行业研究 上周量化私募超额有所回升，中性策略正收益。上周样本量化私募收益及超额如下： 1）300 增强策略周/月/年初以来平均收益分别为-0.4%/-1.3%/-1.8%，周/月/年初以来平均 超额分别为+0.1%/+0.9%/+3.3%；2）500 增强策略周/月/年初以来平均收益分别为+0.4%/- 1.7%/+2.3%，周/月/年初以来平均超额分别为+0.3%/+2%/+7.1%；3）A500 增强策略周/月 /年初以来平均收益分别为-0.2%/+1.5%/+5.6%，周/月/年初以来平均超额分别为- 0.1%/+4.1%/+11%；4）1000 增强策略周/月/年初以来平均收益分别为+0.7%/-1.2%/+5.4%， 周/月/年初以来平均超额分别为+0.5%/+3%/+9.2%；5）空气指增策略周/月/年初以来平均 收益分别为+0.8%/-0.2%/+7.8%；6）市场中性策略周/月/年初以来平均收益分别为 +0.2%/+0.8%/+4.4%。 指数收益情况：1）沪深 300 相对中证 500 周/月/年初以来超额收益分别为-0.4%/- 0.2%/+1.9%；2）中证 1000 相对中证 500 ...

金融资金面跟踪：量化周报（2025/05/05~2025/05/09） 推荐（维持） 成交量有所增长，超额有所回升 行业研究 非银行金融 2025 年 05 月 11 日 | 华创证券研究所 | | | --- | --- | | 证券分析师：徐康 | 证券分析师：刘潇伟 | | 电话：021-20572556 | 邮箱：liuxiaowei@hcyjs.com | | 邮箱：xukang@hcyjs.com | 执业编号：S0360525020001 | | 执业编号：S0360518060005 | | 证 券 研 究 报 告 上周量化私募超额有所回升，中性策略正收益。上周样本量化私募收益及超额如下： 1）300 增强策略周/月/年初以来平均收益分别为-0.4%/-1.3%/-1.8%，周/月/年初以来平均 超额分别为+0.1%/+0.9%/+3.3%；2）500 增强策略周/月/年初以来平均收益分别为+0.4%/- 1.7%/+2.3%，周/月/年初以来平均超额分别为+0.3%/+2%/+7.1%；3）A500 增强策略周/月 /年初以来平均收益分别为-0.2%/+1.5%/+5.6%，周/月/年初以来 ...

[Table_StockNameRptType] 金融工程 专题报告 5 月看好小盘成长风格 ——风格轮动月报 202505 [Table_Rpt 报告日期： Date] 2025-5-7 主要观点： 分析师：严佳炜 执业证书号：S0010520070001 邮箱：yanjw@hazq.com ⚫[Table_Summary] 观点回顾：4 月大小盘轮动超额-0.3%，价值成长轮动超额收益 0.2% 2025 年 4 月，风格轮动模型看多大盘成长风格。本月大小盘轮动组合 相对风格等权基准的超额收益为-0.3%；价值成长轮动组合相对风格等 权基准的超额收益为 0.2%。 联系人：吴正宇 执业证书号：S0010522090001 邮箱：wuzy@hazq.com ⚫ 最新观点：5 月建议配置小盘成长风格 5 月模型判断风格为小盘成长：其中，宏观经济和微观特征指向小盘风 格，而市场状态模型看好大盘。价值成长维度，各子模型均指向成长风 格。 ⚫ 风险提示 本报告基于历史个股数据进行测试，历史回测结果不代表未来收益。未 来市场风格可能切换，Alpha 因子可能失效，本文内容仅供参考。 [Table_CompanyRep ...

关键因素 图利 绝对差值和边际变化 重要指标 II 价值成长轮动策略 II 深圳证券交易所 ( SHENZHEN STOCK EXCHANGE 深交所ETF投资问答(42) 如何的身上了 II t FE ALKE 0 n - 编者按 - 近年来我国指数型基金迅速发展,交易型开 放式指数基金(ETF) 备受关注。为帮助广 大投资者系统全面认识ETF,了解相关投资 方法,特摘编由深圳证券交易所基金管理部 编著的《深交所ETF投资问答》（中国财政 经济出版社2024年版）形成图文解读。本 篇是第42期,一起来看看如何通过ETF构建 风格配置策略。 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。风格轮动的分析框 架需要对比指数间的相对强弱，因此 预测难度更大。 II 影响风格轮动强弱的因素 II 价值和成长两类股票具有明显基本面 的差异。 价值类股票往往具备更好 的安全边际 成长类股票则可能具备更 好的盈利前景 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。 u 大小鱼论动策略 ! 大小盘轮动通常 ...

