2025 年 07 月 07 日 7 月指向 AH 红利均衡 团队成员 投资要点： AH 双市轮动策略，构建收益更优的红利+组合。我们在 2/17 发布的《红 利+：AH 双市轮动策略》通过解析 A 股和 H 股红利的驱动因素，提出 AH 红利轮动策略，提高红利组合整体收益。 策 略 研 究 华福证券 策 略 定 期 报 告 7 月 AH 红利轮动指标：50%A 股红利，50%港股红利。美国扰动百 度指标、VIX 月平均值、汇率、A 股季节效应等驱动因子超出阈值。因子 赋权后，AH 红利轮动指标数值为 50，相对中性。24 年至 25 年 6 月，指 标准确度为 73%。 分析师： 周浦寒(S0210524040007) zph30515@hfzq.com.cn 研究助理： 杨逸帆(S0210124110046) yyf30689@hfzq.com.cn 相关报告 1.【华福策略】红利+：AH 双市轮动策略—— 2025.2.17 2.【华福策略】3 月红利或迎窗口期，AH 轮动构 建红利+组合——2025.3.2 3.【华福策略】4 月或指向港股红利——2025.4.3 风险提示 证 券 历史经验不代表未来 ...

证券研究报告 | 2025年07月05日 AI 赋能资产配置追踪（2025.7） AI 提示货币信用体系占优 策略研究·策略快评 | 证券分析师： | 王开 | 021-60933132 | wangkai8@guosen.com.cn | 执证编码：S0980521030001 | | --- | --- | --- | --- | --- | | 证券分析师： | 陈凯畅 | 021-60375429 | chenkaichang@guosen.com.cn | 执证编码：S0980523090002 | | 证券分析师： | 董德志 | 021-60933158 | dongdz@guosen.com.cn | 执证编码：S0980513100001 | | 联系人： | 郭兰滨 | 010-88005497 | guolanbin@guosen.com.cn | | 事项： 国信总量团队开发了 AI 赋能投研体系，将五大周期框架有机结合，通过动态赋权、回测调优对当月乃至 年内的股债胜率进行预测，以实现分析师主动框架与人工智能多模态分析的协调统一，将人工智能应用于 主动投研领域，定期跟踪预测市场表现。 ...

——7月大小盘轮动观点 报告日期：2025年7月4日 n 分析师：吕思江 n SAC编号：S1050522030001 n 联系人：武文静 n SAC编号：S1050123070007 证 券 研 究 报 告 金融工程月报 小微盘胜率占优，赔率改善 数据来源：wind，华鑫证券研究 | 业绩统计 | 中证2000 | 沪深300 | 基准:等权配置 | 轮动策略 | 轮动策略/基准 | | --- | --- | --- | --- | --- | --- | | 累计收益 | -7.21% | 12.73% | 6.60% | 121.87% | 101.66% | | 年化收益 | -0.81% | 1.31% | 0.70% | 9.04% | 7.92% | | 最大回撤 | 56.49% | 45.60% | 37.97% | 32.46% | 14.94% | | 年化波动率 | 26.35% | 19.00% | 20.95% | 21.10% | 8.63% | | 年化sharpe | -0.03 | 0.07 | 0.03 | 0.43 | 0.92 | | calmar | -0.01 ...

资料显示，2025年以来险资举牌次数已达19次，其中9次是银行股，占比近半。这背后是险资对稳定回报型权益资产的迫切需求。 当前银行板块的高股息率非常具有吸引力，叠加监管鼓励险资加大入市力度，资金持续增配银行板块的方向十分明确，这更有助于支撑板块行情持续发展。 今年以来，听的最多的恐怕就是——银行股又创新高了。 这不，经过短暂的回调后，昨天浦发银行、华夏银行、建设银行A、H股又创下了历史新高。这要是2015年买入银行股，到现在都翻倍了！ | 序号 | 证券代码 | 证券简称 | 近期创历史新高次数 | | | --- | --- | --- | --- | --- | | | | | [交易日期] 2025-7-2 [近N天内] 114 [复权方式] 后复权 | | | 7 | 600000.SH | 浦发银行 | | 24 | | 2 | 600015.SH | 华夏银行 | | ব | | 3 | 601939.SH | 建设银行 | | 17 | | 4 | 0939.HK | 建设银行 | | 社 | ETF方面，两市唯一的跨AH市场的银行类ETF——银行AH优选ETF(517900)又开启了连涨模式， ...

