankings over the past 1 and 5 years, providing insights into valuation and market sentiment [32][43][45][54] - Key indices analyzed include the SSE 50, CSI 300, CSI 500, CSI 1000, CSI 2000, CSI All Share, and ChiNext Index, with detailed metrics such as risk premiums, PE-TTM values, dividend yields, and net asset ratios provided for each index [13][19][32][45][54][58] - The report highlights the relative valuation and investment attractiveness of these indices based on their historical positioning and current market conditions, but does not delve into specific quantitative factor or model construction [43][45][54]

- The report primarily focuses on tracking and analyzing the performance of broad-based indices in the A-share market, including metrics such as daily returns, moving averages, turnover rates, risk premiums, PE-TTM, dividend yields, and net asset ratios[1][3][4] - **Risk Premiums**: The risk premium is calculated using the yield of 10-year government bonds as the risk-free rate. It measures the relative investment value and deviation of indices. For example, the current risk premium for the SSE 50 is 1.64%, with a 5-year historical percentile of 94.21%, while the CSI 500 has a negative risk premium of -0.98% and a 5-year historical percentile of 16.35%[27][31][34] - **PE-TTM**: The PE-TTM (Price-to-Earnings Trailing Twelve Months) is used as a valuation reference. The CSI All Share Index and CSI 500 have the highest 5-year historical percentiles at 99.92% and 99.75%, respectively, indicating high valuation levels. In contrast, the SSE 50 and ChiNext Index have lower percentiles at 85.87% and 62.89%, respectively[39][42][44] - **Dividend Yields**: Dividend yield reflects the cash dividend return rate. The current dividend yield for the SSE 50 is 3.19%, while the CSI 500 and CSI 2000 have lower yields at 1.21% and 0.71%, respectively. The ChiNext Index has a 5-year historical percentile of 56.20%, indicating a relatively high historical level[48][53][55] - **Net Asset Ratios**: The net asset ratio measures the proportion of stocks trading below their net asset value. Currently, the SSE 50 has the highest ratio at 24.0%, while the CSI 2000 has the lowest at 2.35%, reflecting market valuation attitudes[54][57]

Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the abstract concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of signals from 10 selected indicators across these dimensions[9][14] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score between -1 and +1[9] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Style Timing Model (Small-Cap Crowding) - **Model Construction Idea**: The model uses a crowding-based trend approach to time large-cap and small-cap styles. Crowding is measured by the difference in momentum and trading volume ratios between small-cap and large-cap indices[3][20] - **Model Construction Process**: 1. Calculate the momentum difference between the Wind Micro-Cap Index and the CSI 300 Index across 10/20/30/40/50/60-day windows 2. Compute the trading volume ratio between the two indices over the same windows 3. Derive crowding scores for small-cap and large-cap styles by averaging the highest and lowest quantiles of the above metrics, respectively 4. Combine the momentum and volume scores to obtain the final crowding score. A score above 90% indicates high small-cap crowding, while below 10% indicates high large-cap crowding[25] - **Model Evaluation**: The model effectively captures the dynamics of style crowding and provides actionable insights for timing decisions[20][25] 3. