市场情绪自3月20日持续调整，于4月18日下降至低点，数值为0.1。本周市场情绪指标在接近0轴处开始向上反弹，回升至0.5，数值较上周五(4/18)上升0.4，模型转多，市场 情绪有所缓和。 本周A股市场提示市场情绪有一定修复，较上周明显发生变化的指标有科创50成交占比、主力买入力量和期权波动率。主力流出速率减缓和VIX指标体现的恐慌程度减弱是本 周市场情绪回升的主要原因。 科创50成交占比、行业涨跌趋势性、主力买入力量和PCR结合VIX，分别代表了市场风险偏好程度下降，市场情绪不确定性增强，主力流出速度 减缓和期权市场恐慌情绪缓和。其他指标维持和上周一致的判断。 资金当前对成长高估值板块观点不确定性较强。 自上周科创50成交占比指标快速下跌至下轨以下后，本周科创50成交占比指标仍在持续下降。本周主力资金持续从科创板块 流出，累计净流出超过32亿人民币。 投资者信心逐渐恢复，市场的活跃度和投资者参与度都有了明显提升。 除了看到主力资金本周流出科创板，主力资金本周在全A仍然呈现净流出的态势，但流出速度较上周有 所减缓，主力流出主力买入力量指标有所回升。从主力资金净流出绝对量看，主力资金本周累计净流出超过370亿 ...

1. 情绪模型观点：市场情绪进一步下行 根 据 《 从 结 构 化 视 角 全 新 打 造 市 场 情 绪 择 时 模 型 》 文 中 提 到 的 构 建 思 路 ， 目 前 我 们 用 于 构 建 市 场 情 绪 结 构 指 标 所 用 到 的 细 分 指 标 如 下 表 | 指标简称 | 含义 | 情绪指示方向 | | --- | --- | --- | | 行业间交易波动率 | 资金在各板块间的交易活跃度 | 正向 | | 行业交易拥挤度 | 极值状态判断市场是否过热 | 负向 | | 价量一致性 | 资金情绪稳定性 | 正向 | | 科创 50 成交占比 | 资金风险偏好 | 正向 | | 行业涨跌趋势性 | 刻画市场轮涨补涨程度，趋势衡量 | 正向 | | RSI | 价格体现买方和卖方力量相对强弱 | 正向 | | 主力买入力量 | 主力资金净流入水平 | 正向 | | PCR 结合 VIX | 从期权指标看市场多空情绪 | 正向或负向 | | 融资余额占比 | 资金对当前和未来观点多空 | 6 公众号 · 普罗完酒会工 | 在指标合成方法上，模型采用打分的方式，根据每个分项指标所提示的情绪方向和 ...

1. 情绪模型观点：市场情绪进一步下行 根据《从结构化视角全新打造市场情绪择时模型》文中提到的构建思路，目前我们用于构建市场情绪结构指标所用到 的细分指标如下表 在指标合成方法上，模型采用打分的方式，根据每个分项指标所提示的情绪方向和所处布林轨道位置计算各指标分 数，指标分数可分为（-1，0，1）三种情况，最终对各个指标分数等权求和。最终的情绪结构指标为求和后分数的20 日均线，如图1所示，指标整体围绕0轴在[-6,6]的范围内上下波动，近5年A股市场情绪波动较大，其中2023年大部分 时间指标都处于较低位置，直至2024年10月市场情绪得分突破2。 市场情绪自3月20日持续调整，当前已下降接近0轴，为0.1，数值较上周五(4/11)下降0.4，模型维持看空观点。 1.1 从分项指标出发：市场进一步缩量，资金不确定情绪增长 本周A股市场继续提示市场情绪下行，速度没有呈现减缓趋势。本周市场情绪不确定性增强，风险偏好程度下降是市 场情绪进一步调整的主要原因。 下表展示了4月以来的情绪结构各分项指标的分数情况，从分项指标出发，本周明显提示信号切换的指标为科创50成 交占比和300RSI指标，分别代表了市场风险偏好程 ...