50等大盘股微跌，小盘股上涨。 文 | 银行螺丝钉 （转载请注明出处） 今天大盘微涨微跌，波动不大，还在5.1星。 昨天比较坚挺的价值风格，今天微跌，成长风格微涨。 最近市场风格轮动比较明显。 遇到下跌的时候，大盘、价值股相对抗跌； 遇到上涨的时候，小盘股、成长股弹性更大。 盈亏同源。 两者搭配会让组合更稳定一些。 1. 昨天有朋友问，像红利等指数基金，有一些成立以来，年化达到10%以上甚至更高。 看起来红利指数的波动也不大，那投资者岂不是很容易就拿到这个收益？ 那投指数基金还有啥难的？ 确实，是有一些红利指数基金，成立多年，年化达到10%以上（加上分红）。 例如最基础的中证红利。这个多年甚至达到10年以上。 投资者在低估的时候买入红利基金，并长期坚持下来，也会获得这个收益。 当时有价值风格的基金，被投资者赎回超过90%。 2022年-2024年，红利等指数，在熊市中比较有优势，跑赢了大盘。 这两年红利又受欢迎。 从全市场角度， 但难就难在坚持上。 基金投资者，平均持有股票基金的时间长度是几个月。 但是A股是存在风格轮动的。 红利属于价值风格，遇到成长风格牛市的时候，就会跑输市场。 例如在2019-2020年 ...

过去几年，人民币债券是一轮小牛市。 到了2024年，长期债券在上涨后，波动也逐渐变大。 很多朋友都会关心： 这里为大家准备了一门限时免费的课程，详细介绍了债券指数基金的相关问题。 长按识别下方二维码，添加@课程小助手，回复「 债券基金 」即可领取～ ◆◆◆ 文 | 银行螺丝钉 （转载请注明出处） 类似的，估值当前比较低的品种，未来更有 可能会迎来上涨。 不过也需要注意，即便都是低估，但之后上 涨的先后顺序可能会有一些区别。 例如2018年底时，成长风格、价值风格都 处于较低的位置，随后， · 2019-2020年，成长风格率先上涨； ·一直到2021年后，价值风格才开始上涨。 因为整个市场上，投资者的资金就那么多， 不同风格的品种往往不是同涨同跌的，会出 现风格上的轮动。 所以，我们在投资时，可以在熊市底部分散 配置不同风格的低估品种。 · 债券基金的收益和风险，有哪些特点？ · 为何普通投资者，更适合投资债券指数基金？ · 当前哪些债券指数基金，投资价值较高？ 不过,尽管每轮上涨的风格有别,但也会有 一些共同的特征。 比如说，之前估值比较高的品种，之后可能 会迎来估值回归，下跌幅度会比较大。 例如大盘成长， ...

