证券研究报告·金融工程·金工专题报告 "风起云涌"风格轮动系列研究（一） 从微观出发的风格轮动—找到风格切换的领 先特征 2025 年 08 月 20 日 证券分析师 高子剑 执业证书：S0600518010001 021-60199793 gaozj@dwzq.com.cn 证券分析师 凌志杰 执业证书：S0600525040007 lingzhj@dwzq.com.cn 相关研究 《万流归宗多因子系列研究（一）— 基于量价因子的多因子决策树》 2023-09-10 《"海纳百川"行业轮动系列研究（一） 基于微观的五维行业轮动—风格偏离 与导向》 2024-05-10 东吴证券研究所 1 / 17 请务必阅读正文之后的免责声明部分 [Table_Tag] [Table_Summary] ◼ 前言：《从微观出发的风格轮动—找到风格切换的领先特征》作为"风 起云涌"风格轮动系列的第一篇，承接了 "万流归宗"多因子系列研究 第一篇《基于价量因子的多因子决策树》与"海纳百川"行业轮动系列 研究第一篇《基于微观的五维行业轮动—风格偏离与导向》，继续从微 观多因子角度出发，尝试构造风格择时+轮动模型，充实基于微观数据 ...

证券研究报告·金融工程·金工定期报告 金工定期报告 20250801 从微观出发的风格轮动月度跟踪 202508 2025 年 08 月 01 日 [Table_Tag] [Table_Summary] 报告要点 证券分析师 高子剑 执业证书：S0600518010001 021-60199793 gaozj@dwzq.com.cn 证券分析师 凌志杰 执业证书：S0600525040007 lingzhj@dwzq.com.cn 相关研究 《从微观出发的风格轮动月度 跟踪 202507》 2025-07-01 ◼ 2025 年 7 月风格轮动模型收益率为 2.52%。 ◼ 2025 年 8 月最新风格择时方向为：低估值、大市值、反转、低波。 ◼ 2025 年 8 月风格择时最新持仓为： 表1：2025 年 8 月风格择时最新持仓 | 指数代码 | 指数名称 | ETF 代码 | ETF 名称 | | --- | --- | --- | --- | | 399378.SZ | ESG 300 | 159653.SZ | ESG300ETF | | 930839.CSI | 港股通高息精选 | 159691.S ...

- Model Name: Style Rotation Model; Model Construction Idea: The model is built from basic style factors such as valuation, market capitalization, volatility, and momentum, gradually constructing a style timing and scoring system[1][6] - Model Construction Process: 1. Construct 640 micro features based on 80 underlying micro indicators[1][6] 2. Use common indices as style stock pools instead of absolute proportion division of style factors to construct new style returns as labels[1][6] 3. Use a rolling training random forest model to avoid overfitting risks, select features, and obtain style recommendations[1][6] 4. Construct a style rotation framework from style timing to style scoring and from style scoring to actual investment[1][6] - Model Evaluation: The model effectively avoids overfitting risks and provides a comprehensive framework for style rotation from timing to scoring and actual investment[1][6] Model Backtest Results - Style Rotation Model, Annualized Return: 21.63%, Annualized Volatility: 24.09%, IR: 0.90, Monthly Win Rate: 59.12%, Maximum Drawdown: 28.33%[7][8] - Market Benchmark, Annualized Return: 7.21%, Annualized Volatility: 21.56%, IR: 0.33, Monthly Win Rate: 56.20%, Maximum Drawdown: 43.34%[8] - Excess Return, Annualized Return: 13.35%, Annualized Volatility: 11.43%, IR: 1.17, Monthly Win Rate: 66.42%, Maximum Drawdown: 10.28%[7][8] Monthly Performance - June 2025, Style Rotation Model Return: 1.28%, Excess Return: -2.51%[13] - July 2025, Latest Style Timing Directions: Low Valuation, Small Market Cap, Reversal, Low Volatility[13] - July 2025, Latest Holding Index: CSI Dividend Index[13]

Core Viewpoint - The investment strategy of the company has evolved from a singular focus on growth stocks to a diversified approach that adapts to market changes, emphasizing the importance of both sustainable growth and risk management in investment decisions [1][5][9]. Group 1: Investment Strategy Evolution - The company has recognized the limitations of a single investment strategy, especially in the current challenging market for growth stocks, prompting a shift towards diversification [1][4]. - The investment philosophy now incorporates a combination of growth, value, and dividend stocks, with a focus on macroeconomic trends and style timing to enhance portfolio resilience [5][9]. - The company aims to balance investment opportunities with safety, particularly in sectors benefiting from AI advancements and those with reasonable valuations [1][9]. Group 2: Market Outlook and Focus Areas - The company believes that the most uncertain phase of the market has passed, with expectations for better investment opportunities in the fourth quarter, particularly in growth stocks [9]. - Key sectors of interest include the internet sector benefiting from AI development, domestic consumption-related industries, technology with a focus on self-sufficiency, and sectors supported by growth policies like engineering machinery [9][10]. - The semiconductor industry is highlighted for its significant growth potential, driven by increasing domestic production and technological advancements [10].

