1. Report Industry Investment Rating There is no information provided regarding the report's industry investment rating. 2. Core Viewpoints of the Report - This week, the trading activity of ultra - long - term credit bonds decreased, with the average daily trading volume dropping to 2.7 transactions from 2.8 last week. The trading volume of ultra - long - term credit bonds decreased by 42.15% week - on - week to 28 billion yuan, mainly in bonds with a remaining maturity of 7 - 10 years. The institutions' preference for bond duration declined, and the proportion of low - valuation transactions increased [1][9]. - For ultra - long - term urban investment bonds, the trading volumes in Chongqing and Shandong increased, while those in Shaanxi and Guangdong decreased significantly. The proportion of low - valuation transactions in Henan and Beijing increased marginally [2][14]. - For ultra - long - term industrial bonds, the trading volumes in the building decoration and machinery equipment industries increased, while those in the public utilities industry decreased. The proportion of low - valuation transactions in the electronics and basic chemicals industries decreased [4][19]. 3. Summary by Directory 3.1 Ultra - long - term Credit Bonds: Decrease in Trading Volume and Increase in Low - valuation Transaction Proportion - **Trading Activity**: The average daily trading frequency of ultra - long - term credit bonds decreased to 2.7 transactions from 2.8 last week. The daily trading frequency of bonds with a remaining maturity of 15 - 20 years decreased significantly. The trading activity of urban investment bonds was higher than that of industrial bonds [1][9]. - **Trading Volume**: The trading volume of ultra - long - term credit bonds was 28 billion yuan, a 42.15% week - on - week decrease, mainly in bonds with a remaining maturity of 7 - 10 years [1][9]. - **Trading Duration**: The average trading duration of ultra - long - term credit bonds was 9.36 years, a decrease of 0.59 years from last week. The average trading duration of ultra - long - term urban investment bonds decreased by 0.73 years, and that of industrial bonds decreased by 0.56 years [2][12]. - **Trading Price**: The trading yield of ultra - long - term credit bonds was 2.37%, a decrease of 3bp from last week. The proportion of low - valuation transactions increased to 57%, with a significant increase in ultra - long - term industrial bonds from 24% to 60%. The proportion of low - valuation transactions of bonds with a remaining maturity of 20 - 30 years increased by about 31 percentage points [2][12]. 3.2 Ultra - long - term Urban Investment Bonds: Increase in Trading Volume in Chongqing and Shandong, and Marginal Increase in Low - valuation Transaction Proportion in Henan and Beijing - **Trading Volume**: Jiangsu had a high trading volume of 1.65 billion yuan. The trading volumes in Shaanxi and Guangdong decreased by 1.76 billion yuan and 1.27 billion yuan respectively compared to last week, while those in Chongqing and Shandong increased [14]. - **Trading Duration**: The trading duration of ultra - long - term urban investment bonds was 9.23 years. The trading duration in Fujian increased by 0.82 years, while that in Jiangxi decreased by 1.17 years compared to last week [17]. - **Trading Price**: The trading yield of Tianjin's urban investment bonds exceeded 3%. The trading yields in Zhejiang and Shandong increased by 52bp and 51bp respectively, while those in Anhui and Henan decreased by 26bp and 16bp respectively compared to last week. The proportion of low - valuation transactions in Jiangsu decreased by 59 percentage points, while those in Henan and Beijing increased [17]. 3.3 Ultra - long - term Industrial Bonds: Increase in Trading Volume in Building Decoration and Machinery Equipment Industries, and Decrease in Low - valuation Transaction Proportion in Electronics and Basic Chemicals Industries - **Trading Volume**: The public utilities industry had a high trading volume of 9.26 billion yuan. The trading volumes in the building decoration and machinery equipment industries increased by 810 million yuan and 150 million yuan respectively compared to last week, while that in the public utilities industry decreased by about 7.46 billion yuan [4][19]. - **Trading Duration**: The trading durations of ultra - long - term industrial bonds in the coal and social services industries decreased by 5.63 years and 3.83 years respectively, while those in the non - ferrous metals and basic chemicals industries increased by 3.54 years and 2.48 years respectively [23]. - **Trading Price**: The trading yields of ultra - long - term industrial bonds in the machinery equipment and commercial retail industries increased by 33bp and 18bp respectively. The proportions of low - valuation transactions in the social services and communication industries were as high as 100%. The proportions of low - valuation transactions in the electronics and basic chemicals industries decreased significantly [23].