证券研究报告 | 金融工程报告 2025 年 06 月 29 日 当下价值/成长的赔率和胜率几何？ ——风格轮动策略周报 20250627 在《如何从赔率和胜率看成长/价值轮动》报告中，我们创新性地提出了基于 赔率和胜率的投资期望结合方式，为应对价值成长风格切换问题提供了定量模 型解决方案。后续，我们将持续在样本外进行跟踪并做定期汇报。 上周全市场成长风格组合收益 5.49%，而全市场价值风格组合收益为 3.33%。 1、赔率 在前述报告中，我们已经进行了验证，即市场风格相应的相对估值水平 是其预期赔率的关键影响因素，并且两者应该呈现出负相关。由于存在上述 线性关系，我们根据最新的估值差分位数，可推得当下成长风格的赔率估计 为 1.10，价值风格的赔率估计为 1.09。 2、胜率 在七个胜率指标中，当前有 5 个指向成长，2 个指向价值。根据映射方 案，当下成长风格的胜率为 68.88%，价值风格的胜率为 31.12%。 3、最新推荐风格：成长 根据公式，投资期望=胜率*赔率-（1-胜率）。我们计算得最新的成长风格 投资期望为 0.44，价值风格的投资期望为-0.35，因此最新一期的风格轮动模 型推荐为成长风 ...

【金融工程】传统多因子打分行业轮动策略*郭策 李腾。本报告介绍了一种 季频换仓偏配置思路的行业轮动策略，采用传统量化多因子打分的方式，分 别从"估值"、"质量"、"流动性"、"动量"四个维度下各优选 2 个单 因子，再进行等权 rank 复合，形成复合因子。整体策略思路偏配置，优先选 择低估值、低拥挤度、景气度上行、近一年价格动量向上，近 3 年价格处于 低位的行业持有。最终复合策略在回测区间（2014/4/1-2025/6/6）实现年化 收益 19.64%，行业等权基准实现年化收益 7.55%，对应年化超额 12.09%。 期间超额累计净值最大回撤-13.25%。 【机械设备】芯碁微装*苏凌瑶。芯碁微装公告新签 1.46 亿元大单，约占 2024 年营收的 15%。AI 基建热潮投推动 PCB 投资热，公司有望受益于 PCB 厂商 积极扩产潮。 行业表现（申万一级） | 指数名称 | 涨跌% | 指数名称 | 涨跌% | | --- | --- | --- | --- | | 银行 | 1.01 | 汽车 | (1.37) | | 通信 | 0.77 | 非银金融 | (1.20) | | 国防军工 | 0 ...

尤其港股银行H股平均股息率更高，如银行AH指数最新股息率为4.7%，对险资、社保等"长钱"而言，这类"类固收"资产是抵御低利率的天然避 风港，因此尤其青睐。 6月25日，工商银行、建设银行、交通银行等十余家银行股价再创历史新高。这并不是昙花一现——年初至今，申万银行指数已上涨14.11%，在31个一 级行业中高居榜首，市值较年初增长2.05万亿元，几乎相当于"再造两个宁德时代"。 | 序号 | 证券代码 | 证券简称 | 区间涨跌幅 | | | --- | --- | --- | --- | --- | | | | | [区间首日] 2025-1-1 [区间居日] 2025-6-25 | | | | | | [单位] %] | | | 1 | 801780.SI | 银行(申万) | | 15.77 | | 2 | 801050.SI | 有色金属(甲力) | | 115.34 | | 3 | 801760.SI | 传媒(申万) | | 9.97 | | 4 | 801880.51 | 汽车(甲万) | | 9.70 | | 5 | 801890.51 | 机械设备(申万) | | 8.23 | | 6 | ...