Quantitative Models and Construction Methods - **Model Name**: Style Rotation Model **Model Construction Idea**: The model is built from micro-level stock factors, focusing on valuation, market capitalization, volatility, and momentum. It integrates a style timing and scoring system to construct a monthly frequency style rotation framework[1][6] **Model Construction Process**: 1. Start with 80 base micro-level factors selected based on the Dongwu multi-factor system[6] 2. Generate 640 micro-level features from these base factors[6] 3. Replace the absolute proportion division of style factors with commonly used indices as style stock pools to create new style returns as labels[6] 4. Use a rolling training random forest model to avoid overfitting risks, optimize feature selection, and derive style recommendations[6] 5. Construct a framework that transitions from style timing to style scoring and finally to actual investment decisions[6] **Model Evaluation**: The model effectively avoids overfitting risks and provides a comprehensive framework for style rotation[6] Model Backtesting Results - **Style Rotation Model**: - Annualized Return: 21.56% - Annualized Volatility: 24.17% - IR: 0.89 - Monthly Win Rate: 58.82% - Maximum Drawdown: 28.33%[7][8] - Excess Performance (Hedged Against Benchmark): - Annualized Return: 13.45% - Annualized Volatility: 11.47% - IR: 1.17 - Monthly Win Rate: 66.18% - Maximum Drawdown: 10.28%[7][8] Quantitative Factors and Construction Methods - **Factor Name**: Valuation, Market Capitalization, Volatility, Momentum **Factor Construction Idea**: These are foundational style factors used to construct the style rotation model. They are further refined into micro-level features and integrated into the model's scoring and timing system[1][6] **Factor Construction Process**: 1. Valuation: Derived from traditional valuation metrics such as P/E, P/B, and dividend yield[6] 2. Market Capitalization: Categorized into large-cap and small-cap stocks based on market size[6] 3. Volatility: Measured using historical price fluctuations[6] 4. Momentum: Calculated based on past price trends and returns[6] Factor Backtesting Results - **Factor Performance (2025, Multi-Factor Timing Results)**: - Valuation: -2.00% - Market Capitalization: 4.00% - Volatility: -6.00% - Momentum: -8.00%[10][17] - **Factor Performance (2025, Actual Factor Returns)**: - Valuation: 2.00% - Market Capitalization: 6.00% - Volatility: -4.00% - Momentum: -8.00%[10][11] Additional Notes - **Latest Style Timing Directions (May 2025)**: Value, Large-Cap, Reversal, Low Volatility[14] - **Latest Holding Index (May 2025)**: CSI Dividend Index[15]

Core Viewpoint - The article emphasizes the importance of a multi-strategy dynamic allocation approach to capture style rotation opportunities in the market, utilizing quantitative indicators to assess the allocation value of different styles or strategies [1][6]. Summary by Sections Style Timing Framework to Strategy Rotation Model - The style timing model can effectively avoid high-risk phases but may miss some upward opportunities in styles. Historical data is used to identify similar past indicators to predict future performance [3][26]. - A voting method is employed to integrate multiple indicators, resulting in a comprehensive style timing model that has shown to reduce risk while maintaining a lower annualized return compared to holding styles directly [3][31]. Performance Metrics - The style timing model achieved an annualized return of 16.5% during the backtest period from January 1, 2015, to January 31, 2025, with an excess return of 12.7% over the benchmark [3][39]. - The active quantitative strategy rotation model yielded an annualized return of 36.2% during the backtest period from January 1, 2015, to February 28, 2025, outperforming the benchmark by 28.5% [4][39]. Key Indicators for Style Allocation - The article identifies key indicators for measuring style allocation value, including valuation difference, active inflow rate difference, and combination temporal correlation [2][17]. - Historical data shows that a larger valuation difference correlates with better future excess returns, while a significant active inflow rate difference indicates potential overreaction risks [2][10]. Latest Insights and Recommendations - As of March 2025, the recommendation is to favor small-cap and growth styles while maintaining a neutral stance on value and dividend styles [4][35]. - The report suggests holding indices like the CSI 2000 for small-cap and the National Growth Index for growth styles, along with specific active quantitative strategies [4][35]. Multi-Dimensional Timing Indicators - The article discusses the construction of a multi-dimensional timing indicator system that includes valuation difference, market participation, and combination consistency to assess future style performance [18][22]. - The effectiveness of these indicators is tested, showing that they can provide valuable insights into future excess returns across different styles [22][23]. Strategy Rotation and Dynamic Allocation - The article outlines a strategy for dynamic allocation and rotation among styles based on multi-dimensional timing indicators, aiming to optimize returns while managing risks [37][39]. - The dynamic allocation strategy is designed to adjust holdings based on the prevailing market conditions and style performance indicators [37][39].