证券研究报告 | 策略研究 2025 年 4 月 6 日 东升西落之内需消费的崛起：对等关税后 A 股怎么看？ ——A 股投资策略周报（0406） 相关报告 关税提升对于 A 股并不是新鲜事，也不是今年的第一次。阶段性降低全球风险资 产的风险偏好并引发流动性冲击对 A 股产生不利影响。对中国来说，对美出口下 降以及全球需求下滑将会拖累中国出口增速。因此全面支持内需消费成为完成今 年经济发展目标以及应对外部冲击的关键措施。疫情、地产和地方政府化债多重 风险逐一消退后，今年开始财政政策的发力将会成为推动消费增速回升的关键力 量。而 14 亿人口人均消费支出提升空间巨大。即将到来的年报及一季报消费板块 自由现金流拐点和业绩边际改善将会出现。在不确定性加大和低利率环境下，中 国内需消费因前期滞涨估值便宜预期低且内在价值稳定提升，可成为当前全球资 金的避险选择。精神属性、适老化、政策支持和小额可选消费是当前消费股重要 的选股原则。除了 A 股消费，恒生消费指数也值得重点关注。A 股除了消费，农 产品、军工和自主可控也是可以重点关注的选择方向。最终 A 股尤其是 A 股内需 消费应该是全球投资在本轮美国关税冲击中最具韧性 ...

证券研究报告 | 债券点评报告 2025 年 04 月 06 日 地方债迎来配置窗口 ——地方债周报 一、一级市场情况 【净融资】本周地方债共发行 1877 亿元，净融资减少。本周地方债发行量为 1877 亿元，偿还量为 215 亿元，净融资为 1663 亿元。发行债券中，新增一般 债 35 亿元，新增专项债 1322 亿元，再融资一般债 318 亿元，再融资专项债 202 亿元。 【发行期限】本周 30Y 地方债发行占比最高（32%），10Y 及以上发行占比为 91%，与上周相比小幅上升。7Y、10Y、15Y、20Y 和 30Y 地方债发行占比分 别为 8%、25%、14%、21%和 32%，其中 20Y 地方债发行占比上升较多，环 比上升约 6 个百分点。 【发行利差】本周地方债加权平均发行利差为 8bp，较上周有所收窄。其中， 30Y 地方债加权平均发行利差最高，达 11.3bp。本周除 3Y 和 7Y 地方债加权平 均发行利差有所走阔外，其余期限均有所收窄。本周区域分化较大，四川、陕 西地方债加权平均发行利差较高，超过 15bp，而福建、江苏发行利差较低。 【募集资金投向】截至本周末，2025 年以来 ...

Investment Rating - The report maintains a "Recommended" rating for the automotive industry, indicating a positive outlook for the sector [5]. Core Insights - The automotive industry experienced an overall decline of 3.5% during the week from March 30 to April 5, 2025, with various segments showing mixed performance [2][10]. - New energy vehicle deliveries saw significant growth, with Leap Motor leading the new forces with a delivery of 37,095 units in March, a year-on-year increase of over 154% [26]. - The report highlights the strategic partnership between Great Wall Motors and Yushun Technology, focusing on motion control and application development for automotive scenarios [24]. Market Performance Overview - The automotive sector's secondary segments mostly declined, with passenger vehicles and auto parts experiencing notable drops of 4.4% and 3.8%, respectively [12]. - Individual stocks within the automotive sector showed varied performance, with Jiuxi Co. rising by 10.3% and Meichen Technology falling by 34.0% [3][17]. New Vehicle Launches - Several new models are set to launch in April, including the Leap B10, NIO Firefly, and BYD Han L, with prices ranging from 10.98 to 45.8 million yuan [4][22]. Industry Dynamics - The inventory alert index for Chinese automotive dealers was reported at 54.6% for March, indicating a slight improvement in market conditions despite remaining in a sluggish zone [24]. - Tesla's Q1 delivery figures showed a decline of 13% year-on-year, with a total of 336,681 vehicles delivered [29]. - The report notes the implementation of new tariffs on automobiles and parts by the Trump administration, effective from April 3 and May 3, respectively [30].