- Model Name: Short-term Quantitative Timing Model; Model Construction Idea: The model uses various market indicators to generate signals for market timing; Model Construction Process: The model integrates valuation, liquidity, fundamental, and sentiment signals to determine market timing. For example, the sentiment signal is derived from the volume sentiment indicator, which is constructed using the 60-day Bollinger Bands of trading volume and turnover rate. The formula for the volume sentiment score is a linear mapping of the 60-day average within the range of -1 to +1, with extreme values capped at -1 or +1. The weekly average of the 5-year percentile is used as one of the timing judgment signals. If the percentile is greater than 60%, it indicates strong sentiment and gives an optimistic signal; if less than 40%, it indicates weak sentiment and gives a cautious signal; if between 40%-60%, it gives a neutral signal. The formula is: $$ \text{Volume Sentiment Score} = \frac{\text{Current Value} - \text{Mean}}{\text{Standard Deviation}} $$ where the mean and standard deviation are calculated over a 60-day period[21][22][23]; Model Evaluation: The model has shown predictive power for the market's performance in the following week[21][22][23] - Model Name: Growth-Value Style Rotation Model; Model Construction Idea: The model suggests overweighting growth or value styles based on economic cycle analysis; Model Construction Process: The model uses the slope of the profit cycle, the level of the interest rate cycle, and the trend of the credit cycle to determine the style allocation. For example, a steep profit cycle slope and low interest rate cycle level favor growth, while a weakening credit cycle favors value. The model also considers valuation differences, such as the 5-year percentile of the PE and PB valuation differences between growth and value. The formula for the PE valuation difference is: $$ \text{PE Valuation Difference} = \frac{\text{PE of Growth} - \text{PE of Value}}{\text{PE of Value}} $$ The model gives signals based on these indicators, suggesting overweighting growth if the indicators favor growth and vice versa[39][40][41]; Model Evaluation: The model has significantly outperformed the benchmark since the end of 2012, with an annualized return of 11.44% compared to the benchmark's 6.59%[40][43] - Model Name: Small-Cap vs. Large-Cap Style Rotation Model; Model Construction Idea: The model suggests balanced allocation between small-cap and large-cap styles based on economic cycle analysis; Model Construction Process: The model uses the slope of the profit cycle, the level of the interest rate cycle, and the trend of the credit cycle to determine the style allocation. For example, a steep profit cycle slope and low interest rate cycle level favor small-cap, while a weakening credit cycle favors large-cap. The model also considers valuation differences, such as the 5-year percentile of the PE and PB valuation differences between small-cap and large-cap. The formula for the PB valuation difference is: $$ \text{PB Valuation Difference} = \frac{\text{PB of Small-Cap} - \text{PB of Large-Cap}}{\text{PB of Large-Cap}} $$ The model gives signals based on these indicators, suggesting balanced allocation if the indicators favor both styles equally[44][45][46]; Model Evaluation: The model has significantly outperformed the benchmark since the end of 2012, with an annualized return of 12.32% compared to the benchmark's 6.74%[45][47] - Model Name: Four-Style Rotation Model; Model Construction Idea: The model combines the conclusions of the growth-value and small-cap vs. large-cap rotation models to recommend allocation among four styles; Model Construction Process: The model integrates the signals from the growth-value and small-cap vs. large-cap models to determine the allocation among small-cap growth, small-cap value, large-cap growth, and large-cap value. The recommended allocation is based on the latest signals from the individual models. For example, if both models favor growth and small-cap, the allocation would be higher for small-cap growth. The formula for the combined allocation is: $$ \text{Allocation} = \text{Weight from Growth-Value Model} \times \text{Weight from Small-Cap vs. Large-Cap Model} $$ The model gives signals based on these combined indicators[48][49][50]; Model Evaluation: The model has significantly outperformed the benchmark since the end of 2012, with an annualized return of 13.10% compared to the benchmark's 7.15%[48][49][50] Model Backtest Results - Short-term Quantitative Timing Model: Annualized Return 16.39%, Annualized Volatility 14.75%, Maximum Drawdown 27.70%, Sharpe Ratio 0.9675, IR 0.5918[28][32][35] - Growth-Value Style Rotation Model: Annualized Return 11.44%, Annualized Volatility 20.87%, Maximum Drawdown 43.07%, Sharpe Ratio 0.5285, IR 0.2657[40][43] - Small-Cap vs. Large-Cap Style Rotation Model: Annualized Return 12.32%, Annualized Volatility 22.72%, Maximum Drawdown 50.65%, Sharpe Ratio 0.5377, IR 0.2432[45][47] - Four-Style Rotation Model: Annualized Return 13.10%, Annualized Volatility 21.59%, Maximum Drawdown 47.91%, Sharpe Ratio 0.5864, IR 0.2735[48][49][50]

