资产配置 | 动态跟踪 配置关注权益商品，行业聚焦中盘蓝筹 ——资产配置月报 202601 研究结论 报告发布日期 2026 年 01 月 04 日 | 郑月灵 | 执业证书编号：S0860525120003 | | --- | --- | | | zhengyueling@orientsec.com.cn | | | 021-63326320 | | 周仕盈 | 执业证书编号：S0860125060012 | | | zhoushiying@orientsec.com.cn | | | 021-63326320 | | 董翱翔 | 执业证书编号：S0860125030016 | | 权益、商品延续强势，风险资产占优： | 2025-12-30 | | --- | --- | | 20251226 多资产配置周报 | | | 当低利率邂逅风偏回归，资产配置被动为 | 2025-12-12 | | 盾，主动为矛：——2026 年多资产配置展 | | | 望 | | | 交易平稳，预期主导：——资产配置月报 | 2025-12-03 | | 202512 | | | 资产配置策略中低波分化，行业策略转 | 202 ...

资产配置 | 动态跟踪 资产配置策略中低波分化，行业策略转向 ——资产配置模型月报 202512 研究结论 报告发布日期 2025 年 12 月 03 日 | 王晶 | 执业证书编号：S0860510120030 | | --- | --- | | | wangjing@orientsec.com.cn | | | 021-63325888*6072 | | 周仕盈 | 执业证书编号：S0860125060012 | | 资产配置不仅仅是风险分散：——主动型 | 2025-11-27 | | --- | --- | | 资产配置新思路 | | | 全天候模型仓位平稳，行业策略推荐科技/ | 2025-11-03 | | 有色/新能源等板块：——资产配置模型月 | | | 报 202511 | | | 关注权益和商品机会：——资产配置月报 | 2025-11-01 | | 202511 | | | 大类资产仓位平稳，行业策略推荐有色/科 | 2025-10-11 | | 技等板块：——资产配置模型月报 202510 | | | 大类资产风险可控，短期关注交易特征： | 2025-09-29 | | ——"2+1 ...

Quantitative Models and Construction Methods 1. Model Name: Convertible Bond Random Forest Strategy - **Model Construction Idea**: Utilizes the Random Forest machine learning method to identify convertible bonds with potential for excess returns by leveraging decision trees[16][17] - **Model Construction Process**: 1. Data preprocessing and feature engineering to prepare convertible bond datasets 2. Training a Random Forest model with historical data to identify patterns of excess return potential 3. Selecting bonds with the highest predicted scores for portfolio construction 4. Weekly rebalancing of the portfolio based on updated predictions[17] - **Model Evaluation**: Demonstrated strong performance in generating excess returns, indicating high predictive accuracy[16] 2. Model Name: Multi-Dimensional Timing Model - **Model Construction Idea**: Combines macro, meso, micro, and derivative signals to create a four-dimensional non-linear timing model for market positioning[18][19] - **Model Construction Process**: 1. Macro signals: Derived from liquidity, interest rates, credit, economic growth, and exchange rates 2. Meso signals: Based on industry-level business cycle indicators 3. Micro signals: Captures structural risks using valuation, risk premium, volatility, and liquidity factors 4. Derivative signals: Generated from the basis of stock index futures 5. Aggregation: Signals are synthesized into a composite timing signal[18][19][24] - **Model Evaluation**: Effective in identifying market trends and providing actionable signals, with the latest signal indicating a bullish stance[19][24] 3. Model Name: Industry Rotation Strategy 2.0 - **Model Construction Idea**: Constructs an industry rotation strategy based on economic quadrants and multi-dimensional industry style factors[69] - **Model Construction Process**: 1. Define economic quadrants using corporate earnings and credit conditions 2. Develop industry style factors such as expected business climate, earnings surprises, momentum, valuation bubbles, and inflation beta 3. Test factor effectiveness within each quadrant 4. Allocate to high-expected-return industries based on factor signals[69][71] - **Model Evaluation**: Demonstrates strong adaptability to the A-share market, with annualized excess returns of 9.44% (non-exclusion version) and 10.14% (double-exclusion version)[71] 4. Model Name: Genetic Programming Index Enhancement Models - **Model Construction Idea**: Uses genetic programming to discover and optimize stock selection factors for index enhancement strategies[88][93][97] - **Model Construction Process**: 1. Stock pools: Defined for CSI 300, CSI 500, CSI 1000, and CSI All Share indices 2. Training: Genetic programming generates initial factor populations and iteratively evolves them through multiple generations 3. Factor selection: Top-performing factors are combined into a composite score 4. Portfolio construction: Selects top 10% of stocks within each industry based on scores, with weekly rebalancing[88][93][97][102] - **Model Evaluation**: - CSI 300: Annualized excess return of 17.91%, Sharpe ratio of 1.05[91] - CSI 500: Annualized excess return of 11.78%, Sharpe ratio of 0.85[95] - CSI 1000: Annualized excess return of 17.97%, Sharpe ratio of 0.93[98] - CSI All Share: Annualized excess return of 24.84%, Sharpe ratio of 1.33[103] --- Model Backtest Results 1. Convertible Bond Random Forest Strategy - Weekly excess return: 0.64%[16] 2. Multi-Dimensional Timing Model - Latest composite signal: Bullish (1)[19][24] 3. Industry Rotation Strategy 2.0 - Annualized excess return (non-exclusion version): 9.44% - Annualized excess return (double-exclusion version): 10.14%[71] 4. Genetic Programming Index Enhancement Models - CSI 300: - Annualized excess return: 17.91% - Sharpe ratio: 1.05[91] - CSI 500: - Annualized excess return: 11.78% - Sharpe ratio: 0.85[95] - CSI 1000: - Annualized excess return: 17.97% - Sharpe ratio: 0.93[98] - CSI All Share: - Annualized excess return: 24.84% - Sharpe ratio: 1.33[103] --- Quantitative Factors and Construction Methods 1. Factor Name: Industry Business Climate Index 2.0 - **Factor Construction Idea**: Tracks industry fundamentals by analyzing revenue, pricing, and cost dynamics[27] - **Factor Construction Process**: 1. Analyze industry revenue and cost structures 2. Calculate daily market-cap-weighted industry indices 3. Aggregate indices into a composite business climate index[27][30] - **Factor Evaluation**: Demonstrates predictive power for A-share earnings expansion cycles[28] 2. Factor Name: Barra CNE6 Style Factors - **Factor Construction Idea**: Evaluates market performance using 9 primary and 20 secondary style factors, including size, volatility, momentum, quality, value, and growth[45] - **Factor Construction Process**: 1. Calculate factor returns for each style factor 2. Aggregate factor performance to assess market trends[45][46] - **Factor Evaluation**: Size factor performed well during the week, while volatility factor underperformed[46] 3. Factor Name: Industry Rotation Factors - **Factor Construction Idea**: Captures industry rotation dynamics using factors like expected business climate, earnings surprises, momentum, and valuation bubbles[69] - **Factor Construction Process**: 1. Define and calculate individual factors 2. Test factor effectiveness within economic quadrants 3. Combine factors for industry allocation[69] - **Factor Evaluation**: Demonstrates strong historical performance, with factors like expected business climate and momentum showing significant returns[57][59] --- Factor Backtest Results 1. Industry Business Climate Index 2.0 - Current value: 0.913 - Excluding financials: 1.288[28] 2. Barra CNE6 Style Factors - Size factor: Strong performance during the week[46] 3. Industry Rotation Factors - Historical annualized returns: - Expected business climate: 0.40% - Momentum: -0.95% - Valuation beta: 2.37%[57]