Group 1 - The report introduces a quantitative model solution for addressing the value-growth style switching issue based on odds and win rates [1][8] - The overall market growth style portfolio had a return of -0.55%, while the value style portfolio had a return of 0.18% in the last week [8] Group 2 - The estimated odds for the growth style is 1.01, while for the value style it is 1.03, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rate for the growth style is 31.12%, and for the value style, it is 68.88%, based on seven win rate indicators [3][17] Group 3 - The latest investment expectations calculated show a growth style expectation of -0.38 and a value style expectation of 0.40, leading to a recommendation for the value style [4][18] - Since 2013, the annualized return of the style rotation model based on investment expectations is 26.73%, with a Sharpe ratio of 0.98 [4][19]

- 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]

Quantitative Models and Construction Methods 1. Model Name: Interest Rate Price-Volume Multi-Cycle Timing Strategy - **Model Construction Idea**: This model uses kernel regression algorithms to identify the trend patterns of interest rates, capturing support and resistance levels. It integrates signals from long, medium, and short investment cycles to form a composite timing strategy[11][23] - **Model Construction Process**: 1. **Signal Generation**: - Use kernel regression to identify support and resistance levels for interest rate data across different cycles (long, medium, short)[11] - Signals are generated based on whether the interest rate breaks through these levels in an upward or downward direction[11] 2. **Cycle Frequency**: - Long cycle: Monthly signal switching - Medium cycle: Bi-weekly signal switching - Short cycle: Weekly signal switching[11] 3. **Composite Signal Scoring**: - If at least two out of three cycles show a downward breakthrough, the signal is "bullish" - If at least two out of three cycles show an upward breakthrough, the signal is "bearish"[11][23] 4. **Portfolio Construction**: - Full allocation to long-duration bonds when at least two cycles show a downward breakthrough and the trend is not upward - 50% allocation to medium-duration bonds and 50% to long-duration bonds when at least two cycles show a downward breakthrough but the trend is upward - Full allocation to short-duration bonds when at least two cycles show an upward breakthrough and the trend is not downward - 50% allocation to medium-duration bonds and 50% to short-duration bonds when at least two cycles show an upward breakthrough but the trend is downward - Equal allocation across short, medium, and long durations in other cases[23] 5. **Stop-Loss Mechanism**: - Adjust holdings to equal allocation when the daily excess return of the portfolio falls below -0.5%[23] 6. **Benchmark**: - Equal-duration strategy: 1/3 allocation to short, medium, and long durations[23] 2. Model Name: Public Bond Fund Duration and Divergence Tracking - **Model Construction Idea**: This model uses an improved regression model to dynamically track the weekly changes in the duration and divergence of public bond funds[13] - **Model Construction Process**: 1. **Duration Calculation**: - Median, 4-week moving average, and mean values of the duration (including leverage) of medium- and long-term pure bond funds are calculated[13][20] 2. **Divergence Measurement**: - Cross-sectional standard deviation of fund durations is used to measure divergence[14] 3. **Yield-to-Maturity (YTM) Analysis**: - Median, 4-week moving average, and mean values of YTM (including leverage) are calculated for the funds[20] --- Model Backtesting Results 1. Interest Rate Price-Volume Multi-Cycle Timing Strategy - **Long-Term Performance (2007.12.31 to Latest Report Date)**: - Annualized Return: 6.3% - Maximum Drawdown: 1.55% - Return-to-Drawdown Ratio: 2 - Excess Return: 1.78% - Excess Return-to-Drawdown Ratio: 0.92[23][24] - **Short-Term Performance (Since 2023 Year-End)**: - Annualized Return: 8.05% - Maximum Drawdown: 1.62% - Return-to-Drawdown Ratio: 6.91 - Excess Return: 2.78% - Excess Return-to-Drawdown Ratio: 2.85[4][23][24] - **Historical Success Rates (18 Years)**: - Absolute Return > 0: 100% - Excess Return > 0: 100%[24] 2. Public Bond Fund Duration and Divergence Tracking - **Duration Metrics**: - Median Duration: 3.13 years - 4-Week Moving Average: 3.19 years - Mean Duration: 3.4 years - Historical 5-Year Percentile: 91.51%[13][14] - **Divergence Metrics**: - Cross-Sectional Standard Deviation: 2.03 years - Historical 5-Year Percentile: 98.46%[14] - **YTM Metrics**: - Median YTM: 1.99% - 4-Week Moving Average: 2.12% - Mean YTM: 2.1%[20]