- The report mentions the "TWO BETA" model, which continues to recommend the technology sector, focusing on communication equipment and military industry[3][4][9] - The industry allocation model suggests a mid-term focus on sectors experiencing a turnaround, recommending industries such as new energy[3][4][9] - The timing system signal shows that the distance between the 20-day and 120-day moving averages of the Wind All A Index has narrowed to 3.28%, indicating a market in a volatile state[2][4][11] - The report suggests that if the trading volume falls below 1.1 trillion yuan, the market is expected to rebound[2][4][11] - The current PE ratio of the Wind All A Index is around the 60th percentile, indicating a medium level, while the PB ratio is around the 20th percentile, indicating a relatively low level[3][12] - The position management model recommends maintaining a 50% position for absolute return products based on the Wind All A Index[3][12] Model and Factor Construction - **TWO BETA Model**: This model recommends the technology sector, focusing on communication equipment and military industry. The model's construction details are not provided in the report[3][4][9] - **Industry Allocation Model**: This model suggests a mid-term focus on sectors experiencing a turnaround, recommending industries such as new energy. The model's construction details are not provided in the report[3][4][9] - **Timing System**: The timing system uses the distance between the 20-day and 120-day moving averages of the Wind All A Index to determine market trends. The latest data shows the 20-day moving average at 5253 points and the 120-day moving average at 5086 points, with a distance of 3.28%[2][4][11] Model and Factor Evaluation - **TWO BETA Model**: Continues to recommend the technology sector, focusing on communication equipment and military industry[3][4][9] - **Industry Allocation Model**: Recommends a mid-term focus on sectors experiencing a turnaround, such as new energy[3][4][9] - **Timing System**: Indicates a market in a volatile state, with the distance between the 20-day and 120-day moving averages narrowing to 3.28%[2][4][11] Backtest Results - **Timing System**: The distance between the 20-day and 120-day moving averages of the Wind All A Index is 3.28%, indicating a market in a volatile state[2][4][11] - **Wind All A Index**: The current PE ratio is around the 60th percentile, and the PB ratio is around the 20th percentile[3][12] - **Position Management Model**: Recommends maintaining a 50% position for absolute return products based on the Wind All A Index[3][12]

广发证券首席金工分析师 安宁宁 SAC: S0260512020003 SAC: S0260522070006 zhangyudong@gf.com.cn 广发金工安宁宁陈原文团队 摘要 最近5个交易日，科创50指数跌4.16%，创业板指跌3.34%，大盘价值跌0.91%，大盘成长跌2.89%，上证50跌2.38%，国证2000代表的小盘跌 1.89%，石油石化、建筑材料市场表现靠前，计算机表现靠后。 风险溢价，中证全指静态PE的倒数EP减去十年期国债收益率，权益与债券资产隐含收益率对比，历史数次极端底部该数据均处在均值上两倍标准差区 域，比如2012/2018/2020年（疫情突发），2022/04/26达到4.17%，2022/10/28风险溢价再次上升到4.08%，市场迅速反弹，2024/01/19指标4.11%，自2016 年以来第五次超过4%。截至2025/03/21指标3.61%，两倍标准差边界为4.72%。 估值水平，截至2025/03/21，中证全指PETTM分位数54%，上证50与沪深300分别为58%、47%，创业板指接近15%，中证500与中证1000分别为35%、 42%，创业板指风格 ...

Group 1 - The report introduces a quantitative timing strategy for style indices based on valuation, liquidity, and crowding models, emphasizing that "efficient markets" are dynamic processes rather than static states [1][6] - The quantitative timing model effectively captures the characteristics of style index bottoms and tops while mitigating risks associated with crowded trades, achieving an average annual return of 18.54% and an average excess annual return of 16.46% since 2011 [1][6] - The report highlights the performance of the mixed style index model, which has achieved an annual return of 20.10% and an excess annual return of 16.24% since December 2013 [1][6] Group 2 - The style index valuation model includes factors such as PB, PE, PBPE, and equity risk premium, with an average annual return of 10.38% and an average excess annual return of 8.30% since 2011 [1][6][17] - The market liquidity model incorporates factors like buy and sell impact costs and liquidity indices, showing a significant accuracy in bottom timing with an average rebound return of 6.86% [1][6][19] - The trading crowding model serves as a top-timing hedge factor, effectively complementing the valuation and liquidity models, achieving an excess annual return of 4.87% since 2011 [1][6][19] Group 3 - The report outlines a quantitative timing research framework that includes data processing, model factor calculation, model testing, and composite model synthesis [1][6][19] - The valuation factors are constructed by calculating the historical percentile levels of the style index valuation factors, which are then compared against set thresholds to trigger buy or sell signals [1][6][21] - The report emphasizes the need for timing factors to be logical and mean-reverting, with specific thresholds established for different style indices to determine market conditions [1][6][20]