Group 1 - The median return of conservative fixed income + products was 0.09% for the week of June 16-20, 2025, with 294 products reaching historical net value highs [2][20] - The total market size of fixed income + funds reached 1,692.127 billion, with 1,173 products available as of June 20, 2025 [2][10] - The performance of various fund types showed divergence, with median returns for mixed bond type funds being 0.10% for level one and -0.02% for level two [2][12] Group 2 - The macro environment forecast for Q2 2025 indicates inflation, with the Shanghai and Shenzhen 300 index, the China government bond index, and gold showing respective increases of 0.17%, 0.71%, and 1.28% since June [2][3] - The recommended industry ETFs for June 2025 include those focused on securities companies, semiconductors, banks, and major consumer sectors, achieving a combined return of 0.21% for the week [2][3] Group 3 - The stock-bond mixed strategy showed a return of 0.03% for the 20/80 rebalancing strategy, while the risk parity strategy yielded a return of 0.15% [3][3] - The small-cap value style within the stock-bond 20/80 combination performed best with a year-to-date return of 5.17% [3][3] - The cumulative return for the small-cap value combination, adjusted for macro momentum, was 2.55% [3][3]

价值和成长两类股票具有明显基本面差异，价值类股票往往具备更好的安全边际，而成长类股票则可能 具备更好的盈利前景。成长与价值的盈利增速差和两者收益率差呈现高度正相关性，当成长与价值的盈 利增速差值扩大时，成长表现将会超过价值。因此，观察风格间的相对业绩增速趋势，有助于进行风格 配置。除此之外，市场中也有投资者通过估值指数来衡量价值与成长之间的风格轮动。 风格轮动是依据ETF特征进行交易的行为，常见的风格轮动有大小盘轮动、成长价值轮动等。风格轮动 的逻辑也依赖于权益资产价格的两个驱动因素——盈利和估值。盈利是主导风格强弱的关键因素，绝对 差值和边际变化也同样是判断风格强弱的重要指标。 （1）价值成长轮动策略 大小盘轮动通常根据市场环境和经济周期的变化来进行，并根据月频公告的宏观经济数据来进行辅助判 断。大盘股占国民经济中的比重更高，因此大盘股相比于小盘股更容易受到经济周期的影响。在经济增 长上行阶段，大盘股盈利上升速度大概率高于小盘股，大盘风格表现就更为强势。在经济下行阶段，大 盘股受到影响更大，表现可能相对弱势。另外，流动性环境对股票估值有重要影响：在流动性充裕的市 场中，资金会外溢到小盘股中，因此小盘股对流动 ...