量化基金周度跟踪（20250331-20250403） 证券研究报告 | 基金研究（公募） 2025 年 4 月 5 日 主动量化收跌，指增小幅跑赢基准 本报告重点聚焦量化基金市场表现，总结近一周主要指数和量化基金业绩表现、 不同类型公募量化基金整体表现和业绩分布，以及本周收益表现较优的量化基 金，供投资者参考。 ❑市场整体表现： 本周（3 月 31 日-4 月 3 日）A 股市场整体收跌，各类型量化基金平均收益 表现分化。 ❑主要指数表现： 主要股指均下跌，其中中证1000、中证500、沪深300分别跌1.04%、1.19%、 1.37%。 ❑各类基金表现： 本周各类型量化基金平均收益表现分化，主动量化跌 0.85%；市场中性涨 0.06%，57%的市场中性基金获得正收益。各指数增强型基金均小幅跑赢基 准，其中其他指增、中证 1000 指增和中证 500 指增超额收益率在 0.20% 以上。 ❑风险提示：图表中列示的数据结果仅为对市场及个基历史表现的客观描述，并 不预示其未来表现，亦不构成投资收益的保证或投资建议。 徐燕红 S1090524120003 xuyanhong@cmschina.com.cn 江 ...

【债市情绪】上周债市情绪指数为 114.6，较前值回落 0.1；债市情绪扩散指数 49.9%，较前值回升 0.3 个百分点。 【机构久期】上周五基金久期为 2.17 年，较前一周五回升 0.04 年；农商行久 期为 2.70 年，较前一周五回落 0.07 年；保险久期为 6.89 年，较前一周五回落 0.10 年。 【杠杆率】上周质押式回购余额为 11.0 万亿元，较前值回升 0.1 万亿元；大行 净融出余额为 3.2 万亿元，较前值回升 0.1 万亿元；债市杠杆率为 103.5%，较 前值持平。 证券研究报告 | 债券点评报告 2025 年 04 月 05 日 基金久期继续回升——债市晴雨表 【二级成交】上周从换手率来看，30Y 国债换手率为 1.7%，较前值回落 0.3 个 百分点。10Y 国债换手率为 1.2%，较前值持平；10Y 国开债换手率为 23.8%， 较前值回落 4.3 个百分点；超长期信用债换手率为 0.49%，较前值回落 0.16 个 百分点。 【配置力量】债市配置力量来看，上周债基新发行份额为 0 亿元，较前值回落 153 亿元；风险偏好来看，股市风险溢价为 1.48%，较前值回升 0. ...

Investment Rating - The report maintains a positive outlook on the securities industry, indicating that performance elasticity is expected in 2025 due to a bullish market environment and base effect from previous years [1][9]. Core Insights - The report highlights that the securities industry is experiencing a recovery in revenue and net profit, driven by a rebound in equity markets and a strong bond market [1][9]. - The overall revenue for the 21 listed securities firms reached 369.8 billion, a year-on-year increase of 7%, while net profit rose by 15% to 113.9 billion [14][16]. - The report emphasizes the structural differentiation in business performance, with proprietary trading showing significant growth while investment banking revenues are under pressure [24][39]. Summary by Sections Q4 Performance and Recovery - Q4 performance has significantly contributed to the annual recovery, with a quarterly revenue of 107.5 billion, up 30% year-on-year and 10% quarter-on-quarter [16]. - The average return on equity (ROE) for the 21 listed firms was 5.65%, an increase of 0.38 percentage points year-on-year, with leverage ratios rising to 5.14 times [28][29]. Business Segment Analysis - **Brokerage Business**: Brokerage income reached 77 billion, a 9% increase year-on-year, driven by retail investor participation [30][37]. - **Investment Banking**: Investment banking revenue was 22.1 billion, down 28% year-on-year, reflecting ongoing challenges in the market [39][47]. - **Asset Management**: Asset management income was 36.1 billion, a slight decrease of 3% year-on-year, but the scale of managed assets stabilized at 6.1 trillion, up 3% [50][54]. Annual Outlook - The report forecasts total revenue for the securities industry in 2025 to be 468.6 billion, a 4% increase year-on-year, with net profit expected to reach 180.4 billion, an 11% increase [9][13]. - The report suggests that the regulatory environment remains supportive, with expectations of continued inflow of medium to long-term capital into the market [9][10]. Investment Recommendations - The report recommends focusing on high-performing stocks such as CITIC Securities, CICC, Guotai Junan, and GF Securities, indicating a significant potential for valuation recovery [9][10].