Summary of Key Points from the Conference Call Industry or Company Involved - The conference call focuses on the **AI investment strategies** and **ETF market** in the context of the **capital market** as discussed by **Huatai Securities** during their **2025 Spring Strategy Meeting**. Core Points and Arguments - **AI Strategies in Investment**: AI strategies significantly enhance traditional multi-factor models by processing vast amounts of data and complex factors, particularly in volume and price data analysis, optimizing investment decisions [1][4][9]. - **Acceptance of AI in Asset Management**: The asset management industry is increasingly accepting AI strategies, particularly those based on statistical models, due to their strong performance. However, the ability of reasoning-based large language models to reach expert-level performance remains to be validated [1][13][14]. - **ETF Market Growth**: The ETF market has surpassed **3.8 trillion yuan**, with a focus on smart beta strategies to achieve stable returns through industry rotation and asset allocation models [1][22]. - **Investment Strategy Focus**: Huatai Securities emphasizes a robust return strategy, primarily focusing on bond investments, and utilizes global asset allocation models and qualitative analysis for market judgment [1][27]. - **Industry Rotation Strategy**: The industry rotation strategy combines macro, meso, and micro factors with AI identification and qualitative analysis, favoring technology, consumer, and pharmaceutical sectors while adjusting investment targets based on significant events like the Two Sessions [3][31]. - **AI's Role in Financial Engineering**: AI enhances traditional multi-factor frameworks by integrating diverse data types, leading to more precise and efficient data analysis, thus optimizing portfolio design and improving returns while reducing risks [7][18]. - **Performance of AI in Quantitative Investment**: AI strategies outperform traditional multi-factor methods by effectively aggregating information and conducting global analyses, leading to superior excess returns [9][12]. - **Future of Large Models in Finance**: Large models like DeepSeek and ChatGPT show potential in subjective analysis, suggesting a new paradigm of combining subjective and quantitative investment approaches, although their expert-level capabilities need further validation [11][15]. - **ETF Product Development**: Huatai Securities is committed to providing ETF products and solutions, focusing on smart beta strategies and offering professional services, including market reports and strategy analyses [1][23]. Other Important but Possibly Overlooked Content - **Historical Context of AI in Quantitative Investment**: The application of AI in quantitative investment began around 2003, evolving through various phases, with significant adoption starting in 2017, leading to substantial investment returns [2][13]. - **Impact of Two Sessions on Market**: The analysis of the Two Sessions' impact on the market involves reviewing historical key topics and market performance, indicating that different time periods around the event affect market dynamics [32]. - **Investment Heat and Valuation Levels**: The current investment heat in AI-related sectors is at historical highs, with significant trading activity and valuation levels, necessitating cautious investment strategies [62][64]. - **Differentiation of Index Products**: Index products vary significantly in valuation levels and stock resonance, suggesting that investors should choose based on their risk appetite and investment strategy [68][70]. - **Performance of Active Equity Fund Managers**: Different fund managers exhibit varying performance in the AI sector, categorized into stable allocation, focused sector, and flexible adjustment types, highlighting the importance of selecting managers based on their stability and risk-return profile [73][74]. This summary encapsulates the essential insights from the conference call, providing a comprehensive overview of the discussions surrounding AI investment strategies and the ETF market.