Investment Rating - The report maintains a "Strong Buy" investment rating for the company [3][6]. Core Views - The company's Q4 performance shows stabilization in revenue and profit, with significant improvement in operational quality. The growth is driven by opportunities in trusted innovation (信创), AI, and data elements, particularly in cloud and data services [1][6]. - The company is positioned as a foundational player in digital China, with advantages in trusted innovation, data elements, and cloud services, projecting a net profit of 316 million, 428 million, and 495 million yuan for 2025 to 2027 respectively [6][10]. Financial Data and Valuation - Total revenue for 2023 is reported at 9,195 million yuan, with a year-on-year decline of 13%. The projected revenue for 2024 is 7,836 million yuan, with a further decline of 15%, followed by a recovery with expected growth of 10% in 2025 [2][10]. - Operating profit for 2023 is 419 million yuan, with a decrease of 4% year-on-year. The forecast for 2024 shows a significant drop to 233 million yuan, but a recovery is expected with 384 million yuan in 2025, representing a 65% increase [2][10]. - The company's net profit attributable to shareholders is 375 million yuan for 2023, down 1% year-on-year, with a forecast of 316 million yuan for 2025, reflecting a 66% increase [2][10]. - The current price-to-earnings (PE) ratio is 41.7, projected to rise to 81.9 in 2024 before decreasing to 49.5 in 2025 [2][11]. Business Performance - In Q4, the company achieved a revenue of 34.99 billion yuan, down 11.95% year-on-year, with a net profit of 2.19 billion yuan, a decrease of 9.82% [6][10]. - The company’s gross margin improved by 0.60 percentage points to 24.08% for the year, with Q4 gross margin increasing by 2.16 percentage points to 18.05% [6][10]. - The cloud and data services, along with trusted innovation business, are key growth drivers, with cloud and data services revenue growing by 12.34% in 2024 [6][10]. Shareholder Information - The major shareholder is China Electric Tai Chi Group Co., Ltd., holding 28.61% of the shares [3].

1 、《环保公用事业行业周报 （20250330）：碳排放权交易市场首 次扩围，新增三大高碳排行业》 2025-03-30 证券研究报告 | 行业定期报告 2025 年 04 月 06 日 证券研究报告 | 行业定期报告 2025 年 04 月 06 日 深化公用事业价格市场化改革，更好发挥价格信号作用 环保公用事业行业周报（2025/04/06） 周期/环保及公用事业 本周环保与公用事业板块表现出现分化。环保（申万）行业指数下跌 0.25%， 相较市场整体跌幅较小；公用事业（申万）行业指数上涨 2.55%，涨幅领先。 推荐（维持） 行业规模 | | | 占比% | | --- | --- | --- | | 股票家数（只） | 241 | 4.7 | | 总市值（十亿元） | 3676.4 | 4.4 | | 流通市值（十亿元） | 3409.4 | 4.5 | 行业指数 % 1m 6m 12m 绝对表现 5.2 16.9 10.6 相对表现 5.9 20.8 2.4 资料来源：公司数据、招商证券 -20 -10 0 10 20 30 Apr/24 Jul/24 Nov/24 Mar/25 (%) 环保及公 ...

成交价格方面，本周天津城投债成交收益率较高，超过 3%。从变动来看，与上 周相比，本周浙江、山东超长城投债成交收益率较上周上升较大，较上周分别 上升 52bp 和 51bp，本周安徽、河南超长城投债成交收益率较上周下降幅度较 大，较上周分别下降 26bp 和 16bp。另外，江苏低估值成交占比较上周下降 59 个百分点，河南、北京等地低估值成交占比较上周上升较多。 三、超长产业债：建筑装饰、机械设备行业成交量上升，电子、基础化工行业 低估值成交占比下降 蓄势待修复 ——超长信用债交易跟踪 一、超长信用债成交量下降，低估值成交占比上升 本周（2025 年 3 月 31 日-2025 年 4 月 3 日，下同）超长信用债每日成交活跃 度有所下降，超长信用债平均每日成交笔数为 2.7 笔，前值为 2.8 笔。本周剩 余期限在 15-20 年的超长信用债每日成交笔数较上周下降较多。从品种来看， 本周城投债每日成交活跃度高于产业债。本周超长信用债成交量有所下降。本 周超长信用债成交量为 280 亿元，较上周环比下降 42.15%，成交量下降主要体 现在剩余期限在 7-10 年的超长信用债中。 1）在成交期限方面，机构久 ...

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 ...