Quantitative Models and Construction Methods 1. Model Name: "百元转股溢价率" (Premium Rate per 100 Yuan Conversion) - **Model Construction Idea**: This model aims to compare the valuation of convertible bonds and their underlying stocks by calculating a time-series comparable valuation indicator, "百元转股溢价率" (Premium Rate per 100 Yuan Conversion), and using rolling historical percentiles to measure the relative allocation value[4][15]. - **Model Construction Process**: - Fit the relationship curve between the conversion premium rate and conversion value in the cross-sectional space at each time point - Substitute a conversion value of 100 into the fitted formula to obtain the "百元转股溢价率" - Formula: $$y_{i}=\alpha_{0}+\,\alpha_{1}\cdot\,{\frac{1}{x_{i}}}+\epsilon_{i}$$ where \(y_{i}\) represents the conversion premium rate of the \(i\)-th bond, and \(x_{i}\) represents the conversion value of the \(i\)-th bond[44] - **Model Evaluation**: Provides a relative valuation perspective for comparing convertible bonds and their underlying stocks[15] 2. Model Name: "修正 YTM – 信用债 YTM" (Adjusted YTM - Credit Bond YTM) - **Model Construction Idea**: This model isolates the impact of conversion terms on the yield-to-maturity (YTM) of convertible bonds to assess the relative allocation value between debt-heavy convertible bonds and credit bonds[5][15]. - **Model Construction Process**: - Adjust the YTM of debt-heavy convertible bonds by considering the probability of conversion and maturity - Formula: $$\text{Adjusted YTM} = \text{Convertible Bond YTM} \times (1 - \text{Conversion Probability}) + \text{Expected Annualized Return of Conversion} \times \text{Conversion Probability}$$ - Conversion probability is calculated using the Black-Scholes (BS) model, incorporating stock price, strike price, stock volatility, remaining maturity, and discount rate - Calculate the median difference between the adjusted YTM of convertible bonds and the YTM of credit bonds of the same rating and maturity: $$\text{"Adjusted YTM - Credit Bond YTM Median"} = \text{median}\{X_1, X_2, ..., X_n\}$$ where \(X_i\) represents the difference for the \(i\)-th convertible bond[45][46] - **Model Evaluation**: Highlights the cost-effectiveness of debt-heavy convertible bonds compared to credit bonds[5] 3. Model Name: Convertible Bond Style Rotation Model - **Model Construction Idea**: This model captures market sentiment using momentum and volatility deviation indicators to construct a convertible bond style rotation portfolio, with bi-weekly rebalancing[6][27]. - **Model Construction Process**: - Calculate the following sentiment indicators for each convertible bond: - 20-day momentum - Volatility deviation - Rank the indicators in reverse order and sum the rankings to determine the market sentiment capture indicator for each style index: $$\text{Market Sentiment Capture Indicator} = \text{Rank (20-day Momentum)} + \text{Rank (Volatility Deviation)}$$ - Allocate portfolio weights based on the rankings, with a preference for the style index with the lowest indicator value. If rankings are equal, allocate weights equally. If all three styles are selected, allocate 100% to the balanced low-valuation style[28] - **Model Evaluation**: Demonstrates superior performance compared to the equal-weighted convertible bond index, with a focus on balanced low-valuation styles[27][33] --- Quantitative Factors and Construction Methods 1. Factor Name: 转股溢价率偏离度 (Conversion Premium Deviation) - **Factor Construction Idea**: Measures the deviation of the conversion premium rate from its fitted value, enabling comparability across different parities[19][20]. - **Factor Construction Process**: $$\text{Conversion Premium Deviation} = \text{Conversion Premium Rate} - \text{Fitted Conversion Premium Rate}$$ - The number of convertible bonds determines the fitting quality[20] - **Factor Evaluation**: Provides a robust measure for identifying valuation discrepancies in convertible bonds[20] 2. Factor Name: 理论价值偏离度 (Theoretical Value Deviation, Monte Carlo Model) - **Factor Construction Idea**: Quantifies the price expectation gap by considering various convertible bond terms (e.g., conversion, redemption, downward revision, put options) through Monte Carlo simulation[19][20]. - **Factor Construction Process**: $$\text{Theoretical Value Deviation} = \frac{\text{Convertible Bond Closing Price}}{\text{Theoretical Value}} - 1$$ - Simulate 10,000 paths at each time point using the Monte Carlo model, with the same credit term interest rate as the discount rate[20] - **Factor Evaluation**: Effectively captures valuation discrepancies, particularly for equity-heavy convertible bonds[19][20] 3. Factor Name: 转债综合估值因子 (Comprehensive Convertible Bond Valuation Factor) - **Factor Construction Idea**: Combines the rankings of the above two factors to enhance valuation analysis across all domains (equity-heavy, balanced, debt-heavy)[19][20]. - **Factor Construction Process**: $$\text{Comprehensive Convertible Bond Valuation Factor} = \text{Rank (Conversion Premium Deviation)} + \text{Rank (Theoretical Value Deviation)}$$ - **Factor Evaluation**: Demonstrates superior performance in identifying undervalued convertible bonds across different styles[19][20] --- Backtesting Results of Models 1. Convertible Bond Style Rotation Model - **Annualized Return**: 23.38% - **Annualized Volatility**: 16.48% - **Maximum Drawdown**: -15.54% - **IR**: 1.42 - **Calmar Ratio**: 1.50 - **Monthly Win Rate**: 65.12%[33] --- Backtesting Results of Factors 1. 转股溢价率偏离度 Factor - **Equity-Heavy Convertible Bonds**: Enhanced excess return of 0.9% over the past 4 weeks[22] - **Balanced Convertible Bonds**: Enhanced excess return of 1.2% over the past 4 weeks[22] - **Debt-Heavy Convertible Bonds**: Enhanced excess return of -0.3% over the past 4 weeks[22] 2. 理论价值偏离度 Factor - **Equity-Heavy Convertible Bonds**: Enhanced excess return of 0.9% over the past 4 weeks[22] - **Balanced Convertible Bonds**: Enhanced excess return of 1.2% over the past 4 weeks[22] - **Debt-Heavy Convertible Bonds**: Enhanced excess return of -0.3% over the past 4 weeks[22] 3. 转债综合估值因子 Factor - **Equity-Heavy Convertible Bonds**: Enhanced excess return of 0.9% over the past 4 weeks[22] - **Balanced Convertible Bonds**: Enhanced excess return of 1.2% over the past 4 weeks[22] - **Debt-Heavy Convertible Bonds**: Enhanced excess return of -0.3% over the past 4 weeks[22]