Quantitative Models and Construction Methods Single Strategy Models - **Model Name**: High Prosperity Industry Rotation Strategy **Construction Idea**: Tracks industry profitability expectations using multi-factor models based on analysts' consensus data to select industries with upward profitability trends [13][15][16] **Construction Process**: 1. Constructs three types of factors: - Type 1: Long-term profitability factors (e.g., ROE_FY2, ROE_FY1) - Type 2: Quarterly changes in profitability (e.g., EPS_F2_qoq, EPS_F3_mom) - Type 3: Monthly changes in profitability (e.g., EPS_F3_qoq_d1m) 2. Filters industries with extreme valuations using PB percentile thresholds [30] 3. Selects top 3 industries based on composite factor rankings and allocates equally [21][30] **Evaluation**: Demonstrates strong performance in tracking industry cycles and avoiding valuation bubbles [13][26] - **Model Name**: Implicit Sentiment Momentum Strategy **Construction Idea**: Captures "unverified sentiment" by removing the relationship between turnover rate changes and returns, aiming to identify market sentiment-driven opportunities [32][33] **Construction Process**: 1. Uses OLS regression to remove "expected sentiment" from daily industry returns, leaving residuals as "unverified sentiment" [34] 2. Constructs momentum factors based on cumulative "unverified sentiment" returns over various time windows (e.g., 1 month, 12 months) [35] 3. Enhances the strategy by neutralizing fundamental impacts, adjusting for volatility, and applying composite factor methods [36] **Evaluation**: Effectively captures sentiment-driven market dynamics ahead of fundamental data releases [32][37] - **Model Name**: Macro Indicator Style Rotation Strategy **Construction Idea**: Uses macroeconomic indicators to predict industry styles (e.g., value, momentum) and maps them to industry selection [43][44] **Construction Process**: 1. Constructs macro indicators (e.g., PMI, CPI, M1) using historical positioning, surprise, and marginal change metrics [48][49] 2. Builds style factors (e.g., Value, Beta, Momentum) based on industry exposures [50][51] 3. Maps style predictions to industry scores and selects top industries [61] **Evaluation**: Addresses limitations of traditional top-down models by incorporating style-based predictions [43][61] - **Model Name**: Mid-to-Long-Term Momentum Reversal Strategy **Construction Idea**: Explores the "momentum-reversal" structure in industry returns, combining short-term momentum and long-term reversal factors [70][71] **Construction Process**: 1. Constructs momentum factors based on single-month returns and reversal factors based on multi-month returns (e.g., 12-month momentum, 24-36 month reversal) [76][78] 2. Combines factors using rank-weighted methods and adjusts for turnover rates [80][85] **Evaluation**: Balances short-term trends and long-term recovery opportunities effectively [70][84] - **Model Name**: Fund Flow Industry Rotation Strategy **Construction Idea**: Tracks institutional and tail-end fund flows to identify industry momentum [91][92] **Construction Process**: 1. Constructs "institutional trend strength factors" based on net buy amounts [93][94] 2. Constructs "tail-end inflow strength factors" based on post-14:30 net inflow data [96][103] 3. Combines factors and excludes high-concentration industries [100][101] **Evaluation**: Enhances stability by avoiding crowded trades [91][101] - **Model Name**: Financial Report Failure Reversal Strategy **Construction Idea**: Utilizes mean-reversion characteristics of long-term effective financial factors after short-term failures [108][109] **Construction Process**: 1. Constructs financial factors (e.g., ROA, YOY) using profit and balance sheet data [110][114] 2. Identifies "long-term effective factors" and "recently failed factors" based on rolling windows [116][117] 3. Combines factors using zscore methods [117] **Evaluation**: Captures recovery opportunities in temporarily underperforming factors [108][118] - **Model Name**: Traditional Low-Frequency Multi-Factor Scoring Strategy **Construction Idea**: Combines factors from four dimensions (momentum, valuation, liquidity, quality) for quarterly industry rotation [122][123] **Construction Process**: 1. Selects top-performing factors from each dimension (e.g., 1-year momentum, ROE_TTM) [124][125] 2. Combines factors using rank-weighted methods [135] 3. Filters industries with low weights in the CSI 800 index [135] **Evaluation**: Suitable for long-term holding with robust risk control [122][129] Composite Strategy Models - **Model Name**: Volatility-Controlled Composite Strategy **Construction Idea**: Allocates funds across single strategies based on inverse negative volatility [138][139] **Construction Process**: 1. Calculates negative volatility for each strategy over a rolling window (e.g., 63 days) [139][140] 2. Allocates funds proportionally to inverse negative volatility [139][147] 3. Adjusts allocation frequencies to match individual strategy cycles (weekly, monthly, quarterly) [141][146] **Evaluation**: Balances risk and return effectively, achieving high annualized excess returns [138][144] --- Model Backtest Results Single Strategy Results - **High Prosperity Strategy**: Annualized excess return 16.69%, max drawdown -12.95%, IR 1.29 [26] - **Implicit Sentiment Strategy**: Annualized excess return 18.61%, max drawdown -17.83%, IR 1.04 [37] - **Macro Style Strategy**: Annualized excess return 7.01%, max drawdown -23.46%, IR 0.30 [63] - **Momentum Reversal Strategy**: Annualized excess return 11.42%, max drawdown -14.91%, IR 0.77 [84] - **Fund Flow Strategy**: Annualized excess return 11.64%, max drawdown -12.16%, IR 0.96 [101] - **Financial Report Strategy**: Annualized excess return 9.13%, max drawdown -10.54%, IR 0.87 [118] - **Low-Frequency Multi-Factor Strategy**: Annualized excess return 12.00%, max drawdown -13.25%, IR 0.91 [129] Composite Strategy Results - **Volatility-Controlled Composite Strategy**: Annualized excess return 12.2%, max drawdown -6.8%, IR 1.80 [144][147]