Quantitative Factors and Their Construction 1. Factor Name: Small-Cap Factor - **Construction Idea**: The small-cap factor measures the performance of stocks with smaller market capitalization, which historically tend to outperform larger-cap stocks under certain market conditions [17][18] - **Construction Process**: The factor's crowding level is calculated using four indicators: valuation spread, pairwise correlation, long-term return reversal, and factor volatility. These indicators are combined into a composite score to assess the degree of crowding [18] - **Evaluation**: The small-cap factor showed a positive crowding level, indicating relatively strong performance and lower risk of factor failure [19] 2. Factor Name: Low-Valuation Factor - **Construction Idea**: This factor identifies stocks with lower valuation metrics, such as price-to-earnings or price-to-book ratios, which are expected to generate higher returns over time [17][18] - **Construction Process**: Similar to the small-cap factor, the low-valuation factor's crowding level is assessed using the same four indicators (valuation spread, pairwise correlation, long-term return reversal, and factor volatility) and combined into a composite score [18] - **Evaluation**: The low-valuation factor exhibited a slightly negative crowding level, suggesting moderate underperformance or potential risks of factor inefficiency [19] 3. Factor Name: High-Profitability Factor - **Construction Idea**: This factor targets stocks with strong profitability metrics, such as high return on equity (ROE) or net profit margins, which are often associated with stable and superior returns [17][18] - **Construction Process**: The factor's crowding level is calculated using the same methodology as the small-cap and low-valuation factors, combining the four indicators into a composite score [18] - **Evaluation**: The high-profitability factor showed a negative crowding level, indicating potential underperformance or risks of factor inefficiency [19] 4. Factor Name: High-Growth Factor - **Construction Idea**: This factor focuses on stocks with high growth rates in earnings or revenues, which are expected to deliver higher returns in growth-oriented market environments [17][18] - **Construction Process**: The high-growth factor's crowding level is also derived from the four indicators (valuation spread, pairwise correlation, long-term return reversal, and factor volatility) and combined into a composite score [18] - **Evaluation**: The high-growth factor exhibited the most negative crowding level among the factors analyzed, indicating significant underperformance and a higher risk of factor failure [19] --- Backtesting Results of Factors 1. Small-Cap Factor - **Valuation Spread**: 1.74 [19] - **Pairwise Correlation**: -0.32 [19] - **Market Volatility**: -0.38 [19] - **Return Reversal**: 1.43 [19] - **Composite Score**: 0.62 [19] 2. Low-Valuation Factor - **Valuation Spread**: -0.33 [19] - **Pairwise Correlation**: 0.05 [19] - **Market Volatility**: 0.15 [19] - **Return Reversal**: -0.29 [19] - **Composite Score**: -0.10 [19] 3. High-Profitability Factor - **Valuation Spread**: -1.23 [19] - **Pairwise Correlation**: -0.05 [19] - **Market Volatility**: 0.28 [19] - **Return Reversal**: -0.44 [19] - **Composite Score**: -0.36 [19] 4. High-Growth Factor - **Valuation Spread**: -2.04 [19] - **Pairwise Correlation**: 0.08 [19] - **Market Volatility**: -0.65 [19] - **Return Reversal**: -1.02 [19] - **Composite Score**: -0.91 [19]

广发证券首席金工分析师 安宁宁 最近5个交易日，科创50指数涨2.67%，创业板指涨1.61%，大盘价值涨1.01%，大盘成长涨1.28%，上证50涨1.63%，国证2000代表的小盘涨4.00%，有色金 属、国防军工市场表现靠前，石油石化表现靠后。 SAC: S0260512020003 anningning@gf.com.cn 广发证券资深金工分析师 张钰东 SAC: S0260522070006 zhangyudong@gf.com.cn 广发金工安宁宁陈原文团队 摘要 风险溢价，中证全指静态PE的倒数EP减去十年期国债收益率，权益与债券资产隐含收益率对比，历史数次极端底部该数据均处在均值上两倍标准差区域，比 如2012/2018/2020年（疫情突发），2022/04/26达到4.17%，2022/10/28风险溢价再次上升到4.08%，市场迅速反弹，2024/01/19指标4.11%，自2016年以来第五 次超过4%。截至2025/03/07指标3.65%，两倍标准差边界为4.71%。 估值水平，截至2025/03/07，中证全指PETTM分位数55%，上证50与沪深300分别为59%、47%，创业 ...