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model applies genetic programming to directly extract factors from industry indices' price, volume, and valuation data, without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[28][32][33] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| (information coefficient) and NDCG@5 (normalized discounted cumulative gain for top 5 groups) 2. Combine weakly collinear factors using a greedy strategy and variance inflation factor to form industry scores 3. Select the top 5 industries with the highest multi-factor scores for equal-weight allocation, rebalancing weekly[32][34] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks, making it a robust tool for industry rotation[32][34] 4. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro-factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively overweights favorable quadrants based on macro momentum[39][42] - **Model Construction Process**: 1. Divide macro risks into four quadrants based on growth and inflation expectations: growth above/below expectations and inflation above/below expectations 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, which combine buy-side momentum from asset prices and sell-side momentum from economic forecast surprises[42] - **Model Evaluation**: The strategy effectively integrates macroeconomic insights into portfolio construction, achieving enhanced performance through active allocation adjustments[39][42] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.78% - Annualized Volatility: 17.32% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.20 - Calmar Ratio: 0.88[15] 2. Style Timing Model (Small-Cap Crowding) - Annualized Return: 28.46% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.89 - YTD Return: 11.85% - Weekly Return: 5.25%[26] 3. Industry Rotation Model (Genetic Programming) - Annualized Return: 32.92% - Annualized Volatility: 17.43% - Maximum Drawdown: -19.63% - Sharpe Ratio: 1.89 - Calmar Ratio: 1.68 - YTD Return: 6.80% - Weekly Return: 3.37%[31] 4. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.93% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.92 - Calmar Ratio: 1.89 - YTD Return: 3.59% - Weekly Return: 1.54%[43] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Crowding Factor - **Factor Construction Idea**: Measures the crowding level of small-cap style based on momentum and trading volume differences between small-cap and large-cap indices[20][25] - **Factor Construction Process**: 1. Calculate momentum differences and trading volume ratios for multiple time windows 2. Derive crowding scores by averaging the highest and lowest quantiles of these metrics 3. Combine momentum and volume scores to obtain the final crowding score[25] 2. Factor Name: Industry Rotation Factor (Genetic Programming) - **Factor Construction Idea**: Extracts factors from industry indices using genetic programming, optimizing for monotonicity and top-group performance[32][34] - **Factor Construction Process**: 1. Perform cross-sectional regression of standardized daily trading volume against daily price gaps to obtain residuals (Variable A) 2. Identify the trading day with the highest standardized volume in the past 9 days (Variable B) 3. Conduct time-series regression of Variables A and B over the past 50 days to obtain intercepts (Variable C) 4. Compute the covariance of Variable C and standardized monthly opening prices over the past 45 days[38] --- Factor Backtesting Results 1. Small-Cap Crowding Factor - YTD Return: 11.85% - Weekly Return: 5.25%[26] 2. Industry Rotation Factor (Genetic Programming) - Training Set IC: 0.340 - Factor Weight: 18.7% - YTD Return: 6.80% - Weekly Return: 3.37%[31][38]