文 | 银行螺丝钉 （转载请注明出处） ◆◆◆ 长按识别下方二维码，添加@课程小助手，回复「 美元债券 」即可领取～ · 想投资美元债券类资产，有哪些方式？ · 美元债基金，当前投资价值如何？ · 投资美元债基金，会有哪些风险？ 大家对美债的关注度日渐提高。 这里为大家准备了一门限时免费的课程，全面讲解美元债券基金投资。 比如： 如果以跑赢跑输看待红利策略，那就会患得 患失。 实际上,能长期坚持投资红利类品种的,通 常是从股息率的角度看待红利。 例如保险和养老金机构，是需要每年获得现 金流。 大家一般什么时候喜欢投红利呢? 往往是在熊市,这时往往其他品种下跌了, 红利类品种还是上涨的。 但等到下一轮牛市来临时，其他品种大涨， 红利却可能涨幅不大。 这时很多人就会骂红利，怀疑这个策略是不 是有问题,干脆卖掉手里的红利类品种,追 涨成长类品种。 等到下一轮牛熊市,还是这样反反复复,相 当于刚好做了一个反向操作,也就是我们常 说的追涨杀跌。 其实对红利这类品种，如果想要长期坚持下 来，最好不要以短期跑赢跑输市场来看待。 因为风格轮动的存在，红利肯定会在某几年 跑输市场。这在长达几十年的投资中,几乎 是一定会遇到的。 ...

- 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]

Group 1: AI Industry and Ecosystem - The AI industry chain covers multiple levels, including infrastructure, model, platform, application, and service layers, with key areas such as chips, computing, storage, networking, and software [1] - DeepSeek has achieved significant performance improvements in model training and inference through innovative training methods and architectures, resulting in lower training costs compared to industry averages [1] - OpenAI's GPT series, including GPT-4, has driven the development of the AI industry, particularly in utilizing pre-training principles like Scaling Law [1] - The rapid growth in AI computing power demand will benefit related industries such as semiconductors, storage, servers, optical modules, PCBs, and power supplies [1] Group 2: Home Appliance Sector Investment Opportunities - The home appliance sector, particularly the three major white goods (air conditioners, refrigerators, washing machines), is characterized by low valuations, high dividends, and stable growth, making it an attractive investment area [2] - The second-hand housing market has shown a rapid recovery, with a 92.2% week-on-week increase in transaction area in key cities from February 8 to 14, indicating strong market resilience [2] - Air conditioner domestic sales growth reached 11.4%, reflecting positive effects from seasonal stocking and national subsidy policies [2] - The kitchen appliance sector is expected to benefit from the recovery in the second-hand housing market, suggesting a focus on undervalued kitchen appliance stocks [2] Group 3: Economic Policies and Inflation - The effectiveness of "capacity reduction" and "consumption promotion" policies is debated, with a greater emphasis on expanding consumer demand to stimulate economic recovery [3] - Service prices have reached historical lows, and a significant increase in service prices could positively impact the GDP deflator index [3] - A dual approach of "capacity reduction" and "consumption promotion" is necessary, but prioritizing consumption promotion is deemed more effective [3] Group 4: Commercial Aerospace Development - The number of global commercial space launches has doubled in the past three years, surpassing non-commercial launches, with commercial launches accounting for 56% of the total [4] - The construction of commercial space launch facilities and satellite manufacturing capabilities in Hainan is accelerating, with expectations of significant increases in launch capacity by 2026 [4] - The market potential for China's commercial aerospace upstream satellite manufacturing is estimated to be between 368.8 billion to 463.7 billion yuan from 2024 to 2030 [4] Group 5: AI and Consumer Trends - Technology is driving consumption upgrades, with the consumer sector expected to become a new growth driver for the economy as macroeconomic conditions stabilize [5] - AI technology is anticipated to revolutionize the home appliance industry, particularly in products like robotic vacuum cleaners, which have substantial market potential [6] - The application of AI in education (2C AI) presents significant market opportunities, especially in scenarios with large user bases and essential needs [7] - Emerging e-commerce brands have rapidly developed by leveraging online platforms, contributing to the consumer stock market boom post-2016 [8]