Group 1 - The report emphasizes the construction of a high-dimensional macroeconomic factor system to analyze the impact of macroeconomic variables on asset prices and to predict future trends in broad market indices and industry profitability [2][8][9] - It identifies five dimensions of macroeconomic variables: economic prosperity, inflation, interest rates, inventory, and credit, to improve the stability of macroeconomic assessments [9] Group 2 - Current macroeconomic conditions indicate a weak recovery, with overall indicators dropping from 72% to 61%, and industrial output and GDP growth rates remaining flat [17][19] - The report highlights that while inflation remains low, liquidity conditions are stable, and credit indicators show signs of improvement, suggesting a gradual recovery in financing demand [19][20] Group 3 - A broad market timing strategy based on macroeconomic variables has achieved an annualized return of 16.2% from January 2012 to December 2025, outperforming the industry by 10.26% [3][30] - The dividend index timing strategy has yielded an annualized return of 10.78%, exceeding the industry benchmark by 8.42% during the same period [3][37] Group 4 - The style rotation strategy, constructed from macroeconomic variables, has produced an annualized return of 14.15%, outperforming equal-weighted style indices by 6.08% from September 2014 to December 2025 [3][50] - The report suggests maintaining a balanced allocation between dividend and value stocks, while being cautious with growth and performance stocks due to current macroeconomic conditions [23][60]

- The report primarily focuses on tracking and analyzing the performance of broad-based indices in the A-share market, including metrics such as daily returns, moving averages, turnover rates, risk premiums, PE-TTM, dividend yields, and price-to-book ratios[1][2][3] - The turnover rate of indices is calculated using the formula: $ \text{Turnover Rate} = \frac{\Sigma(\text{Component Stocks' Free Float Shares} \times \text{Component Stocks' Turnover Rate})}{\Sigma(\text{Component Stocks' Free Float Shares})} $ This metric reflects the liquidity and trading activity of the indices[15] - The risk premium is measured relative to the 10-year government bond yield, serving as a benchmark for assessing the relative investment value and deviation of indices. The report highlights that indices like CSI 500 and SSE 50 exhibit high 5-year percentile values for risk premiums, indicating relatively attractive valuations[25][26][29] - The PE-TTM (Price-to-Earnings Trailing Twelve Months) is used as a valuation metric, with indices such as CSI 500 and CSI All Share showing high 5-year percentile values (99.92%), suggesting elevated valuations compared to historical levels[37][40][42] - Dividend yield is analyzed as a measure of cash return to investors, with indices like the ChiNext Index and CSI 300 showing relatively high 5-year historical percentile values (57.93% and 31.65%, respectively), indicating their attractiveness during periods of market downturns or declining interest rates[46][51][53] - The price-to-book ratio (P/B) is evaluated through the "break net ratio," which measures the proportion of stocks trading below their book value. The report notes that indices such as SSE 50 and CSI 300 have higher break net ratios, reflecting market sentiment and valuation levels[52][55]

Group 1 - The article emphasizes that different regional stock markets do not move in unison, allowing investors to seize more investment opportunities by understanding multiple markets [2] - Global investment can significantly reduce volatility risk, highlighting the benefits of diversifying investments across different markets [2] - A free course is offered to teach methods for investing in global stock markets through index funds, aiming to share the long-term gains of global markets [2][3] Group 2 - The article discusses the composition of the CSI 800, CSI 1000, and CSI 2000 indices, explaining that they collectively cover over 90% of the market, making them similar to the CSI All Share Index [5][6] - The CSI 800 includes the largest 1-800 A-shares, the CSI 1000 includes the next 801-1800, and the CSI 2000 includes the next 1801-3800, providing a comprehensive view of the A-share market [5] - It suggests that once the number of index funds for CSI 2000 increases, a portfolio based on the combination of CSI 800, CSI 1000, and CSI 2000 could be considered for a full market index fund advisory [6][7]

Content: --------- <doc id='1'>证券研究报告·金融工程报告 2025 年 12 月 18 日 江海证券研究发展部 金融工程定期报告 金融工程研究组 A 股市场快照：宽基指数每日投资动态 2025.12.18 ◆市场表现：2025 年 12 月 17 日, 各宽基指数（表 1）全部上涨，其中创业板指（3.39%） 投资要点：</doc> <doc id='2'>分析师：梁俊炜 执业证书编号：S1410524090001 A 股市场快照：宽基指数每日投资动 和中证 500（1.95%）涨幅最大。当年涨跌情况，创业板指（48.3%）涨幅最大，其 次是中证 2000（30.48%）和中证 500（24.66%），中证 1000（22.34%）和中证全指 （21.17%）涨幅扩大，而上证 50（11.43%）涨幅最小。 ◆均线比较：除了中证 1000 和中证 2000，其余跟踪指数已突破 5 日及 20 日均线。 创业板指率先突破 60 日均线。各跟踪指数单日修复程度较大。 ◆资金占比与换手：2025 年 12 月 17 日, 沪深 300（25.34%）交易金额占比最高， 相关研究报告</doc> <doc id='3'>态 2025.12.17 A 股市场快照：宽基指数每日投资动 态 2025.12.16 A 股市场快照：宽基指数每日投资动 态 2025.12.15 其次是中证 2000（23.94%）和中证 1000（20.51%）。各宽基指数当前换手率分别 为中证 2000（3.88），创业板指（2.41），中证 1000（2.24），中证全指（1.63）， 中证 500（1.56），沪深 300（0.54）和上证 50（0.23）。 ◆日收益率分布：创业板指的峰度负偏离最大，中证 1000 的峰度负偏离最小。上 证 50 和创业板指的负偏态最大，中证 1000 和中证 2000 的负偏态最小。 ◆风险溢价：2025 年 12 月 17 日, 创业板指（97.14%）和中证 500（96.59%）风险 溢价近 5 年分位值较高，中证 1000（88.41%）和中证 2000（69.05%）较低。</doc> <doc id='4'>◆PE-TTM：中证 500（95.04%）和中证 1000（93.47%）分位值较高，而中证 2000 （79.17%）和创业板指（57.69%）分位值较低。 ◆股债性价比：没有指数高于其 80%分位，中证 500 低于其 20%分位。 ◆股息率：创业板指（63.22%）和中证 1000（51.65%）所处近 5 年历史分位值较 高，而中证 2000（31.32%）和中证 500（24.55%）较低。 ◆破净率：当前，各指数破净率为上证 50（22.0%），沪深 300（16.33%），中证 500（11.2%），中证 1000（8.3%），中证 2000（3.8%），创业板指（nan%）和中 证全指（6.51%）。 ◆风险提示：本报告可能存在数据缺失、数据错误、数据不及时、模型处理错误 等风险。本报告仅从金融工程角度，对重要指数的市场数据进行跟踪、统计、分 析，不构成对市场指数、行业或个股进行预测或推荐。</doc> <doc id='6'>| 市场衣乳… | | --- | | 1.1 指数表现 … | | 1.2 指数与均线的比较 | | 1.3 资全占比与换手率 … | | 2 日收益分布 | | 2.1 收益区间分布对比 | | 22 分布形态变化对比 | | 3 风险溢价 … | | 3.1 各宽基指数的风险溢价 | | 32 风险溢价历史分布 | | 4 PE-TTM. | | 4.1 各宽某指数 PE-TTM 和分位值 | | 4.2 PE-TTM 历史对比… | | 4.3 股债性价比历史对比… | | 5 吸血率… | | 5.1 近一年各宽某指数股息率变化情况, | | 5.2 股息率历史对比… | | 6 玻璃率 | | 7 风险提示 . |</doc> <doc id='8'>| 表 1、各宽基指数表现情况 | | --- | | 表 2、各宽基指数与均线、近250交易日高位和低位的比较 … | | 表 3 、各宽基指数分布形态变化 … | | 表 4、各宽基指数和十年期国债即期收益率的风险溢价 | | 表 5、各宽基指数 PE-TTM 分位值和历史值 | | 表 6、各宽基指数当前股息率和历史情况 | | 图 1、各宽基指数交易全额占比和换手率 | | 图 2、各宽基指数每日收益率分布情况 | | 图 3、各宽基指数相对十年国债即期收益率的风险溢价 | | 图 4、各宽基指数相对沪深 300 的风险溢价的近 5年分布 . | | 图 5 、各宽基指数 PE-TTM 及其分位值 | | 图 6、各宽基指数的股债性价比… | | 图 7、各宽基指数股息率 | | 图 8、各宽基指数破净个股数和占比………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………… 10 |</doc> <doc id='10'>1 市场表现 本报告将从指数涨跌幅、连阴连阳、上涨下跌分布等维度对各宽基指数进 行评价和跟踪。 1.1 指数表现 </doc> <doc id='11'>2025 年 12 月 17 日, 各宽基指数（表 1）全部上涨，其中创业板指（3.39%） 和中证 500（1.95%）涨幅最大。当周涨跌情况，各跟踪指数全部下跌，其中 中证 2000（-1.22%）和中证 1000（-1.12%）跌幅最大。当月涨跌情况，各跟踪 指数涨跌各现，其中创业板指（4.04%）和中证 500（1.51%）涨幅最大，而中 证 2000（-0.89%）和中证 1000（-0.62%）下跌。当季涨跌情况，各跟踪指数除 了上证 50（0.09%）外全部下跌，其中中证 1000（-3.78%）和中证 500（-3.7%） 跌幅最大。当年涨跌情况，创业板指（48.3%）涨幅最大，其次是中证 2000（30.48%） 和中证 500（24.66%），中证 1000（22.34%）和中证全指（21.17%）涨幅扩大， 而上证 50（11.43%）涨幅最小。 表 1、各宽基指数表现情况 指数名称 指数代码 当日涨幅% 当周涨幅% 当月涨幅% 当季涨幅% 当年涨幅% 日K </doc> <doc id='12'>| 指数名称 | 指数代码 | 当日涨幅% | 当周涨幅% | 当月涨幅% | 当季涨幅% | 当年涨幅% | 日K 连阴连阳 | 周K 连阴连阳 | 月K 连阴连阳 | 季K 连阴连阳 | 年K 连阴连阳 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 上证50 | 000016.SH | 1.25 | -0.10 | 0.74 | 0.09 | 11.43 | | | |